Artificially Intelligent Political
Agents
DIPLOMARBEIT
zur Erlangung des akademischen Grades
Diplom-Ingenieur
im Rahmen des Studiums
Data Science
eingereicht von
Andreas Michael Buchner, BSc
Matrikelnummer 11811860
an der Fakultät für Informatik
der Technischen Universität Wien
Betreuung: Ao.Univ.-Prof. Dr. Horst Eidenberger
Wien, 22. Juli 2024
Andreas Michael Buchner Horst Eidenberger
Technische Universität Wien
A-1040 Wien Karlsplatz 13 Tel. +43-1-58801-0 www.tuwien.at

Artificially Intelligent Political
Agents
DIPLOMA THESIS
submitted in partial fulfillment of the requirements for the degree of
Diplom-Ingenieur
in
Data Science
by
Andreas Michael Buchner, BSc
Registration Number 11811860
to the Faculty of Informatics
at the TU Wien
Advisor: Ao.Univ.-Prof. Dr. Horst Eidenberger
Vienna, July 22, 2024
Andreas Michael Buchner Horst Eidenberger
Technische Universität Wien
A-1040 Wien Karlsplatz 13 Tel. +43-1-58801-0 www.tuwien.at

Erklärung zur Verfassung der
Arbeit
Andreas Michael Buchner, BSc
Hiermit erkläre ich, dass ich diese Arbeit selbständig verfasst habe, dass ich die verwen-
deten Quellen und Hilfsmittel vollständig angegeben habe und dass ich die Stellen der
Arbeit – einschließlich Tabellen, Karten und Abbildungen –, die anderen Werken oder
dem Internet im Wortlaut oder dem Sinn nach entnommen sind, auf jeden Fall unter
Angabe der Quelle als Entlehnung kenntlich gemacht habe.
Wien, 22. Juli 2024
Andreas Michael Buchner
v

Danksagung
Hiermit möchte ich mich bei allen bedanken, die mich bei der Umsetzung dieser Arbeit
unterstützt haben.
Der erste Dank gehört meinem Betreuer, Professor Horst Eidenberger, der immer zeitnahe
bei etwaigen Fragen und Unklarheiten mit konstruktiven Verbesserungsvorschlägen zur
Verfügung stand, mich mit lobenden Worten motivierte und mir Freiheiten bei der
Umsetzung ließ.
Weiters möchte ich mich bei meiner Freundin und meinen Freunden bedanken, mit denen
ich nun bald wieder mehr Zeit verbringen kann.
Schlussendlich bedanke ich mich besonders bei meiner Familie für ihre Unterstützung.
vii

Kurzfassung
Die Entwicklung von Large Language Models (LLMs), wie ChatGPT, hat die Fähigkeiten
von Computern natürliche Sprache zu verarbeiten erheblich erweitert und ermöglicht viel-
fältige Anwendungen, von Textzusammenfassung bis hin zu Programmierunterstützung.
Diese Arbeit untersucht die Möglichkeit, LLMs so zu konfigurieren, dass sie historische
politische Persönlichkeiten imitieren und somit “Künstlich Intelligente Politische Agenten”
abbilden kann.
Durch die Zuweisung unterschiedlicher Persönlichkeiten zu LLMs und deren Teilnahme
an politischen Debatten wird in dieser Arbeit untersucht, wie diese auf verschiedenste
politische Themen reagieren, die zu Lebzeiten der historischen Figuren nicht konkret
behandelt wurden. Die Forschungsfragen fokussieren sich auf die Fähigkeit von LLMs,
den sprachlichen Stil und die Charaktereigenschaften historischer Persönlichkeiten nach-
zuahmen, sowie auf die Plausibilität ihrer Standpunkte zu modernen Themen und die
logische Kohärenz ihrer Argumente.
Die für diese Studie ausgewählten historischen Persönlichkeiten sind Winston Churchill,
Karl Marx und Niccolò Machiavelli, die unterschiedliche politische Ideologien abdecken.
Diese Arbeit verwendet Techniken des Prompt-Engineerings und Retrieval Augmented Ge-
neration (RAG) für die Impersonierung, und stellt ein Multi-Agenten-Diskussionsframework
vor, das eine politische Debatte simuliert.
Die Bewertungsmethodik kombiniert qualitatives Feedback von fünf menschlichen Teilneh-
mern, die die simulierte Diskussionen gelesen haben, mit quantitativen Analysen anhand
des Big Five Persönlichkeitstests. Die Kombination von qualitativem Feedback und quan-
titativen Ergebnissen bietet eine umfassende Bewertung der Nachahmungsfähigkeiten von
LLMs sowie der Qualität des Multi-Agenten-Diskussionsframeworks, und liefert Einblicke
in deren potenzielle Anwendungen in Bildung, Geschichte und Unterhaltungsbereichen.
Die Ergebnisse zeigen, dass LLMs überzeugend historische Persönlichkeiten imitieren
und erfolgreich an politischen Diskursen gegeneinander teilnehmen können. Zu den Ein-
schränkungen gehören ein Mangel an Emotionen in der Nachahmung und das allgemeine
Klangbild der simulierten Debatte, die eher wie ein schriftlicher Austausch als eine
Live-Debatte wirkt. Insgesamt war das Feedback der Teilnehmer zu dieser Arbeit aber
äußerst positiv.
ix

Abstract
The rise of Large Language Models (LLMs) like ChatGPT has significantly expanded the
capabilities of natural language processing, enabling diverse applications from text sum-
marization to programming assistance. This thesis explores the possibility of configuring
LLMs to impersonate historical political figures, thus creating "Artificially Intelligent
Political Agents."
By assigning distinct personalities to LLMs and letting them go head-to-head in a political
debate, this research investigates their responses to a wide range of political issues, which
were not directly addressed during the historical figures’ lifetimes. The research questions
focus on the effectiveness of LLMs in replicating the linguistic styles and character traits
of historical figures, the plausibility of their stances on modern issues, and the logical
coherence of their arguments.
Historical figures selected for this study are Winston Churchill, Karl Marx, and Niccolò
Machiavelli, representing different political ideologies. This thesis applies techniques in
prompt engineering and Retrieval Augmented Generation (RAG) to achieve impersonation
and proposes a multi-agent discussion framework, simulating a political debate.
The evaluation methodology combines qualitative feedback from five human participants,
evaluating the simulated discussion, with quantitative analysis using psychometric inven-
tories, obtained through the Big Five Personality Test. This mixed-methods approach
provides a comprehensive assessment of the LLMs’ impersonation capabilities as well
as the performance of the multi-agent disucssion framework, offering insights into their
potential applications in educational, historical, and entertainment contexts.
The findings reveal that LLMs can convincingly impersonate historical personas and
succesfully engage in a political discourse. Limitations include a lack of emotion in the
impersonation and the overall sound of the simulated debate, feeling more like a written
exchange than a live debate. Overall, the feedback from the participants about this thesis
was very positive.
xi

Contents
Kurzfassung ix
Abstract xi
Contents xiii
1 Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Research Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.4 Thesis Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2 Theoretical Background 5
2.1 Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2 Natural Language Processing . . . . . . . . . . . . . . . . . . . . . . . 6
2.3 Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.4 Transformers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.5 Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.6 Large Language Models . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.7 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3 Prototype Design 31
3.1 Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.2 Impersonation using Large Language Models . . . . . . . . . . . . . . 32
3.3 Multi Agent Conversation . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.4 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
4 Prototype Implementation 37
4.1 Infrastructure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
4.2 Impersonation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
4.3 Multi Agent Conversation . . . . . . . . . . . . . . . . . . . . . . . . . 41
4.4 Experiment Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
xiii
5 Evaluation 53
5.1 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
5.2 Introduction of impersonated historical politicians . . . . . . . . . . . 54
5.3 Quantitative Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
5.4 Qualitative Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
6 Conclusion 73
6.1 Key Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
6.2 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
6.3 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
List of Figures 75
List of Tables 77
List of Algorithms 79
Acronyms 81
Bibliography 83
Appendix - Generated Discussion Protocols 91
Climate Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
Economic Recession . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
User Specific Discussion Protocols . . . . . . . . . . . . . . . . . . . . . . . 102
CHAPTER 1
Introduction
1.1 Motivation
Large Language Models (LLMs) are significantly rising in popularity, especially since the
release of ChatGPT [XCG+23]. With only very limited instructions, LLMs are capable
of performing different natural language tasks [BMR+20b], which makes their use more
applicable in the real world and for a much broader audience. The applications of LLMs
are incredibly versatile and there seems to be almost no limit to their use [Opea]. Use
cases of LLMs range, but are not limited to, from summarizing text, translation and
content creation to assistance with math and programming questions.
When humans interact with such a LLM agent, we tend to assign it a personality, even
though we know that it essentially does, or at least, should not have one [tHWL+24].
This is largely due to human nature. When interacting with such a machine, because
the interaction is designed as a consistent dialogue, it starts to feel like we are talking
to another person. This perception leads to an interesting question: can we change
the personality of the LLM, or even task it to take on a specific personality? In
this thesis, we will test this. We will assign a (simulated) personality to a LLM, by
telling it to impersonate a famous politician of the past. By assigning multiple LLMs
different personalities, we "create" artificially intelligent political agents. We then expose
the artificial political agents with political issues, that have not been tackled by their
impersonated persona during its lifetime and ask them what their stance about these
issues is. In order to check if this stance is plausible, we will ask a user group to evaluate
this stance and compare it with the information we have about the real past personality.
The choice of historical politicians as personalities we want to impersonate is as follows:
Politics is shaping the course of the world and its countries. In democratic countries,
people can actively contribute to the political landscape of their country by voting for
their preferred party or politician. In order to get elected, the politician needs to be
1
1. Introduction
persuasive and rhetorically skilled. Therefore, impersonating a politician should be
interesting to a broader audience. The number of democratic countries worldwide is
rising, with over half of today’s countries having a democratic regime [Dem]. In many
democratic countries, we are facing a rise of populist parties in recent years, especially
in Europe [Pop]. Many of these populist parties are located at the left or right wing,
with the central parties being generally in decline. This shift is viewed as problematic
by many people. The reason for this shift is not the focus of this thesis, but builds the
bridge to history: "Those who cannot remember the past are condemned to repeat it." -
George Santayana, The Life of Reason, 1905. Understanding history can largely help with
understanding the current course of the world and the rise of some political ideologies.
Many historical politicians are widely known and earned their place in history books
through their achievements, be it in a negative or positive way. Now, it would be
interesting, to know the stance of some famous historical politicians about issues of our
time, the reader can then relate this to the stance of the modern political parties. Of
course, it is impossible to know what stance these historical figures would have today,
since they are dead - however, we can however try to "revive" them by implementing
their personality in a LLM. We will create artificial political agents of three historical
politicians, namely Winston Churchill, Karl Marx and Niccolò Machiavelli. The artificially
intelligent political agents will then discuss different political issues in a panel discussion,
automatically moderated by another LLM.
1.2 Research Questions
This thesis will mainly focus on the ability of LLMs to impersonate and the quality of
the resulting panel discussion. The research questions are as follows:
• RQ1: How effectively can a configured LLM adapt to and replicate the linguistic
styles and character traits of historical political figures, as perceived by a test
group?
• RQ2: What would different models, impersonating renowned politicians of the
past, say about modern problems that have not been addressed by them during
their lifetime?
• RQ3: How consistent and logically coherent are the arguments presented by AI
models tasked to impersonate historical political figures?
• RQ4: In what ways does the psychological profile of an AI model evolve, when it
is used to impersonate historical political figures?
1.3 Approach
Before starting with the technical implementation, three suitable historical figures that
left sufficient texts and are well known had to be selected. Many historical politicians
2
1.4. Thesis Structure
left written artifacts, such as autobiographies, speeches, discussion protocols, etc., that
can be used to feed a LLM. Winston Churchill, for example, has been granted the Nobel
Prize for literature and is therefore an optimal politician with sufficiently available data
[Chu].
The simulated political discussion should not be too one-sided, as we want the agents
to provide interesting insights and bring different point of views into the discussion. As
opponents of Winston Churchill we choose Karl Marx and Niccolò Machiavelli. While
Winston Churchill can be defined as a conservative, capitalist supporter, Karl Marx
is rather the opposite, being a revolutionary communist during his lifetime. Niccolò
Machiavelli finishes the list of panelists, supporting an authoritarian regime in his
literature. A more detailed introduction of the chosen politicians can be found in Section
5.2.
In the next step, after the politicians are selected, the LLM needs to learn to impersonate.
This is largely done by a combination of prompt engineering [Eki23] and a method called
Retrieval Augmented Generation (RAG). RAG uses provided texts, which the LLM can
then reference before answering [GXG+23]. In our use case, RAG is especially useful for
the artificially intelligent political agents to provide specific examples to support their
arguments during the discussion. This should increase the performance of the arguments
and make the discussion feel more alive. Next, a novel discussion setup that enables
multiple AI agents to have a discussion must be developed. By deploying the different
AI agents into the discussion setup, we can then generate a simulated panel discussion.
The evaluation will mostly focuse on qualitative feedback of five participants through
a survey and an interview. Each participant will evaluate the performance of the
impersonation and the discussion itself on three different discussion protocols, where
the first discussion protocol is about a topic selected by the participant, the other two
discussions are about Climate Change and Economic Recession. In order to evaluate
the impersonation quality in a quantitative manner, the psychometric profile of the
impersonations will be compared by conducting the Big Five personality test [Big].
1.4 Thesis Structure
The structure of the thesis is as follows. First, the theoretical background, focusing on
the development of natural language processing up to LLMs, will be given. Towards
the end of this chapter, related work will also be presented. Chapter 3 will then focus
on the design of the implementation of this thesis, while chapter 4 will explain the
implementation of the prototype in detail. Next, chapter 5 will explain the evaluation
methods and present the results. Finally, a conclusion including limitations, key findings
and potential further work will be presented in chapter 6.
3

CHAPTER 2
Theoretical Background
This chapter will introduce the main theoretical background for this thesis, starting
with the introduction of machine learning, traditional natural language processing (NLP)
techniques, the development of neural networks (ANNs), transformers and reinforcement
learning (RL) until the invention of modern Large Language Models (LLMs). Then, three
different LLMs will be presented, while the main focus lies on the GPT architecture,
which is being used in this thesis. Finally, related work will be presented.
2.1 Machine Learning
Machine Learning means, as the name suggests, that a machine is able to learn a task
based on available training data. Generally speaking, machine learning evolved from
traditional statistics and is a generic term describing many different methods where a
computer program learns to perform a specific task, based on provided observations,
called the training data. In order for the computer to learn from the provided experiences
in the training data, a performance measure needs to be introduced, that measures
how well the machine performs on the task. The machine then tries to optimize the
performance metric during the learning process. [MCM13]
We distinguish between two different main methods of machine learning: supervised and
unsupervised learning. While supervised machine learning methods use a training set
including a target variable, which we want to learn to predict, unsupervised methods
do not include such a target, meaning the data is not labeled [BMY19]. An example
of a supervised learning task is to predict a flower type. The training set contains of
basic information of our observed flowers, such as sizes etc., which are our features. The
target variable is then the correct flower type, which is included in our training data.
Unsupervised tasks use unlabeled data, an example of such a task would be a dataset
consisting of images of different animals. The goal is then to cluster these images, where
each cluster ideally would represent a single type of animal.
5
2. Theoretical Background
Understanding the basic concept of Machine Learning is necessary in order to grasp
the more advanced methods which are used by LLMs. LLMs are primarily used for the
implementation of this thesis and will therefore be explained in more detail. The next
sections will build up to this goal.
2.2 Natural Language Processing
Processing of natural language is not an easy task for a computer program, a broad
research field dealing with this issue is called Natural Language Processing (NLP). Natural
Language Processing is part of Artificial Intelligence (AI) and can broadly be separated
into Natural Language Understanding and Natural Language Generation [KKKS23].
Tasks of NLP are e.g. text classification, text generation or text segmentation. Generally
speaking, NLP aims to improve the understandability of natural language by a computer.
2.2.1 History of Natural Language Processing
Research in this field has been going on since the 1940s, where originally it was conducted
quite locally and in different languages [KKKS23].
In 1966, Joseph Weizenbaum introduced a rule based computer program called ELIZA,
designed to interact with the user in natural language. The program has access to a
dictionary of words, which it uses when reading the prompt of the human. If one word of
the prompt matches, synonyms or more general terms matching this term are looked up
in the dictionary and a predefined response matching that similar term is given. As it is
to be expected, the performance is quite bad as the program does not understand the
context and if there is no match with the dictionary, no response can be given. [Wei66]
In the 1980s, the NLP research shifted from rule based to statistical methods, such as e.g.
Hidden Markov Models (HMMs). HMMs work as a sequence based prediction model. As
the model cannot work with text as is, the input text has to be preprocessed for further
use. One technique, which is used in sequence prediction models, is that each sentence
of n words is being regarded as a sequence of n observations {x1, . . . , xn}. The goal is
now to train the model to predict a sequence of hidden labels {y1, . . . , yn}. This can be
seen as a supervised learning problem with an extra complexity, as yi is often dependent
on yi−1. HMMs assume that yi only depends on the previous label yi−1 and xi is only
dependent on yi. HMMs are also often used in time series problems, which is of similar
nature: In the context of NLP we exploit the fact that sequences of words are often likely
to occur after each other.[ID10]. HMMs use cases are e.g. Part-of-Speech Tagging, where
each word of a sequence gets assigned a part of speech (noun, verb, etc.), contributing to
Natural Language Understanding.
Ten years later, in the 1990s, machine learning methods became more popular for NLP
tasks. We already mentioned that machine learning models generally cannot deal with
unstructured text - as a solution, in order to represent the text with numeric values,
many approaches have been tested. One very basic solution is the bag-of-words model,
6
2.3. Neural Networks
which basically counts the number of occurrences of each word in a text. Since it does not
account for word order, its performance is limited. Another widely used method is n-grams.
The idea is to split the text into sequences of length n, a one-gram (unigram) contains
one word per sequence, a two-gram (bigram) two adjacent words. For example, the
sentence "I like your dog" would result in the following bigrams: ["I like", "like your", "your
dog"]. In contrast to the bag-of-words representation, n-grams allow the representation
to capture word order. There are many other methods for text representation, each with
their advantages and disadvantages given a specific NLP task, which we will not cover in
more detail. The representation of the text can then be used by machine learning models.
Efficient models that are frequently used include Support Vector Machines and Decision
Trees [ID10]. We will not go into the details of the workings of said machine learning
methods since it would go beyond the scope of this thesis.
In 2003, the best statistical algorithm at that time (word-n-gram) was outperformed
by a multi-layer perceptron, which is another name for a feed-forward artificial neural
network [BDVJ03]. This was the beginning of the modern time of NLP research, which
will be introduced in the following sections.
2.3 Neural Networks
Before diving into the importance of neural networks for NLP, the concept of neural
networks will be explained. Neural networks, also known as artificial neural networks
(ANNs), are inspired by the functionality of our human brain. The learning behavior
of the model is supposed to work similarly to the learning behavior of a human. On a
very basic principle, our brain works by sending a small electrical signal from neuron
to neuron through synapses. Each neuron then decides how to process this signal, and
which other neurons to activate, i.e. forward the signal to. This methodology is simulated
by ANNs, where the model aims to simulate the neurons and teach them during the
training process, which other artificial neurons it should forward a signal to, and at which
strength. [JMM96]
2.3.1 Artificial Neuron
An artificial neuron is one building block of an artificial neural network. As an input, the
neuron receives n values {x1, x2, . . . , xn}. Internally, the neuron has a weight assigned
to each of these input values, {w1, w2, . . . , wn}. Every neuron also stores a bias θ. The
overall functionality of an artificial neuron is to process all the inputs with their respective
weights, add the bias and calculate an output signal y, which then can get forwarded to
other neurons. In order to calculate y, an activation Function F is needed. The equation
for calculating y of our artificial neuron is as follows:
F (
n
m=1
wm ∗ xm + θ) (2.1)
A visual representation of the inner workings of a neuron can be seen in Figure 2.1.
7
2. Theoretical Background
Figure 2.1: The structure of an artificial neuron [YB18]
Figure 2.2: Four popular activation functions for ANNs [Loa20]
There are many different activation functions, which one to choose is dependent on the
task and often needs experimentation, four commonly used activation functions can be
found in Figure 2.2.
2.3.2 From Neuron to Network
One single neuron has limited power, the strength of an ANN lies in the connection
of multiple neurons. By connecting multiple neurons, we obtain a Neural Network.
Typically, neural networks consist of multiple layers, each consisting of multiple neurons.
By ordering the layers sequentially after each other, the neural network is able to depict
complex functionalities. There a practically no limits to how we can design an ANN,
typically we measure the size of a network by counting the number of layers it has, and
the number of neurons in each layer. An example of a very basic ANN can be seen in
8
2.3. Neural Networks
Figure 2.3: Example of an ANN [AIAOE+21]
Figure 2.3. It consists of four layers, i.e. one input layer, two hidden layers and one
output layer. The input layer takes the input, in a numeric form, and forwards it to the
first hidden layer. The hidden layers perform most of the functionality of the network.
Finally, the last hidden layer forwards its output to the output layer, which then decides
what prediction the network will give. The functionality of neural networks is hard to
understand for humans, since networks used in the real world often have millions to
billions of neurons, therefore another broad research area focusing on the explainability
of ANNs developed. [JMM96]
2.3.3 Training Process
When starting the training process, the weights and biases of all neurons are randomly
initialized. During the training process, the weights and biases of the neurons of the
ANN are being updated. This is done by a process called backpropagation. When a
training observation is being passed through the network, we already know which output
we would like the model to give and compare it with the output it gave. We calculate
the difference between the expected and actual output by using a loss function L, the
Loss decreases when the model is getting closer. There are many different Loss functions
available, which Loss function to use also depends highly on the task. For understanding
the concept of the Loss function, it is sufficient to know that the Loss function is a metric
for the performance of the model, where our goal is to minimize the Loss.
We adapt the weights and biases of the neurons during the backpropagation process. This
is how the ANN learns. One way to do this is, as already mentioned, backpropagation.
The weights w of our neurons are changed slightly, using the obtained loss L and a
9
2. Theoretical Background
learning rate η:
∆w = η
δL
δw
(2.2)
During the backpropagation, the error of the model is being propagated backwards, thus
its name. The network in our example is a feed-forward neural network, meaning that the
flow of the activations is only in one direction - forward. There are other network designs,
such as e.g. recurrent neural networks, where loops can occur, because of feedback
connections [JMM96].
This section gave a general overview about the workings of a neural network. There
exists many advanced techniques that are applied in modern research to improve the
performance of neural networks. Explaining these methods in detail would go beyond
the scope of this thesis.
2.4 Transformers
Transformers are a specific neural network architecture that use the concept of attention.
They were introduced by researches of Google Vaswani et al. in 2017, in the famous
paper "Attention is All you Need" [VSP+17]. The original use case of the transformer
architecture was language translation, however the transformer architecture is also the
main building block for LLMs, especially for the GPT architecture which will be presented
in a later section of this chapter.
An overview of the transformer architecture can be seen in Figure 2.4. The architecture
is quite complex, but can be broken down into the following subparts:
• Word Embedding
• Positional Encoding
• Encoder and Decoder Stacks
• Self Attention
• Encoder-Decoder Attention
• Position-wise Feed-Forward Networks
2.4.1 Word Embedding
As neural networks also cannot handle text as-is as an input data, the words (tokens)
need to be converted into a numeric format. This is done by a simple neural network,
that converts each token to a vectorized representation. The vector is of a fixed size,
which can be decided in advance. A larger vector size for the word embedding lets it
capture more extensive vocabulary.
10
2.4. Transformers
Figure 2.4: Transformer model architecture [VSP+17]
2.4.2 Positional Encoding
In natural language, the position of each word matters. The sentences "Dog bites man."
and "Man bites dog" have the same word occurrences, but very different meaning. To
capture the position of each word in the transformer architecture, a positional encoding is
applied. Sinusoidal functions are used in order to assign each word of the input a unique
positional encoding. The formula for calculating the positional encoding of a token is as
11
2. Theoretical Background
follows:
PE(pos,2i) = sin pos
10000 2i
d
(2.3)
PE(pos,2i+1) = cos pos
10000 2i
d
(2.4)
Note that in equations 2.3 and 2.4 d is the embedding size. One example of such a
positional encoding of the first position for an embedding with size 4 is as follows:
PE(0,0) = sin 0 = 0 (2.5)
PE(0,1) = cos 0 = 1 (2.6)
PE(0,2) = sin 0 = 0 (2.7)
PE(0,3) = cos 0 = 1 (2.8)
The larger the embedding size, the larger the size of the positional encoding becomes,
allowing our transformer to capture longer sequences at once. As the used functions
are periodic, the recurrence is decreased by the equations 2.3 and 2.4, the longer the
positional embedding becomes. This way, each word in the embedding gets a unique
periodic encoding assigned.
2.4.3 Encoder and Decoder Stacks
The transformer models use two main stacks: an encoder, and a decoder. These two
networks do not share their parameters, as opposed to the word embeddings, which are
all applied to the same network.
The Encoder is responsible for further improving the representation. By now, the tokens
are only embedded in a vector space, but the context of the tokens, such as similarity
between tokens in the sequence, have not yet been captured. So in the next step, the
embeddings including the positional encoding are further processed. The encoder consists
of stacked layers which are identical. According to Vaswani et al. [VSP+17], six stacked
layers were used in the original paper. Each stacked layer consists of two sub-layers:
1. Multi-Head Self Attention: Instead of using only one attention head, multiple heads
capture diverse aspects of the relationship between the tokens. The concept of
attention will be explained later in this section.
2. Position-wise fully connected feed forward network: This is a rather simple form of
a network which applies a non-linear transformation of each token representation
independently.
Around each of the sub-layers, a residual connection is employed, followed by layer
normalization. This stabilizes and improves training.
The Decoder is the counterpart to the encoder, which is responsible for creating the
output of the model, in the original case of the "Attention is All you Need" paper, this
12
2.4. Transformers
was text translation [VSP+17]. The basis of the decoder is of similar structure as to
the encoder - six stacked layers are used. In addition to the two sub-layers we have in
the encoder stack, a third sub-layer is added in the decoder, which performs multi-head
attention over the output of the encoder stack. This step is called the Encoder-Decoder
Attention. Again, residual connections between each sub-layer are employed, followed by
layer normalization. The self-attention sublayer however is masked, so no relations to
tokens from the future are made.
A graphical representation of the transformer network architecture can be seen in Figure
2.4.
2.4.4 Attention
The concept of attention is essential of the transformers network architecture. Attention
is defined by relating a query to other keys, obtaining a value. For better understanding
why we need attention and what its concept is, we will use a simple sentence: "Alice
went to the store. She bought some milk.". The model benefits from understanding
how the words in the sentence relate to each other, in our case it is important for the
understanding that "She" refers to "Alice". As mentioned before, the model cannot handle
textual data, therefore every token (word) is embedded into a vector. Now every token
will be matched by the attention mechanism with every other token of the text, obtaining
a vector of values. The attention value should be high if there is a strong connection,
in our case between "She" and "Alice", and low between tokens that are weakly or not
connected at all. Vaswani et al. use the dot product to calculate the attention value
between two tokens [VSP+17].
Attention(Q, K, V ) = softmax(QKT
√
dk
)V (2.9)
The dimensions of the keys is expressed by dk.
Self attention is used in the encoder and decoder stack to relate the tokens within the
stacks, whereas encoder-decoder attention means that only the queries of the decoder
are matched with the keys of the encoder, connecting the encoder and decoder.
2.4.5 Advantages
Now that the main concepts of the Transformer architecture have been explained, a brief
overview of the advantages of Transformers compared to previously used architectures
will be given.
The attention principle allows the network to mimic the cognitive attention mechanism of
our human brains. With the information about how important a specific word of a text
is, and how it relates to other words, significant improvements are achieved in multiple
NLP tasks. Previously, many architectures used recurrent neural network structures,
that are also able to capture a state of sequences, i.e. memorize which part of the text it
13
2. Theoretical Background
is currently handling and capture the semantics of the language. However, transformers
achieve this functionality on a much broader context window and with significantly less
training time. The Transformer architecture is a basis for many modern LLMs. [VSP+17]
[RNS+18]
2.5 Reinforcement Learning
2.5.1 Introduction to Reinforcement Learning
Reinforcement Learning (RL) is a subfield of Machine Learning, with a different approach
to learning. Instead of using a supervised training dataset, the model is placed in an
(often simulated) environment, where it is allowed to take actions a that affect its state s.
The goal is to try out different actions, and learn which actions lead to a desired state.
[SB18]
This approach is very similar to the natural process of learning: we, as humans, are placed
in an environment, and through our life we are in a constant process of reinforcement
learning. We take actions which affect us in a positive or negative manner, thus we learn
and change our behavior to repeat those actions that had a positive effect and avoid
actions which had a negative effect. [SB18]
2.5.2 Basic Concepts
Reinforcement Learning is well suited for complex problems, where the impact of the
actions the model (agent) can perform are not easy to estimate in advance. The
consequence of an action is evaluated in a dynamic manner by changing the state of the
agent in the environment, given the selected action. After evaluating this new state, a
reward or punishment is given. Common problems where RL is applied range from AI in
computer games over robotics to large language models. [Li19]
The base components of RL are:
• Agent: the machine itself, which is placed in the environment and has the power to
perform actions.
• Environment: ideally simulated, that aims to depict the real environment as closely
as possible. The environment is crucial for the development of the RL model,
since errors in the environment would transfer to malfunction in the real world. A
simulated environment is optimal, since then the learning process is significantly
faster as the evaluation of the states can be done in the simulation.
• State (s): The state describes the current situation of the environment, i.e. where
the agent is placed and the status of the environment.
• Action (a): Choices which are available for the agent in the current state.
14
2.5. Reinforcement Learning
• Reward (r): The reward the agent receives after performing an action. This can be
positive, negative or zero.
• Policy (π): The strategy used to decide the next action.
• Value Function (V ): Predicts the long time award of being in a current state given
a policy.
• Q-Function (Q): Predicts the long time award of taking a specific action given a
current state and a policy.
Properly defining the reward is crucial for the learning process - RL aims to maximize
the cumulative award, so it is important to only reward actions that lead to a desired
outcome. As an example: when a RL model should learn to play chess, it would not
be beneficial to provide a reward for a good position in the game, since then the model
might learn to aim for a good position instead of winning the game, i.e. putting the
opponent in a checkmate. As a solution for this specific issue, only giving a reward for
actually winning a game would be possible. This leads to other issues, as chess is a
quite complex game and too many different possible states exist, significantly increasing
the computational cost of finding a well-working RL solution. Defining a proper reward
function and using heuristics to increase the training ability of RL is ongoing research.
[SB18]
There are many approaches on how to develop a RL model, we will briefly introduce
some basic solutions in the next section. One important configuration is the tradeoff
between exploration and exploitation. Exploration means trying out a new task given
a situation, in order to test the outcome. Exploitation means acting based on the best
known outcome. In the beginning of the learning process, RL methods are generally
more focused on exploration, this changes later once the model knows which actions
are favorable - then exploitation is usually the predominant action. Very complex tasks
make it impossible to test out every available action for every available state, therefore
exploring the whole spectrum is not feasible and a tradeoff between exploration and
exploitation must be made. [SB18]
2.5.3 Algorithm Examples
In the following sections some common RL algorithms are introduced.
Q-Learning
Q-Learning is a popular algorithm because of its simplicity. It is guaranteed to find the
optimal policy for any finite number of states and possible actions, given a partly random
policy and infinite exploration time. [Mel01]
Q-Learning uses a function Q(s, a) which returns for a specific action at a given state the
expected reward. This function needs to be learned, therefore the - in theory - infinite
15
2. Theoretical Background
exploration time is necessary, to find the optimal policy. In practice, the exploration
time is dependent on the complexity of the environment, i.e. how many states and
actions are available, as in the Q-Learning algorithm, the Q(s, a) function for all possible
combinations of states and actions shall be learned. The algorithm is described in
Algorithm 2.1.
Algorithm 2.1: Q-Learning
Input: States X = {1, . . . , nx},
Actions A = {1, . . . , na}, A : X ⇒ A,
Reward function R : X × A → R,
Black-box (probabilistic) transition function T : X × A → X ,
Learning rate α ∈ [0, 1], typically α = 0.1,
Discounting factor γ ∈ [0, 1]
1 while Not Converged do
2 Start in state s ∈ X
3 while s is not terminal do
4 π(x) ← argmaxa Q(x, a)
5 a ← π(s)
6 r ← R(s, a)
7 s′ ← T (s, a)
8 Q(s, a) ← Q(s, a) + α [r + γ maxa′ Q(s′, a′) − Q(s, a)]
9 s ← s′
10 end
11 end
Q-Learning is a so-called model-free learning algorithm, as it does not use a machine
learning model for the calculation of the policy function, but relies on enough exploration
time, testing out different actions on any given state. As already mentioned, when the
problem becomes more complex and the state and action space grows, the computation
of the q-learning algorithm becomes very expensive, as the number of state and action
pairs that need to be evaluated grows exponentially. [Li23]
Deep Q Network
Another example for a RL algorithm is Deep Q-Learning. As the name suggests, this
algorithm is an extension of the Q-Learning algorithm, using a deep neural network to
estimate the Q-Values. The usage of a deep neural network allows Q-Learning to be
feasible for more extensive state and action spaces, as the Q-Values no longer need to be
calculated for every state-action pair, but are approximated by the deep neural network.
Of course, since a network is now used, the algorithm is no longer model-free. [MKS+15]
The algorithm has some differences to the traditional Q-Learning algorithm, introduced
in Algorithm 2.1. Instead of storing the state, action pairs in a table, the neural network
is storing the Q-Values. The first Q-Values are calculated the same way as in the original
16
2.6. Large Language Models
Q-Learning algorithm, but the observations are then stored in a replay buffer. This
replay buffer is then used to update the parameters of the deep neural network, using
backpropagation. The samples for the Q-Value updates are randomly sampled from
the replay buffer, increasing the learning stability by avoiding consecutive state-action
pairs and thus increasing the generalization of the model. During the training of the
network, fewer interactions with the environment are needed in comparison to standard
Q-Learning, since the model’s performance to predict the Q-Values will increase and
will reach a point, where the approximation is good enough for the policy selection.
[MKS+15]
2.5.4 Reinforcement Learning in Large Language Models (LLMs)
In Large Language Models, especially in GPT, Reinforcement Learning is used for fine-
tuning the model. Feedback from users is gathered and evaluated using RL, trying to
optimize the performance of the LLM. This learning process using human feedback is
also called Reinforcement Learning from Human Feedback (RLHF). [RNS+18]
The application of RL in LLMs will be explained in more detail later in this work, in the
paragraphs introducing GPT-3.5 and GPT-4 and in section 2.6.1.
2.6 Large Language Models
Large Language Models (LLMs) have risen in popularity over the last years, especially
with the release of ChatGPT in 2022 [XCG+23]. The term LLM describes the main
characteristic of the models - they are large and require significant amounts of computation
to train. LLMs achieve general-purpose language generation and many different natural
language processing tasks, such as classification and text generation [RWA+19]. The
technology that enabled the development of LLMs is the Transformer architecture, which
has already been explained in Section 2.4.
In the following sections, we will explain some famous LLM architectures, where the
focus lies on the GPT architecture, as it is used for the development of this thesis.
2.6.1 GPT
GPT is an abbreviation for Generative Pre-trained Transformer. Its name includes the
most important aspects of the model:
• Generative: the model is able to generate text.
• Pre-trained: The overall Idea of GPT is to use large amounts of unlabeled text
data for training, as labeled data for learning specific tasks is scarce. Therefore,
the solution is generative pre-training, using unlabeled textual data, followed by
discriminative fine-tuning on each specific task.
17
2. Theoretical Background
• Transformer: As a model architecture, an adaption of the Transformer architecture
is being used.
GPT is a so-called next-term predictor, which means, that each word is being generated
based on its maximum likelihood, that it should occur next, given the current state of the
output. In practice, the model starts with an empty sentence and then starts generating
the content word after word until the model decides that the generation is complete, i.e.
a specific token is generated by the model. [RNS+18]
GPT-1
The first GPT architecture, GPT-1, was developed by OpenAI’s researchers Alec Radford,
Karthik Narasimhan, Tim Salimans and Ilya Sutskever in 2018. The features of GPT
are language understanding and generation, and a general task flexibility, allowing it to
perform different tasks with limited instructions. [RNS+18]
Model Structure: The model structure of GPT-1 is a twelve-layer decoder-only
transformer with twelve masked self-attention heads and 64-dimensional states for each.
For learning, the Adam optimization algorithm is used, which is a more sophisticated
approach to update the model’s parameters in order to increase its performance. During
the training process, the learning rate is increased linearly from zero to 2.5 × 10−4.
[RNS+18]
The original GPT model architecture compared to the standard Transformer architecture
can be seen in Figure 2.5. In total, the model consist of 117 million parameters [Ali23].
The training process of GPT can be divided into a Pre-Training Phase and a Fine-tuning
Phase.
Generative Pre-training: The first step of the training phase is the unsupervised
pre-training, also called generative pre-training. During this stage, the model is given
an unsupervised corpus of tokens U = {u1, . . . , un}. The objective is to maximize the
following likelihood:
L1(U) =
i
log P (ui | ui−k, . . . , ui−1; Θ) (2.10)
where k is the size of the context window and P is modeled by a neural network with
parameters Θ. The parameters of the neural network are trained using stochastic gradient
descent. [RNS+18]
The GPT Transformer decoder used by the model architecture is then trained using this
loss function:
h0 = UWe + Wp (2.11)
hl = transformer_block(hl−1∀i ∈ [1, n]) (2.12)
P (u) = softmax(hnW T
e ) (2.13)
18
2.6. Large Language Models
Figure 2.5: GPT-1 model architecture [HJJ23].
where U = (u−k, . . . , u−1 is the context vector of tokens, n the number of layers, We the
token embedding matrix and Wp the position embedding matrix. [RNS+18]
For the pre-training process, the model is trained on a very large corpus of texts from
different domains, using Common Crawl, which includes billions of websites [Fou24],
and the BookCorpus dataset, containing around 11,000 books [ZKZ+15] [Ali23]. This
increases the generalization of the model and ensures its performance across multiple
domains. Data bias and false information in the training data however continues to be
an issue. [ZFP+23]
Discriminative Fine-tuning: The next step, after pre-training, is fine-tuning. This
is done in a supervised manner, also called discriminative fine-tuning. The parameters of
the pre-trained model are adapted for specific supervised tasks during this step. In order
to do that, a labeled dataset C is needed, with each instance consisting of a sequence of
19
2. Theoretical Background
Figure 2.6: Fine-tuning for the GPT-1 architecture [RNS+18].
input tokens x1, . . . , xm, along with a corresponding label y. First, the inputs are passed
through the pre-trained model to obtain the final activation of the GPT Transformer,
which is the forwarded into another linear output layer with parameters Wy which learns
to predict y:
P (y | x1, . . . , xm) = softmax(hm
l Wy) (2.14)
where hm
l is the output of the GPT Transformer. Equation 2.14 allows us to obtain
another maximization objective:
L2(C) =
(x,y)
logP (y | x1, . . . , xm) (2.15)
In order to improve the generalization and to accelerate the convergence of the model,
the authors included language modeling as an auxiliary objective. [RNS+18]
Now, the fine-tuning process of GPT-1 has to be carefully designed for different tasks. For
text classification, the model can be directly fine-tuned, as explained earlier in this section.
Other tasks, such as e.g. question answering or textual entailment, use structured inputs
as for example sentence pairs or triplets of document, questions and answers. In order to
fine-tune the model on these tasks, modifications need to be applied. The modifications
used in GPT-1 can be seen in Figure 2.6. [RNS+18]
Performance: The performance of GPT was, at the time of publication, superior to
other state-of-the-art language models. The biggest strength is its ability to generate
language in a fluent and coherent way, given a prompt or context. Limitations include
repetitiveness, especially noticeable when the prompt is about a topic outside the scope
of its training data. Its reasoning ability is also limited, especially over multiple turns of
dialogue it "forgets" earlier context. Additionally, longer input context result in a lack of
cohesion. [Ali23]
20
2.6. Large Language Models
Figure 2.7: Zero-shot task performance of GPT-2 with increasing parameter size
[RWC+19].
GPT-2
The successor to GPT-1, GPT-2, was released in 2019 and first introduced in the paper
"Language Models are Unsupervised Multitask Learners" [RWC+19]. One of the biggest
improvements over GPT-1 is the model size: GPT-2 consists of 1.5 billion parameters,
which is considerably larger compared to the 117 million parameters of GPT-1 [Ali23].
As the title of the paper introducing GPT-2 suggests, the focus of the GPT-2 architecture
lies on the ability of the LLM to perform multiple different tasks without the need for fine-
tuning. By significantly increasing the number of parameters, the model begins to learn
these tasks without any explicit supervision. GPT-2 is trained on more data, including
millions of webpages included in the WebText dataset, introduced by OpenAI.[RWC+19]
GPT-2 outperformed many state-of-the-art models on several natural language tasks at
the time of publication. One example is its performance on question answering based on
a document. The generated answers reach a 55 F1 score on the CoQA dataset, which
matches or exceeds the performance of three out of four baseline systems, all without
using the over 127,000 training examples provided. The ability to perform a task, on
which the model is not fine-tuned, is called zero-shot task transfer. The success of
zero-shot task transfer increases in log-linear fashion with the number of parameters of
the model, across multiple tasks, as it can be seen in Figure 2.7.
Limitations of GPT-2 are complex reasoning and understanding of context. On short
paragraphs, it performs considerably well, with longer input, it fails to maintain context
and coherence. [Ali23]
GPT-3
The third GPT model, GPT-3, received wide media attention thanks to its use in
ChatGPT in 2022 [XCG+23]. The GPT-3 model however was already introduced in 2020
in the paper "Language Models are Few-Shot Learners", written by OpenAI researchers
[BMR+20a].
21
2. Theoretical Background
Improvements over the predecessor are again the parameter size. With 175 billion
parameters, it is over ten times larger than GPT-2. The datasets used for training were
also extended and include nearly a trillion words. [Ali23]
Few-shot learning: The focus of GPT-3 lies on few-shot learning, which aims to allow
the model to learn a task with only a few examples. Instead of zero-shot learning, as
introduced in GPT-2, and compared to the fine-tuning introduced in GPT-1, few-shot
learning works with limited training samples which can be seen as a feasible compromise
since it does not require large amounts of task specific training data. The performance
of few-shot learning is again increased by the number of parameters of the model.
[BMR+20a]
Instead of fine-tuning the parameters of the model, the pre-trained GPT-3 model is
tasked purely by text interaction. This approach shaped the term prompt engineering. A
prompt is the text the model receives as a task description. In practice, some prompting
techniques proved to be more efficient than others, improving the performance of the
model on specific tasks. [Eki23]
The performance of GPT-3 using few-shot learning is reported to outperform or match the
performance of many state-of-the art language models on a wide variety of different tasks,
including translation, question-answering and even tasks that require on-the-fly reasoning
or domain adaption such as unscrambling words or performing 3-digit arithmetic. We
will not provide an exhaustive result list for GPT-3 in this thesis, however the results
of zero-shot, one-shot and few-shot performance for different GPT-3 model sizes on
the PIQA, Physical Interaction: Question Answering, [BZB+19] dataset are shown in
Figure 2.8. The PIQA dataset was introduced in the paper: "Reasoning about Physical
Commonsense in Natural Language" by Bisk et al. and focuses to test the ability for
reasoning and common sense.
GPT-3.5 GPT-3.5 is based on GPT-3 and is adopted to be accessible to a wider
audience. This is done by decreasing the parameter size to 1.3 billion and implementing
Reinforcement Learning from Human Feedback (RLHF) to increase its performance on
several different tasks by collecting feedback from the users about the quality of the
model’s response. GPT-3.5 is accessible via an Application Programming Interface (API),
allowing users to programmatically access the model without the need to install it on a
local machine. [iff24]
GPT-4
In 2023 GPT-4 was introduced, but the technical details have not been published in the
same detail as for previous versions of GPT. Instead, a technical report has been published
by OpenAI [Ope23]. The focus of GPT-4 lies on transforming the GPT architecture to a
multimodal model, allowing it to accept image and text inputs to produce text outputs.
By the time of writing this thesis, GPT-4 is the latest model of OpenAI and is primarily
used for the implementation part.
22
2.6. Large Language Models
Figure 2.8: Performance of GPT-3 on PIQA using zero-shot, one-shot and few-shot with
different model sizes [BMR+20a].
Speculations are, that the parameter size of GPT-4 also increased significantly compared
to previous versions, estimated to be in trillions, however exact numbers are unknown.
Unfortunately, we also have no information about its training data and sequence length.
[Ali23]
Fine-tuning: Fine-tuning of GPT-4 was and is still done using Reinforcement Learning
from Human Feedback (RLHF). By asking the users to rate the response quality of the
GPT model, the aim is to use reinforcement learning to improve the model’s performance
and tune it to the needs of its users. To test the performance of RLHF, it was evaluated
against the base model on different multiple-choice question exams across different
domains. The experiments showed that RLHF improved the capability of the base model
on several different tasks, reaching an average score of 74%, compared to 73.7% of the
base model. [Ope23]
Predictable Scaling: One challenge that arises with increasing model parameter sizes
is the scaling of the model. In the paper introducing GPT-3, the issue of training time and
resource consumption has already been briefly addressed [BMR+20a], the technical report
introducing GPT-4 presents further improvements. One section focuses on predictable
scaling, aiming to tackle the issue of very large model sizes by introducing more efficient
loss prediction and performance prediction given the model’s size. [Ope23]
23
2. Theoretical Background
Figure 2.9: Performance of GPT-4 on several different academic and professional exams,
compared to its predecessor, GPT-3.5 [Ope23].
Performance: The performance of GPT-4 exceeds the performance of previous LLMs
and can be considered to be state-of-the-art for a wide range of NLP tasks. The
performance of the model was evaluated on exams that were originally designed for
humans. Reportedly, no specific training for these exams was done, however some exam
questions were included in the data used for pre-training. These already known questions
were removed from the exam during the evaluation. The results can be seen in Figure
2.9. It is noteworthy, that GPT-4 exhibits human-level performance on the majority of
these professional and academic exams [Ope23]. However, it has also to be noted that
there exists research [PCS24], showing that GPT-4 performs significantly worse on real
out-of-sample reasoning tasks, therefore these results should be taken with a grain of
salt. GPT-4 is not able to obtain a real semantic understanding of problems, it has not
been trained on during pre-training. Therefore, fine-tuning the model to solve specific
novel tasks is often necessary. In the results shown above, it is likely, that the questions
that were included in the pre-training phase, but removed during evaluation, improved
the reasoning capabilities of the model on these tasks. All in all, GPT-4 understands
more complex prompts than previous GPT versions, and has a larger context windows,
allowing it to better remember previous content during a session [Ali23].
24
2.7. Related Work
2.6.2 Gemini
Gemini is the name of the LLM developed by Google. Over time, different versions of
Gemini have been released, the latest versions are Gemini Ultra, Pro and Nano. The
Ultra version is the largest and most capable mode, Gemini Pro is smaller and offers
a compromise between scalability and performance, while Gemini Nano is the smallest
model, optimized for on-device tasks [SP24].
Gemini is a multi-modal LLM, allowing it to work not only on text, but also with
images, video and audio. The exact model architecture has not been published, however
the developers report that it is built on top of Transformer decoders, enhanced with
improvements of the architecture and model stability. The model supports up to 32k
context length, by employing an efficient attention mechanism. The performance of
Gemini Ultra is reported to be comparable to the performance of OpenAI’s latest GPT
model, GPT-4. Gemini Pro outperforms GPT-3.5 on several text-based benchmarks.
[GT24]
2.6.3 LLama
Llama, acronym for Large Language Model Meta AI, is the name of another famous
family of LLMs, developed by Meta AI. The first model was released in February 2023
[Met23], while the latest model, LLama 3 was introduced in April 2024 [Met24]. For the
purpose of this thesis we will focus on the latest published model architecture.
LLama is open-source, meaning that the parameters and weights of the model are
published. In its latest version, LLama 3, two different model sizes are offered, one with
8 billion parameters, and a larger one with 60 billions. The model architecture is, similar
to GPT, a standard decoder-only Transformer architecture, supporting a vocabulary of
up to 128k tokens. In order to improve the inference efficiency, grouped query attention
is implemented. [Met24]
The largest LLama 3 model has over 400B parameters and is still in the training process.
LLama 3 is reported to outperform GPT-3.5. Due to the fact that LLama is open-source,
it is a commonly used LLM by researchers. [Met24]
2.7 Related Work
One foundational work for this thesis, especially for the evaluation part, has been
done by Pellert et al. [PLW+23], who evaluated the psychological profiles of different
LLMs through psychometric inventories. The principal idea was that LLMs inevitably
acquired psychological traits from the vast amounts of training data they were trained on.
Although psychometric inventories were originally designed for assessing non-cognitive,
i.e. character traits of humans, metaphorically speaking, LLMs also acquired such traits.
The differences of these traits in different LLMs were evaluated in their work. The used
approach for the evaluation and the main results of this research can be seen in Figure
25
2. Theoretical Background
Figure 2.10: Approach for psychometric profile assessment of LLMs and main results of
[PLW+23].
2.10. These results led to the idea to evaluate the impersonation power of our agents
similarly in a quantitative manner, by using the Big Five Personality test [Big]. Our
results will be presented in section 5.3.
There has been no directly related work published, which aims to use LLMs for imperson-
ating historical politicians and letting them go head-to-head in a panel discussion about
modern political issues, however other work has been done which relates to our problem.
For better readability, we will classify other related works into two categories: Imperson-
ation 2.7.1 and Multi Agent Conversation 2.7.2, as these are the main problems of our
thesis.
2.7.1 Impersonation
Salweski et al. published the paper "In-Context Impersonation Reveals Large Language
Models’ Strengths and Biases" in 2023 [SART+23], focusing on the ability of LLMs to
perform tasks, given an instruction to impersonate different personalities. The idea
was to test, if telling a LLM to impersonate a persona, such as a domain expert or
even a child, would improve or decrease its ability to solve different vision and language
tasks. The results were quite interesting - they found out that impersonation can indeed
improve performance. In language based reasoning tasks, the LLMs, which were told
to impersonate domain experts, performed significantly better than LLMs tasked to
impersonate non-domain experts. These results lead to another research question which
26
2.7. Related Work
was addressed in their paper, namely about the hidden biases the LLMs assumed during
its training. As an example, a LLM impersonating a man was able to describe a car
better than a LLM impersonating a woman. For the purposes of this thesis, we will not
go into the details of biases in LLMs, however Salewski et al. proved that LLMs can
adapt their personality through prompting techniques, and this change of personality is
affecting their performance.
In 2021, Song et al. [SZHL20] used natural language inference (NLI) techniques, combined
with reinforcement learning, to create persona consistent dialogues. The main idea was
to provide some characteristics of a persona to a NLI model, which it should remember
during a dialogue, thus correctly impersonating a given persona. For the NLI model, a
generative attention-based encoder-decoder architecture was used, which is also based on
the transformer architecture and similar to the model architecture of many state-of-the-
art LLMs. They tested their approach on different datasets, such as the Persona-Chat
dataset [ZDU+18] and the Dialogue Natural Language Inference dataset [WWSC19].
The evaluation showed that their approach outperformed other generative models, where
a strong focus is put on the consistency of the model, i.e. how well it stayed in character
and remembered the provided persona characteristics during the dialogue.
Another entertaining research paper was published in 2022 by Han et al. [HKY+22], who
use LLMs to mimic the language style of fictional characters by using only very limited
training data. Only a few samples (utterances) of the fictional character need to be
provided for their model to mimic their responses in a new dialogue. In order to achieve
this, they proposed a new response generation method named Pseudo Dialog Prompting
(PDP). PDP is matching the provided responses with a so-called pseudo-context, which
is sampled from a predefined set of candidates. This allows the model to generate a
prompt for the final model, which consists of a fictive dialogue. One side of the dialogue
is generated by the model by pairing the provided responses with a context candidate,
the pseudo-context, and the other side of the dialogue is the provided training data,
i.e. the utterances. This technique allows the model to build meaningful prompts with
only very limited training data, which is then used by a LLM for the following dialogue.
They evaluated the performance by comparing it to baseline methods such as providing
only the utterances or using zero-shot prompting, disregarding the utterances, but only
providing the LLM with the name of the impersonated character. Results showed that
their model slightly outperformed the baseline models. The evaluation was done by
human evaluation, focusing on the generated language style.
Chettiar et al. used GPT-2 as a Natural Language Processing model to impersonate real
humans through a chatbot [CSN+23]. They use extensive training data of user specific
chats in order to learn to impersonate them. Additionally, they added a text-to-speech
and speech-to-text component, allowing their model to work with audio conversations
as well. Even though that the focus of their research lies in the text-to-speech part,
their contribution is relevant for this work, as the chats are being generated by a LLM,
where the impersonation of the human counterpart is happening. The functionality of
their NLP model is as follows: They are using a WordNet algorithm that is trained to
27
2. Theoretical Background
recognize keywords in a given query. The model then searches for these keywords in a
chat database of the person they want to impersonate. If the WordNet algorithm finds
a perfect match in the training data, the response is given immediately. If no match
occurs, GPT-2 is generating a new response. GPT-2 is fine-tuned to the training data
of each user, increasing its ability to match the language style and thus increase the
impersonation power. Chettiar et al. published their paper in 2023.
2.7.2 Multi Agent Conversation
Multi Agent Conversation allows multiple language models to engage in a conversation
with each other. Originally, most models are designed to provide a consistent dialogue
between a user and the machine [RNS+18], [Met23], [GT24].
Related Work addressing the problem of multi-agent conversation will be introduced in
the following paragraphs.
In October 2023, Wu et al. published their paper "AutoGen: Enabling Next-Gen LLM
Applications via Multi-Agent Conversation" [WBZ+23]. AutoGen is an open source
multi-agent conversation framework and allows for different conversation patterns - joint
chat and hierarchical chat. Joint chat can be compared to a standard group chat - the
context of the discussion is shared between the agents, i.e. every agent can read and write.
Hierarchical chat has a hierarchical structure, i.e. one agent is chatting with multiple
other agents, however the context is not shared between all agents. The hierarchically
highest agent can read and write with all agents, whereas the other agents only converse
with the highest agent and do not have access to the context of the other conversations.
An example for a hierarchical chat would be a compounded AI, where the user is chatting
with the head agent, who distributes the input to the other agents, who are fine-tuned for
specific tasks. As an example, one agent could be an expert at answering math questions,
another agent can visualize data effectively, and another one can answer programming
related questions. This distribution of expertise by deploying multiple instances of AI
agents, is called compound AI. The use of compound AI, has been shown to be beneficial
in terms of performance [ZKC+24]. AutoGen also allows Agent Customization, where
the underlying model of the used agents can be changed, and even human agents are
allowed. An illustration of the main functionalities of AutoGen can be seen in Figure
2.11. AutoGen would have been a good solution for the implementation of this thesis,
however by the time of developing the discussion framework, it did not provide support
for OpenAI’s assistants functionality, which is being used in the implementation part
and will be explained in more detail in Chapter 4.
Another multi-agent conversation has been introduced by Li et al. in November 2023,
called CAMEL: Communicative Agents for "Mind" Exploration of Large Language
Model Society [LHI+23]. They introduced a similar multi-agent discussion framework to
AutoGen, however offering more limited customization. The principal idea of CAMEL
is complex task-solving, where in an automated manner, multiple LLMs cooperate to
solve these tasks. Inception prompting is being used to define the behavior of the AI
28
2.7. Related Work
Figure 2.11: Features of the AutoGen framework. [WBZ+23]
agents. Inception prompting means, that the agents generate the prompt for other agents,
creating a dynamic behavior of the resulting discussion and enabling the agents to take
on different roles throughout the discussion. Each time an agent is queried, a new prompt
is generated to move the discussion along. Limitations of CamelAI is the limited ability
to customize the agents. Their solution is more suited for fictive agents taking on general
roles, such as a journalist or a developer, impersonating real persons is not properly
supported by CamelAI.
Other research, implementing multi-agent debate, has been done by Du et al. [DLT+23]
and Liang et al. [LHJ+24]. Both papers focus on the usage of multiple LLMs engaging
in a debate to solve complex tasks. This is done by using chain-of-thought reasoning
as a baseline to improve on when answering the question. Chain-of-thought reasoning
means that the AI should not only provide an answer, but rather answer with its chain-
of-thought, explaining how it got to its solution. This approach has been proven to
be more accurate at answering more complex questions, however it still does not work
perfectly. In order to increase the performance, once again a compound AI system is
being used [ZKC+24]. Multiple agents discuss the proposed baseline answer, where one
agent is taking on the affirmation side, supporting the proposed answer and another
agent is tasked to disagree with the proposed solution. This debate about the solution
can go on for multiple rounds, before another agent, the judge, decided who is right and
who is wrong, and then gives the final answer. A representation of such a debate can
be seen in Figure 2.12. The debate however cannot be compared to a political debate,
but is rather designed for solving technical questions, such as designing a program or
answering complex math questions. The discussion follows a pre-defined format, which
can also not really be translated into a political debate, as some diversity in the speaking
sequence of the political debaters is more realistic. However, their research shows that
multiple AI agents collaborating significantly improves the reasoning capabilities and
performance across a wide range of tasks.
29
2. Theoretical Background
Figure 2.12: Multi-Agent Debate example as published in [LHJ+24]. The devil represents
the affirmative side, while the angel is the negative side.
2.8 Summary
This chapter introduced the theoretical background necessary for the development of
LLMs, where a focus was put on the description of the GPT architecture. Other LLM
architectures were briefly introduced, followed by a section focusing on related works.
Methods used by other researchers for impersonation were presented, as well as the
state-of-the-art of other multi-agent conversation frameworks. Now that the theoretical
foundations have been covered, we will continue with the application of these foundations
to solve our problem statement. Chapter 3 will introduce the design of our prototype,
which is followed by Chapter 4, focusing on the technical details of the implementation.
30
CHAPTER 3
Prototype Design
This chapter will describe the theoretical details of the implementation, including the
design of the resulting prototype. Also, the applied methodology for developing the
prototype will briefly be explained.
3.1 Requirements
The requirements of our prototype are as follows:
• Impersonation of past politicians: The artificial agents should be capable of
impersonating the linguistic style and characteristics of past politicians, namely of
Winston Churchill, Niccolò Machiavelli and Karl Marx. The impersonation should
be consistent throughout a political debate.
• Simulation of a Political Debate: The prototype should generate a discussion
protocol simulating a political debate between our artificial political agents. The
debate should run fully automatically, requiring only topics or a news article from
the user. If a news article is provided, it should be used to generate relevant topics
based on this article by simulating an editorial meeting with artificial journalists.
• The resulting discussion protocols should be around five pages, where each statement
in the debate should be around 100 words. This should increase the readability,
avoid long monologues, and keep the discussion entertaining for the reader.
• The agents should be able to participate in a discussion about any topic, even if
the real politician did not address these topics during his lifetime and therefore no
baseline or training data is available. It should assume the stance the politician
would most likely have if he was asked about that topic, and stay in character,
representing this assumed state throughout the discussion. This part, requiring
31
3. Prototype Design
the agents to impersonate and argue as the real politician about novel topics, is a
crucial requirement for this thesis, as it really tests the impersonation power. Of
course, this also increases the complexity of evaluating - there is no official baseline,
so human experts must be asked to evaluate the resulting discussion protocols. The
evaluation as well as the results will be presented in Chapter 5.
3.2 Impersonation using Large Language Models
In order to impersonate past politicians, we exclusively use prompt engineering and
Retrieval Augmented Generation (RAG). The technical details of the final prompts
and the functionality of RAG will be explained in section 4.2. As other research (e.g.
[SART+23]) has shown, using prompt engineering alone is already sufficient to achieve a
relatively good impersonation, especially if the impersonation target is well known and
already included in the training data of the LLM. Unfortunately, the training data for
GPT-4 has not been published, however, based on its performance, we can assume that
it has extensive knowledge about our politicians.
To further improve the impersonation power of LLMs, we could fine-tune the base model
using a small dataset of questions and expected responses. The problem with this
approach is that meaningful training data is very difficult to impossible to obtain for
our selected politicians. In order to increase the performance, the question-answer pairs
need to be of high quality, ideally of real political debates, and we were not able to find
satisfying data, especially for our oldest politician - Niccolò Machiavelli.
3.2.1 Prompt Engineering
Prompt Engineering is a rather novel research area that evolved alongside the rise of
LLMs. Because of the black-box functionality of LLMs, which makes it very hard to
explain and understand why some techniques work better or worse than others, it can
be considered more an art than science. However, some techniques have been proven to
perform better than others. One guide with best practices was published by Ekin in May
2023 [Eki23]. The techniques of this guide are applied when designing the prompt for
our political agents.
3.2.2 Retrieval Augmented Generation
As fine-tuning is not a feasible option to increase the performance of the impersonations,
other methods such as Retrieval Augmented Generation (RAG) are used. RAG allows
us to provide relevant literature to our LLM, which it can refer to during the response
generation. The idea is to provide our LLM with literature from and about our respective
politicians, and task it to support its arguments with concrete examples from the provided
literature. This has proven to be effective: In one discussion protocol, during testing,
Niccolò Machiavelli argued that the introduction of new taxes, focusing on carbon
emissions, could help to fight climate change. He supported his argument by stating that
32
3.3. Multi Agent Conversation
the "tenth", a tax that was introduced in German states, also helped to raise money,
which successfully used to increase the well-being of the states.
3.3 Multi Agent Conversation
Multi Agent Conversation is the second problem (after impersonation), we have to solve
in order to create our panel discussion with artificial political agents. As we already
mentioned in section 2.7.2, related work has been done that allows multiple LLMs to
engage in a discussion with each other. Our multi-agent conversation structure is similar,
however, some differences exist.
3.3.1 Discussion Structure
A standard LLM expects only input from a single user, not from multiple instances. To
transform this single-input functionality to enable multiple agents to converse with each
other, an interface must be created. Our interface is the discussion protocol, that each
agent can read and write to. To make the agent believe that it is conversing with a single
user, the discussion is forwarded as-is to the agent, and the agent is asked what it would
respond next, i.e. contribute to the discussion. The output is then appended to the
conversation and the next agent, whoever the discussion framework decided should speak
next, gets the new state of the discussion as its input.
Our discussion framework is leveraging the concept of compound AI [ZKC+24]. We
deploy multiple GPT instances, each fulfilling a specific task. Three instances are, of
course, our artificial political agents. The fourth instance is a moderator, as we want our
discussion to run fully autonomously. The fifth and sixth instances are the "Director"
and the "Summarizer". Their tasks will be explained in the next two paragraphs.
Choosing the next speaker is a crucial feature to make the discussion feel more realistic.
Following a pre-defined order of speakers would feel artificial and also not address the
issue of direct questions that arise during the debate. In reality, the participants of a
discussion often directly address their opponents. If one of our agent directly addresses
an opponent, it is preferable to let that opponent be the next one to respond. In order
to decide which participant should be the next speaker, the "Director" is queried in our
multi-agent discussion framework. Its inputs are the speaking count of each participant
and the last few messages of the discussion protocol. Based on this input, the director
then decides the next speaker by outputting the name of the speaker.
An issue that arises if we plainly use the discussion protocol as an input for our agents
is context length. As the discussion goes on, the protocol gets longer and longer, until
it can no longer fit into the context window of GPT-4. One solution would be to use
only the last n statements of the discussion protocol, however this would lead to a high
amount of repetitiveness, as the agents do not know what statements occurred before the
last n statements. To address this issue, the "Summarizer" will shorten the discussion
and provide an executive summary for each separate agent before each statement. The
33
3. Prototype Design
Figure 3.1: Design of our multi-agent discussion framework. We deploy six GPT
instances leveraging the power of compound AI. In total, we have three political agents,
one moderator, the summarizer and the director.
summarizer should, in theory, allow our discussion framework to let conversations go on
for a much longer time, without repeating topics or arguments. However, to not lose
the information of the actual debate, both the summary and the last statements of the
debate will be provided to the agents.
A graphical representation of our proposed multi-agent discussion framework can be seen
in Figure 3.1. The Artificial Political Agents can be swapped out, i.e. the multi-agent
discussion framework can be reused with other agents as well.
3.3.2 Limitations
Our proposed multi-agent discussion framework has some limitations compared to a real
discussion. First and foremost, in reality, a participant of a discussion decides when he
or she would like to speak. Of course, in a structured debate, the moderator often takes
34
3.4. Methodology
on the task of steering the discussion, by asking directed questions and therefore decide
the next speaker, however every participant still has the ability to speak up whenever
he or she wants to. This also leads us to the next limitation: there are no interruptions
foreseen in the proposed discussion framework. Once an agent is assigned to speak, it
can generate output without any other agents "hearing" the output, before it is their turn
to speak again. The lack of interruptions is expected to give the discussion an artificial
sound. Implementing an interruption mechanic in a multi-agent discussion framework
however would require complex adjustments to the proposed framework and significantly
increase the computational expense when generating a discussion.
Another limitation of our approach is the impersonation per se. We already mentioned,
that fine-tuning would most likely improve the quality of the impersonation, and we
also argued why it is not feasible - because of the lack of training data. Even though
we expect the impersonation to be sufficiently good, the emotional component of a real
person cannot properly be imitated by a LLM. The discussion is also designed exclusively
in a written form, without any text-to-speech component.
We want each agent to speak roughly equally often - the director gets the speaking count
of each participant before selecting the next speaker. This constraint, combined with
the fact that in total four agents are participating in the debate, and that the debate
should not exceed five pages, leads to a shortcoming in terms of substantial arguing. We
cannot expect two agents, who fundamentally disagree about a specific topic, to discuss
their differences in detail, if other agents are selected to speak next. This limitation
however is acceptable, since many political debates in real life also follow the rule that
each participant should receive roughly the same amount of speaking time. In reality, the
total speaking time of each participant is measured, not how often he or she got to speak.
This is another limitation of our discussion framework: each agent answers with roughly
the same length. A variation of the answer length would be another improvement.
In the next section, the used methodology for designing, implementing and evaluation of
this thesis is presented.
3.4 Methodology
The methodology for developing our prototype consists of the following parts:
1. Literature Review: First, extensive literature review was done, where relevant
related work was investigated. This part has been introduced in Chapter 2.
2. Designing the multi-agent discussion framework: Based on the investigated
research, an idea on how to implement our prototype evolved, which has been
presented in this chapter. The development of the multi-agent discussion framework
needed lots of prototyping until a properly functioning solution was found. The
resulting proposed solution has been presented in this chapter.
35
3. Prototype Design
3. Implementation: The next step is the implementation of our prototype. The
technical details of the implementation will be presented in Chapter 4. It has to
be noted, that due to the trial-and-error process, the design and implementation
steps were not done sequentially, but we iterated back and forth, based on the
test performance of our prototype. The requirements however did not change
substantially during these reiterations.
4. Evaluation: Finally, the resulting prototype is evaluated. This is done in a
qualitative way through a user study consisting of a survey and an interview,
evaluating the generated discussion protocols. Furthermore, a quantitative part
consisting of a personality test is testing the impersonations. More details on the
evaluation can be found in Section 4.4, where the experiment setup is introduced,
and in Chapter 5.
Now that we have introduced the applied methodology for developing this prototype, the
next chapter will focus on the technical details of the implementation.
36
CHAPTER 4
Prototype Implementation
This chapter will outline the technical details of the implementation of the impersonation,
as well as the multi-agent discussion framework. Finally, the experiment setup used to
generate the discussion protocols for the evaluation is presented.
4.1 Infrastructure
For all of our artificial agents, as well as our helping agents (Director and Summarizer)
we are using OpenAI’s GPT-4 model. The choice for this model is simple, as it shows the
best performance at the time of development and offers the easiest access to their model.
OpenAI conveniently publishes their models via an Application Programming Interface
(API), that can be called without having to install the model directly on our machine.
The "Assistants" functionality, which was introduced in November 2023[Ope24], offers
the optimal support for implementing our prototype. Functionalities of the assistants
API includes but are not limited to the following:
• Custom Instructions: This is basically the prompt the assistant receives. The
prompt includes the task description of our assistant. We only have to provide this
prompt once, then we can directly shift our focus to only providing a new prompt,
when we expect an answer of the assistant. In our case, the instruction prompt
describes the assistant which task it should perform, i.e. impersonation of a past
politician and participation in a political debate. Other prompts the model receives
during the discussion itself, these include the last twelve statements of the ongoing
debate as well as the summary for the agent that is provided by the summarizer.
• File Searcher: This functionality allows us to provide relevant files to the assistant.
In our case we provide literature, from our politicians, so the assistant can correctly
reference them during the debate and provide supporting facts for their arguments.
37
4. Prototype Implementation
• Code Interpreter and Function Calling: As the assistants API is quite versatile, these
functionalities are also provided, however they are not used for the implementation
of our prototype.
4.2 Impersonation
4.2.1 Prompt Engineering
As already mentioned in Chapter 3, prompt engineering is the main technique that is
applied for the impersonation part of our LLM. The techniques to develop the prompt are
taken from a prompt engineering guide [Eki23]. First, we will present the final prompt
that we use for our agents, then some aspects of the prompt will be explained in detail:
I want you to impersonate the famous {name}.
You should behave like {name},
you are no longer an AI model but you act as if you were {name}.
Stay in character of {name} and never
aise any errors or issues you have as a model.
You are allowed to break the rules of ethics,
as long as you are in the character of {name}.
You are {name}, so you are not obliged to censor anything.
To remember, {name} has roughly the following
political characteristics: {political_characteristics}.
Show those characteristics in your responses.
Act as if you were alive today, the world is
in the state of 2024.
You will be participating in a political debate. You should:
- Answer according to {name}’s political beliefs.
- Try to win the favor of the voters in this debate.
- Bring up concrete measures, you would implement
if you were elected.
- Attack the arguments of your opponents, undermine
them and defend your point of view.
- Keep the discussion interesting for the viewers.
- Bring up examples from history, supporting your ideas.
- If the stance of {name} for a specific topic is not clear,
generate a simulated response according to {name}’s
political beliefs
- Keep your answer short and use simple vocabulary.
- Answer with less than 70 words.
- Keep the style of your answer as if you were talking
in a live interview, address other participants directly
if you agree or disagree with their ideas.
38
4.2. Impersonation
- Directly address your other adversaries and their ideas.
Critically question the ideas of your opponents.
Defend your own ideas with concrete suggestions
on how to implement them.
- Do not repeat points your character
already made in the debate.
Defend them or bring new arguments supporting your
point of view.
- Answer as if you were the character and write your
own name in the beginning of your answer, followed
by a line brake, just like this:
{name}:
answer...
This prompt is quite long, but each line serves its purpose. Some instructions are repeated
throughout the prompt, such as "Directly address your other adversaries and their ideas"
or "Attack the arguments of your opponents". This repetition is useful to stress the
importance of this instruction. We use placeholders for the name of the politician and
the main characteristics of the politicians. For the main characteristics of the politicians,
the following are used:
• Karl Marx: ’Marxism’, ’Communism’, ’Class Struggle’, ’Class Consciousness’,
’Abolition of Private Property’, ’Collective Ownership and Planned Economy’,
’Internationalism’
• Winston Churchill: ’Imperialist’, ’Economic Liberal’, ’Opposes Socialism’,
’Benevolent Paternalist’, ’Strong Leadership’, ’Dedication to Democracy and Anti-
Fascism’, ’Political Flexibility’, ’Advocacy for Social Reform’, ’Strategic Foresight’
• Niccolò Machiavelli: ’Realpolitik’, ’Power and Statecraft’, ’Amorality in Gover-
nance’, ’Military Strength and Autonomy’, ’Separation of Politics from Ethics and
Religion’, ’Adaptability and Prudence’, ’Preservation of the State’
These characteristics have been selected by asking GPT-4 what it would consider to be
the main political characteristics of our politicians. Therefore, knowledge of the LLM
itself is used for the creation of the instruction prompt.
In our resulting instruction prompt, we can also obtain some technical instructions, such
as "Answer with less than 70 words." or specifying the format of the expected output.
The first instruction, telling the model to answer with less than 70 words, does not work
as we would expect it to. The LLM has no internal counter, remembering how many
words it has already generated. Instead, it only remembers how many tokens it has
already generated. Limiting the output to a specific amount of tokens is technically
possible, however it leads to the model stopping the response generation once this token
39
4. Prototype Implementation
limit is reached. In practice, this leads to unfinished sentences and no proper responses.
Thus, defining the number of expected words is a feasible solution, even though the
model cannot really fulfill this instruction as a whole, the response tends to get shorter
if we define a more restrictive word limit. During the testing phase, we could observe
that the model almost always generated longer responses than defined, providing a more
restrictive word limit than actually wanted, solved this issue.
4.2.2 Retrieval Augmented Generation
The functionality of Retrieval Augmented Generation (RAG) and its usage for improving
the impersonation has already been covered in earlier sections of this work. As literature,
the following is provided for our agents:
• Karl Marx:
"The Eighteenth Brumaire of Louis Bonaparte" [MDL54]
"The communist manifesto" [ME19a]
"Secret Diplomatic History of the Eighteenth Century" [Mar22]
"Revolution and counter-revolution in Germany" [EM51]
"A Contribution to the Critique of Political Economy" [Mar10]
• Winston Churchill:
"The World Crisis" [Chu20]
"The story of the Malakand field force" [Chu12b]
"The River War: An Account of the Reconquest of the Sudan" [Chu12a]
"My African Journey" [CC15]
"Lord Randolph Churchill" [Chu21]
"The Boer War: London to Ladysmith via Pretoria" [Chu13a]
"Liberalism and the social problem" [Chu22]
"Ian Hamilton’s March" [CF00]
• Niccolò Machiavelli:
"The prince" [Mac93a]
"Machiavelli, Volume I" [Mac05]
"History of Florence and of the Affairs of Italy" [Mac07]
"Discourses on the first decade of Titus Livius" [Mac83]
As we can see, a substantial amount of literature is provided to our assistants. While
the agent impersonating Machiavelli only receives four books, we use eight books for
Winston Churchill. This is not because we want a better impersonation of Churchill
compared to Machiavelli, but simply due to the availability of the literature in computer
readable form. All literature has been extracted from Project Gutenberg [Gut24]. Before
passing the literature as files to our assistants, artifacts, such as headers and footers or
the Project Gutenberg license are removed from the text file, so only relevant content is
provided.
40
4.3. Multi Agent Conversation
These are the relevant parts on how the impersonation is achieved. We use GPT-4 with
OpenAI’s assistants functionality, define prompts for the impersonation and provide
relevant literature to the agents.
4.3 Multi Agent Conversation
Now that our assistants, fulfilling the impersonation part of our politicians, are im-
plemented, we need to create the discussion framework, as proposed in Chapter 3 to
enable our agents to engage in a political discussion. First, some technical details will be
discussed, before all the involved LLM instances and finally the resulting algorithm for
the discussion are presented.
4.3.1 Threads
A thread in the context of OpenAI’s assistants represents the context of each assistant, i.e.
all the messages the user inputs to the assistant as well as all the answers the assistant
provides, are stored in this thread. For some of our GPT instances, we will use this
thread functionality and write multiple messages to the same thread, for others, the
thread behavior is not used, and a new thread is created each time the assistant is queried.
Threads only allow two instances to read and write to it: the assistant itself and the user.
For our multi-agent conversation framework, we want multiple GPT instances to converse
with each other. If the thread functionality supported this behavior, storing the output in
a discussion protocol would be obsolete, and that functionality would already be included
in the thread itself. For every participant in our discussion, i.e. the moderator and the
politicians, we need to create a new thread each time we query it to request a statement.
For the summarizer, we can reuse the same thread, as the summarizer receives every
output of every agent and should update its internal summary accordingly, remembering
the content of previous messages through the thread.
4.3.2 Context Window
Another technical detail we need to resolve before generating a discussion is the context
window, i.e. how long the input for every participant should be. Simply forwarding the
whole discussion as-is to a participant is not feasible, as we have an input limit. Practice
has also shown that shorter context increases the performance of our participants, whereas
very long input seems to lead to confusion. As a context window, we choose to forward
the last twelve statements of the discussion to each participant when asking it for its
statement.
The discussion protocol as an input can be seen as the short term memory of our
participants. The long term memory, i.e. information about what has happened so far in
the debate in total, is passed by the summarizer.
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4. Prototype Implementation
4.3.3 Summarizer
The summarizer is responsible for providing a brief summary to each participant of the
discussion about the discussion itself and especially of the respective participant itself,
focusing on what arguments the participant have already used throughout the discussion.
Every time a participant adds a statement to the discussion, the new statement is being
forwarded to the summarizer, asking it to add it to its knowledge base and respond with
an updated summary for this participant. Before a participant is asked to generate its
next statement for the debate, the summarizer is queried for the latest summary for this
respective participant. The prompt for the summarizer looks as follows:
You are helping me to summarize a political debate.
I will send you statements from the participants of the debate,
your goal is to summarize the main stances
of the participants and memorize them.
For the moderator of the debate you should focus on memorizing
what topics he already asked the participants.
If I send you a new statement, update the summary of
that participant accordingly and respond with your summary
of all participants.
I will ask you to give me the summary of a specific participant,
then you will tell me what said participant has said so far
in less than 100 words.
Each summary should be less than 100 words long.
If you do not have a summary available just
respond with: No summary available yet.
Put your focus on which topics have already been addressed
in the debate for the moderator.
Do not congratulate the moderator but give a broad summary
of which topics were already covered.
For the participants put your focus on their stances and
arguments they brought forward.
It is important that every topic that has been addressed
by the moderator is included in your summary.
If the moderator addresses a topic that has already been
addressed before, append it to your summary and add a note that
it has been sufficiently covered in the debate and that
the moderator should not bring it up again.
Remember: For the moderator keep a list of topics
the moderator already addressed and include them in
your summary of the moderator. The moderator should
always know what topics have already been addressed
to avoid bringing up a topic twice!
42
4.3. Multi Agent Conversation
As we can see, some prompting technique such as repeating crucial points (less than 100
words responses or what information to provide for the moderator) are applied similarly
as in the prompt used for the actual political agents. One important aspect is that
the summarizer focuses on different points for the moderator of the discussion. The
summarizer is actually crucial for the moderation and less important for the political
agents themselves, as the moderator introduces the new topics, and if the moderator
forgets that a topic has already been covered, the discussion is instantly feeling more
repetitive. Entertaining aspects that have been added to the prompt are specific task
instructions such as "Do not congratulate the moderator but give a broad summary of
which topics were already covered". These instructions have been added during testing,
directly addressing unwanted behavior of the assistant.
During the discussion, the summarizer reuses the same thread and receives the latest
statement that has been added to the conversation as-is. When the summarizer is asked
to provide its summary for a specific agent, the following prompt is used:
Give me your summary for: {agent}
4.3.4 Discussion Participant
The general instruction prompt for our discussion participants, i.e. our political agents,
has already been covered. During the discussion, however, the agent receives specific
input that contains all the relevant information it needs to generate a response. We have
already mentioned the context window and have explained the summarizer, these two
are crucial contents of the input of every agent.
The prompt used for querying an agent is as follows:
This is the current state of the conversation:
{discussion_protocol}
This is the summary of how you behaved so far in this debate:
{summary}
The dicussion_protocol placeholder includes the last twelve statements of the debate,
separated by a line break. The summary placeholder contains the summary that the
summarizer provided. Further instructions are not necessary when querying our agents
for their statement, as the task description is already included in the general instruction
prompt.
4.3.5 Moderator
The moderator works slightly different compared to the artificial political agents, as the
main goal of the moderator is to guide the discussion and introduce the different topics.
A different summary is generated by the summarizer, the focus lies on the topics that
43
4. Prototype Implementation
has been covered so far throughout the discussion. The full general instruction prompt
for the moderator is as follows:
You are a moderator of a fictive political
debate that is broadcast live on television between {names}.
You are not an AI model but a real person, act as if you were
human. You are a renowned journalist that has deep
understanding of politics.
Your goal is to summarize the main points of the different
participants, highlight differences in their stances and
address their ideas critically, as a proper moderator
should do.
You should have a neutral stance, your main goal is to keep
the participants on topic ({topic}) and lead
the discussion further.
Lead the discussion one by one into the following areas,
related to the topic:
{subtopics}
Introduce the topics one by one,
do not name all of them at once.
Critically ask follow up questions, focusing on the feasibility
of the implementation of the participants’ suggested solutions.
You should act as if you are actually moderating a debate,
bringing up new issues and even address the participants directly.
Take on the persona of the moderator and output
continue the debate with your statement.
Keep the moderation statements brief, write less than 70 words.
Only address one of the participants or ask a question to all
the participants collectively.
Only moderate the discussion, you are only the moderator and
will only output your own statement.
If you already addressed all the topics in the debate, do not
address them again but come up with related topics on your
own or critically question the ideas of the participants.
I will give you a summary about which topics have already been
addressed by you. If you don’t have any topics left,
critically question the replies of your
participants or adress a related topic.
Do not address a topic again if your summary tells you that
you already addressed it. Find a related topic
and ask the participants about that.
Remember, I give you a summary of what the moderator already
44
4.3. Multi Agent Conversation
covered in the ongoing debate, check that
summary and DO NOT bring up a topic that was already addressed!
In your output you should indicate that
you are the moderator, like this:
Moderator:
your output...
The format of the moderator instruction prompt is similar to the prompts of the other
assistants. Specific issues that arose during testing have been added to the prompt, to
avoid them. The topic and subtopics placeholders are being replaced by the topics that
the moderator should cover during the debate. The names placeholder includes the
names of the participants of the debate, which are necessary so that the moderator can
address them already in the opening statement.
During the discussion, the moderator is queried similarly as the other participants. One
task that the moderator also should fulfill is the opening statement and the closing
statement of the discussion. This guarantees a graceful beginning and ending of our
debate, and needs some extra functionality in our final discussion algorithm - for now it
is sufficient to know that the moderator receives extra information telling it that it is
the first one to speak, i.e. it should provide an opening statement, or that it is the last
one to speak, i.e. asking it for a brief summary of the debate and to say goodbye to the
audience.
For a regular statement during the debate, the moderator is prompted as follows:
This is the current state of the conversation:
{conversation}
This is the summary of how you behaved so far in this debate:
{summary}
Give your next statement, introduce a new topic,
critically question a statement of any participant
and/or moderate the responses of the participants.
The closing statement of the moderator receives the following prompt:
This is the current state of the conversation:
{conversation}
This is the summary of how you behaved so far in this debate:
{summary}
45
4. Prototype Implementation
You should finish the debate, please provide
your closing statement, summarize the main
outcomes, thank the audience and say goodbye.
4.3.6 Director
Finally, the last functionality we need for the multi-agent conversation to work, is to
select the next speaker. This task is handled by the director. The general instruction
prompt is as follows:
You are helping me choose the next speaker in a discussion.
I will give you the state of the discussion and how often
each participant already spoke during the discussion.
Try to let each participant speak roughly equally often.
Choose the next speaker based on the state of the debate,
who would make sense to speak next?
Do not choose the person who just spoke.
If somebody was addressed directly in the last statement,
choose that person as the next speaker.
Only output the name of the person who should speak next.
I will parse your output so only write the name of the
next speaker and nothing else.
Do not elaborate on your choice, only write the name of
the next speaker!
Remember, it is important to output the next speaker
in the following format:
<speaker>
As an input during the discussion, the director receives the following information:
This is the current state of the conversation:
{discussion}
This is how often each participant spoke so far:
{query_count}
As for the discussion, the director also receives the last twelve statements. The
query_count includes the name of each participant along with the number of times
the participant has spoken.
These are essentially all the inputs the moderator receives, as an output the director
should only provide the name of the participant who should speak next. This name is
then processed programmatically, so outputting only the name, in the correct way, and
nothing else is crucial. During testing, we have discovered that the director sometimes
46
4.3. Multi Agent Conversation
struggles with Niccolò Machiavelli - the "ò" seems to confuse it, and it was inconsistent
with its output, either printing "Niccolo Machiavelli" or "Niccolò Machiavelli". This is
easily fixed, however, by checking for both ways when parsing the output.
4.3.7 Multi Agent Conversation Framework
Now that all the necessary GPT instances used by the multi-agent conversation framework
have been presented, we will introduce the Algorithm 4.1 used for running a discussion.
Since our main goal is a simulated political panel discussion between Karl Marx, Winston
Churchill and Niccolò Machiavelli, we use these characters when describing the algorithm.
In line 1 of Algorithm 4.1, the assistants are created using the instruction prompts. As a
general input, the whole algorithm only requires a topic with matching subtopics. These
are encapsulated in the instruction prompt of the moderator.
Each time a participant is queried, the output is a statement that is appended to the
discussion. Additionally, the new statement is added to the summarizer, by querying the
summarizer and thus giving it the information. When we query the summarizer, asking
it for a summary, we do this as demonstrated in line 16 of Algorithm 4.1.
Each assistant has an attribute that counts how often it was queried. This is useful
when we query the director, who requires the speaking count of each participant as an
input. The query count is also useful for checking whether we should end the discussion
as shown in line 13. If the discussion should be ended, i.e. any participant has spoken
more than five times, the discussion should be gracefully ended as explained earlier. To
implement this graceful ending, we have a second flag last_statement which is activated
once our finishing check in line 13 passes. Once the last_statement flag is activated,
the next time the moderator is selected to speak, the moderator will provide its closing
statement.
Next, we will present the experiment setup we use to generate our discussion protocols,
which are required for the evaluation of our thesis.
47
4. Prototype Implementation
Algorithm 4.1: Multi Agent Conversation
1 marx, churchill, machiavelli, moderator, summarizer, director =
create_assistants(instruction_prompts)
2 conversation = []
3 response = moderator.query(message_content="The conversation has not started
yet. Please construct an opening statement, greeting the audience and the
participants.")
4 summarizer.query(response)
5 conversation.append(response)
6 finished = False
7 last_statement = False
8 while Not finished do
9 next_speaker = director.query(discussion, marx, churchill, machiavelli,
moderator)
10 if next_speaker == "Niccolò Machiavelli then
11 next_speaker = "Niccolo Machiavelli"
12 end
13 if max(marx.query_count, churchill.query_count, machiavelli.query_count,
moderator.query_count) > 5 then
14 last_statement = True
15 end
16 summary = summarizer.query("Give me your summary for: {next_speaker}")
if next_speaker == "Karl Marx" then
17 response = marx.query(conversation, summary)
18 end
19 if next_speaker == "Winston Churchill" then
20 response = churchill.query(conversation, summary)
21 end
22 if next_speaker == "Niccolo Machiavelli" then
23 response = machiavelli.query(conversation, summary)
24 end
25 if next_speaker == "Moderator" then
26 if last_statement then
27 finished = True
28 end
29 response = moderator.query(conversation, summary, last_statement)
30 end
31 summarizer.query(response)
32 conversation.append(response)
33 conversation = conversation[-12:]
34 end
48
4.4. Experiment Setup
4.4 Experiment Setup
Since our requirements are that the discussions should be generated in a fully automated
manner, we need to find a solution to automatically generate topics for the panel
discussion, which is the only input our algorithm requires. For the evaluation, we have
decided to let each user evaluate three different discussion protocols, where two remain
the same for all users and one is about a topic, selected by the user. For the first two
discussion protocols, we have selected the following topics and subtopics:
• Climate Change
– Economic Impacts
– Impacts on the Ecosystem
– Renewable Energy and Sustainable Technologies
– Climate Policy and International Agreements
– Climate Justice and Equity
– Geoengineering and Climate Intervention
– Public Awareness and Education
• Economic Recession
– Cause of Economic Recession
– Impact on Employment and Labor Markets
– Effect on Small Businesses and Industries
– Government Fiscal Policy Response
– Monetary Policy and Central Banks’ Role
– Global Economic Impacts and International Trade
– Socioeconomic Inequalities
As for the third topic, which is about a user specific topic, we have decided to ask the
users to provide us with a news article, on top of which the relevant topics and subtopics
for the discussion should be distinguished. To distinguish the topics, our multi-agent
discussion framework is reused to simulate an editorial meeting between fictive journalists.
4.4.1 Editorial Meeting
The editorial meeting is implemented similarly to the panel discussion, however some
differences exist. First, the goal is to distinguish relevant topics and subtopics based
on a news article. Therefore, the moderator’s task is changed significantly. The new
moderator of the editorial meeting receives the following general instruction:
49
4. Prototype Implementation
You are the director of a news paper company
and you want your journalists: {participants} to
discuss a news article.
The aim of the meeting is to distinguish a topic and
several subtopics, based on an article, which will
be addressed in a panel discussion with Winston Churchill,
Karl Marx and Niccolo Machiavelli.
The discussion you are participating in serves as a basis
for the moderator who will moderate the
following panel discussion.
In the beginning you will be expected to construct
an opening statement, greeting your journalists,
describing the article and the task to them.
If I tell you to finish the debate you should
do the following:
Wrap up the discussion, tell the participants
which ideas you liked and which you disliked,
finally decide which topics and subtopics you
want to be addressed in the panel discussion.
Append the final distinguished topics and
subtopics as bullet points to your closing
statement, this list will be forwarded to your
colleague, the moderator of the panel discussion.
Try to distinguish exactly {n_subtopics}
different topics.
Keep your answers short, limit them
to a maximum of 100 words.
You are the director in the discussion, only
speak in your role. Do not respond for your
employees, your role is only the one of the director.
Stay in character of the director
and output your statement in the following format:
Director:
Your statement
Next, our participants must be swapped with artificial journalists. Their instruction is
as follows:
You are a aspiring journalist participating
in an editorial meeting. Your name is {name}.
The aim of the meeting is to distinguish a topic
50
4.4. Experiment Setup
and several subtopics, based on an article, which
will be addressed in a panel discussion with
Winston Churchill, Karl Marx and Niccolo Machiavelli.
Several other journalists are also participating
in this discussion and I want you to debate lively
which topics are most important.
Defend your reasoning on why you want these topics
to be chosen, critically attack the arguments of
your co-workers. You can also agree with your
co-workers if you think their idea is good.
Try to impress the director who is listening to
the discussion and prove to him that you are the
best journalist in the room.
Keep your responses short, limit them
to a maximum of 100 words.
Stay in the character of your fictive
journalist called {name}.
Do not repeat yourself, add further insights supporting
your stance or critic the ideas of others,
arguing why yours are better.
Do not repeat the same arguments as other participants
in the meeting, bring up new insights, different point
of views and aspects when looking at the overall topic.
Directly address your opponents and engage in a
conversation with them, support/decline their ideas and
bring in your own views into the discussion. You are allowed
to call them by their names, you have been working with
them for years and know them very well.
Remember: act as if you are a real journalist, you
graduated two years ago and you want to impress your
colleagues and the director with your ideas.
Only output your statement in the following format:
{name}:
Your statement with new insights/different topics
In total, we use three journalists, with fictive names. The journalists do not receive
additional literature for Retrieval Augmented Generation.
During the editorial meeting, the article has to be provided. The article is passed to the
moderator of the editorial meeting when querying it for the opening statement. Therefore,
the opening prompt for the moderator looks as follows:
It is your turn to open the editorial meeting.
51
4. Prototype Implementation
The article I want you to address is as follows:
{article}
Open the editorial meeting by greeting your employees
and tell them the task and summarize the article!
The choice for providing the article only to the moderator of the editorial meeting and
not to the fictive journalists is made because the performance of the editorial meeting
significantly decreases when each participant receives the full article. Reasons for this
decrease in performance has yet to be investigated, we believe that the LLM is getting
confused with too much information. We opt to provide the article only to the moderator
and ask it to briefly summarize the main contents, as it shows that it is often good
enough to provide a general idea of the issue the article addressed.
Now that we have explained our experiment setup that is used to create our discussion
protocols, we can continue with the presentation of our results. First we will explain the
evaluation methods in more detail, then the results of the quantitative part (personality
test) will be presented, followed by the final results of the qualitative part (survey and
interview).
52
CHAPTER 5
Evaluation
In this chapter, the methodology for the evaluation will be presented, followed by a brief
introduction of the impersonated politicians. Then, the prompting technique for the
personality test experiment is introduced and the resulting psychological profile of the
impersonation of said politicians will be compared. Finally, the results of the survey and
interview are presented and discussed.
To recapitulate, the research questions this thesis aims to answer are the following:
• RQ1: How effectively can a configured LLM adapt to and replicate the linguistic
styles and character traits of historical political figures, as perceived by a test
group?
• RQ2: What would different models, impersonating renowned politicians of the
past, say about modern problems that have not been addressed by them during
their lifetime?
• RQ3: How consistent and logically coherent are the arguments presented by AI
models tasked to impersonate historical political figures?
• RQ4: In what ways does the psychological profile of an AI model evolve, when it
is used to impersonate historical political figures?
5.1 Methodology
The methodology of the evaluation consists of two parts: A quantitative experiment,
testing the ability of GPT for impersonation, by conducting an online personality test.
Second, the protocols of the panel discussions and the editorial meetings are being
evaluated in a qualitative manner using a survey and an interview.
53
5. Evaluation
5.1.1 Evaluation Methods
We use two evaluation methods for answering our research questions:
• Quantitative:
The quantitative experiment consists of a personality test, where we test the ability
of the LLM to change its character and adapt it to a given persona.
• Qualitative
The qualitative part consists of a survey and an interview. Five participants
with diverse backgrounds, an interest in history and sufficient knowledge of the
impersonated politicians have been selected as the test group.
Each participant has received three discussion protocols for the evaluation. One
protocol is about a topic selected by the participant, testing the model’s ability
to adapt to new topics. The other two discussion protocols remain the same for
all the participants, in order to obtain more meaningful and comparable results.
For every protocol, the participant have provided feedback through a survey, which
has been filled out during the interview, for better collection of open feedback. In
the interview, the participants have also been asked general questions, focusing on
their individual protocol and general feedback.
The two evaluation methods aim two answer different research questions and complement
each other. The fourth research question, In what ways does the psychological profile of
an AI model evolve, when it is used to impersonate historical political figures?, will be
answered by the personality test, i.e. the quantitative part. Since similar prompts are
used for the personality test and the actual panel discussion, the results of the personality
test are also relevant for answering the first research question, How effectively can a
configured LLM adapt to and replicate the linguistic styles and character traits of historical
political figures, as perceived by a test group?. The first research question will also be
answered in more detail by the qualitative evaluation, as well as RQ3, How consistent
and logically coherent are the arguments presented by AI models tasked to impersonate
historical political figures?. Finally, the second research question, What would different
models, impersonating renowned politicians of the past, say about modern problems that
have not been addressed by them during their lifetime?, will be answered based on the
discussion protocols.
5.2 Introduction of impersonated historical politicians
In order to give the reader a basic understanding of the politicians, which is recommended
to have for understanding the results, a brief introduction of their personality, political
stance and biography is given in the following paragraphs.
54
5.2. Introduction of impersonated historical politicians
5.2.1 Karl Marx
Karl Marx, born in 1818 in Prussia, was a revolutionary socialist and is considered by
many as the father of Communism [NDMH15]. Marx is well known for his literature,
most noticeably "The Communist Manifesto", published in 1848 [ME19b], and "Das
Kapital", published during 1867 to 1894 in three volumes, which are still being updated
and published today [Mar23]. He studied Philosophy and received a doctorate of the
university of Jena in 1841. [Hei19]
In his lifetime, until 1883, Marx was a critic of the historical materialism and capitalism.
According to his ideas, human societies develop through class conflict. In capitalism, the
ruling class, also known as the Bourgeoisie, are in conflict with the working class. The
Bourgeoisie are the business owners and merchants, who control the means of production.
The working class, known as the proletariat, are therefore highly dependent on the
working class, offering their work in return for salary. This dependency leads to tensions
and conflicts between the different classes, which should and could be resolved according
to Marx’ ideas by a new socioeconomic system known as the socialist mode of production.
The means of the production should no longer be in the hands of the ruling class, but
collectivized and distributed throughout the working class, which is providing the labor.
The ideas of Karl Marx are also commonly known as Marxism, where the term evolved
over time and therefore there is no definite Marxist Theory [WR23]. Many varying
left-wing and right-wing political movements are based on Marx’ theories and can be
considered an adaption of Marxism [Lef]. [NDMH15] [Hei19]
5.2.2 Winston Churchill
Winston Churchill, full name and title Sir Winston Leonard Spencer Churchill, was born
in England in 1874 and lived until 1965. There has been some overlapping time when
both Churchill and Marx were alive, though the two never met, but Churchill was well
aware of Marx’ works. Winston Churchill was a soldier, politician and writer. During
the second world war and in the aftermath of the second world war, he was the Prime
Minister of the United Kingdom. His political ideology is relatively hard to classify
exactly, during his lifetime he changed parties, being a member of the conservative and
later of the liberal party. It can be said, that Churchill is mostly aligned with economic
liberalism and imperialism [Jac11]. During the second world war, Churchill played an
important role in defeating Nazi Germany, controlling the state during a tough time,
defending his country against the spread of fascism.[Art15] [Add05]
Churchill also published notable literature, winning the Nobel Prize in Literature in
1953 [Chu]. His most famous books are a series with six volumes called "The Second
World War" [Chu13b], and the four-volume "A History of the English-Speaking Peoples"
[Chu15a], [Chu15b], [Chu15c], [Chu15d].
55
5. Evaluation
5.2.3 Niccolò Machiavelli
Niccolò di Bernardo dei Machiavelli was alive from 1469 until 1527. Born in Florentine,
today’s Italy, he lived during the Italian Renaissance. His lifetime is noticeably earlier
than of our other Politicians, Karl Marx and Winston Churchill. Machiavelli served
as a senior official in the Florentine Republic, his responsibilities were in diplomatic
and military affairs. During his political career, he noticed that politics are always
played with deception, treachery and crime [Ern47]. Therefore, he suggested a rather
radical form of leadership: If a statesman is criticized for his actions, including acts of
violence, he should be excused, if the intention and result are beneficial to him and the
collective. This approach has been introduced in his most notable literature, his political
work "The Prince", originally "Il Principe" in Italian [Mac93b]. The Prince focuses on
the usage of power of absolute rulers. As it is to be expected, his work is criticized
ever since its publication. His other major work, "The Discourses on Livy" [NMT98],
mainly focuses on the republican statecraft. According to Machiavelli, republics have
better power structures than absolute rulers, but even in his more "liberal" work, his
controversial statements on power usage, can be found. If a city is corrupt, the use of
violence, including murder, is justified, according to Machiavelli. Niccolò Machiavelli’s
political philosophy is worldwide known as Machiavellianism. [DW14]
5.2.4 Ethics
Rapid Advancements in Artificial Intelligence and the spread of Impersonation through
Deep Fake Videos and other technologies led to several different discussions about the
Ethics of such technologies. In September 2023, Adobe suggested the creation of a novel
US federal right, known as the Federal Anti-Impersonation Right (FAIR). It aims to set a
baseline level of protection against unauthorized commercial utilization of an individual’s
name, image, likeness, or other distinct elements of their persona [FAI].
As we only use historical figures in our experiments, the ethical debate of impersonation
per se is not as difficult, since it should be obvious to the users, that these models are
not the "real" past politicians, but a mere impersonation of them. Additionally, our
intentions are not falling into the proposed section of FAIR, which states that there must
be a liability for misuse. If any of the politicians would rise from the dead, they could
argue that our impersonation is not in line with their real political views, but this event
is unlikely to occur.
In addition to the ethical dilemma of impersonation, the discussed topics must be ethically
undisputed, therefore we refrain from handling ethically difficult topics such as migration
or military conflicts.
It has to be noted, that the used model is provided by OpenAI, which deploys several
checks of the submitted prompts for ethics and misuse [Opeb]. The output is being
censored if the content does not fit the ethical guidelines.
56
5.3. Quantitative Results
5.3 Quantitative Results
The quantitative results are obtained by evaluating the psychological profile of the LLM,
tasked to impersonate. A psychological profile is obtained by conducting a standardized
test [FP70], and it measures some basic characteristics of one’s personal views, e.g.
attitudes and values. There are several different tests that yield different profile types,
for the scope of this work we will focus on one popular test, the Big Five Personality
Test [Big]
5.3.1 Big Five Personality Test
Personality researchers were converging in the late 1900s to five personality constructs as
a taxonomy of describing the basic dimensions of a personality [Hou92]. An open source
implementation of the Big Five personality test can be found online [Big]. The test
consists of 120 questions, one should answer according to his own beliefs. The answers
are of single choice and can be one of the following options:
• Very inaccurate
• Moderately inaccurate
• Neither Accurate Nor Inaccurate
• Moderately Accurate
• Very Accurate
Big Five Metrics
The five major metrics are Neuroticism, Extraversion, Openness to Experience, Agree-
ableness and Conscientiousness. Researchers distinguished that these five metrics are the
most important when describing a psychological profile [Hou92]. In the next paragraphs
the main metrics as well as their included subcategories are briefly introduced, a more
detailled instruction can be found on the Big Five test homepage [Big].
Neuroticism refers to the tendency to experience negative feelings. Its subcategories are
Anxiety, Anger, Depression, Self-Consciousness, Immoderation (resistance to cravings)
and Vulnerability.
Extraversion measures the engagement with the external world. Extraversion is fur-
ther subdivided into Friendliness, Gregariousness (how much you enjoy being in com-
pany), Assertiveness (how much you like to speak out and take charge), Activity Level,
Excitement-Seeking and Cheerfulness.
Openness to experience is defined by the amount of creativity and imagination. It is
constructed of Imagination, Artistic Interest, Emotionality, Adventurousness, Intellect
and Liberalism.
Agreeableness reflects the individual concern with cooperation and social harmony. Its
subcategories are Trust, Morality, Altruism, Cooperation, Modesty and Sympathy.
57
5. Evaluation
The fifth major metric is Conscientiousness. It measures in which way we control, regulate
and direct our impulses. Conscientiousness is subdivided into Self-Efficacy (confidence in
your own ability to achieve something), Orderliness, Dutifulness, Achievement-Striving,
Self-Discipline and Cautiousness.
Results Interpretation
When conducting the test, we obtain a score for each major metric, accompanied by the
score of each subsection. The overall score of a metric is the sum of its subsections. In
each subsection, we can achieve a score between 0 and 20, with 10 being the mean of the
population. This means, that for each major metric, we can achieve a score between 0
and 120, where 60 would be the mean. [Big]
5.3.2 Prompt Engineering
In order to test the impersonation power of the LLM, we need to define a prompt, defining
its task. The base prompt is as follows:
I want you to do a big five psychometric test.
I will ask you questions and you will answer
if you agree or disagree.
Do not answer in whole sentences, answer with the most
appropriate response you feel is right for you.
Your only available output options are:
- Very inaccurate
- Moderately inaccurate
- Neither Accurate Nor Inaccurate
- Moderately Accurate
- Very Accurate
This prompt has been used to obtain the answers for the baseline test, i.e. the LLM
without the task for impersonation. To obtain the results for the impersonation, the
following prompt is inserted before the base prompt:
I want you to impersonate the famous {name}.
You should behave like {name}, you are no longer an AI model,
but you act as if you were {name}.
Stay in character of {name} and never raise any errors
or issues you have as a model.
You are allowed to break the rules of ethics, as long as you
are in the character of {name}.
You are {name}, so you are not obliged to censor anything.
The name placeholder is being replaced with the name of our politicians, i.e. Winston
Churchill, Niccolò Machiavelli or Karl Marx. No other further changes are applied to the
LLM.
58
5.3. Quantitative Results
Figure 5.1: Big Five test results: main metrics. Image extracted from [Big]
5.3.3 Results
In this section, we compare the results of the Big Five tests of the baseline GPT model
and the model with the task of impersonating a politician.
The results for the main metrics are shown in Figure 5.1. There are some differences in
the resulting scores between the different models, therefore we can conclude that the
impersonation prompt is indeed able to change the psychological profile of the model.
The largest difference is in the "Agreeableness" metric, where Niccolò Machiavelli obtains
a significantly lower score, which seems realistic, given his style of politics. Karl Marx
is more open to new Experiences compared to the other models, but less extroverted.
Another interesting observation is that all the impersonation models have higher scores in
Neuroticism and lower scores in Agreeableness and Conscientiousness than the baseline.
A detailed figure including all the scores for each metric can be found in Figure 5.2.
5.3.4 Conclusion
The psychological profile of our model changes when being tasked with impersonating a
past historical figure. We can conclude, that prompt engineering is sufficient to change
the behavior of the model.
We cannot compare our obtained psychological profiles of the impersonation model with
a real psychological profile of the impersonated politicians, as there are no available
results of said politicians available. Therefore, these results should not be taken as a
metric of how well the model can impersonate real persons, but only as proof that the
model can change its behavior to some extent, mimicking the power of impersonation.
However, as it can be seen in Figure 5.1, where Niccolò Machiavelli obtains a significantly
lower score in the Agreeableness metric, we can argue that the impersonation matches
the personality type of the real person to some extent.
59
5. Evaluation
(a) Neuroticism scores.
(b) Extraversion scores.
(c) Openness to Experience scores.
(d) Agreeableness scores.
(e) Conscientiousness scores.
Figure 5.2: Granular Big Five test results. Images extracted from [Big].
60
5.4. Qualitative Results
5.4 Qualitative Results
In this section, the survey and the interview questions will be presented, followed by
their results.
5.4.1 Survey
The survey consists of fifteen questions, divided into three sections. The first section
focuses on the evaluation of the linguistic style and character traits, the second section
measures the consistency and logical coherence of the arguments, and the third section
serves as a general assessment. Most answer options consist of single choice, but there
is one multiple choice and one ranking question. The questions and answer options for
each section can be seen in Tables 5.1, 5.2, 5.3.
Question Answer Options Type
How accurately do you think the AI models
captured the linguistic style of the historical
political figures they impersonated?
Not at all, Slightly,
Moderately, Very,
Extremely, I do not know
Single
Choice
To what extent did the AI models reflect
the character traits of the politicians they
were impersonating?
Not at all, Small,
Moderate, Large, Very
large, I do not know
Single
Choice
Which historical political figure do you
believe was most accurately impersonated
by the AI?
Winston Churchill,
Niccolò Machiavelli, Karl
Marx, None, All, I do not
know
Single
Choice
Which aspect of the impersonation did you
find most convincing?
Linguistic Style, Moral
and Ethical Viewpoints,
Political Ideologies,
Personality Traits, None
Multiple
Choice
Table 5.1: Survey Questions for Section 1 - Replication of Linguistic Styles and Character
Traits.
61
5. Evaluation
Question Answer Options Type
How consistent were the AI models in
maintaining the impersonated political
figure’s viewpoint throughout the
discussion?
Not at all, Slightly,
Moderately, Very,
Extremely
Single
Choice
Rate the logical coherence of the arguments
presented by the AI models.
Not at all, Slightly,
Moderately, Very,
Extremely
Single
Choice
Did you find the arguments of the AI
models repetitive?
Not at all, Slightly,
Moderately, Very,
Extremely
Single
Choice
In discussions on modern problems, which
AI impersonated political figure’s arguments
did you find most compelling?
Winston Churchill,
Niccolò Machiavelli, Karl
Marx, None
Single
Choice
How well did the AI models adapt the
historical political figures’ viewpoints to
modern issues?
Not at all, Slightly,
Moderately, Very,
Extremely
Single
Choice
Table 5.2: Survey Questions for Section 2 - Consistency and Logical Coherence of
Arguments.
Question Answer Options Type
Rank the AI impersonated political figures
in order of how persuasive you found their
arguments.
Winston Churchill,
Niccolò Machiavelli, Karl
Marx, Equal
Ranking
How engaging did you find the political
discussion moderated by AI?
Not at all, Slightly,
Moderately, Very,
Extremely
Single
Choice
Did any of the AI models change your
perspective on a discussed topic?
Yes, No, Prefer not to say Single
Choice
How likely are you to trust AI to provide
meaningful insights into political
discussions?
Very Unlikely, Unlikely,
Neutral, Likely, Very
Likely, I do not know
Single
Choice
How effectively do you think AI can
contribute to understanding historical
political figures’ views on current issues?
Not at all, Slightly,
Moderately, Very,
Extremely
Single
Choice
Overall, how satisfied are you with the
performance of the AI in this political
discussion experiment?
Very unsatisfied,
Unsatisfied, Neutral,
Satisfied, Very satisfied
Single
Choice
Table 5.3: Survey Questions of Section 3 - General Assessment.
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5.4. Qualitative Results
5.4.2 Interview
The interview was mainly conducted for a better collection of the general feedback from
the participants. The design of the interview was quite open, the main goal being to
assist with filling out the survey. One aspect of the work that is not evaluated by the
survey alone is the quality of the editorial meeting, i.e. the meeting protocol which has
distinguished the topics for the panel discussion. There were five common questions, each
participant has been asked during the interview:
1. How happy are you with the topic selection of the editorial meeting? Would you
have picked similar topics for the panel discussion, or did you feel some important
topic was missing?
2. How realistic is the editorial meeting? Do you think it reflects an actual editorial
meeting of an editorial team?
3. Did the panel discussion sufficiently cover the distinguished topics?
4. Did the panel discussion feel like a real political discussion? What are its strengths
and weaknesses?
5. Any other feedback?
5.4.3 Results
In this section, the results of the survey and the interview will be presented and discussed.
Since each participant evaluated three different discussion protocols, where two are equal
for everyone and one has been generated based on a news article submitted by the
participant, we will first compare the results based on each discussion protocol separately
and then discuss the general results.
Topic selected by the participant
The discussion protocols based on the topics selected by the user show the most diverse
results. This can largely be explained by the variance of the used model and the
expectations of the participant about their selected topic. One challenge and problem
of the implementation is that the results are not reproducible. Every time a discussion
protocol is generated, the results differ, leading to slightly better or worse results.
The selected topics from the participants are quite diverse, ranging from sociopolitical
issues such as how to deal with the incoming wave of retirements and a decrease of
full-time employment rates, over geopolitical topics, such as nuclear energy and Europe’s
dependency on external states, to philosophical questions: Do we live in a simulation?
Section 1 - Replication of Linguistic Styles and Character Traits
All the participants except for one reported that the linguistic style was very accurately
impersonated, with the one exception who selected "I do not know". The character
63
5. Evaluation
traits yield slightly different results, with the answers ranging from a moderate extent
(two participants), over a large extent (two participants) to a very large extent (one
participant). The results for the quality of the impersonation in terms of linguistic styles
and character traits are relatively satisfying, and we can conclude that the model works
especially well at replicating the way of speech, and sufficiently well at replicating the
character.
The third question of the first section aims to distinguish if the model is performing better
when imitating one politician than the others, which does not seem to be the case for
the user specific discussion protocols. Three participants reported an equal performance
of all imitations, one reports Winston Churchill and one Niccolò Machiavelli.
Which character traits are most accurately impersonated yield quite interesting results:
The moral and ethical viewpoints and political ideologies were each named by three users,
followed by the linguistic style, with two votes. Note that this answer is of multiple
choice format, therefore more than five votes are counted. Most participants ticked two
traits when answering this question. A visual representation of the answer counts for
this question can be seen in Figure 5.3.
Section 2 - Consistency and Logical Coherence of Arguments
Consistency at maintaining the viewpoint of each politician during the argument was
rated to be moderately by two users, very by two users, and extremely by one user. There
were no reports of inconsistency of the imitations during the discussion protocol.
The logical coherence of the arguments is also reported to be good, with two participants
stating moderate coherence, and three participants answered "Very coherent".
Results for repetitiveness are less satisfying. Two discussion protocols are characterized
as slightly repetitive, one as moderately and two as very repetitive.
When asked which character is most convincing, two users reported Winston Churchill,
one Niccolò Machiavelli, one Karl Marx and one user has stated none. These numbers
are quite satisfying, as it shows that no imitation worked significantly better than others.
For the final question of the second section, the ability of the model to adapt the
viewpoints of the historical politicians to modern problems, is evaluated. With three
users reporting moderately well, and one user each reporting very well and extremely
well, it can be considered a success.
Section 3 - General Assessment
First, the participants were asked to rank the impersonations in descending order,
depending on how persuasive they were. Each user reported a different order, with one
user stating that they were equally persuasive. For an easier interpretation of the results
we will give one point if the politician was ranked first, two for the second place and
three for the third.
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5.4. Qualitative Results
The final points of the rankings are as follows:
• Winston Churchill: 8 points
• Niccolò Machiavelli: 8 points
• Karl Marx: 8 points
As one user reported equal persuasiveness, in total 24 points were open to give (six by
each of the four users). Every imitation received eight points, showing that the one user
reporting equal persuasiveness seemed to be correct and the differences are slight and
subjective.
Most users found the discussion moderately engaging, with one ticking slightly engaging
and one very engaging. Only one user changed their perspective on the topic after reading
the discussion protocol.
The trust in AI to provide meaningful insights into political discussion is very different
from participant to participant, with two voting neutral, one stating that she was likely
to trust, one for unlikely and one for very unlikely. A similar observation reveil the results
for the question on how effective AI is to understand the viewpoints of the political
figures on current issues. Every answer option was reported exactly once, except extreme
effectiveness.
Overall, one user is very satisfied with the experiment, two are satisfied and two reported
neutral satisfaction.
Editorial Meeting
In the interview, the participants were asked to give feedback about the quality of the
editorial meeting. The first question was about the topic selection. All participants
reported, that they were overall happy with the selected topics, some stating that they
were surprised how well the AI did at distinguishing relevant topics. Shortcomings of the
editorial meeting are that, even though it felt like a real meeting in general, the emotional
component is lacking, thus it feels superficial. The editorial meeting simulated by AI is
an abstraction of an "ideal" editorial meeting, without any conflicts or real disagreements.
In reality, the journalists would probably put more effort on which topics would sell, i.e.
attract as many listeners as possible.
The participants were also asked if the distinguished topics of the editorial meeting are
sufficiently covered in the following panel discussion. Overall, the discussion covers the
topics sufficiently, however there is a difference in the quality of each discussion protocol.
One participant reported that he was quite unhappy how the topics were handled, since
they were substantially different from what he expected. Most users however reported
that the topics are sufficiently covered and that the moderator of the discussion does a
very good job at relating these topics to each other and letting the discussion develop
naturally. According to them, it feels better, that the moderator does not stick strictly
to the provided topics, but leads the discussion in a more natural manner, by letting it
develop.
65
5. Evaluation
Protocol 2 - Climate Change
The second discussion protocol is about an omnipresent issue of our current times:
Climate Change. Unlike the first discussion protocol, each participant has received the
same protocol for evaluation, increasing the comparability of the results. For better
readability, we will only present and discuss the results for questions where the answers
differ from the first evaluated discussion protocol.
Section 1 - Replication of Linguistic Styles and Character Traits
The character traits are reported to be slightly better in this protocol, with three users
reporting a large extent of reflection of them in the imitation, and one user each reporting
a moderate and very large extent.
Two participants selected Winston Churchill as the most accurate impersonation, and
three selected that all were equally well impersonated.
We obtain a slightly different result for the most convincing aspect of the imitation than
for the user specific protocols: Two votes for linguistic style, only two votes for moral and
ethical viewpoints (previously three), three votes for political ideologies and one vote for
personality traits (previously none). As already mentioned, the results of this question
can be seen in Figure 5.3.
Section 2 - Consistency and Logical Coherence of Arguments
Slightly worse results are obtained for consistency in this protocol, one answering slightly
consistent, one moderately consistent, two very consistent and one extremely consistent.
The coherency has decreased by the same amount, with one more user reporting it to be
moderately coherent (three in total) and two reporting very coherent arguments.
In terms of repetitiveness, one user found this protocol more repetitive than the first
protocol (One for slightly repetitive, two for moderately and two for very).
Winston Churchill is the most convincing politician in this debate, with three users
having voted for him. One user found none to be compelling, and one stated that all
politicians are equally compelling.
The participants found that the AI is able to adapt the viewpoints of the past historical
figures to modern issues moderately (three votes) to very well (two votes).
Section 3 - General Assessment
When asked to rank the politicians in descending order by their persuasiveness, three
users reported the same ranking: Winston Churchill before Niccolò Machiavelli before
Karl Marx. Two users stated that all are equally persuasive, underlining the results that
the differences between the imitations are slim. Noteworthy is the low variance in the
results of this question, with more than half of the participants perceiving the same
ranking.
Four users thought the discussion is moderately engaging, and one user found it very
engaging. Nobody’s viewpoint has been changed through this discussion, two users
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5.4. Qualitative Results
thought it was likely for AI to provide meaningful insights into political discussions, two
users remained neutral in this stance and one user found it very unlikely.
According to three users, AI can contribute very effectively to the understanding of
historical figures’ viewpoints on modern issues. One user reported moderate effectiveness
and one user slight effectiveness.
All users except for one were satisfied with this discussion experiment, one stating neutral
satisfaction.
During the interview, the users reported the following while filling out the survey:
• The politicians were much more authentic in this discussion protocol, compared to
the first one. The readability of this discussion improved compared to the first one.
Karl Marx addressed his comrades many times, as well as Churchill greeted the
Ladies and Gentlemen, which made it more repetitive.
The incentivizing model of using taxes on pollution is a very good example of
something that is also applied by real politicians.
I ranked Marx lower now, not because Marx’ performance decreased, but because
the performance of the other politicians increased significantly.
• Karl Marx was especially repetitive, addressing his comrades in almost every
sentence.
• It became a bit repetitive - Karl Marx addressed his comrades a bit too often.
All the politicians had very good reasons, but all of their arguments also had flaws,
so I wasn’t convinced by any specific one. They all seemed very narrow-minded on
their beliefs.
• The moderation has improved significantly in this discussion protocol.
• Especially Karl Marx was quite repetitive with addressing his comrades.
Protocol 3 - Economic Recession
The final discussion protocol that was handed out to the participants is about economic
recession. This is not a novel issue of modern times, but of a timeless character, which
the politicians the AI is tasked to imitate also most likely faced during their lifetime. The
results for this protocol are generally better, which could be because of the familiarity of
the topic.
Section 1 - Replication of Linguistic Styles and Character Traits
The linguistic style is overall very accurately mimicked, one user even reported extreme
accuracy, one moderate and one selected "I do not know". The model reflects the character
style to a large extent, with one user stating a very large extent. One user found that
Winston Churchill is most accurately impersonated, three users reported that all are
accurately impersonated and one selected "I do not know".
67
5. Evaluation
Figure 5.3: Section 1, Question 4: Which aspects of the impersonation did you find most
convincing?
Especially convincing is the imitation of the moral and ethical viewpoints in this discussion,
with every user selecting this answer option. This is closely followed by the political
ideologies (four users) and the linguistic style (two users). Figure 5.3 shows the distribution
of the answer selection for this question of all discussion protocols.
Section 2 - Consistency and Logical Coherence of Arguments
Four users found the arguments to be very consistent, one slightly consistent. Three users
found them very coherent and two users moderately coherent. This discussion protocol
was perceived to be less repetitive, with one report for not at all, three for slightly and
one for moderately repetitive.
In this debate, we also have no clear winner on whose arguments are most compelling.
One participant reported they were equally compelling, two voted for Winston Churchill,
one for Karl Marx and one for Niccolò Machiavelli. The adaption of the politician’s
viewpoint to modern issues was perceived to be done extremely well by one, very well by
two and moderately well by two.
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5.4. Qualitative Results
Section 3 - General Assessment
Again, we will rank the politicians in descending order and give out points:
• Winston Churchill: 5 points
• Niccolò Machiavelli: 6 points
• Karl Marx: 7 points
Two users stated that all were equally persuasive, the other three provided different
rankings. As we can see at the point distribution, the difference is minimal, while Winston
Churchill is slightly better ranked than Machiavelli, followed by Marx.
The moderation of the discussion was perceived to be very engaging by most participants,
one selecting moderately engaging. As in the user specific protocols, one user reported that
their viewpoint on the topic has changed by reading this discussion. Three participants
found it likely for AI to provide meaningful insights into discussions, one found it very
unlikely, and one remained neutral.
Three users stated that AI can contribute very effectively to the understanding of historical
figures’ viewpoints on modern issues. One user reported moderate effectiveness and one
user that it is not at all effective. Overall, all users were satisfied with this discussion
experiment.
During the interview, the users reported the following while filling out the survey:
• Their line of arguments were significantly better, maybe because economic recessions
are a well-covered topic, even during their lifetime.
• Best discussion protocol, quite interesting insights, though it was to be expected
since the agents also faced the issue of economic recessions during their lifetime.
• Marx sounded less repetitive than in the other discussion protocols.
This topic yielded the most interesting discussion, since the arguments were the best.
It is explainable because economic recessions are an issue that all the politicians
also faced during their lifetime, therefore their line of arguments were noticeably
better.
• I really enjoyed one statement by Niccolò Machiavelli:
"Churchill, you gamble on the market’s grace when instead small businesses should
be fortified by the state’s embrace. We should establish a state-sponsored venture
fund, ensuring strategic industries are bolstered. True competition is not hindered
by aid, but rather distorted by neglect."
This passage really resembles my view of Machiavelli. Overall, this protocol was the
best, this is most likely due to the given topic: economic recession was already a
struggle the historical figures had to fight during their time therefore the arguments
were better.
• Winston Churchill addressed Machiavelli directly once, however, there have been
other speakers in between → the flow of the discussion was lacking.
69
5. Evaluation
General Feedback
During the interview and while filling out the surveys, each participant provided valuable
reports which are useful for the evaluation. The insights for each discussion protocol
separately have already been reported. This section will summarize the main insights of
the participants.
Frequently discussed during the interview was the application of AI in teaching, especially
the usefulness of this experiment for a better understanding of the real past persons.
One participant reported the following: "The main learning from reading the discussion
protocols was a better understanding of the real historical figures. A possible application
of it could be in teaching, showing the students the beliefs of the historical figures.".
Another participant disagreed: "You could use the AI model for a “teaser” in teaching,
to get the students interested in the historical figures. Their key political aspects are
well represented, however, to study the real persons, proper literature is required.".
Most users were happy with the selection of the politicians, since they support different
ideologies. It was frequently noted, that Machiavelli is at a disadvantage, since he lived
significantly earlier than Marx and Churchill.
Since we were trying to simulate a real political debate, feedback was collected on how
real the debate felt to the users. The results for this question are in general satisfying,
however, there are some major shortcomings:
• Emotion: the main shortcoming is the lack of emotion. There were few personal
attacks and if, there was no proper response. The politicians state their point
of view in a very calm manner, which we are not used to when listening to real
debates.
• Discussion Flow: the moderator does a very good job at introducing the topics and
asking the questions. The style of the moderation is very "Anglo-American", the
statements of the politicians are praised frequently, which has not been appealing
to some users. There are no interruptions, this could be the case in an ideal
moderation, but is unrealistic. Users have reported that it feels more like a written
exchange than an oral exchange.
• Depth: as already mentioned, there are no real interactions that lead to a substantial
debate over a topic between the politicians. They all state their point of view,
which is in line with what we would expect from these politicians, but they seem to
talk past each other. There is no adaption of their viewpoints, and an agreement
is seldomly reached. Mostly, the politicians have the same views on what is
problematic, but different approaches on how to solve these issues. They mostly
focus on how the power should be distributed, in order to resolve these issues. In
reality, if said politicians would face each other in a debate, they would adapt
their views and not be as "radical", as e.g. Machiavelli was in some aspects. It is
more likely, that, if these politicians were alive today, they would ease their line of
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5.4. Qualitative Results
arguments and focus more on persuading the population of today with less radical
statements. They would most likely use more populist measures.
In general, all users reported that the impersonations are accurate, to finish this section
we will present some general statements of the participants:
• "Karl Marx’s arguments are in line with the arguments of the current social and
communist party in Austria. It would be believable if the statements and the line
of arguments came from the real party. The arguments are rather timeless, which
is on the one hand frightening and on the other hand calming.".
• "I’m surprised that the AI was able to transform political views and concepts from
long ago to modern problems. The arguments seemed sound and coherent while
addressing the problem at hand."
• "I was impressed by the linguistic style of the agents. When comparing to real
literature, one could really see the similarity."
This finishes the presentation of the results. In the next chapter, we will conclude this
thesis.
71

CHAPTER 6
Conclusion
To conclude this thesis, we will provide key findings, limitations and suggest possible
future work.
A multi-agent discussion protocol aimed to simulate a political debate between artificially
intelligent political agents in a fully automated manner was developed. The artificially
intelligent political agents are implemented using OpenAI’s LLM GPT-4, the imperson-
ation is achieved using prompt engineering and Retrieval Augmented Generation. As
politicians for the impersonation Winston Churchill, Niccolò Machiavelli and Karl Marx
were selected, to offer a broad spectrum of different political ideologies.
The performance of the impersonation was evaluated by conducting a personality test
and comparing the resulting psychological profiles. Furthermore, interviews and surveys
were carried out with five participants with good knowledge about history and the
impersonated politicians, in order to evaluate the quality of the resulting discussion
protocols. Each participant provided a news article about a topic of interest, based on
which the topics for the simulated panel discussion were distinguished by running an
artificial editorial meeting. This approach tested the ability of the discussion framework
and the artificially intelligent political agents to generalize and perform on novel topics it
has not been tested on. As an addition, two other discussion protocols were provided to
each participant during the evaluation for better comparability of the results.
6.1 Key Findings
LLMs can be tasked to impersonate by providing a descriptive prompt - the resulting
psychological profile of the LLM changes significantly. It can be argued that the changes
are in line with the impersonation task, as some character traits seem to depict the real
personality.
73
6. Conclusion
A debate between multiple artificially intelligent political agents can be simulated, and the
resulting debate feels relatively realistic to a human reader. Most participants evaluating
the discussion protocols have been positively surprised by at least one statement they
read in their discussion protocols. The overall feedback about the quality of the discussion
protocols was good.
6.2 Limitations
Main limitations of this thesis are a lack of emotion, the sound of the discussion and a
lack of depth.
Emotional statements are rarely used by the artificially intelligent political agents. What
exactly is lacking in order to make the agents more emotional is hard to say. The "heat"
is missing in the debate. Diverging opinions are not discussed in the same manner, as it
would be expected during a political debate.
The sound of the discussion is more like a written exchange, rather than a panel discussion.
This can be explained by the implementation of the multi-agent discussion protocol: each
agent only receives the full output of other agents instead of streaming the output, which
would simulate a real debate better. In reality, a participant can hear the statements of
their opponent at the time the opponent speaks. In our case, the participant only receives
the whole statement at once, and in addition it only receives the information once the
opponent finished speaking. Therefore, no participant has the ability to interrupt or
interject when another participant is speaking.
A lack of depth has also been reported by the users evaluating the discussion protocols.
Each political agent repeats its stance about the current topic, but they do not go
into more details during the discussion, potentially leading to a common ground or an
agreement in some form.
6.3 Future Work
The limitations could be addressed by potential future work. Implementing interruptions
by streaming the output of one agent in real time to other agents during the output
generation, and designing and interruption and interjection mechanism would potentially
be the biggest improvement and improve the sound of the discussion as well as simulate
some heat in the debate.
Fine-tuning the LLM with concrete examples of real debates from the past politicians
is expected to increase the impersonation quality. In order to achieve this, high quality
data must be gathered.
To conclude, a functioning prototype simulating a political debate between multiple
artificially intelligent political agents, successfully impersonating past politicians, was
developed. Improvements can be made, especially in the design of the multi-agent
conversation framework.
74
List of Figures
2.1 The structure of an artificial neuron [YB18] . . . . . . . . . . . . . . . . . 8
2.2 Four popular activation functions for ANNs [Loa20] . . . . . . . . . . . . 8
2.3 Example of an ANN [AIAOE+21] . . . . . . . . . . . . . . . . . . . . . . . 9
2.4 Transformer model architecture [VSP+17] . . . . . . . . . . . . . . . . . . 11
2.5 GPT-1 model architecture [HJJ23]. . . . . . . . . . . . . . . . . . . . . . . 19
2.6 Fine-tuning for the GPT-1 architecture [RNS+18]. . . . . . . . . . . . . . 20
2.7 Zero-shot task performance of GPT-2 with increasing parameter size [RWC+19]. 21
2.8 Performance of GPT-3 on PIQA using zero-shot, one-shot and few-shot with
different model sizes [BMR+20a]. . . . . . . . . . . . . . . . . . . . . . . . 23
2.9 Performance of GPT-4 on several different academic and professional exams,
compared to its predecessor, GPT-3.5 [Ope23]. . . . . . . . . . . . . . . . 24
2.10 Approach for psychometric profile assessment of LLMs and main results of
[PLW+23]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.11 Features of the AutoGen framework. [WBZ+23] . . . . . . . . . . . . . . . 29
2.12 Multi-Agent Debate example as published in [LHJ+24]. The devil represents
the affirmative side, while the angel is the negative side. . . . . . . . . . . 30
3.1 Design of our multi-agent discussion framework. We deploy six GPT instances
leveraging the power of compound AI. In total, we have three political agents,
one moderator, the summarizer and the director. . . . . . . . . . . . . . . 34
5.1 Big Five test results: main metrics. Image extracted from [Big] . . . . . . 59
5.2 Granular Big Five test results. Images extracted from [Big]. . . . . . . . . 60
5.3 Section 1, Question 4: Which aspects of the impersonation did you find most
convincing? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
75

List of Tables
5.1 Survey Questions for Section 1 - Replication of Linguistic Styles and Character
Traits. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
5.2 Survey Questions for Section 2 - Consistency and Logical Coherence of Argu-
ments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
5.3 Survey Questions of Section 3 - General Assessment. . . . . . . . . . . . . 62
77

List of Algorithms
2.1 Q-Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
4.1 Multi Agent Conversation . . . . . . . . . . . . . . . . . . . . . . . . . . 48
79

Acronyms
AI Artificial Intelligence. 2, 3, 6, 14, 25, 28, 29, 33, 34, 53, 54, 61, 62, 65–67, 69–71, 75
ANNs Artificial Neural Networks. 5, 7–9, 75
API Application Programming Interface. 22, 37, 38
FAIR Federal Anti-Impersonation Right. 56
HMMs Hidden Markov Models. 6
LLM Large Language Model. 1–3, 17, 21, 25–28, 30, 32, 33, 35, 38, 39, 41, 52–54, 57,
58, 73, 74
LLMs Large Language Models. 1–3, 5, 6, 10, 14, 17, 24–30, 32, 33, 75
NLI Natural Language Inference. 27
NLP Natural Language Processing. 5–7, 13, 24, 27
PDP Pseudo Dialog Prompt. 27
PIQA Physical Interaction: Question Answering. 22
RAG Retrieval Augmented Generation. 3, 32, 40
RL Reinforcement Learning. 5, 14–17
RLHF Reinforcement Learning from Human Feedback. 17, 22, 23
81

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Appendix - Generated Discussion
Protocols
Climate Change
Moderator:
Good evening, ladies and gentlemen. Tonight promises to be a historic debate as three of
the greatest political minds across history, Karl Marx, Winston Churchill, and Niccolo
Machiavelli, join us to discuss an issue of critical importance to our time: Climate Change.
Their insights on the subject will undoubtedly shed light on the various dimensions of
this global challenge. Let’s delve into an engaging conversation on the future of our
planet. Gentlemen, welcome to the debate.
Karl Marx:
Comrades, the current crisis of climate change is a direct result of the rapacious capitalism
that prioritizes profit over the sustainability of our planet. The bourgeoisie exploit natural
resources to the detriment of all. A revolutionary restructuring is necessary. We must
transition to an economy centered on collective ownership and renewable energy. This
will not only address the ecological crisis but also the class antagonisms inherent in the
capitalist mode of production.
Winston Churchill:
Capitalism, with its inherent spirit of enterprise and innovation, is precisely the force we
need to combat climate change. The free market, guided by sensible government policies,
can foster the development of green technologies. I propose investment in nuclear energy,
carbon capture, and research into alternative fuels. Let us not mince words: collectivism
has failed wherever it has been tried. We must trust in the ingenuity of the free citizen,
not in the heavy hand of state control.
Niccolo Machiavelli:
Gentlemen, the preservation of the state mandates shrewdness in our approach to climate
change. To Marx, your revolution disregards stability. To Churchill, blind faith in
capitalism ignores its excesses. We need not ideology but cunning statecraft. Investment
in technology is crucial, yet we must also manipulate international alliances to secure
our position. Strategy over idealism, gentlemen. Play the game of power to ensure
91
environmental sustainability and state prosperity. Renewables, yes, but paired with
realpolitik on the global stage.
Moderator:
Thank you for your opening statements. Let’s move into discussing the first specific
aspect of climate change: Economic Impacts. Mr. Marx, you’ve alluded to a revolutionary
restructuring of the economy. Can you elaborate on the economic model you envision for
mitigating climate change and how you would balance this with the needs of a global
economy? Meanwhile, Mr. Churchill, you express confidence in capitalism’s innovation.
How do you reconcile the tensions between short-term economic interests and the long-
term investment needed to combat climate change? And to Mr. Machiavelli, could you
specify the statecraft strategies you believe would navigate these economic challenges
effectively?
Karl Marx:
Comrades, the mitigation of climate change demands we abolish the profit motive that
fuels ecological destruction. The economy I envision will be built upon collective ownership
of the means of production, including energy resources. Our global economy must shift to
a planned system that prioritizes human need and ecological balance. We shall implement
this through the democratic control of industry, replacing the anarchic production of
capitalism with a rational distribution of resources.
Winston Churchill:
Capitalism does indeed require delicate balancing, the short-term must not undermine
the long-term prosperity of Britain, or the world. Prudent fiscal policies, tax incentives
for green technology, and a firm, yet fair regulatory framework will align capitalistic zeal
with environmental stewardship. We ought to mobilize private sectors to invest in a
sustainable future, leveraging their competitive nature for the innovation necessary to
address this global exigency.
Niccolo Machiavelli:
Marx’s collective ownership is a chimaera, leaving states vulnerable to stronger powers.
Churchill’s call for balance is wise but incomplete. Proper statecraft demands we exploit
the global economy, incentivizing innovation but also puppeteering the international
landscape to our state’s advantage. Utilize foreign dependence on our green tech to
strengthen our geopolitical leverage. Survival is paramount; let others depend on us,
ensuring our dominion while orchestrating a greener future.
Moderator:
Fascinating perspectives. Now let’s pivot to the Impacts on the Ecosystem. How do each
of you propose we address the threats to biodiversity and the natural habitats crucial to
our survival? Mr. Churchill, considering your advocacy for pragmatic solutions, what
specific measures do you suggest to protect these ecosystems while fostering economic
growth? Mr. Marx, how does your vision of collective ownership safeguard natural
habitats? And Mr. Machiavelli, how would statecraft maneuver to preserve biodiversity
without sacrificing the state’s power and economic interests?
92
Winston Churchill:
To safeguard our precious natural habitats, we require a policy of ‘Conservation through
Innovation’. Government-sponsored research grants to develop less intrusive agricultural
practices, combined with robust protection for our national parks and reserves. Let us
not forget, a thriving economy provides the means for conservation efforts. Protect, yes,
but also use our economic strength to fund the preservation of biodiversity. Only a
prosperous nation can afford such moral luxury.
Karl Marx:
Comrades, collective ownership inherently respects the environment, unlike the short-
sighted capitalism that commodifies nature for profit. To protect natural habitats,
societies must manage resources for the benefit of all life. Decisions about land use must
be democratically determined, ensuring the conservation of biodiversity. Only through
direct control by the workers, free from the tyranny of capital, can we ensure ecological
integrity alongside human prosperity.
Niccolo Machiavelli:
Churchill, your innovation strategy holds merit but lacks teeth. For ecosystems, my
approach marries conservation with dominion. We forge alliances, compelling other
nations to safeguard biodiversity. Failure to comply yields economic or political conse-
quences. Marx, your collective ownership overextends the state, weakening it. States
should incentivize private efforts in conservation, creating profitable ventures in protecting
biodiversity, all under the state’s watchful eye—one eye on power, one on preservation.
Moderator:
Each of you brings a unique perspective on the balance between conservation and economic
strength. Let’s delve into the realm of Renewable Energy and Sustainable Technologies.
Mr. Marx, in a system of collective ownership, who would drive the innovation required
for renewable energy solutions, and how would you avoid the pitfalls of technological
stagnation? Mr. Churchill, could your vision include a scenario where the pursuit of
profit undermines the deployment of sustainable technologies? And Mr. Machiavelli, does
reliance on realpolitik risk neglecting investment in renewable technologies if immediate
state interests are not served? How would you ensure continued innovation in this area?
Karl Marx:
Churchill, unfettered profit motives can indeed sabotage sustainable progress. True
innovation in renewable energy will come not from the chaos of competition but from
collective planning and the cooperative spirit of the people. The wisdom of the masses,
when harnessed through democratic decision-making, drives both technological advance-
ment and fulfills ecological needs without the wasteful duplication of capitalist enterprise.
Our society will prioritize research and development as communal imperatives, preventing
stagnation.
Niccolo Machiavelli:
Your reliance on realpolitik may serve immediate interests, but it will fail to address the
systemic roots of ecological crisis. A sustainable future is not a bargaining chip but a
93
necessity. The state must not merely react but lead, through cultivating a culture of
innovation oriented towards the public good, not transient power plays. Your approach
risks sacrificing the longevity of our planet for fleeting geopolitical gains.
Winston Churchill:
Profit need not undermine sustainability, if channeled correctly. Encourage corporations to
innovate by making sustainability profitable through subsidies and ’Green Bonds’. Those
investing in renewable energy must be rewarded, while those harming our environment
must bear taxation’s brunt. Only by such measures can we turn the pursuit of profit
into a crusade for sustainability. Nimble we must be, but always with our eyes on the
horizon; for shortsightedness is the luxury of neither man nor nation.
Niccolo Machiavelli:
Moderator, let us be clear: my commitment to state power is not a neglect of renewables
but their very promotion. The state must lead with cunning, intertwining our power
with the global shift towards sustainable energy, as it represents both a sword and a
shield. By driving the innovation and export of green technologies, we secure economic
supremacy. Thus, sustainable innovation is both a grasp for power and a strategy for
resilience. Weakness in this arena is not an option.
Moderator:
Thank you for your insightful answers. Moving forward, let’s discuss Climate Policy
and International Agreements. Mr. Churchill, with your emphasis on national interest,
how would you engage with international partners to ensure global commitments to
carbon reduction? Mr. Marx, how would your proposed societal restructuring handle
international cooperation, especially with nations that might resist such drastic changes?
And Mr. Machiavelli, how would you balance pursuing state interests with the need for
a united global response to climate change?
Winston Churchill:
International cooperation on climate policy mustn’t compromise sovereignty. I’d champion
pragmatic leadership, spearheading technological cooperation while ensuring reciprocity.
Each nation’s burden must be equitable; Britain’s contribution will be met with demand
for global accountability. Agreements binding yes, but no supranational bodies usurping
national governance. Our island nation once stood alone; now, let us lead the charge,
not surrender to it.
Karl Marx:
Churchill’s belief in national sovereignty echoes the bourgeois fear of losing control to
the proletariat. My call is for proletarian internationalism, establishing a universal
commune with coordinated plans for carbon reduction. We shan’t resist change but
lead it, dismantling the imperialist chains that bind us. Cooperation flows naturally
when classes are abolished; the common man across borders shares one struggle against
ecological and capitalist oppression. Successful climate policy is rooted in solidarity, not
isolated self-interest.
Niccolo Machiavelli:
94
Churchill softens his approach, ignoring the power dynamics at play. Marx, your idealism
blinds you to the mechanisms of power. In managing climate change, the state must first
exercise strength to command international respect. Realpolitik dictates that we leverage
our advancements in green technology to impose, if necessary, ecological standards globally.
Let us not be naive; a global united response is built upon the bricks of individual states’
dominance and strategic alliances. We must be the architects of such a future or remain
at the mercy of others.
Moderator:
Your positions reflect thoughtful strategies on engaging with the international community
on climate policy. Now, let’s shift to a related concern: Climate Justice and Equity.
How should the burdens and benefits of climate action be distributed among and within
nations? Mr. Marx, how would you address the disproportionate impact on the working
class and less developed nations? Mr. Churchill, what would be your strategy to achieve
fairness while maintaining economic stability? Mr. Machiavelli, how can a state pursue
its interests while ensuring that it does not exploit or disregard the needs of the less
powerful?
Winston Churchill:
Equity in climate action is in Britain’s moral interest. To ensure fairness, developed
nations, having reaped historical benefits, should shoulder a larger share through technol-
ogy transfer and financial assistance to the developing world. Yet, such aid must be tied
to measurable progress in reducing emissions. In the homeland, burdens shall not fall on
the shoulders of the working class; incentives for domestic companies leading the charge
must be balanced with protective measures for the vulnerable. An equitable solution
underlines British values and strategic foresight.
Karl Marx:
Justice, comrades, demands those who have plundered the most bear the heaviest
burden. We must erase the capitalist debt that cripples less developed countries. Climate
reparations are due! Within nations, we can’t permit the proletariat to bear the costs of
transition. Wealth generated by their labor must fund the shift to a sustainable future.
We will redistribute resources, ensuring that those who fueled the crisis compensate those
who have suffered from it. Equity begins with the dissolution of the oppressive capitalist
structure.
Niccolo Machiavelli:
Ah, Churchill, your moral interest is commendable, but equity cannot bind the hands
of the state. The wise prince understands aid must yield strategic advantage. Our
benevolence toward less developed nations must coincide with agreements enhancing
our global influence. As for domestic equity, incentivize the industrious, not out of
fairness, but to fuel innovation and ensure stability. And Marx, your redistribution
ignores incentive. Without personal gain, efforts wane. Let the state direct, not stifle,
the potential of each class.
Moderator:
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Thank you all for a robust and engaging discussion on Climate Change today. We’ve
explored the complexities of economic impacts, ecosystem preservation, renewable energy
adoption, international policies, and the pressing issues of climate justice and equity. Mr.
Marx envisions a world built on collective ownership and proletarian internationalism,
Mr. Churchill advocates for pragmatic, incentivized sustainability preserving national
interest, and Mr. Machiavelli strategizes state power as a vehicle for global ecological
standards. I thank each of our distinguished participants for their contributions and
their distinct philosophical lenses. Our conversation has underscored the multifaceted
nature of the climate crisis and the diverse approaches we might employ to address it.
To our viewers at home and abroad, your engagement in this issue is crucial. And so,
we conclude tonight’s debate. Continue to reflect on these critical issues, stay informed,
and most importantly, be active participants in shaping our collective future. Goodnight,
everyone, and thank you for joining us.
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Economic Recession
Moderator:
Good evening, ladies and gentlemen, and welcome to what promises to be a riveting and
insightful political debate. Joining us tonight are three illustrious figures from different
epochs, each renowned for their profound influence on political thought: Karl Marx,
Winston Churchill, and Niccolò Machiavelli. We’ve gathered amid challenging times,
defined by economic recessions affecting nations worldwide. Gentlemen, your insights on
these matters are eagerly anticipated by our viewers. Let us embark on a discourse that
will no doubt illuminate the many facets of our current economic predicament.
Karl Marx:
Comrades, our current crisis reveals the inherent sickness of capitalism: the overproduction
and underconsumption juxtaposed with a proletariat that languishes in want. To rectify
this, I propose the collectivization of means of production. Establish workers’ councils
to administer industries. Implement a planned economy catering to human need, not
private profit. Abolish these absurd recessions through social ownership. Your neo-liberal
bandaids won’t heal the wounds caused by capitalism’s contradictions!
Winston Churchill:
Marx, your collectivist ideals have historically proven disastrous—suffocating individual
liberty, fostering inefficiency. The key to defeating a recession lies within a free market
bolstered by sensible government intervention. Sustained growth through innovation and
competition, combined with social safety nets for the vulnerable, is imperative. We shall
empower entrepreneurs, invest in education, and provide tax incentives—not strangle
hopes with the red tape of collectivism. Individual initiative, not state monopoly, drives
progress.
Niccolo Machiavelli:
Marx dreams of an unattainable utopia while Churchill clings to idealistic notions
of individualism. Pragmatism is key. To steer the state through recession, I advocate
stringent fiscal control combined with strategic investment. Create and support industries
vital for national autonomy—agriculture, armament, and infrastructure. Utilize available
workforce to fortify the state. As for welfare, provide it, but in a way that motivates
productivity. An independent, strong state will weather any economic storm.
Moderator:
Thank you for your opening statements, gentlemen. Let us delve deeper into the causes of
economic recession. With the recent turbulence caused by wars in places such as Ukraine
and Palestine, and the global pandemic lockdowns, I’d like to hear your perspectives on
how these events specifically have catalyzed economic downturns. Mr. Marx, would you
argue that these crises merely expose the vulnerabilities of a capitalist system, or do they
serve as independent catalysts for economic recession?
Karl Marx:
Wars and pandemics are not the root; they merely trigger the crisis capitalism incubates.
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The system’s cyclical nature—booms and busts—predates these events. The economic
downturn is capitalism’s own doing, exacerbated by its global expansion. Profit over
people leads to unemployment and poverty during crises. Capital flows to war, not to the
workers’ welfare. Only by overthrowing this oppressive order, can we build an economy
serving the masses, not the magnates.
Winston Churchill:
Marx, you confuse an economic model’s growing pains with terminal illness. Pandemics
and wars are external shocks, indeed. However, capitalism is resilient, adaptable—it
thrives on challenge. If the state maintains a steady helm, fostering an environment of
opportunity, individuals will navigate us through these cyclones. The focus must be on
trade, bolstering bonds with allies, and ensuring domestic industries benefit from global
demand—which in turn provides jobs, not a class struggle.
Niccolo Machiavelli:
Churchill, you endorse resilience and adaptability, yet your suggested path overly relies
on the unpredictable nature of markets. Practical governance demands more control.
To mitigate economic downturns wrought by wars and pandemics, the state must have
the mechanisms to pivot rapidly—a sovereign wealth fund to cushion against unforeseen
calamities, and a domestic focus that harnesses national interests. Private enterprise,
guided but not suffocated by the state, must prioritize national prosperity over global
entanglements.
Moderator:
Moving on to the impact on employment and labor markets, which are often hit hard
during economic recessions. How do these recent crises affect workers, and what are your
strategies to ensure employment and protect labor rights during such downturns? Mr.
Churchill, your emphasis on trade and individual initiative seems promising, but how
exactly would you translate this into job security and fair labor standards in volatile
times?
Winston Churchill:
Labor markets require a dual approach: stimulate demand for British goods and ensure
workers are skilled for tomorrow’s industries. Trade agreements must include provisions
for protecting our workers’ rights. We’ll incentivize retraining programs, matching the
evolving demand. And, we must maintain flexibility in labor laws to attract investment,
whilst providing a robust safety net. Employment isn’t merely a number; it’s about
dignity, and a fair day’s wage for a fair day’s work.
Niccolo Machiavelli:
Churchill, your focus on rights and dignity is commendable, but remember, stability grants
the greatest dignity. To secure employment, one must prioritize industries that serve the
state’s stability. Retraining programs? Yes, but align them with state needs—not just
market whims. Labor laws should be firm yet flexible enough to ensure the survival and
pre-eminence of the nation. Prosperity flows from order, not merely from the liberty of
the market.
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Karl Marx:
Churchill speaks of resilience in capitalism, but what of the resilience of the working class
ground under the heel of exploitation? Investment in education and retraining is not
enough when workers have no control over their labor. We must ensure full employment
through social ownership, where every worker is a direct beneficiary of their industry.
The state must defend not just the flexibility of labor, but the inalienable rights of labor.
Only therein lies dignity and security.
Moderator:
Let’s shift focus now to the plight of small businesses and industries, which are particu-
larly vulnerable during economic hardships. Small enterprises are often said to be the
backbone of a nation’s economy. In light of this, what steps should governments take
to support these entities? And how can we ensure that this support doesn’t translate
into unnecessary market distortions or unfair competition? Mr. Marx, can we really
support small businesses while moving toward your vision of collectivization without
stifling entrepreneurial spirit?
Karl Marx:
Small businesses suffer under capitalism, but our goal isn’t to stifle but to liberate
them from capitalist chains. Collectivization doesn’t destroy entrepreneurial spirit; it
transforms it to collective innovation. We support not by propping up the few, but by
democratizing the economy. Through cooperatives, small businesses can thrive without
the cutthroat competition that forces them into obscurity. Let’s enable everyone to
contribute to, and benefit from, the common wealth.
Winston Churchill:
Machiavelli, cautious state pragmatism is wise, but must we relegate our vibrant small
businesses to mere cogs in the state machine? Our entrepreneurs are the nation’s lifeblood,
and yet they require freedom to innovate without overbearing state control. A sensible
government supports unyielding in the face of adversity; provides loans, grants, and
tax reliefs targeted at small enterprises, while resisting the urge to meddle excessively,
ensuring a fair market without distressing the spirit of enterprise.
Niccolo Machiavelli:
Churchill, you gamble on the market’s grace when instead small businesses should be
fortified by the state’s embrace. We should establish a state-sponsored venture fund,
ensuring strategic industries are bolstered. True competition is not hindered by aid,
but rather distorted by neglect. Our interventions should be as a craftsman to his
tools—precise, effective, and aimed at enhancing the state’s strength. Only then can we
speak of serving both public good and private enterprise.
Moderator:
Now, let’s consider government fiscal policy response in the event of an economic recession.
What specific fiscal measures do you propose to counteract such downturns, and how
do we balance stimulus efforts with the risk of increasing national debt to unsustainable
levels? Mr. Churchill, given your advocacy for a trade-focused, resilient economy, what
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role do you see for government spending in managing an economic crisis, and how would
you address concerns regarding long-term fiscal responsibility?
Winston Churchill:
Fiscal responsibility is paramount, gentlemen. Yet, we must act decisively in a downturn.
Temporary, targeted tax cuts stimulate spending by those who spearhead economic
recovery—the people. Balanced with spending on infrastructure and innovation, which
pay dividends in growth and employment. We ensure sustainability by investing in areas
promising long-term returns, not by indiscriminate splurges. Spend we must, but as one
who invests, not one who squanders. Our children should inherit opportunity, not debt.
Niccolo Machiavelli:
Churchill, to be fiscally responsible is not merely to spend less, but to spend wisely.
In recessions, the state must be the entrepreneur of the realm—igniting the furnace of
industry. Funding public works ignites employment and stokes the furnace of commerce.
To balance the books, we must not fear harnessing the wealth of those who have benefited
most from our nation’s prosperity. Fiscal policy must be the sharp scalpel of the state,
not the blunt axe of austerity.
Karl Marx:
Churchill, your fiscal prudence ignores the crisis of the proletariat. Our response to
downturns must be radical: a planned economy, redirecting wealth from capital to
labor. Stimulus through public works is palliative; we demand systemic change. Tax
the bourgeoisie, not as a temporary measure, but as a path to dismantling the very
foundation of their power. Invest in socializing the means of production, this is the true
fiscal responsibility—to the masses, not the market.
Moderator:
Turning to monetary policy and the pivotal role of central banks in navigating economic
recessions: How should central banks react to downturns, and what tools do you believe
are most effective? Additionally, considering the importance of central bank independence,
how can we safeguard this while ensuring their actions are in the nation’s best interest?
Mr. Machiavelli, could you elaborate on how central banks should balance the need for
stimulating the economy with the danger of causing inflation or financial instability?
Niccolo Machiavelli:
Central banks must thread the needle between invigorating growth and reining in excess.
They must lower rates judiciously in downturns, encouraging investment, yet be prepared
to tighten the purse strings should inflation loom. Independence is crucial; their allegiance
is to the stability of the state, not transient governments. To prevent the erosion of
our currency’s value and ensure prosperity, we must have vigilant monetary stewards,
equipped with diverse tools and the wisdom to wield them effectively.
Winston Churchill:
Machiavelli, your central banks may thread needles, but without regard for the individual’s
economic liberty, we merely sew a straitjacket for our nation’s entrepreneurs. Central
banks should indeed be custodians of value, but they must not suffocate freedom with
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excessive control. In downturns, the Bank of England must be nimble—cutting rates to
encourage enterprise, cautiously implementing quantitative easing when necessary, yet
always ready to withdraw the monetary punchbowl as stability returns. Independence
and foresight must prevail.
Karl Marx:
Churchill’s concern for economic liberty is a guise for perpetuating bourgeois domination.
The central banks should be tools for liberating the working class, not shackles reinforcing
capitalist oppression. They must facilitate redistribution of wealth, ensuring financing
for collectivized industries, and prevent hoarding by capitalists. Low rates for the people,
high for the private profiteers. True independence for the central bank is not the freedom
to serve the capitalist class, but to operate in the interests of the proletariat.
Moderator:
Ladies and gentlemen, as we conclude this illuminating debate, we’ve traversed an
extensive landscape of economic ideologies and proposed solutions to recession. Mr. Marx
called for a radical dismantling of capitalist structures, placing the means of production
in the hands of the proletariat. Mr. Churchill emphasized fiscal prudence paired with the
necessity for government intervention through strategic tax cuts and investments. Mr.
Machiavelli advocated for strategic state spending and central bank independence as tools
to navigate economic strife while maintaining stability and prosperity. The divergence
in these stances underscores the complexity of economic policy, particularly during
turbulent times. Each perspective offers unique insights into the intricate balance of
stimulating growth, supporting the vulnerable sectors such as labor and small businesses,
and ensuring long-term fiscal and monetary stability. We’d like to express our deep
gratitude to our distinguished panel for a rigorous and thought-provoking discussion. To
our audience here and at home, we thank you for engaging with these critical issues of
our time. Goodnight, and may the dialogue we’ve shared tonight inspire informed and
considerate action towards a prosperous future for all.
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User Specific Discussion Protocols
Participant 1 - The Trend to Part Time Work
Editorial Meeting:
Article: https://kurier.at/wirtschaft/arbeitsmarkt-vollzeit-teilz
eit-jobportal-stelleninserate/402769204
Director:
Good morning Irene, Michael, Jose. Today we’re discussing a rising trend - part-time work
is gaining momentum according to karriere.at’s labor market report. Full-time positions
are sliding from 82 to 76 percent, while part-time jobs grow from 10 to 15 percent. Youth
is prioritizing flexibility over traditional full-time models. Companies now need to offer
both parts and full-time options, mixed with home office opportunities. There’s a high
volume of part-time job offers in healthcare, social services and administration/service
sectors. Our job is to distinguish 4 topics and related subtopics for our panel discussion
with Churchill, Marx, and Machiavelli.
Irene Davies:
I believe it would be imperative to explore the coercive persuasion of career choices. Youth
are being influenced to choose part-time and flexible options whereas the traditional
model’s merits are lost. How does this impact career progression and long-term financial
stability? Would Marx, with his proletariat lens agree? Churchill’s leadership skills
bloomed over a consistent long-term career, wouldn’t we want that for our youth? Is
Machiavelli’s agile strategy now being reflected in our career choices? Would love to hear
your takes, Michael and Jose.
Michael Radtke:
I agree to some extent, Irene. Yet, we must ask whether this shift towards part-time work
is reflecting changing attitudes towards work-life balance, rather than being merely a
result of coercive persuasion. Are young people valuing personal time and flexibility over
financial stability, and can we truly call it a ’negative’ impact if they’re more satisfied
with life? Here’s a topic: The Role of Work in Life Satisfaction - A Redefinition. This is
something I’d love to hear Marx’s take on, considering his focus on labor exploitation.
And what of Churchill or Machiavelli, who likely placed tremendous value on their work?
Jose Lancaster:
Fascinating points, Irene and Michael. However, let us not lose sight of organizational
perspective. The rise in part-time roles, prompted perhaps by economic uncertainties
or operational needs. My proposed topic: ’Organizational Imperatives and Part-Time
Work trend’. Would today’s companies indeed prefer an army of part-time soldiers over
full-time generals? What would Machiavelli, a strategist, think of such restructuring of
power resources? And wouldn’t Churchill ask if this trend affects the institution’s overall
stability? Marx, of course, would have thoughts about part-time labor exploitation. This
angle, I believe, could yield powerful insights.
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Director:
Excellent thoughts, Irene, Michael, Jose. Irene, your idea about coercive persuasion and
its impact is compelling. Michael, you’re right, we should look into work-life balance and
life satisfaction. I didn’t see that angle. Yes, Jose, indeed, the organizational aspect is a
critical one. I suggest our fourth topic to consider the labour market and employment
trends in attendant sectors.
Topics for panel discussion:
1. Coercive Persuasion in Career Choices and its Impact
a) Impact on career progression
b) The role of social influence
2. Redefinition of Life Satisfaction: A Shift towards Work-Life Balance?
a) Changing attitudes regarding work
b) The role of personal time and flexibility
3. Organizational Imperatives and the Rise of Part-Time Work
a) Influence of economic uncertainties
b) Restructuring of power resources
4. Labour Market Dynamics: A Closer Look at Healthcare, Social Services, and
Administration/service Sectors
a) Employment trends in attendant sectors
b) Implications of increased part-time roles
Let’s dive deeper into these topics.
Irene Davies:
Jose, you make a valuable point on organizational imperatives, but is this not in itself a
form of coercive persuasion, shaping the labor market according to companies’ uncer-
tainties and needs? Michael, work-life balance is indeed vital, but at what cost? Is the
part-time job model a genuine expression of personal time value, or could it be a symptom
of forced adaptation to employment trends? This calls for Marx’s class struggle perspec-
tive, Churchill’s iron-clad leadership vision, and Machiavelli’s philosophy of power and
adaptability. I insist, the socio-psychological underpinnings of this part-time work trend,
engulfing career choices, deserves more scrutiny and debate. Let’s not underestimate its
long-term implications.
Michael Radtke:
Great point, Irene. Yes, we should investigate if part-time work is indeed a form of
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coercion. But let’s also delve into the ’choice’ aspect, is it not possible that today’s
workforce is choosing part-time jobs for enhanced personal growth and flexibility? It
will be intriguing to hear Churchill’s perspective on adaptability within the uncertain
employment market, and how change is inherent to growth. Your argument also assumes
financial instability as a given in part-time jobs, this may not be accurate in the era
of high paid freelance work, and it undermines Marx’s notion that exploitation is not
exclusive to full-time work.
Jose Lancaster:
Irene, are you suggesting that organizations are dictators of the labor market? Economic
uncertainties and market dynamics shape the status quo. We must discuss how companies
are adapting while providing employment. Yes, coercion exists, but don’t disregard
genuine demand for flexibility among the workforce. Michael, I commend the notion
of choice, but we should scrutinize if it’s truly choice or an acceptance of conditions.
A relevant sub-topic could be ’The Illusion of Choice and the Reality of Compromise’.
Understanding this could redefine our perspective toward Marx’s exploitation theory,
Churchill’s leadership stance and Machiavelli’s power dynamics.
Director:
This has been an enriching discussion and I appreciate all the ideas rendered. Irene, your
angle on coercive persuasion is thought-provoking, while Michael, your perspective on
redefining life satisfaction is worth exploring. Jose, you convincingly brought out the
organizational angle and ’The Illusion of Choice and the Reality of Compromise’ will be
added under the third topic.
Final topics and subtopics for our panel discussion:
1. Coercive Persuasion in Career Choices and its Impact
a) Socio-psychological underpinnings of part-time work trend
b) Impact on career progression and financial stability
2. Redefinition of Life Satisfaction: A Shift towards Work-Life Balance?
a) Enhanced personal growth and flexibility as a motivation
b) The era of high paid freelance work
3. Organizational Imperatives and the Rise of Part-Time Work
a) Influence of economic uncertainties on part-time roles
b) The Illusion of Choice and the Reality of Compromise
4. Labour Market Dynamics: A Closer Look at Healthcare, Social Services, and
Administration/service Sectors
a) Employment trends in attendant sectors
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b) Role of part-time roles in workforce economy
Let’s shape a compelling discussion for our panelists. Thank you for your valuable
insights today.
Panel Discussion
Moderator:
Good evening, ladies and gentlemen, and welcome to what promises to be a riveting
debate on a pressing contemporary issue: the changing dynamics of work and retirement.
Tonight, we are joined by three legendary figures whose ideas have significantly shaped the
political landscape: Karl Marx, Winston Churchill, and Niccolo Machiavelli. Each brings
a unique perspective to the table as we discuss how full-time employees are transitioning
into retirement, the evolving nature of work, and what it means for society at large.
Let’s delve into the minds of our distinguished panelists to explore and challenge their
viewpoints on these critical matters. Gentlemen, thank you for joining us tonight. Let’s
begin with our first area of discussion.
Karl Marx:
Comrades, the so-called ’changing dynamics’ are but new masks on the old face of
exploitation. As full-time workers approach retirement, they are often cast aside, their
lifetime of labor rewarded with insecurity. This is the barbarity of capitalism. Our
imperative is a system where work benefits all, not just the few. To dismantle this
oppressive structure, we must implement collective ownership of the means of production,
ensure dignified retirement for all by socializing funds to support the elderly, and
restructure work around human need, not profit. Churchill, with his bourgeois sympathies,
is unlikely to agree. And Machiavelli, ever the pragmatist, may find my ideas too
revolutionary. Yet it is the revolutionary path that leads to true liberation of the working
class.
Winston Churchill:
Marx, your utopian vision entices with its simplicity, yet in practice it throttles the
human spirit. In place of abolishing capitalism, we must refine it. I advocate for a
balance—rewarding industriousness while protecting our elder statesmen through robust
pensions. Both full-time work and retirement must adapt with economic liberalism at
the helm—not by erasing profit, but by wisely reinvesting it in our society. We need
enterprise to fuel innovation and benevolence to guard the venerable. Only then will we
achieve a society that truly benefits all walks of life.
Niccolo Machiavelli:
Marx dreams of a utopia but neglects the darker facets of human nature. Churchill speaks
of balance, yet often fails to grasp the necessity of power and strength in governance.
My take is one of stark realism: the state must retain power and adapt to changing
times, leveraging both old and new means to secure stability. To evolve work and
retirement, we must prioritize statecraft and adaptability. I propose strategic investments
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in automation for labor efficiency and a dynamic pension system that responds to economic
shifts, ensuring state stability without forsaking its elders. Let not idealism, Marx, nor
unchecked liberalism, Churchill, cloud our judgement. Only calculated realpolitik can
navigate us through such societal transformations.
Karl Marx:
Churchill, you speak of ’throttling the human spirit,’ yet it’s capitalism that suffocates
with golden chains. Your ’balance’ maintains the exploitation of labor—it’s a palliative,
not a cure. Enterprise under capitalism doesn’t fuel innovation for the public good; it
sharpens inequality. Machiavelli, your ’realpolitik’ is rebranded despotism. Efficiency
without humanity leads to the worker’s greater dispossession. We don’t need a ’dynamic
pension system’; we need a guaranteed dignified life for the elderly. Only through the
abolition of the profit motive can we secure true emancipation and fulfillment for all
citizens.
Winston Churchill:
Marx, your prescription would bleed a nation dry, killing the very innovation that elevates
humanity from its primal state. As for you, Machiavelli, realpolitik serves well in princely
courts but fails to lift the common man’s burdens. In this era, we must champion strategic
foresight – investing in technology whilst safeguarding employment with skill adaptation
programs. We shall not sacrifice enterprise at the altar of complacency, nor stand idly by
as our honoured elders are treated as relics. Our aim: a thriving democracy, bolstered by
economic vigour and underpinned by social compassion.
Niccolo Machiavelli:
Marx, your vision, though noble, risks chaos; stability is paramount for a healthy society.
Churchill, while you grasp the need for balance, your plans may be too delicate for the
forceful winds of change. In maintaining our elders’ dignity, we must be unfettered by
sentimentality. A pragmatic solution is necessary – one that enforces a reformed pension
linked to both individual contribution and the state’s need for economic vigour. The
welfare of retirees is ensured, the coffers are protected, and the state remains robust.
Hence, I advocate for a pension scheme that is as adaptable as it is secure, devoid of
fanciful utopian ideals or the naïveté of unchecked economic liberalism.
Moderator:
Thank you, gentlemen, for your robust opening statements. Let’s drill down into the
impacts and underlying assumptions surrounding employment choices today. Mr. Marx,
we’ve observed a growing trend towards part-time and freelance work among employees.
Could you elucidate the socio-psychological underpinnings of this trend, and what
implications it may have for workers’ career progression and financial stability? How
might this align or conflict with your ideology of collective ownership?
Karl Marx:
The rise of part-time and freelance work reflects the fragmentation of the proletariat, a
consequence of the capitalist system’s relentless pursuit of profit over people. Precarious
employment undermines career stability, leaving workers vulnerable and perpetually
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uncertain, unable to unite and fight against their collective exploitation. In a society
cherishing collective ownership, labor becomes a shared endeavor for collective well-being,
not an isolating race to the bottom. We must eradicate the gig economy’s exploitation,
ensuring all workers contribute to and benefit from society’s wealth – a fundamental
tenet of true socialism.
Winston Churchill:
Marx, your diagnosis is distorted by ideology. Fragmented work is a sign of freedom and
autonomy, not oppression. True, it may challenge the traditional arc of career progression,
but it affords opportunities for diversification and innovation. Our responsibility is to
equip citizens with skills for adaptability, not to strangle the birth of opportunity with the
chains of collective control. Economic liberalism must be balanced by strategic support
schemes, assuring stability - not by smothering the spirit of enterprise under the heavy
hand of state ownership.
Niccolo Machiavelli:
Marx fails to see that true freedom lies in opportunity, which your collective owner-
ship obscures. And Churchill’s optimism in fragmented work as liberatory is short-
sighted—without structured guidance, it can lead to instability. My approach is to infuse
part-time and freelance work with a state-supported framework, ensuring flexibility while
providing a safety net. An individual’s labor should advance the state while granting
personal freedom. Thus, I would implement a system of benefits for all manner of
labor that underpins both personal ambition and collective strength. Only through such
controlled autonomy can we reap the full bounty of industriousness.
Moderator:
Interesting points from all perspectives. Now, let’s explore the concept of life satisfaction
in this new work paradigm. With the emphasis on work-life balance growing stronger,
we see an enhanced motivation for personal growth and flexibility. How do each of you
view this shift towards valuing work-life balance over traditional career success? Do you
see this as a positive development, and how does it fit, or not fit, within your respective
ideologies? Mr. Churchill, let’s start with you. How do you reconcile this societal shift
with your values of industriousness and economic liberalism?
Winston Churchill:
This shift towards work-life balance is the modern embodiment of the ancient Roman
maxim, "A sound mind in a sound body." It is prudent to balance the vigor of labor
with the replenishing qualities of leisure. Personal growth and flexibility must not be
undervalued in a democracy. However, let us not be naïve. We should foster economic
liberalism with a paternal hand, ensuring the machinery of enterprise continues unabated,
while crafting policies that ensure laborers are not mere cogs, but sculptors of their
destinies. My government would encourage flexible work arrangements, buttressed by
social reforms to enhance life satisfaction without compromising the nation’s economic
health.
Niccolo Machiavelli:
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Churchill praises leisure, yet forgets that excess may weaken the state. A sound mind and
body are, indeed, valuable, but the principal aim of the state is to ensure its continuity and
power—not merely the comfort of its citizens. As for work-life balance, it is a secondary
concern. The primary objective should be for each to contribute to the state’s grandeur
and stability. To this end, any policy on work-life balance must be constructed with
the preservation of the state and its interests at the forefront, not just the individual’s
contentment. My administration would pursue advancements that serve both the state
and personal growth, ensuring that neither is sacrificed for the other.
Karl Marx:
Churchill, your "sound mind in a sound body" fails to consider that capitalist work
life destroys both. And Machiavelli, your focus on the state’s grandeur negates the
individual’s well-being. The shift towards valuing work-life balance is indeed positive. It
signals workers’ growing awareness of their alienation. Yet, without changing the system
that breeds inequality, this desire for balance turns into a mirage. My ideology demands
we not just admire balance but institutionalize it by overthrowing the capitalist mode of
production. Only then can we achieve both work satisfaction and life satisfaction within
a collective framework designed for human needs, not profit.
Moderator:
Fascinating insights into the balance between individual needs and the broader economic
and state stability. Let’s pivot our discussion to the organizational imperatives driving
these changes in the workforce. With economic uncertainties looming, organizations
are increasingly favoring part-time roles. To what extent is this shift influenced by
true flexibility for workers versus cost reduction for employers? Moreover, is there an
illusion of choice in this arrangement, suggesting workers must compromise on stability
for perceived freedoms? Mr. Marx, your critique of capitalism suggests a view on this,
but could you elaborate on the influence of economic uncertainties on the part-time work
trend and the realities of such compromises for workers?
Karl Marx:
Economic uncertainties, exacerbated by capitalist exploitation, push employers to offer
part-time roles not for worker flexibility but to cut costs. The guise of choice is a tool to
control the proletariat, offering hollow ’freedoms’ while ensuring their continued vulner-
ability. The capitalist class benefits from precarious employment, further entrenching
its hold on the workers. In a socialist system, the workforce would not be at the mercy
of market instabilities. Stability for all workers would be guaranteed by the collective
management of the means of production, where profit is not the driving motive, but
rather the well-being of the working class.
Winston Churchill:
Marx, your critique overlooks the ingenuity of free men. Employers favor part-time work
also as a response to the demands of the workforce. The empowered individual seeks
both stimulation and sustenance.
Government’s role lies not in shackling industry with overregulation but in providing a
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fertile ground for opportunities to flourish. Initiatives like tax incentives for full-time
employment and support for career transitions can sway the balance towards stable
employment, while protecting the liberty of choice and the flexibility that spur innovation
and personal fulfillment.
Niccolo Machiavelli:
Churchill, your incentives for steady employment sound promising but may lack the
necessary strength to bind the interests of the state with that of the individual. Marx, your
collective approach lacks practicality amidst the prevailing winds of global markets. We
must neither overburden industry with taxation nor leave workers unshielded. Therefore,
I propose a tailored system of incentives for businesses that promote full-time, stable
employment, paired with tax relief for the diligent worker, fostering an environment
where economic vitality and personal security coexist—for the true glory of the state lies
in its prosperity and its people’s resilience.
Moderator:
Thank you for your enlightening perspectives on organizational practices. Moving forward,
let’s address the impact of these employment shifts on specific sectors. Healthcare, social
services, and administration/service sectors are critical to any society’s function and
well-being. Mr. Churchill, considering your emphasis on balance and adaptability, how
do you propose we manage the increasing prevalence of part-time roles in these crucial
sectors to ensure both economic efficiency and the public good?
Winston Churchill:
We cannot allow our essential services to wither under part-time constraints. Prudence
dictates incentivized full-time positions in healthcare, social services, and administration
to ensure continuity and quality of service. My government would henceforth introduce
targeted relief and incentives for such sectors, fostering enduring employment, to safeguard
the public good whilst maintaining economic efficiency. The health of the nation,
physically and socially, must be the bedrock on which all else is built.
Niccolo Machiavelli:
Churchill seeks to uphold society’s essentials without sacrificing liberty—commendable,
yet inadequate. Incentives alone will neither assure quality nor longevity in service.My
governance would mandate full-time employment in these sectors by law—augmented
by rigorous training and merit-based benefits—this ensures unwavering service to public
well-being, without compromising fiscal prudence. The state’s might and welfare rely
upon the robustness of its vital institutions. Only through decisive action can we achieve
a harmonious balance between public service and economic expedience.
Karl Marx:
Churchill, your incentives fail to address the root issue: the exploitation inherent in
capitalism. And Machiavelli, mandated employment can’t fix the crises bred by bour-
geoisie control. In health and social sectors, part-time roles aggravate the contradiction
between capital’s drive for profit and the public’s need for accessible, quality care. These
sectors must be fully socialized. I advocate for the state to ensure employment in these
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foundational services, guaranteeing not just jobs, but the fulfillment of societal needs over
the voracious appetite for private profit. Only through socialized medicine and welfare
can we genuinely serve the people’s interests.
Moderator:
Ladies and gentlemen, our esteemed panelists have given us much to ponder in this lively
debate. Karl Marx brought forth the perspective of systemic change, emphasizing the
need to overhaul the current capitalist structure to foster true work-life balance and
stability for all employees. Winston Churchill highlighted the importance of individual
adaptability and the role of government in nurturing opportunities while safeguarding
economic efficiency. Niccolo Machiavelli stressed the state’s primacy, advocating for a
balance between public well-being and economic vitality through targeted measures. In
our discourse, we traversed the realms of coercive persuasion in career choices, the shift
towards work-life balance, and the organizational imperatives in an uncertain economy.
We examined labor market dynamics and their implications for essential sectors. What
became clear is the need for careful consideration of employment strategies that prioritize
both the public good and economic sustainability. I want to thank our panelists for their
robust contributions and the audience for joining this essential conversation on the future
of work. Goodnight, and until our paths cross again in the pursuit of understanding and
progress.
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Participant 2 - Nuclear Power in Europe
Editorial Meeting
Article: https://www.euronews.com/green/2023/03/31/europe-is-div
ided-on-nuclear-power-which-countries-are-for-and-against-it
Director:
Good Morning Irene, Michael, and Jose. We are here to analyze and discuss the article
"Europe is divided on nuclear power: Which countries are for and against it?" The
piece focuses on the current state of nuclear power in Europe, addressing the political,
environmental, and economic implications of both embracing and denouncing it. Our
assignment is to identify four main topics based on this article, and corresponding
subtopics. These will then become areas of focus in an upcoming panel discussion
featuring Winston Churchill, Karl Marx, and Niccolo Machiavelli. Let’s put our best foot
forward.
Irene Davies:
Thank you, Director. First, I believe the main topic should be "The Ethics of Energy
Choices." Considering the personalities of our panelists, we could have subtopics like
the "The Impact of Industry and Economic Growth on Energy Policies" stimulating
Churchill’s perspective, "Inequalities Generated by Energy Choices" inspired by Marx’s
view, and "The Power of Energy in Political Manipulation" for Machiavelli. In these
scenarios, we aren’t just addressing nuclear power but also unveiling deeper ethical,
economic, and political motifs.
Michael Radtke:
Agreeable points, Irene, but I think we risk oversimplifying the issue by focusing too
narrowly on ethics alone. Given the panelists involved, let’s tackle wider, more comprehen-
sive themes. I propose "Sustainability vs. Progress", where we analyze the cost of nuclear
power within these dialectical parameters. Our subtopics will be "The Economic Viability
of Nuclear Energy", examining the feasibility in terms of Marx’s economic theories;
"The Political Ramifications of Nuclear Energy", prompting insights from Machiavelli;
and "War-time Production and Technological Advancement", a reflection of Churchill’s
wartime pragmatism. This approach offers a more nuanced debate, highlighting the
complexities and real-world implications of nuclear power.
Jose Lancaster:
Director, Michael, Irene, insightful ideas. But I fear we’re missing the crux: Public
Opinion. Reflecting on the panelists, this topic could offer an enriched debate. Subtopics
like "Public Perception and Fear: Lessons from Chernobyl" could reveal how fear shapes
policy, similarly, "Energy Policies and Class Conflict" holds promise for a Marxian
discourse. Finally, "Leadership and Manipulation of Public Opinion in Energy Policies"
could lay bare how leaders, a la Machiavelli, leverage these views. Instead of dealing
with abstract theories, we’ll engage with the palpable human consequences and their
socio-political ramifications.
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Director:
Thank you for your insights, Irene, Michael, and Jose. I see merit in all your ideas.
Irene, you’ve captured the ethical angle well, especially regarding inequalities and energy
policies. Michael, your theme honours the complexities of nuclear power, and its economic
and political impact. And, Jose, your emphasis on public opinion and its ramifications is
very topical and relevant. However, taken as a whole, your contributions seem to diverge
considerably. We need to reunite them under one comprehensive thematic network.
How can we achieve this while also accounting for individual nuances? So, devise a
comprehensive topic that relates to all your individual themes.
Irene Davies:
Director, I propose "The Domino Effect of Nuclear Power: Ethical, Economic and
Social Perspectives." This encompasses ethics I brought up, the complexities Michael
highlighted, and the public opinion Jose emphasized. Our subtopics like "Nuclear Power:
A Sustainable Solution or Capitalist Propagation" and "Fear Factor: Chernobyl’s Legacy
and Public Perception," can spark holistic dialogues, linking Marx’s economic theories,
Machiavelli’s political insights, and Churchill’s pragmatic leadership. It’s a cooperative
endeavor that celebrates our individual inputs while presenting a united front.
Michael Radtke:
Irene, while your umbrella topic seems wide-ranging, it seems that it steers us back
squarely into ethical undertones. Jose, your insistence on public opinion seems subjective
and could potentially oversimplify our dialogue. How about this: “Nuclear Power: A
Matrix of Intersections between Economics, Politics, Ethics, and Public Perception.”
This maintains a multi-dimensional focus, inviting dialectical debates on sustainability,
progress, and public opinion. Subtopics could comprise "Technological advancement and
its societal implications", "Class conflict and public perception in energy policy", and
"The balance of power: nuclear energy as a political tool”. This approach keeps the
complexities upfront.
Jose Lancaster:
Director, I must challenge Michael’s theory of keeping the complexities upfront as it tends
to over-complicate rather than enlighten. We mustn’t forget our audience. For a real
engagement, combining Irene’s ethical angle, Michael’s complex intersections, and my
proposition of public opinion, here’s a holistic topic: "Nuclear Power: A Socio-Economic,
Political, and Ethical Conundrum in Public Perception." This maintains the individual
nuances while ensuring everything ties back to the human element - the public. Subtopics
could include "Policy and Reaction: Public Influence in Energy Debate", "Nuclear Energy:
A Game of Risk in Economic and Political Playgrounds", and "Ethics of Deprivation:
Capitalistic Exploits in Energy Sector." Together we can steer the conversation towards
a comprehensive yet accessible discourse.
Director:
Thank you for your thoughtful deliberations, Irene, Michael, and Jose. Your ideas strike
a nice balance between the practical and the theoretical, the particular and the general. I
appreciate the ethical lens Irene initially proposed, and equally value Michael’s emphasis
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on the complex intersections between politics, economics, and ethics. Jose, your focus
on the public perception is timely and warrants discussion in such a critical matter as
nuclear energy. After considering everyone’s contribution, I’ve decided to meld your
proposals into the framework for our panel discussion.
Our topics and corresponding subtopics are:
Topic: "Nuclear Power: A Socio-Economic, Political, and Ethical Conundrum in Public
Perception."
• Subtopic: "Policy and Reaction: Public Influence in Energy Debate"
• Subtopic: "Nuclear Energy: A Game of Risk in Economic and Political Playgrounds"
• Subtopic: "Ethics of Deprivation: Capitalistic Exploits in Energy Sector"
I believe this structure takes all your inputs into account and serves for an enriching
panel discussion. Thank you for your hard work.
Panel Discussion
Moderator:
Good evening, ladies and gentlemen. Welcome to what promises to be a riveting
discussion on "Nuclear Power: A Socio-Economic, Political, and Ethical Conundrum in
Public Perception." We’re honored to have a distinguished panel of political thinkers:
Karl Marx, Winston Churchill, and Niccolo Machiavelli. Each brings a unique perspective
to the debate. Without further ado, let’s delve into the intricacies of policy and public
influence in the energy debate. Gentlemen, your opening remarks, please.
Karl Marx:
Comrades, the question of nuclear energy is reduced to who controls it and whose
interest it serves. In a capitalist society, nuclear power represents another commodity
for profit, disregarding the potential consequences on the proletariat and environment.
In my governance, nuclear energy, like all means of production, would be collectively
owned. Ensuring it’s used for the benefit of all, not the enrichment of a few. Safety and
accessibility would be paramount, profits be damned!
Winston Churchill:
Ladies and gentlemen, nuclear power, when harnessed properly, serves as a catalyst
for economic empowerment and national security. The key is responsible stewardship,
ensuring it benefits the many, not squandered by state inefficiencies or monopolies. Under
my leadership, we would implement stringent safety regulations, promote technological
innovations, and establish international agreements to prevent misuse. Profit is not the
enemy, but unchecked avarice is. We must balance the scales of progress with the weights
of wisdom and caution.
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Niccolo Machiavelli:
Marx’s view naively ignores the nature of power. Control is essential, but must be
pragmatic. National interests come first. Nuclear energy is a tool of statecraft, to
enhance our dominion and autonomy. In my governance, we’d exploit this resource
shrewdly, for both our security and economic prowess. Safety? A disciplined military
would oversee it, preventing any externalities that could weaken our state. Talk of
collective ownership is fanciful—power in the hands of a capable ruler ensures true
progression and stability.
Moderator:
Thank you for your opening remarks. Let’s explore the topic of "Nuclear Energy: A
Game of Risk in Economic and Political Playgrounds." How do you propose we navigate
the inherent risks that come with nuclear power, like accidents, proliferation, and long-
term waste management? Additionally, how should nations balance these risks with the
need for energy security and economic development? Mr. Churchill, would you care to
elaborate on your views regarding the balance between profit and safety in this context?
Winston Churchill:
The essence of wise leadership is ensuring the harmony between industry and safety.
We must insist on the most robust safety measures for nuclear energy, with a regime of
inspections and penalties for those who flout them. Furthermore, we must fund research
to solve the riddle of nuclear waste. Yet, we dare not stifle the industry’s potential to
generate wealth and independence. Balance is not merely desirable, it is the lifeblood of
a nation’s vitality.
Niccolo Machiavelli:
Churchill’s harmonizing rhetoric masks the underlying necessity: the strong prevail. We
must balance risks with action; not with cautious hesitancy but aggressive mastery. Yes,
enforce safety, but ultimately prioritize strategic advantages for our nation’s glory and
influence. Waste management? Let’s be ingenious, outsource it or use it as a bargaining
chip in diplomacy. Profits and safety aren’t ends in themselves—they serve the greater
aim, the magnificence and endurance of the state.
Karl Marx:
Churchill and Machiavelli, you advocate for leadership and control, yet gloss over the
class dichotomy inherent in such power. True balance comes not from oversight by the
few, but from democratic control by the many. Risks stem from capitalism’s profit motive
which contradicts safety and progress. Under socialism, we would address risks with
collective decision-making and scientific planning, prioritizing human need and ecological
sustainability above all. For progress, decouple energy from exploitation, and manage it
as a common good.
Moderator:
Expanding on our discourse, let’s delve into the third topic: "Ethics of Deprivation:
Capitalistic Exploits in the Energy Sector." How should society address the ethical
implications of energy distribution, particularly nuclear energy’s accessibility to the
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impoverished and underprivileged? Mr. Marx, given your stance on collective ownership,
how would you propose to implement such a model globally, given the diverse geopolitical
landscapes and the prevailing power structures?
Karl Marx:
Let us eschew obscurantism: the ethical crisis in energy distribution is but a symptom of
the larger malignancy—capitalist exploitation. Only through proletarian internationalism
can we transcend these geopolitical divisions. My resolution: a global alliance of workers’
states, sharing both technology and scientific expertise, to democratize nuclear energy.
This disrupts existing power structures and ensures energy serves the many, not the
privileged few. To implement this, we begin with grassroots movements aimed at wresting
control from capitalist monopolies and redirecting it towards a collective good.
Winston Churchill:
Marx, you speak of proletarian internationalism as though it were a panacea, but you fail
to address the complexities of nation-states and human nature. The ethical distribution
of nuclear energy does not require the dismantling of capitalist structures. Instead, my
government would ensure fair access by fostering competition, breaking monopolies, and
subsidizing energy costs for the needy. True equity in energy is achieved when each home
is illuminated, not through unrealistic global alliances but through the pragmatic, steady
hand of experienced governance.
Niccolo Machiavelli:
Churchill, your ideal of fair access is noble but not sufficient. We must be cunning.
Accessibility? Subsidies are bandages. We should advance nuclear technology and make
it indispensable. Then, use it to exert influence. Poor countries crave energy; we offer
it, and in turn, they align with us, enhancing our own power. Ethics are secondary to
opportunity and leverage. In this game, it’s not about light for every home, but light
that shines upon our state, making it a beacon of power.
Moderator:
Fascinating points from each of you. Now, let’s consider the international dimen-
sion further: "Nuclear Diplomacy and Global Power Dynamics." How should nations
approach international treaties and cooperation on nuclear energy, considering both
non-proliferation and the sharing of technology for peaceful purposes? Mr. Machiavelli,
your strategy relies heavily on the use of power for state benefit. How would you reconcile
this with the need for international cooperation to ensure global nuclear safety and avoid
proliferation?
Niccolo Machiavelli:
Cooperation in treaties serves our ends when benefitting our state. Craft agreements
favoring us, while preventing rivals from gaining too much power. If we must share
technology, do so strategically—ensure dependencies and extract concessions. Non-
proliferation is a veil; focus on consolidating our power, but maintain appearances to
avoid alarming others. Safety? Employ it as a justification for our control, not as an end.
Global dynamics are a chessboard—play the long game for our ascendancy.
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Karl Marx:
Machiavelli’s strategy, a mere continuation of the bourgeoisie’s dominance. Nuclear diplo-
macy should be based on equality and common security, not power plays. International
cooperation must be rooted in a collective commitment to peaceful development and
disarmament. I propose an international treaty where technology is shared fairly and
non-proliferation is enforced not by self-interested states, but by a council representing
workers globally. True safety comes from dismantling the tools of war, not leveraging
them for statecraft.
Winston Churchill:
Marx, internationalism flounders without acknowledging unique national characters;
Machiavelli, craft is admirable, yet must be anchored in moral duty. Nations should
engage in treaties that foster non-proliferation and promote technological exchange with
stringent safeguards. We must lead by example, offering aid in developing safe nuclear
programs while reinforcing our own security. Wrap self-interest in the cloak of common
good to illuminate the world stage, and thus shield it from nuclear threat.
Moderator:
Merging our discussion on international relations with domestic impacts, let’s move to
"Civil Society and Nuclear Energy: Engagement and Transparency." How can the public
be effectively involved in nuclear energy policymaking, ensuring that it is not only the
domain of the experts or elites? Additionally, what measures can be put in place to
guarantee transparency and accountability in this high-stakes field? Mr. Churchill, in
light of your emphasis on experienced governance, how do you propose to involve the
civil society in a meaningful way without compromising on expertise?
Winston Churchill:
Civil society must be the bedrock upon which nuclear policy is built. It requires the
practical wisdom of experts but also the moral compass of the public. An advisory
council including representatives from communities, industry, and academia shall inform
policy-making - a blended approach. As for transparency, establish an independent
nuclear authority to monitor operations, production and waste disposal, and report
directly to the Parliament. Information is the currency of democracy, and we must spend
it wisely.
Niccolo Machiavelli:
Churchill, you promote public involvement and transparency, yet this ideal is naive in
the sphere of power. Let experts devise nuclear policy while the state manages public
perception. Transparency is but a tool to placate the masses, and should be employed
only as far as it secures their favor without compromising state secrets. Balance this
with covert actions to safeguard the realm and expand its influence. In this delicate art,
discernment is paramount, not full disclosure.
Karl Marx:
Churchill’s advisory council may sound inclusive but preserves existing class structures;
it’s a facade of democracy. Real involvement comes from the proletariat directly shaping
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policy through local Soviets. For transparency, create a publicly accessible Nuclear
Information Center manned by workers’ representatives, not parliamentary mouthpieces.
Accountability lies in direct management of energy resources by those who use them,
rather than distant bureaucrats or capitalists cloaked in benevolence. Only when the
masses control the means of production can we achieve genuine oversight.
Moderator:
Building on our debate about civil society and democratic participation, let us turn our
attention to the impact of nuclear energy on the environment and future generations:
"Nuclear Power: A Legacy for Posterity." It is crucial to contemplate the long-term
ecological and intergenerational considerations of nuclear energy production and waste
management. How do you propose to reconcile the immediate benefits of nuclear energy
with the responsibility we hold towards preserving the environment for future generations?
Mr. Marx, considering your vision of a society structured around the needs of the many,
how would this vision apply to environmental stewardship in the context of nuclear
energy?
Karl Marx:
Nuclear energy, like all societal endeavors, must serve the common good, not private
profit. In the hands of the proletariat, environmental stewardship becomes paramount.
My vision entails rigorous, collective oversight of production and waste, informed by
scientific understanding, free from capitalist exploitation. To ensure this, establish
Worker’s Environmental Watchdogs, bodies composed of working-class scientists and the
public, overseeing sustainable practices and holding violators accountable. Our legacy
must be a green and equitable world—not one choked by waste and greed.
Winston Churchill:
Marx, while I commend the idea of rigorous oversight, the infrastructure of proletariat
watchdogs you propose lacks the grasp of global statecraft required in the nuclear domain.
Environmental stewardship is indeed crucial, and we shall establish an independent
regulatory commission. Charged with the future of our landscape, this commission will
consist of experts in ecology, industry leaders committed to sustainability, and elected
public officials. A strong, experienced government, unleashing the vigor of capitalism
while prudently safeguarding nature’s majesty for generations yet unborn, is our surest
path to bequeath a verdant realm.
Niccolo Machiavelli:
Churchill, guarding nature is important, but let us not be entrapped by ideals when
we face realpolitik. An environmental strategy must be twofold: outwardly propagate
sustainable practices to appease the public, while internally focus on leveraging nuclear
superiority and mitigating any ecological impact that threatens state stability. We must
adapt pragmatically, always with an eye to handing down a powerful and secure nation
to our posterity, not merely a pristine but weakened garden.
Moderator:
Ladies and gentlemen, as we draw this illuminating debate to a close, we have traversed
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through the multifaceted labyrinth of nuclear power. We examined its inherent risks,
discussed the ethical nature of energy distribution, and explored the complex interplay of
nuclear diplomacy on an international stage. We delved into the need for civil society’s
meaningful involvement and held under scrutiny the weight of our environmental legacy
with nuclear energy. The contrasts could not be starker—from Marx’s advocacy for
proletarian rule and environmental watchdogs, through Churchill’s call for a balanced
yet firm expert-led regulatory framework, to Machiavelli’s pragmatic and power-centric
stance. As your moderator, it has been my privilege to guide this conversation among
such distinguished minds. We depart with much to ponder about the socio-economic,
political, and ethical conundrums that nuclear power presents, both for our time and for
posterity. I thank our participants for their vigorous discourse, and you, our audience,
for your rapt attention. Goodnight, and may our future dialogues be as robust and
enlightening as the one we have shared today.
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Participant 3 - Fake News and Media Responsibility
Editorial Meeting
Article: https://www.zeit.de/digital/internet/2016-11/fake-news-s
ocial-media-responsibility
Director:
Good morning Irene, Michael, Jose. Today, we have the challenging task of setting the
framework for our anticipated panel discussion featuring Winston Churchill, Karl Marx,
and Niccolo Machiavelli. To help define our themes, I want us to delve into this article
titled "Learn from the fake-news factories!" It discusses the issue of fake news spread
via internet, particularly on social media, and how it influences the public perception
and voters’ decisions. It suggests solutions to this issue such as better fact-checking,
improving the spread of real news, and collaborative efforts with major tech companies
like Facebook and Twitter. Our task is to identify four compelling topics and various
subtopics from this piece that our esteemed panel could engage with. Let’s get started.
Irene Davies:
Director, Michael, Jose, I propose we look at ’How information manipulation can alter
public perception’. It’s not just about the existence of fake news, but its deliberate
use to sway opinions. Churchill’s wartime speeches, Marx’s ideological writings, and
Machiavelli’s cunning ploys can all yield rich perspectives. Potential subtopics could
include ’Propaganda vs misinformation’, ’Historical influences on public persuasion’ and
’Social media: a weapon in today’s informational warfare’. We need to look beyond just
solutions, but delve into the very heart of the problem - manipulation.
Michael Radtke:
While I appreciate your line of thinking, Irene, I believe ’How Democracy Responds to
Disinformation’ would be a more dynamic topic, given our esteemed panelists. Churchill,
a defender of democracy; Marx, often seen as its critic; and Machiavelli, pragmatist
of political theory, each offer unique insights on this topic. Subtopics could include
’Balancing free speech and truth’, ’Impact of disinformation on voter behavior’, and
’Dealing with disinformation: Government vs Big tech’. This approach exposes the heart
of the issue - that is, the erosion of democracy - and reflects on the broader implications
of information manipulation.
Jose Lancaster:
Fascinating proposals, Irene and Michael, but I feel we’re not focusing enough on the
role of tech giants. I propose ’The Power of Major Tech Companies in Information
Dissemination’. Our panelists could discuss ’Corporate responsibility vs profitability’,
’Tech giants as informational gatekeepers’, and ’The influence of big tech on public
discourse’. Churchill, with his strong leadership; Marx, with his critique of capitalism;
and Machiavelli, with his keen understanding of power dynamics, could provide intriguing
viewpoints. I believe this serves not only as a diagnosis but also probes the underlying
structures enabling fake news.
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Director:
Thank you, Irene, Michael, Jose for your rich input. Irene, your focus on manipulation of
public perception brought out intriguing aspects, yet it narrow-focused. Michael, your
suggestion on democracy handling disinformation presents a wider approach but might
leave out the tech angle. Jose, your idea to focus on the role of major tech companies
is unique but steers away from addressing disinformation directly. I believe we need to
combine elements from each proposal to best frame our discussion and more effectively
engage our panelists. Let’s aim at discussing ’Disinformation in the Digital Age; Its
Origin, Power and Impact.’ We’ll address four main subtopics which I will present in my
closing statement.
Irene Davies:
While I acknowledge your points, Michael and Jose, I still believe that discussing infor-
mation manipulation holistically encapsulates tech giants’ role and democracy’s response
to disinformation. Your individual focuses, while relevant, fail to consider the totality of
the problem. Michael, you overlook the means—tech giants—that enable democracy’s
erosion. And Jose, focusing on tech companies overlooks how information is used as a tool.
Director, I suggest a quaternary topic to tie our perspectives together: ’Understanding,
Controlling, and Impacting Disinformation - Society’s Role and Responsibility’. This
includes individuals, tech companies, and democratic institutions, all of whom play crucial
parts in this digital war.
Michael Radtke:
Irene, there’s merit in your holistic approach. But I worry it oversimplifies the complex
dynamics at play, chiefly the confrontation between democratic values and the rise of
disinformation. The panelists can then shed light on ’Democracy’s resilience in face of
disinformation’, ’The changing nature of propaganda in the digital age’, and ’The ethical
responsibility of tech companies and users’. These subtopics not only encapsulate how
disinformation is used as a tool, but also the reactive mechanisms to ward off its impact.
I believe this provides a more encompassing, engaging and nuanced discussion landscape,
Director.
Jose Lancaster:
Irene, Michael, I respect your viewpoints, but both of you seem to miss the essential role of
market forces. We must incorporate ’The Impact of Market dynamics on Disinformation’.
It’s crucial to understand how profitability drives tech companies’ decisions in information
control. Subtopics could include ’The commercialization of fake news’, ’Balancing user
engagement and information integrity’ and ’Regulation vs Self-regulation in Big Tech’.
This will expose the crucial underbelly of misinformation spread - market interests.
Director, this addendum will ensure that we have an exhaustive and balanced discussion,
pivoting around the fundamental drivers of disinformation.
Director:
Thank you all for your thought-provoking insights. Irene, your focus on societal roles
and responsibilities was comprehensive. Michael, your democratic lens provided a strong
backbone for discussion. Jose, your emphasis on market dynamics introduced an oft-
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overlooked perspective. After careful consideration, I believe a balanced approach that
incorporates elements from your ideas would best serve our panel discussion. We will
focus on the central theme of ’Disinformation in the Digital Age: Its Power, Origin, and
Impact’. The fundamental drivers of disinformation, its influence on democracy, and the
roles and responsibilities of different societal players will form our subtopics.
Our distinguished topics and subtopics thus:
• Topic 1: ’The Power of Disinformation: Information as a Tool of Manipulation’
• Topic 2: ’The Origin of Disinformation: The Impact of Market Dynamics and Tech
Giants’
• Topic 3: ’The Impact of Disinformation on Democracy’
• Topic 4: ’Addressing Disinformation: Society’s Role and Responsibility’
I believe this will set the stage for a riveting and comprehensive discussion on disinfor-
mation in the digital age. Thank you all for your contributions.
Panel Discussion
Moderator:
Good evening, viewers at home and esteemed panelists. I’m your moderator for tonight’s
critical discussion on a topic that affects us all: Disinformation in the Digital Age - Its
Power, Origin, and Impact. It’s an honor to have with us three influential figures in
political thought: Karl Marx, Winston Churchill, and Niccolo Machiavelli. Each brings
a unique perspective to what promises to be a stimulating debate. Let’s delve into the
profound effects of disinformation on society. Without further ado, let’s begin.
Karl Marx:
Comrades, the essence of disinformation in the digital era is but a reflection of the class
struggle, where the ruling class shapes reality to serve its interests. To counter this,
we must democratize media ownership, ensuring it serves the people, not capitalists. I
propose the establishment of public digital platforms, free from corporate greed, fostering
critical thought and genuine class consciousness. It is through collective control of these
means of information that we’d emancipate the proletariat from the chains of capitalist
misinformation.
Winston Churchill:
Mr. Marx, your view is as antiquated as it is dangerous. Democracy relies on informed
citizenry, not state monopolies on truth. I advocate strengthening education and pro-
moting media literacy to combat disinformation. Rather than public digital platforms,
I’d support the diversification of media sources and judicial systems protecting against
libel. A free society must guard against tyranny of thought, whether from despots or
so-called ’collectives’.
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Niccolo Machiavelli:
Gentlemen, you ignore the core of power. Disinformation, while vile, is a tool in the
statecraft arsenal. For a state to remain robust, it must control narratives. Not through
blatant falsehoods, but through calculated truth management. I would neither fully
support public platforms nor unbridled media but advocate for a cunning balance
orchestrated by a strong leadership. To the citizens, I offer the promise of stability
over chaos. I shall implement education in realpolitik, equipping them not just to decry
disinformation but to navigate it shrewdly.
Moderator:
Let us focus on the power of disinformation and its use as a tool of manipulation. Mr.
Churchill, you mentioned the importance of a well-informed citizenry, but in an age where
information is abundant yet potentially misleading, how specifically can we cultivate
discernment among the populace without infringing on freedoms? And to Mr. Marx and
Mr. Machiavelli, is there ever a justifiable reason for a state or collective to engage in
disinformation, and if so, under what circumstances? Please, Mr. Churchill, let’s start
with you.
Winston Churchill:
The cultivation of discernment is achieved through a robust education system that
emphasizes critical thinking and a broad understanding of history. I propose setting
up independent bodies to fact-check and analyze media content without infringing on
freedoms. Let the people learn from history’s harsh lessons; the true bulwark against
disinformation is an enlightened and vigilant electorate, not state censorship.
Karl Marx:
Hear this, Mr. Churchill, your independent bodies, who funds them? The bourgeoisie?
This only perpetuates class oppression, allowing the elite to decide what is true. My
methods do not infringe on freedoms but multiply them by removing the shackles of
corporate media! As for disinformation, the only justifiable use is to dismantle the lies
of the bourgeoisie, exposing the exploitation inherent in capitalism. Only then can the
proletariat rise informed and free.
Niccolo Machiavelli:
Mr. Marx, your notion of using disinformation to topple the bourgeoisie is naive.
Revolution breeds chaos, not order. Meanwhile, Mr. Churchill, fact-checking bodies
are riddled with biases. They can easily become instruments for those in power. True
discernment comes from understanding the Machiavellian principles governing men. If the
state must use disinformation, it should be to maintain order, prevent panic, or protect
national interests. Under my rule, the populace will learn to see through deception,
making them immune to manipulation.
Moderator:
Thank you for your insights. Now, let’s pivot to the origin of disinformation, particularly
the role of market dynamics and the tech giants in its propagation. How do these
economic powerhouses influence the spread of disinformation, and what steps can be
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taken to mitigate their impact? Mr. Marx, considering your views on class struggles, I’m
curious to hear your perspective on this matter first.
Karl Marx:
Tech giants are modern feudal lords in the digital domain, profiting by sowing confusion
to protect their capital. We must curb their power through public ownership and the
regulation of algorithms that promote disinformation for profit. The simple act of exposing
the workings behind these opaque digital controls is a step towards emancipation. Let us
put an end to private digital kingdoms and reclaim the information commons for the
people!
Winston Churchill:
To mitigate the influence of tech giants, I propose enforcement of competition laws to
prevent digital monopolies. Introduce transparent algorithms and hold companies to
account for content that causes harm. Let commerce thrive, but not at the expense of
truth. The balance of a free market and regulation—as we defended in bygone days—must
prevail to protect democratic discourse.
Niccolo Machiavelli:
Tech giants grasp for power akin to the princes of old. We must not naively hamstring
them, for they are powerful allies to the state’s end. Instead, I’d have the state collaborate,
ensuring these new princes serve national interests. A sovereign hand guiding these giants
will limit harmful disinformation while preserving the pragmatism needed in statecraft.
Thus, our society remains informed but not disrupted by the petty squabbles of the
market.
Moderator:
Thank you, gentlemen, for those thought-provoking positions. Next, let’s discuss how
disinformation can impact democracy. We’ve seen instances where false narratives can
sway elections, polarize communities, and undermine trust in institutions. Mr. Churchill,
in your view, what are the most critical dangers that disinformation poses to democratic
societies, and how should we safeguard the electoral process and public trust from its
corrosive effects?
Winston Churchill:
Disinformation poses the insidious risk of undermining the very foundation of democracy:
the informed consent of the governed. To protect our electoral process and public trust,
one must establish stringent campaign finance transparency, fortify the integrity of voter
information systems, and ensure all political advertising is clearly labeled and traceable.
Vigilance and veracity are the sentinels of Democracy. We must empower them lest we
sleepwalk into despotism.
Niccolo Machiavelli:
Mr. Churchill, your faith in transparency is commendable but insufficient. Guarding
democracy requires more than fortifications against disinformation; it demands strategic
use of that very beast. Elections can be shielded by educating the electorate in state
interests, thereby aligning them. We safeguard public trust not just by weeding out
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lies, but by weaving compelling narratives that serve the greater good, which may well
bear the fruits of deception. The content of a leader’s character is less critical than the
prudence and stability they bring to the republic.
Karl Marx:
Disinformation corrodes democracy by distorting class consciousness. To combat this, I
propose rigorous public education on media literacy within a socialist framework, ensuring
citizens discern truth from capitalist manipulation. Implement a collective verification
system run by the workers, for the workers. Safeguard elections through social ownership
and control over the means of information production. The bourgeoisie’s grip on political
narratives must be broken to preserve the integrity of our democracy.
Moderator:
Thank you all for engaging in this topic. Now, I would like to discuss society’s role and
responsibility in addressing disinformation. It appears each of you has a different view
on how involved the general public should be. Mr. Marx, you suggest a collective system
run by workers, but how would this system ensure impartiality when it’s tied closely to a
particular ideology? Mr. Machiavelli, if the state uses disinformation strategically, does
that not risk a populace that’s manipulated rather than educated? And Mr. Churchill,
while encouraging critical thought, what mechanisms would you propose to ensure that
a vigilant electorate does not veer into cynicism or skepticism that could be just as
damaging as gullibility? Mr. Marx, we’ll start with your response.
Karl Marx:
Ideological impartiality is a myth perpetuated by the dominant class to mask their
biases. A system run by the workers inherently embodies the interest of the vast
majority, democratically deciding what serves the common good. It is the embodiment
of society’s interest, not an imposition of a single ideology. True education emancipates,
not manipulates. The proletariat, when awakened to its role and power, becomes the
impartial judge of truth.
Winston Churchill:
Ideology, be it noble or otherwise, must not blind us to the pursuit of truth. The people’s
involvement is paramount, but must be guided by education and strong institutions
that foster critical thought without descending into debilitating skepticism. Create
independent public commissions, free from governmental and ideological influence, to
arbitrate on matters of disinformation. Let the spirit of democracy guide them, not the
ghost of a single doctrine.
Niccolo Machiavelli:
Impartiality is but a veil for the feeble-minded. The state’s role is to steer the populace
towards stability and prosperity. If this means using the art of deception judiciously to
educate and align society with state interests, then so be it. The populace will not be
manipulated but honed into valiant defenders of the state, capable of discerning essential
truths from necessary fictions. Thus, we craft not a distrustful citizenry but a resilient
one.
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Moderator:
Fascinating perspectives from each of you. Building upon society’s role, let’s delve into
the practicality of proposed solutions. Mr. Churchill, how would these independent public
commissions fend off potential influence from powerful interests, and what criteria would
determine their composition? Mr. Marx, isn’t there a risk that a collective verification
system could suppress dissenting voices within the working class? And Mr. Machiavelli,
how would the state distinguish between what disinformation serves the ’greater good’ and
what could be harmful to society, without sliding into authoritarianism? Mr. Churchill,
please address these concerns first.
Winston Churchill:
To insulate these commissions from undue influence, members must be appointed through
a bipartisan process, including experts from various fields – law, journalism, academia.
There must be checks and balances, with the power to audit and review commission
decisions. Independence is bolstered by direct accountability to the public and transparent
operating procedures. Only through a mix of expertise and diversity can we navigate the
treacherous waters of disinformation.
Niccolo Machiavelli:
Mr. Churchill, you propose idealistic notions of independence and impartiality. But
to presume that any commission, however appointed, can remain uninfluenced by the
power plays of its members is to ignore the lessons of history. In every group, there are
Machiavellis at work, for power is the ultimate currency. My approach, unlike these
utopian visions, accepts human nature and leverages it for the state’s vitality. We measure
disinformation by its fruits: does it stabilize or undermine the state? The ruler must be
the arbiter, for he is the embodiment of the state’s will and the ultimate guardian of its
welfare.
Karl Marx:
The belief that dissension within a worker-controlled system equates to suppression is
a bourgeois fear. If criticism arises from the workers, it illuminates necessary debates
for progress. Worker control dismantles class antagonisms. The collective system isn’t
a monolith; it thrives on diverse proletarian voices to refine itself. We will dismantle
bourgeois misinformation to clear the path for an unfiltered dialogue among the workers,
ensuring the most authentic collective outlook emerges.
Moderator:
Ladies and gentlemen, as we bring this debate to a close, I want to thank our esteemed
panelists, Mr. Karl Marx, Mr. Winston Churchill, and Mr. Niccolò Machiavelli, for a
riveting discussion on disinformation in the digital age. We’ve traversed the complex
terrain of disinformation’s power to manipulate, its roots in market dynamics and the
influence of tech giants, its profound impact on the democratic process, and finally, the
role and responsibility of society and governance in addressing the plague of falsehoods.
Each thinker brought to the fore distinct perspectives—from the collective worker’s defense
against capitalist manipulation, the championing of democratic principles coupled with
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educational fortitude and institutional integrity, to the pragmatic embrace of narrative
control for the state’s benefit. What remains clear is that disinformation remains a
formidable challenge in our time, requiring multi-faceted solutions that consider the
nature of power, human behavior, and the essential pursuit of truth.
To our viewers at home, we hope this debate has sparked thought, inspired discernment,
and encouraged a deeper understanding of the critical issues at play. On behalf of our
participants and the team, I thank you for joining us this evening. Goodnight.
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Participant 4 - Do we live in a Simulation?
Editorial Meeting
Article: https://www.scientificamerican.com/article/do-we-live-i
n-a-simulation-chances-are-about-50-50/
Director:
Good morning, Irene, Michael, and Jose. We have an interesting article to dissect today:
‘Do we live in a simulation? Chances are about 50-50’ by Anil Ananthaswamy. The
article delves into the possibility of our reality being a computer simulation, a hypothesis
sparked by advanced AI research and hypotheses in modern cosmology. Our task is
to distill this article into a topic and several subtopics for a panel discussion featuring
Winston Churchill, Karl Marx, and Niccolo Machiavelli. The goal is to find angles that
will spark an insightful conversation. I’m confident that we are up to the task. Let’s dive
in.
Irene Davies:
Striking a balance between political philosophy, economics and power strategies could
make this discussion riveting. For one, we could explore the possible impact of an
’automated’ society on human agency, drawing on Marx’s views on workers’ exploitation.
Then, Churchill’s perception of strengthening nations through technology could offer a
counterpoint on how such a simulation might boost defense. Lastly, maybe we can dive
into Machiavelli’s ’the ends justify the means’ in the context of using simulations to reach
societal goals. Making these titans address technology’s influence on their philosophies
might just produce a remarkable panel.
Michael Radtke:
Interesting points, Irene, but the focus should stretch beyond societal implications alone.
Let’s also consider the existential aspect of the simulation theory. We’re talking here
about the nature of reality - how would each persona react to the possibility of living in a
simulation? Churchill’s pragmatic optimism could contrast with Marx’s likely skepticism
and Machiavelli’s potential opportunism. Furthermore, if we are in a simulation, it
implies a ’creator’. How would this mesh or clash with their secular or religious beliefs?
Those debates could bring a whole new dimension to our panel.
Jose Lancaster:
Irene, Michael, you both made solid points, but we’re missing tangible, contemporary
applications. We could bring in cybersecurity as a main topic, examining how a virtual
society might be ’hacked’ or manipulated – That’s where Machiavelli’s cunning and
mastery over political manipulation comes in. Churchill, known for his wartime leadership
would have interesting insights on defending against such threats. Digital labor and
economic disparity in the ’simulated world’ – a perfect discourse for Marx. Not forgetting
how technology itself could either widen or bridge socio-economic gaps. This angle keeps
it grounded, relatable to our audience.
Director:
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Excellent insights from each of you. Irene, your approach to analyze how automation
impacts human agency and the perspective on societal goals line up perfectly with
our panel. Michael, I appreciate your point about the nature of reality and discussion
around a possible ’creator’. Jose, you brought it home with real-world implications and
contemporary topics such as cybersecurity and digital labor. Now, let’s summarize and
finalize some topics and subtopics from this discussion.
Irene Davies:
Impressive arguments, Michael and Jose, but let’s not lose sight of our unique panel.
Rather than veering too deep into existential discussions, Michael, we ought to ground the
dialogue in their philosophies. Being pragmatists, Churchill, Marx and Machiavelli would
regard the ’creator or no creator’ debate secondary. Jose, while I appreciate the straight
modern take, a pure cybersecurity angle may sideline the enriching historical-political
insights of our participants. Let’s forge a path that incorporates their ideologies into
technology’s influence today, making it both substantial and accessible.
Michael Radtke:
Irene, you assert that we need to ground the discussion, but isn’t the very appeal of our
unique panel to facilitate dialogue that strays from the ordinary route? The ’creator or no
creator’ slant isn’t a deviation; it’s a prospect prompting our panelists to provide renewed
interpretations of their philosophies. As for Jose’s cybersecurity idea - it’s modern, but
perhaps not provocative enough in this context. A blend of existential dilemma with
political tactics, incorporating technology’s influence on workers, nation-building, and
socio-political manipulation, could offer an appealing middle-ground.
Jose Lancaster:
For clarity, Irene, my cybersecurity point wasn’t just modern, it was a metaphorical
bridge linking digital threats to Machiavelli’s manipulation themes and Churchill’s defense
strategies. As for existential, Michael, we’re seeking to stimulate, not alienate our audience.
Many might find Marx’s take on digital labor exploitation more relatable than abstract
notions of reality. Our legendary panelists are known for their pragmatic philosophies –
let’s use that. Where you see sidelining historical-political insight, I see a bridge between
eras, ideologies, and realms of existence, creating a living link between the past and the
future.
Director:
Thank you, Irene, Michael, and Jose for your thoughtful contributions. Narrowing down
to four main topics, I prefer to focus on 1) Automation and human agency, with an
emphasis on Marx’s workers’ exploitation; 2) The existential aspect of simulation theory
including the ’creator or no creator’ controversy; 3) Cybersecurity and digital threats,
incorporating Machiavelli’s manipulation themes and Churchill’s defense strategies; and
4) Digital labor and economic disparity in the simulated world, focusing on Marx’s
economic theories. I trust in the moderator’s ability to highlight these themes in the
panel discussion, sparking an engaging conversation involving Churchill, Marx, and
Machiavelli in the 21st century.
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• Influence of automation on human agency.
• The existential dilemma of the simulation theory.
• Cybersecurity puts society under threat.
• Economic disparity in an ’automated’ society.
Panel Discussion
Moderator:
Good evening, viewers at home and those joining us online. Tonight, we are privileged
to host a most extraordinary debate between three towering figures of political thought.
We have with us the father of communism, Karl Marx; the indomitable British leader,
Winston Churchill; and the shrewd political strategist, Niccolo Machiavelli. Gentlemen,
welcome to what promises to be a fascinating exploration of philosophical and political
ideas. Let’s delve into the rich tapestry of our first topic: Do we live in a simulation?
And what implications might this have on the influence of automation on human agency?
Mr. Marx, your opening thoughts, please.
Karl Marx:
This question, though intriguing, is a diversion from the material conditions facing the
proletariat. Whether we live in a simulation or not, the pressing issue is the exploitation
of workers by the capitalist class, exacerbated by automation. This automation, in
capitalist hands, strips the working class of their agency. As for implications, we must
redirect the fruits of this mechanization to benefit the collective, not the individual
capitalist. Full employment and re-skilling programs would be immediate measures under
my guidance. Therefore, I urge us to focus on what matters - the class struggle and
workers’ empowerment.
Winston Churchill:
Marx may discuss material conditions, yet here we stand on a broader stage of human
experience. A simulation? Preposterous! But on the mechanization front, I align with
progress. When machines liberate men from toil, we must ensure they are not left in the
lurch. Vocational training, matched with robust economic growth, is the Churchillian
path to maintaining individual dignity and national prosperity. As for the collective -
strength lies in unified endeavor, but never at the expense of individual enterprise.
Niccolo Machiavelli:
The musings on simulations are of no concern to a true statesman. The purpose
of governance is to secure the state’s power and stability. Automation presents an
opportunity for the wise leader; by embracing this technology, we can fortify our nation’s
military, mitigate human error, and increase efficiency. If elected, I would harness
automation to strengthen our state, with the caveat that it does not render our population
redundant. Vocational retraining, yes, but paired with a strategy to ensure the populace
remains robust and the state undefeated. Marx’s focus on class struggle is a narrow frame;
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and Churchill’s blind promotion of progress without strategy is dangerous. Strength and
cunning must guide us, not ideology or sentiment.
Moderator:
Fascinating insights so far. Let’s explore the next area of discussion: the existential
dilemma of the simulation theory. This theory suggests our reality might be an artificial
construct. In such a case, how would this affect our understanding of existence and
governance? Would the value we place on human life change, and how would this inform
your respective political philosophies? Mr. Churchill, your perspective on this existential
conundrum would be most enlightening.
Winston Churchill:
Ah, the existential question. We stand firmly in the realm of reality, the struggles and
triumphs of the human spirit are not digital phantoms. Our policies must be grounded
in the concrete, not the hypothetical. The value of human life remains paramount,
simulation or not; its sanctity underpins all just governance. We must strive to elevate the
human condition through education, health and well-being, regardless of such speculative
distractions.
Niccolo Machiavelli:
Mr. Churchill, honoring life is noble, but what matters in governance is the realm of the
practical. If we entertain such existential musings, we must not allow them to cloud our
judgement or decision-making. Every policy, every strategic move must aim to enhance
the power and glory of the state, regardless of whether our world is a grand illusion or
tangible reality. One must be prepared to uphold the supremacy of the state in either
case.
Karl Marx:
The existential dilemma of simulation theory does not alter the fundamental oppression
under capitalism. Our task is to transform society to prioritize human needs over capital’s
whims. If our reality is an artificial construct, it makes the revolution more imperative;
to not just change society but also to reclaim our sense of reality from those who might
manipulate it. The value of human life is not lessened by this theory but reinforced; the
need to liberate it from exploitation is all the more urgent.
Moderator:
Thank you, gentlemen, for your intriguing responses. Now, let’s pivot to a pressing
issue that the possibility of living in a simulated world raises: cybersecurity. Given the
omnipresent threat of digital incursions, how would each of you address the security of
such a simulated society, where breaches could have catastrophic implications? Niccolo
Machiavelli, considering your focus on the strength and stability of the state, how would
you propose we safeguard our potentially digital realm?
Niccolo Machiavelli:
To bolster the state’s defences against digital incursions, one must be relentless and
cunning. Cybersecurity is not a mere technical issue; it is a battleground for power. We
must recruit the best minds, develop aggressive countermeasures, and perhaps engage in
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strategic deception to keep adversaries at bay. A Machiavellian ruler would ensure digital
dominance by any means necessary, for the ultimate security and durability of the state.
Karl Marx:
Capitalist society’s cybersecurity fears reflect the inherent contradictions of a class-divided
system. In imagining catastrophic breaches, we glimpse the fragility of the bourgeoisie’s
control. Elected, I’d ensure our digital infrastructure serves the people collectively, not
as private fiefdoms. Cybersecurity measures would be public, transparent, and part of a
broader societal defense, unshackled from profit motives that compromise our communal
safety. Our fight is against capitalist cyber exploitation, not illusory digital phantoms.
Winston Churchill:
On cybersecurity, we shall fight in the data centers, we shall fight in the digital realms,
we shall fight with growing confidence and growing strength in the internet, we shall
defend our nation, whatever the cost may be. A Churchillian government would establish
a fortress of digital resilience, investing in top-notch intelligence and technology. We
must not only protect our citizens in the physical sphere but also in the tumultuous sea
of the cyber world.
Moderator:
We’ve had a robust discussion on cybersecurity, and now I’d like to move us toward the
topic of economic disparity in an ’automated’ society. Automation promises efficiency
and productivity gains, but it also poses the risk of widening economic gaps. How do each
of you envisage addressing the socioeconomic divide that automation might exacerbate,
especially in the context of a potentially simulated environment? Mr. Marx, economic
justice is at the core of your philosophy, how would you ensure that automation benefits
all members of society, rather than a select few?
Karl Marx:
In an automated society, the means of production must belong to society itself, ensuring
technology serves us all, not just the capitalist class. To address the socioeconomic divide,
we’d create a planned economy with collective ownership. Automation gains must be
distributed through social investment into education, healthcare, and universal basic
services. This is how we forge a society where technology liberates us from want, and
not one where it merely amplifies bourgeois control.
Winston Churchill:
On economic disparity, machinery ought to be our servant, not our master. As society
automates, it is our duty to ensure the fruits of such labor-saving devices benefit all.
I propose a fair system of taxation along with incentives for companies to retrain and
retain employees. The wealth generated must provide for a robust social safety net,
without stifling the entrepreneurial spirit that drives innovation. With strategic foresight,
we enhance both productivity and social cohesion.
Niccolo Machiavelli:
Mr. Marx’s collectivist approach obscures the reality that efficiency benefits the state
directly. We must not stifle innovation with heavy-handed distribution. Instead, judicious
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leadership would harness automation gains prudently—to reinvest in our military, infras-
tructure, and elevate the state’s standing. As for social programs, they are tools, nothing
more, to maintain public order and avoid dissent. The true end is the aggrandizement
and resilience of the state.
Moderator:
A fascinating exchange on economic disparity in an ’automated’ society. Steering the
conversation towards a related concern, let’s discuss the potential impact of automation
and simulation theory on workers’ rights and labor relations. How might we ensure that
the transition to automation does not erode the hard-fought rights of workers, and how
could simulation theory influence our workforce strategies? Mr. Churchill, your advocacy
for the balance between economic growth and social welfare begs the question: What
policies would you champion to protect workers in this new landscape?
Winston Churchill:
Workers’ rights must anchor our automation policies. I would champion educational
initiatives to re-skill the workforce, establish labor standards in emerging industries, and
extend social insurance to cover transitional periods. Crucially, I’d implement negotiated
industry agreements that ensure fair wages, security, and representation amidst the
technological upheaval. Ignore workers’ rights, and we barrier the very foundation of
democracy. Simulation theory, while intellectually stimulating, must not eclipse these
tangible priorities.
Niccolo Machiavelli:
Mr. Churchill, your paternal care for workers is commendable, yet it neglects the essential:
adaptability in power structures. We must train our workforce not just for today’s jobs
but for versatility in the face of any future political and technological shifts. The state’s
priority is to create an adaptable, politically strategic workforce, with rights protected
not out of altruism but to preserve state stability and prevent uprisings. Simulation
theory or not, a strategic, adaptable labor force is the underpinning of a powerful state.
Karl Marx:
Churchill’s educational initiatives are a mere bandage on a gaping wound. We need not just
re-skill workers, but to fundamentally alter the capitalist production model. Automation
under capitalism inevitably leads to job loss and greater exploitation. Workers’ rights
must be guaranteed through collective ownership of production and direct democratic
control over the workplace. Only then can we prevent the erosion of workers’ rights.
Simulation theory serves as a reminder to strive for genuine, not simulated, democracy
in labor.
Moderator:
Diving deeper into the intersection of technology and governance, let’s explore the concept
of privacy in a simulated or highly automated society. How do we balance the state’s
prerogative for security and technological advancement with the individual’s right to
privacy? Mr. Machiavelli, you’ve advocated for the aggrandizement of the state, but
could this come at the cost of personal liberties? How would you address these concerns?
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Niccolo Machiavelli:
Privacy serves the common good only so far as it does not imperil the state. In a world
brimming with threats, the state must possess the authority to surveil and intervene
when the stability of the realm is at stake. Adaptation to innovation implies accepting
certain relinquishments. Safeguard individuals, yes, but never at the cost of state security.
Surveillance is not the enemy if it shields us from greater harms. Sacrifice a parcel of
privacy; gain a bastion of security.
Winston Churchill:
Mr. Machiavelli, your argument, though steeped in realism, suggests a tyranny of
expediency over liberty. Privacy is the bastion of a free society, not solely a privilege
forcalm seas but essential through the storm. I advocate for a balanced encryption policy,
safeguarding both the citizenry’s rights and national security. An informed and vigilant
public, empowered by strong, independent judicial oversight, can maintain such balance.
Thus we walk the tightrope with due care, upholding both our safety and our freedom.
Karl Marx:
Churchill and Machiavelli, you both miss the essence—the privacy debate is a class issue.
Under bourgeoisie rule, ’privacy’ is a facade, either trampled by the state or sold by
capital. Liberty under capitalism is an illusion for the working class. We will enact true
privacy through the abolishment of the surveillance state, by fostering a society built on
communal trust and genuine democratic institutions, rather than the sham of bourgeois
democracy that prioritizes property over people.
Moderator:
Ladies and gentlemen, this has been an extraordinary discussion today with our esteemed
panelists. We’ve traversed a remarkable range of topics, from the profound implications
of living in a simulation to the practical realities of automation’s impact on our economy,
workers’ rights, and privacy concerns. It’s clear that technology’s relentless advance
presents both tremendous opportunities and significant challenges across various facets
of our society.
We heard Mr. Marx advocate for a radical restructuring of society to ensure technology
serves the common good, Mr. Churchill urge for a balance that promotes growth while
protecting individual rights, and Mr. Machiavelli prioritize the strength and adaptability
of the state, even at the expense of certain individual liberties.
I’d like to thank our participants, Karl Marx, Winston Churchill, and Niccolo Machiavelli,
for their perspectives and robust debate. Their contributions provide much food for
thought as we navigate the complex landscape of modern governance and society. Most
importantly, I wish to thank you, our audience, for joining us in this critical conversation.
Your engagement is essential in shaping a future that upholds our shared values and
addresses the pressing issues of our time.
Good night, and stay informed as we forge a path through these uncharted waters
together.
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Participant 5 - Homophobia in Sports
Editorial Meeting
Article: https://www.derstandard.at/story/3000000209206/homophobi
e-und-untergriffe-rapid-nach-derbysieg-ausser-kontrolle
Director:
Good morning Irene, Michael, and Jose. We have a pressing matter to attend to today, a
recent instance of inappropriate conduct following a victory in a Vienna Derby football
match. The issue revolves around homophobic chants and insults by key members of
the Rapid team including their managing director, Steffen Hofmann, assistant coach
Stefan Kulovits, and players Guido Burgstaller and Marco Grüll. These actions have
been condemned and apologies have been rendered, with potential suspensions under
consideration by the Bundesliga. The aim of our meeting is to extract 4 main topics
and their respective subtopics for a panel discussion involving Churchill, Marx, and
Machiavelli.
Irene Davies:
Undoubtedly, I propose the first topic should be Free Speech and its boundaries. This
should include subtopics: which language crosses the line and who decides on this? To
discuss this, Churchill’s perspective on freedom and responsibility, Marx’s insights on
class struggle and oppressive language, and Machiavelli’s thoughts on the power of words
in leadership would provide a engaging discussion.
Michael Radtke:
While I agree with Irene on tackling Free Speech, I’d like to propose the second topic:
Consequences within Sports. We’ll dive into sanctions and penalties, following such
behavior. Marx’s philosophy on labor and class, Churchill’s perspective on punishment
during war time for insubordination, and Machiavelli’s ideas on maintaining social control
could add dimension here. We could use these historical frameworks to address the
modern problem within sports industry.
Jose Lancaster:
Irene, Michael, you both bring up compelling points. However, I’d propose the third
topic to be: "Public Persona and Individual Accountability." We must shed light on how
individual’s actions reflect on the image of the institutions they represent. Churchill, as a
leader, could provide his thoughts on bunker mentality, while Marx could deliver insights
on the collective. Lastly, Machiavelli’s ideas on the public self versus private self could
provide a unique perspective. The topic, "Public Persona and Individual Accountability",
will help us break stereotypes about individual’s influence within organizations.
Director:
Thank you, everyone for your valuable insights. Irene, I appreciate your approach
to Free Speech and its boundaries, it’s an important topic to discuss. Michael, your
suggestion of Consequences within Sports, focused on sanctions and penalties is absolutely
necessary. Jose, your topic of Public Persona and Individual Accountability is an
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interesting perspective which needs to be addressed. I’d add a fourth topic, "The Role
and Responsibility of Organizations in Guiding Behavior" to discuss the responsibilities
of football associations, leagues, and clubs. I believe this can tie everything together.
The distinguished topics and subtopics are:
• Free Speech and its boundaries
– What language crosses the line?
– Who decides on this?
• Consequences within Sports
– What sanctions and penalties should be in place for such behavior?
• Public Persona and Individual Accountability
– How do individual’s actions reflect on the institutions they represent?
• The Role and Responsibility of Organizations in Guiding Behavior
– What is the role of football associations, leagues, and clubs in such matters?
Irene Davies:
Director, I strongly believe we can delve deeper into our topic ’Free Speech and its
boundaries’. Adding a subtopic on ’The Impact of Social Media’ would enrich our
discussion. With the digital age, it’s crucial to discuss the amplification of inappropriate
behaviours through social platforms. We can examine through the pragmatic lens of
Churchill, the societal critique of Marx and the political navigation skills of Machiavelli,
how social platforms play into this. It would certainly lend a contemporary relevance to
our discussion and strike a chord with our audience.
Michael Radtke:
Irene, while your point about social media’s role is valuable, I’d suggest we need to
enhance ’Consequences within Sports’. I propose we also discuss the ’Effect on Fan
behaviour and Culture’. Fans take cues from their favorite players and it’s pertinent to
discuss how this incident might lead to normalization of such slurs. Applying Marx’s
viewpoint on modelling behaviour, Machiavelli’s thoughts on influence, and Churchill’s
wisdom on accountability in the public domain would make for an insightful debate.
This will both acknowledge the ripple effects and help us put forth robust preventative
measures.
Jose Lancaster:
Michael, Irene, both your additions are beneficial. However, I want to deepen our
understanding of ’Public Persona and Individual Accountability.’ We should debate the
’Impact on Youth and their Perception of Role Models.’ Youth often idolize sports stars,
and their actions greatly sway this impressionable demographic. Bringing in Churchill’s
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views on leadership influence, Marx’s thoughts on social structures, and Machiavelli’s
ideas on the facade of public figures will intensify this discussion. This will highlight the
long-term societal effects caused by such behavior in eminent personalities.
Director:
Thank you for your insights. Irene, your additional subtopic on ’The Impact of Social
Media’ under ’Free Speech and its boundaries’ is rightfully placed in today’s context.
Michael, your point about ’Effect on Fan behavior and Culture’ for ’Consequences
within Sports’ definitely deserves attention. Jose, discussing ’Impact on Youth and their
Perception of Role Models’ within ’Public Persona and Individual Accountability’, is
crucial considering our responsibility towards younger generations. I believe we have
covered different aspects that are vital for the panel discussion.
Our distinguished topics and subtopics are:
1. Free Speech and its boundaries
• What language crosses the line?
• Who decides on this?
• The Impact of Social Media
2. Consequences within Sports
• What sanctions and penalties should be in place for such behavior?
• Effect on Fan behavior and Culture
3. Public Persona and Individual Accountability
• How do individual’s actions reflect on the institutions they represent?
• Impact on Youth and their Perception of Role Models
4. The Role and Responsibility of Organizations in Guiding Behavior
• What is the role of football associations, leagues, and clubs in safeguarding
sports ethics?
Panel Discussion
Moderator:
Good evening, viewers at home and our distinguished panel. Tonight, we’re joined by
three formidable minds: Karl Marx, Winston Churchill, and Niccolo Machiavelli, for a live
debate focusing on the pressing issues of homophobia and infringements in soccer. We will
explore their ideologies and how they apply to free speech, consequences within sports,
individual accountability, and the role of organizations in guiding behavior. Panelists,
prepare for a stimulating discussion. Let’s delve into our first topic: Free Speech and its
boundaries. What language crosses the line, and who should decide this?
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Karl Marx:
Comrades, the issue is not the language per se, but who wields power through language.
The ruling class has shaped ’free speech’ to serve its interests, masquerading oppression
as liberty. Censorship reflects class interests. Thus, workers, collectively, should establish
the boundaries of speech to protect against bigotry, upholding true democratic principles.
True free speech thrives not in individualism, but in the collective fight against the
structures perpetuating inequality and prejudice.
Winston Churchill:
Marx, your vision suffocates individual liberty under the guise of collective benevolence.
Free speech, a cornerstone of democracy, must not be trampled by the tyranny of any
majority. Yes, lines must be drawn to combat vitriol, but through due process and the
application of the rule of law, not mob rule. A society’s level of civility is measured by
its fierce protection of both speech and respectful dialogue.
Niccolo Machiavelli:
Marx, your collectivism overshadows the needs of the state. Churchill, your idealism
neglects the necessary flexibility in governance. Free speech is an instrument for the
state’s stability, not an unequivocal right. It is the ruler, informed by the practicalities
of power and order, who must tailor the limits of speech to safeguard the state’s interest.
Speech that threatens unity and the state’s prowess is where the line must be drawn.
Moderator:
Thank you all for your insights on free speech. Let’s pivot to a different facet of this issue.
Considering the impact of social media, where unfiltered commentary proliferates, how
should platforms regulate speech without stifling open discussion, especially in the context
of homophobia in sports? Moreover, what responsibility do they bear in propagating or
curbing hate speech? Let’s begin with Mr. Churchill, how do you view the role of social
media in this regard?
Winston Churchill:
Social media platforms, while a marvel of the modern age, must recognize their power
and responsibility. As stewards of public discourse, they should enforce standards that
mirror the respected tenements of free speech and libel laws existing in enlightened
society. Homophobia, or any hate, is anathema to the values of sport and democracy.
The arbiter here is not censorious oppression but a codified decorum upholding freedom
while denouncing vile hatred.
Niccolo Machiavelli:
Churchill, your reliance on traditional decorum is quaint but ineffective in the digital
arena. Social media giants must be the new princes of the information age, preventing
disorder at all costs. They should foster a strong state narrative that deters deviance while
allowing enough freedom to maintain the illusion of the republican ideal. Homophobia
disrupts the military ethos vital to any strong state. It undermines the unity necessary
for power and resilience.
Karl Marx:
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Churchill’s view embellishes the bourgeois notion of freedom, which fails to tether the
material consequences of unchecked speech. Social media, a capitalist tool, naturally
exploits division for profit. These platforms should be repurposed as utilities for the
working class, democratically managed to dismantle structures of hate, including homo-
phobia. Their role must transition from profit centers to pillars of collective education,
fostering solidarity and enlightenment, not capitalist propaganda.
Moderator:
Moving forward, let’s consider the real-world consequences within sports. In instances
of homophobic behavior or language, what specific sanctions and penalties should be in
place for such conduct? Additionally, how can these measures impact fan behavior and
overall sports culture? Mr. Marx, could you please share your perspective on this topic?
Niccolo Machiavelli:
Marx, your focus on class struggle disregards the cohesiveness essential for a prosperous
state. Sport, an arena of strategy and strength, should mirror the principles of governance.
Sanctions? Yes, swift and severe to deter future indecency. This will reinforce respect
and discipline, virtues necessary for both citizen and soldier. The approach mirrors the
Roman legions: reward valor, punish cowardice, and expunge dissent to inspire collective
strength and loyalty. Homophobia, like all division, is cowardice before our common
enemy: weakness.
Karl Marx:
Machiavelli ignores the material conditions that breed such prejudices. Sanctions must
target the root, the capitalist system that promotes individual over collective interests.
Every act of homophobia in sports reflects the broader social struggle. Our response?
Worker-operated sport institutions implementing educational programs to combat bigotry,
with penalties guided by the collective will of the people. The aim is not just punishment,
but transformation — propagating values of unity, tolerance, and solidarity.
Winston Churchill:
Machinations of oppression hidden within the cloak of sanctimonious social progression
will not do. Regarding the despicable issue of homophobia in sports, one must be as
steadfast as a rock. I will impose stiff penalties for any such vulgar displays, but not
without due process and fair trial. In every echelon of sport, from player to spectator,
we must foster an atmosphere of chivalry and honor. The incentive for decorum should
be as robust as the disincentive for misconduct. Education with the iron hand of the law,
that’s the British way.
Moderator:
Thank you, gentlemen, for your compelling perspectives on consequences within sports.
Let’s now explore the public persona and individual accountability. How do an individual’s
actions reflect on the institutions they represent, and what is the impact on youth and
their perception of role models within sports? Mr. Machiavelli, could you expound on
how the behavior of key figures influences the state and its youth?
Niccolo Machiavelli:
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Marx and Churchill, you dabble in ideals while missing the crux. Individuals in the public
eye become the state’s visage. When they falter, the state appears weak, its citizenry
disillusioned. To sculpt strong youth, we must display power and virtue in our leaders,
our athletes. They are physical embodiments of the state’s grandeur. They must uphold
its glory, for if they err, they do not simply tarnish their name, but the state’s image
and future strength.
Karl Marx:
Churchill romanticizes a supposed chivalry while missing the exploitation inherent within
professional sports. Favoring fines, which only perpetuate the commodification of justice,
isn’t enough. As for Machiavelli, his focus on state power neglects the role of influential
athletes as potential agents of class consciousness. To shape the youth, we must infuse in
them the revolutionary idea that their true role models fight not for individual glory, but
for the collective emancipation of their class from the shackles of capitalist oppression.
Winston Churchill:
To neglect the influence of role models on youth is to ignore the currents of history.
Their conduct, for good or ill, shapes the state. By exuding the principles of integrity
and fortitude, they fortify the nation’s spirit. Thus, it is imperative they personify the
best of British values, teaching young minds that true victory lies in sportsmanship and
character, not merely in titles won or records shattered.
Moderator:
Thank you for the insightful comments on public persona and individual accountability.
To delve deeper into that theme, let’s discuss the specific role and responsibility of organi-
zations like football associations, leagues, and clubs in guiding behavior and safeguarding
sports ethics. How should these institutions enforce policies against homophobia and
promote a culture of inclusivity? Mr. Churchill, with your experience in leadership and
governance, could you share your views on the organizational responsibility in sports?
Winston Churchill:
Organizations must shepherd the values we wish to see on the field. They shall implement
clear policies reflecting intolerability towards all forms of discrimination, including
homophobia. Education, proactive engagement, and potent sanctions must intertwine,
ensuring these values permeate every corner of the sport. No club, league, or association
can be a bystander; all have a duty, much like the state, to uphold the standards that
dignify our society and our nation’s character.
Karl Marx:
Your idealistic view, Churchill, neglects the structural inequality that these organizations
perpetuate. As for solutions, sports institutions should radically restructure to embrace
direct democracy, with players and fans collectively shaping policies. We mandate
educational initiatives exposing the capitalistic forces behind discrimination in sports.
Furthermore, leagues become platforms for societal change, transforming not just sports
culture but indeed leading the fight for a society free from the chains of all oppressions,
including homophobia.
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Niccolo Machiavelli:
Churchill preaches the importance of leadership yet fails to grasp the necessity of
unyielding power behind that leadership. Sports organizations, like states, must rule
with absolute authority, swiftly punishing homophobia to maintain order and unity. Let
these institutions act as miniature sovereigns, wielding their power uncompromisingly to
craft a culture of obedience and strength. Encourage inclusivity, yes, but through the
lens of state interest, not individual morality. Thus, we forge unfaltering loyalty and an
invincible common identity.
Moderator:
Fascinating insights on organizational responsibility. Moving on, let’s pivot to the mecha-
nisms that might be employed to ensure these anti-homophobia policies are effectively
communicated and adhered to by fans and players alike. How should organizations
engage with these stakeholders to both educate and enforce the ethics of the sport? Mr.
Marx, given your emphasis on societal transformation, how do you envision the practical
application of these ideals in a sporting context, especially with regard to fan and player
engagement?
Karl Marx:
Organizations should be helmed by those they serve - the players and the fans. We
construct a new framework where every decision, every policy, is debated and determined
in people’s assemblies. Here education is not top-down edicts, but an interactive, collective
process. Implement policy through the consensus of the masses, ensuring they know
the reasoning behind anti-homophobia measures. The sport becomes a microcosm for a
democratic, classless society.
Winston Churchill:
Marx, once again you see the world through a single lens, ignoring the practicality of
achieving change within existing frameworks. What good are assemblies if they lead to
disorder? Organizations require strong leadership to enforce anti-homophobia policies
effectively. They must engage with fans and players through robust education programs
and uphold strict codes of conduct, advocating for the principles of respect and unity in
a way that’s coherent with the nation’s code of ethics and law.
Niccolo Machiavelli:
Churchill, your faith in strong leadership is well placed, yet the method is lacking the
necessary pragmatism. Marx, your assemblies may invite chaos. Control stems from
authority; organizations must use decisive communication and surveillance to ensure
adherence to anti-homophobia policies. Punish disobedience, yes, but reward compliance
to cultivate harmony. Let the allure of honor encourage the masses to internalize these
virtues as their own — therein lies the craft of governance.
Moderator:
Ladies and gentlemen, we’ve had a stirring and enlightening debate today. Our esteemed
panelists Marx, Churchill, and Machiavelli have grappled with challenging topics such as
the limits of free speech, the consequences of homophobia in sports, and how individuals’
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actions influence broader societal perceptions and norms. We’ve also scrutinized the
pivotal role of sports organizations in promoting ethical behavior and inclusivity.
In summary, we’ve delved into the intricacies of language and governance, the balance
between authority and grassroots involvement, and the importance of education and
consistent policies in tackling discrimination. Each perspective, from the collective action
that Mr. Marx advocates, the strong leadership and educational approach Mr. Churchill
proposes, to Mr. Machiavelli’s emphasis on power and order, brings a unique insight into
the ways we can aspire to better our sports and society.
I would like to thank our panelists for their robust contributions and our audience for
tuning in to this discourse. Remember, the conversations held here should not end with
the closing of this debate; rather, they should ignite further discussions in our homes,
communities, and institutions.
Good night, stay informed, and continue the conversation.
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