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This book uses game theory to analyse the creation, evolution and function of economic and social institutions. The author illustrates his analysis by describing the organic or unplanned evolution of institutions such as the conventions of war, the use of money, property rights and oligopolistic pricing conventions. Professor Schotter begins by linking his work with the ideas of the philosophers Rawls, Nozick and Lewis. Institutions are regarded as regularities in the behaviour of social agents, which the agents themselves tacitly create to solve a wide variety of recurrent problems. The repetitive nature of the problems permits them to be described as a recurrent game or 'supergame.' The agents use these regularities as informational devices to supplement the information contained in competitive prices. The final chapter explores the applicability of this theory, first by relating it to previous work on the theory of teams, hierarchies, and non-maximizing decision theory, and then by using it to provide a new approach to a variety of questions both within and outside economics.

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This paper investigates belief learning. Unlike other investigators who have been forced to use observable proxies to approximate unobserved beliefs, we have, using a belief elicitation procedure (proper scoring rule), elicited subject beliefs directly. As a result we were able to perform a more direct test of the proposition that people behave in a manner consistent with belief learning. What we find is interesting. First to the extent that subjects tend to "belief learn," the beliefs they use are the stated beliefs we elicit from them and not the "empirical beliefs" posited by fictitious play or Cournot models. Second, we present evidence that the stated beliefs of our subjects differ dramatically, both quantitatively and qualitatively, from the type of empirical or historical beliefs usually used as proxies for them. Third, our belief elicitation procedures allow us to examine how far we can be led astray when we are forced to infer the value of parameters using observable proxies for variables previously thought to be unobservable. By transforming a heretofore unobservable into an observable, we can see directly how parameter estimates change when this new information is introduced. Again, we demonstrate that such differences can be dramatic. Finally, our belief learning model using stated beliefs outperforms both a reinforcement and EWA model when all three models are estimated using our data.