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Thesis

Distributed dynamics and learning in games

Abstract:

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.

We consider learning dynamics that are based on trial-and-error and aspiration-based...

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Institution:
University of Oxford
Division:
MPLS
Department:
Mathematical Institute
Research group:
Oxford-Man Institute of Quantitative Finance
Oxford college:
Mansfield College
Role:
Author

Contributors

Role:
Supervisor
Role:
Supervisor
Publication date:
2015
Type of award:
DPhil
Level of award:
Doctoral
Awarding institution:
Oxford University, UK
Language:
English
Keywords:
Subjects:
UUID:
uuid:37185594-633c-4d78-a408-dfe4978bacb7
Local pid:
ora:11888
Deposit date:
2015-07-27

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