Franklin

Strategic learning and its limits / H. Peyton Young, Johns Hopkins University and University of Oxford.

Author/Creator:
Young, H. Peyton.
Publication:
Oxford : Oxford University Press, 2004.
Series:
Ryde Lectures
Arne Ryde memorial lectures ; 2002
Ryde Lectures
Format/Description:
Book
1 online resource (178 p.)
Subjects:
Knowledge management.
Organizational learning.
Form/Genre:
Electronic books.
Language:
English
Summary:
In this book an economist suggests a conceptual framework for studying strategic learning, one of the key theoretical developments in current economics. He discusses the interactive learning problem; reinforcement and regret; equilibrium; conditional no-regret learning; and much more.
Contents:
Cover; Contents; Acknowledgements; 1. The Interactive Learning Problem; 2. Reinforcement and Regret; 2.1. Reinforcement learning; 2.2. Learning in stationary environments; 2.3. Criteria of performance; 2.4. Regret; 2.5. Regret matching; 2.6. Realized payoffs; 2.7. The logic of regret matching; 3. Equilibrium; 3.1. Forms of equilibrium; 3.2. Examples; 3.3. A generalization of correlated equilibrium; 3.4. Learning coarse correlated equilibrium; 3.5. Concepts of convergence; 4. Conditional No-Regret Learning; 4.1. Conditional versus unconditional regret; 4.2. Blackwell's approachability theorem
4.3. Eliminating conditional regret4.4. Simple rules minimizing conditional regret; 4.5. A generalization of Blackwell's Theorem; 4.6. Summary; 5. Prediction, Postdiction, and Calibration; 5.1. Prediction of an unknown process; 5.2. An impossibility theorem of Oakes; 5.3. Random forecasting rules; 5.4. Foster's forecasting rule; 5.5. Calibrated forecasting and correlated equilibrium; 6. Fictitious Play and Its Variants; 6.1. Predictive learning rules; 6.2. Smoothed fictitious play; 6.3. Better versus best reply; 6.4. Finite memory and inertia; 6.5. Convergence for weakly acyclic games
7. Bayesian Learning7.1. The inference problem; 7.2. An example; 7.3. Strategies and beliefs; 7.4. Optimality and equilibrium; 7.5. Uncertainty and robustness; 7.6. An impossibility theorem; 7.7. Further implications; 8. Hypothesis Testing; 8.1. Cognitive learning theory; 8.2. Cognitive learning in games; 8.3. The structure of hypothesis testing; 8.4. Naive hypothesis testing; 8.5. Dynamics of hypothesis testing; 8.6. Learning Nash equilibrium; 8.7. Hypothesis testing: the general case; 8.8. Models, hunches, and beliefs; 8.9. Convergence in probability; 8.10. Learning to predict
9. ConclusionReferences; Index; A; B; C; D; E; F; G; H; I; J; K; L; M; N; O; P; Q; R; S; T; U; V; W; Y; Z
Notes:
Description based upon print version of record.
Includes bibliographical references and index.
Description based on online resource; title from PDF title page (ebrary, viewed February 6, 2014).
ISBN:
0-19-153342-4
1-280-75767-1
9786610757671
1-4237-8672-6
OCLC:
70253680
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