Strategic Learning and its Limits

by H. Peyton Young

Overview

In this concise book based on his Arne Ryde Lectures in 2002, Young suggests a conceptual framework for studying strategic learning and highlights theoretical developments in the area. He discusses the interactive learning problem; reinforcement and regret; equilibrium; conditional no-regret learning; prediction, postdiction, and calibration; fictitious play and its variants; Bayesian learning; and hypothesis testing. Young's framework emphasizes the amount of information required to implement different types of learning rules, criteria for evaluating their performance, and alternative notions of equilibrium to which they converge. He also stresses the limits of what can be achieved: for a given type of game and a given amount of information, there may exist no learning procedure that satisfies certain reasonable criteria of performance and convergence. In short, Young has provided a valuable primer that delineates what we know, what we would like to know, and the limits of what we can know, when we try to learn about a system that is composed of other learners.

Product Details

ISBN-13:	9780191500732
Publisher:	OUP Oxford
Publication date:	11/04/2004
Series:	Ryde Lectures
Sold by:	Barnes & Noble
Format:	eBook
File size:	5 MB

About the Author

H. Peyton Young is Senior Fellow in Economic Studies and Governance Studies and Co-Director of the Center on Social and Economic Dynamics at the Brookings Institution. He is also Scott and Barbara Black Professor of Economics at Johns Hopkins University and a Member of the Science Steering Committee at the Santa Fe Institute. His main areas of research and expertise are game theory, the design of legislative systems, public sector pricing, social norms, and public policy, in all of which he has published extensively.

Table of Contents

1. The Interactive Learning Problem
2. Reinforcement and Regret
3. Equilibrium
4. Conditional No-Regret Learning
5. Prediction, Postdiction, and Calibration
6. Fictitious Play and Its Variants
7. Bayesian Learning
8. Hypothesis Testing
9. Conclusion