Haret, A., Lackner, M., Pfandler, A., & Wallner, J. P. (2020). Proportional Belief Merging. In V. Conitzer & F. Sha (Eds.), Proceedings of the AAAI Conference on Artificial Intelligence (pp. 2822–2829). AAAI Press. https://doi.org/10.1609/aaai.v34i03.5671
E192-02 - Forschungsbereich Databases and Artificial Intelligence E192 - Institut für Logic and Computation
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Published in:
Proceedings of the AAAI Conference on Artificial Intelligence
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Date (published):
2020
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Event name:
The Thirty-Fourth AAAI Conference on Artificial Intelligence
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Event date:
7-Feb-2020 - 12-Feb-2020
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Event place:
New York, NY, United States of America (the)
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Number of Pages:
8
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Publisher:
AAAI Press
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Peer reviewed:
Yes
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Keywords:
General Medicine
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Abstract:
In this paper we introduce proportionality to belief merging. Belief merging is a framework for aggregating information presented in the form of propositional formulas, and it generalizes many aggregation models in social choice. In our analysis, two incompatible notions of proportionality emerge: one similar to standard notions of proportionality in social choice, the other more in tune with the logic-based merging setting. Since established merging operators meet neither of these proportionality requirements, we design new proportional belief merging operators. We analyze the proposed operators against established rationality postulates, finding that current approaches to proportionality from the field of social choice are, at their core, incompatible with standard rationality postulates in belief merging. We provide characterization results that explain the underlying conflict, and provide a complexity analysis of our novel operators.
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Project title:
DK - Logic EMBArg HyperTrac: hypergraph Decompositions and Tractability: P30930-N35 (Fonds zur Förderung der wissenschaftlichen Forschung (FWF)) Algorithms for Sustainable Group Decision Making: P31890-N31 (Fonds zur Förderung der wissenschaftlichen Forschung (FWF))
