This paper reviews a framework for numerically analyzing dynamic interactions in imperfectly competitive industries. The framework dates back to Ericson & Pakes (1995), but it is based on equilibrium notions that had been available for some time be-fore, and it has been extended in many ways by different authors since. The framework requires as input a set of primitives which describe the institutional structure in the industry to be analyzed. The framework outputs profits and policies for every incum-bent and potential entrant at each possible state of the industry. These policies can be used to simulate the distribution of sample paths for all firms from any initial industry structure. The sample paths generated by the model can be quite different depending on the primitives that are fed into it, and most of the extensions were designed to enable the framework to accommodate empirically relevant cases that required modification of the initial structure. The sample paths possess similar properties to those observed in (the recently made available) panel data sets on industries. These sample paths can be used either for an analysis of the likely response to a policy or an environmental change,

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Many identify inflated credit ratings as one contributor to the recent financial market turmoil. We develop an equilibrium model of the market for ratings and use it to examine possible origins of and cures for ratings inflation. In the model, asset issuers can shop for ratings – observe multiple ratings and disclose only the most favorable – before auctioning their assets. When assets are simple, agencies’ ratings are similar and the incentive to ratings shop is low. When assets are sufficiently complex, ratings differ enough that an incentive to shop emerges. Thus, an increase in the complexity of recently-issued securities could create a systematic bias in disclosed ratings, despite the fact that each ratings agency produces an unbiased estimate of the asset’s true quality. Increasing competition among agencies would only worsen this problem. Switching to an investor-initiated ratings system alleviates the bias, but could collapse the market for information.

I apply the notion of a self-confirming equilibrium (SCE) to study how information feedback in first price auctions (e.g. whether all bids or only the winning bid are revealed at the end of each auction) influences bidders’ perceptions about their strategic environment, and consequently their bidding behavior. In a private values setting, revealing the two highest bids is sufficient for bidders to have correct beliefs (which justifies the standard assumption of Nash equilibrium). In contrast, in every symmetric SCE of a symmetric, affiliated, private values model, bidding strategies and revenue are (weakly) higher when only the highest bid is revealed compared to the case where at least the two highest bids are revealed. I also obtain results when valuations are interdependent and discuss the implications for the empirical auction literature.

This paper proposes an equilibrium approach to belief manipulation and deception in which agents only have coarse knowledge of their opponent’s strategy. Equilibrium requires the coarse knowledge available to agents to be correct, and the inferences and optimizations to be made on the basis of the simplest theories compatible with the available knowledge. The approach can be viewed as formalizing into a game theoretic setting a well documented bias in social psychology, the Fundamental Attribution Error. It is applied to a bargaining problem, thereby revealing a deceptive tactic that is hard to explain in the full rationality paradigm.

This paper examines how a decisionmaker who is only partially aware of his temptations learns about them over time. In facing temptations over time, individuals use their experiences to forecast future self-control problems and choose the appropriate level of commitment. I demonstrate that rational learning can be perpetually partial and need not result in full sophistication. The main result of this paper characterizes necessary and sufficient conditions for learning to converge to full sophistication. This result has implications for commonly studied self-control environments such as savings and addiction. Keywords: self-control, partial awareness, Bayesian learning, sophistication,

This paper proposes a model in which the decision maker builds an optimally simplified representation of the world which is “sparse,”i.e., uses few parameters that are non-zero. Sparsity is formulated so as to lead to well-behaved, convex maximization problems. The agent’s choice of a representation of the world features a quadratic proxy for the benefits of thinking and a linear formulation for the costs of thinking. The agent then picks the optimal action given his representation of the world. This model yields a tractable procedure, which embeds the traditional rational agent as a particular case, and can be used for analyzing classic economic questions under bounded rationality. For instance, the paper studies how boundedly rational agents select a consumption bundle while paying imperfect attention to prices, and how frictionless firms set prices optimally in response. This leads to a novel mechanism for price rigidity. The model is also used to examine boundedly rational intertemporal consumption problems and portfolio choice with imperfect understanding of returns.

Within a social-learning environment, we investigate the implications of the assumption (formalized in various ways) that players may naively believe that a previous player’s actions solely reflect that player’s private information. This can lead players to become very confident about the true state of the world, even in environments where rational players would never become certain, as well as to herd on incorrect actions, even in environments where fully rational players never incorrectly herd. Keywords: Innit?, Didn’t I?

I empirically analyze the sources and magnitude of asymmetric information between competing lenders in residential mortgage lending. I exploit that property developers often cooperate with vertically integrated mortgage lenders to provide financing to buyers of their newly constructed homes. These integrated lenders might have superior information about both mortgage collateral quality and borrower characteristics. I construct a dataset of all housing transactions and associated mortgages in Arizona between 2000 and 2010. This allows me to test for asymmetric information by comparing the return of initially similar houses in the same development financed by different lenders. I find that houses financed by an integrated lender outperform similar houses financed by non-integrated competitors by 50 basis points annually. They are also less likely to enter into foreclosure. These differences persist during the ownership period of the second owner of the house. The outperformance of houses financed by an integrated lender is over twice as large amongst houses built on expansive soil, which makes housing return more sensitive to construction quality. Non-integrated lenders charge 10 basis points higher interest rates

We test for strategic behavior in common-value elections by using an experimental design that distinguishes the mistake of failing to account for the information content of others ’ strategies from other mistakes that arise naturally when making inferences. Depending on the treatment, between 50-80% of subjects fail to vote strategically. This mistake is robust to experience and hints about pivotality, and it is mainly driven by the failure to make inferences from hypothetical, rather than actual, events. Finally, the mistake also arises in more general settings where players have no private information.