Distributed dynamics and learning in games
<p>In this thesis we study decentralized dynamics for non-cooperative and cooperative games. The dynamics are behaviorally motivated and assume that very little information is available about other players' preferences, actions, or payoffs. For example, this is the case in markets where e...
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| Format: | Thesis |
| Language: | English |
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2015
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| _version_ | 1826316301365149696 |
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| author | Pradelski, B |
| author2 | Young, H |
| author_facet | Young, H Pradelski, B |
| author_sort | Pradelski, B |
| collection | OXFORD |
| description | <p>In this thesis we study decentralized dynamics for non-cooperative and cooperative games. The dynamics are behaviorally motivated and assume that very little information is available about other players' preferences, actions, or payoffs. For example, this is the case in markets where exchanges are frequent and the sheer size of the market hinders participants from learning about others' preferences.</p> <p>We consider learning dynamics that are based on trial-and-error and aspiration-based heuristics. Players occasionally try to increase their performance given their current payoffs. If successful they stick to the new action, otherwise they revert to their old action. We also study a dynamic model of social influence based on findings in sociology and psychology that people have a propensity to conform to others' behavior irrespective of the payoff consequences.</p> <p>We analyze the dynamics with a particular focus on two questions: How long does it take to reach equilibrium and what are the stability and welfare properties of the equilibria that the process selects? These questions are at the core of understanding which equilibrium concepts are robust in environments where players have little information about the game and the high rationality assumptions of standard game theory are not very realistic.</p> <p>Methodologically, this thesis builds on game theoretic techniques and prominent solution concepts such as the Nash equilibrium for non-cooperative games and the core for cooperative games, as well as refinement concepts like stochastic stability. The proofs rely on mathematical techniques from random walk theory and integer programming.</p> |
| first_indexed | 2024-03-06T20:49:29Z |
| format | Thesis |
| id | oxford-uuid:37185594-633c-4d78-a408-dfe4978bacb7 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-12-09T03:42:31Z |
| publishDate | 2015 |
| record_format | dspace |
| spelling | oxford-uuid:37185594-633c-4d78-a408-dfe4978bacb72024-12-07T13:38:59ZDistributed dynamics and learning in gamesThesishttp://purl.org/coar/resource_type/c_db06uuid:37185594-633c-4d78-a408-dfe4978bacb7MathematicsGame theory,economics,social and behavioral sciences (mathematics)Operations research,mathematical programmingMicroeconomicsSocial influenceEconomicsEnglishOxford University Research Archive - Valet2015Pradelski, BYoung, HTarres, P<p>In this thesis we study decentralized dynamics for non-cooperative and cooperative games. The dynamics are behaviorally motivated and assume that very little information is available about other players' preferences, actions, or payoffs. For example, this is the case in markets where exchanges are frequent and the sheer size of the market hinders participants from learning about others' preferences.</p> <p>We consider learning dynamics that are based on trial-and-error and aspiration-based heuristics. Players occasionally try to increase their performance given their current payoffs. If successful they stick to the new action, otherwise they revert to their old action. We also study a dynamic model of social influence based on findings in sociology and psychology that people have a propensity to conform to others' behavior irrespective of the payoff consequences.</p> <p>We analyze the dynamics with a particular focus on two questions: How long does it take to reach equilibrium and what are the stability and welfare properties of the equilibria that the process selects? These questions are at the core of understanding which equilibrium concepts are robust in environments where players have little information about the game and the high rationality assumptions of standard game theory are not very realistic.</p> <p>Methodologically, this thesis builds on game theoretic techniques and prominent solution concepts such as the Nash equilibrium for non-cooperative games and the core for cooperative games, as well as refinement concepts like stochastic stability. The proofs rely on mathematical techniques from random walk theory and integer programming.</p> |
| spellingShingle | Mathematics Game theory,economics,social and behavioral sciences (mathematics) Operations research,mathematical programming Microeconomics Social influence Economics Pradelski, B Distributed dynamics and learning in games |
| title | Distributed dynamics and learning in games |
| title_full | Distributed dynamics and learning in games |
| title_fullStr | Distributed dynamics and learning in games |
| title_full_unstemmed | Distributed dynamics and learning in games |
| title_short | Distributed dynamics and learning in games |
| title_sort | distributed dynamics and learning in games |
| topic | Mathematics Game theory,economics,social and behavioral sciences (mathematics) Operations research,mathematical programming Microeconomics Social influence Economics |
| work_keys_str_mv | AT pradelskib distributeddynamicsandlearningingames |