<div class="csl-bib-body">
<div class="csl-entry">Graß, D. (2025). Optimal control models: exploring the limits of predictive power. <i>Central European Journal of Operations Research</i>, <i>33</i>(2), 609–630. https://doi.org/10.1007/s10100-025-00983-y</div>
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dc.identifier.issn
1435-246X
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/216063
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dc.description.abstract
This paper examines the application of optimal control models across disciplines, highlighting both their strengths and limitations. While these models are valuable tools in biological and socio-economic contexts, their use requires careful consideration of inherent constraints. A key advantage of such models is their ability to facilitate structural analysis. The second part of the study focuses on the continuation algorithm and its role in understanding dynamic optimization. Through two examples, the paper illustrates this approach and emphasizes the need for a critical perspective, especially when modeling human behavior and interactions.
en
dc.language.iso
en
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dc.publisher
SPRINGER
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dc.relation.ispartof
Central European Journal of Operations Research
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dc.subject
Continuation algorithm
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dc.subject
Objectivity
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dc.subject
Optimal control theory
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dc.subject
Socio-economic applications
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dc.title
Optimal control models: exploring the limits of predictive power
