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URN to cite this document: urn:nbn:de:bvb:703-epub-8267-5
Title data
Galeazzi, Paolo ; Madsen, Mathias W.:
Learning Decision Criteria from Play.
In: Dynamic Games and Applications.
(2024)
.
ISSN 2153-0793
DOI der Verlagsversion: https://doi.org/10.1007/s13235-024-00595-2
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Abstract
This paper investigates population games under ambiguity in which players may adopt decision criteria different from one another. After defining equilibria for these situations by extending well-known decision-theoretic criteria to the game-theoretic context, we apply these concepts to examine the case of two-person games played within a population whose relative proportions of decision criteria are unknown to the players. We state necessary and sufficient conditions under which such games prompt the players to reveal their decision criterion through their actions, and we show when the relative proportions may be learned by observing the increasingly informed agents play.
Further data
| Item Type: | Article in a journal |
|---|---|
| Keywords: | Decision criteria; Learning; Population games |
| DDC Subjects: | 100 Philosophy and psychology > 100 Philosophy |
| Institutions of the University: | Faculties > Faculty of Cultural Studies > Department of Philosophy Faculties Faculties > Faculty of Cultural Studies |
| Language: | English |
| Originates at UBT: | Yes |
| URN: | urn:nbn:de:bvb:703-epub-8267-5 |
| Date Deposited: | 03 Mar 2025 11:09 |
| Last Modified: | 03 Mar 2025 11:09 |
| URI: | https://epub.uni-bayreuth.de/id/eprint/8267 |
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