Information and Learning in Markets represents the culmination of fifteen years of Xavier Vives' work in applying Bayesian ideas to game theoretic models of finance and the real economy. Vives' homepage with his working papers is here.
The book starts off by building simple Cournot models of large markets, showing where and when the informational equilbria correspond to efficient allocative equilibria. The first few chapters are tough going (especially chapter 2), but once you've gotten your head around the general approach, which is, assume a continuum or assets and workers, and draw statistical inferences from the assumed interactions of these players, which correspond to equilibria in defintie settings according to standard Bayesian criteria.
The goal is to show the equivalence of anomalous results in behaviural economics to rational expectations equilibria, in different assumption-settings.
I found the first part of the book really interesting, and will use the approach Vives develops in my financial economics course, EC4024, next year. The second half of the book really loses me, however, especially chapter 9, sections 9.0--9.2, where we see a rational expectations equilibrium emerging, even though some of the traders are 'slow learners'. I can't get my head around why a slow learner in a financial market could persist for long enough to attain an equilibrium. We see that market microstructure really matters for the information revelation properties of prices, but we don't see the connection with the real world.
All in all though, this book is a nice, well paced, and at the end a very technical introduction to Bayesian thinking about price dynamics in markets with asymmetric information. The going is tough, but in parts the work one puts in will really worth it. Expect to see more of Vives' work on my financial economics course next semester, in elementary from of course.
Courses this book will be good for: PhD financial economics and mathematics, industrial organisation, or a Bayesian decision making seminar.