Prosocial Behavior in Games
Bounded Rationality and Reinforcement Learning
Prosocial Behavior in Games
Bounded Rationality and Reinforcement Learning
Description
Game theory often prescribes actions that people cannot perform. This book identifies where bounded agents can learn their way to equilibrium-and where they cannot.
Equilibrium concepts assume agents with no cognitive limits and complete knowledge of their strategic circumstances. When these conditions fail, equilibrium reasoning collapses, leaving agents without normative principles for their strategic situations. Existing approaches to bounded rationality-ecological rationality, epistemic game theory, evolutionary models, the learning-in-games tradition, and procedural rationality-provide no unified criteria for evaluating how bounded agents should adapt to uncertain, dynamic environments.
Ashton T. Sperry-Taylor introduces strategic bandits, a framework that closes this normative gap. It models opponent behavior as Memory-m rules-behavioral patterns that players discover through repeated observation, not equilibrium reasoning. Decision policies provide rigorous regret bounds that yield determinate, testable predictions about learning dynamics: not what bounded players should believe about opponents, but what they can discover through experience. Strategic bandits identify two independent boundaries for action-level learning: the strategic boundary, where myopic and strategic optimization diverge, and the estimation boundary, where learning dynamics prevent the identification of even the myopic optimum.
Sperry-Taylor examines five canonical games: the Battle of the Sexes, the Centipede Game, Divide the Cake, the Prisoner's Dilemma, and the Stag Hunt. Each game reveals distinct features of learning under bounded rationality: unconditional convergence in dominance-solvable games, coordination failure driven by estimation dynamics, and the contingent efficiency of robust versus adaptive algorithms.
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Table of Contents
Chapter 1: Bounded Rationality and Learning in Games
Chapter 2: The Epistemic Relaxation Model
Chapter 3: Foundations of Reinforcement Learning and Stochastic Games
Chapter 4: Explanatory Underdetermination in Sequential Games
Chapter 5: Strategic Bandits and Learning in Games
Chapter 6: The Prisoner's Dilemma-Analytical Results
Chapter 7: The Prisoner's Dilemma-Empirical Results
Chapter 8: The Centipede Game-Analytical Results
Chapter 9: The Quasi-Centipede Game-Empirical Results
Chapter 10: The Stag Hunt-Analytical Results
Chapter 11: The Stag Hunt-Empirical Results
Chapter 12: Divide the Cake-Analytical Results
Chapter 13: Divide the Cake-Empirical Results
Chapter 14: Battle of the Sexes-Analytical Results
Chapter 15: Battle of the Sexes-Empirical Results
Appendix to Chapter 5: Technical Proofs and Finite-State Analysis
Appendix to Chapter 8: Technical Proofs for the Quasi-Centipede
References
Index
Product details
| Published | 07 Jan 2027 |
|---|---|
| Format | Ebook (Epub & Mobi) |
| Edition | 1st |
| Pages | 256 |
| ISBN | 9781978764439 |
| Imprint | Bloomsbury Academic |
| Publisher | Bloomsbury Publishing |
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