Monopoly Pricing when Consumers are Antagonized by Unexpected Price Increases: A "Cover Version" of the Heidhues-Koszegi-Rabin Model

This paper reformulates and simplifies a recent model by Heidhues and Koszegi (2005), which in turn is based on a behavioral model due to Koszegi and Rabin (2006). The model analyzes optimal pricing when consumers are loss averse in the sense that an unexpected price hike lowers their willingness to pay. The main message of the Heidhues-Koszegi model, namely that this form of consumer loss aversion leads to rigid price responses to cost fluctuations, carries over. I demonstrate the usefulness of this "cover version" of the Heidhues-Koszegi-Rabin model by obtaining new results: (1) loss aversion lowers expected prices; (2) the firm's incentive to adopt a rigid pricing strategy is stronger when fluctuations are in demand rather than in costs.monopoly pricing, loss aversion, price variation antagonism, price rigidity, price stickiness

Testing Threats in Repeated Games

I introduce a solution concept for infinite-horizon games, called “Nash equilibrium with added tests”, in which players optimize with respect to relevant threats only after having tested them before. Both the optimal response and the tests are part of equilibrium behavior. The concept is applied to repeated 2×2 games and yields the following results: 1) Sustained cooperation in games such as the Prisoner’s Dilemma is preceded by a “build up” phase, whose comparative statics are characterized. 2) Sustainability of long-run cooperation by means of familiar selfenforcement conventions varies with the payoff structure. E.g., “constructive reciprocity” achieves cooperation with minimal buildup time in the Prisoner’s Dilemma, yet it is inconsistent with long-run cooperation in Chicken. 3) Nevertheless, a “folk theorem” holds for this class of games.Game Theory, Prisoner's Dilemma

"But Can't we Get the Same Thing with a Standard Model?" Rationalizing Bounded-Rationality Models

This paper discusses a common criticism of economic models that depart from the standard rational-choice paradigm - namely, that the phenomena addressed by such models can be "rationalized" by some standard model. I criticize this criterion for evaluating bounded-rationality models. Using a market model with boundedly rational consumers due to Spiegler (2006a) as a test case, I show that even when it initially appears that a bounded-rationality model can be rationalized by a standard model, the rationalizing models tend to come with unwarranted "extra baggage". I conclude that we should impose a greater burden of proof on rationalizations that are offered in refutation of such models.Bounded rationality, methodology, theory selection, rationalizations

Competition over agents with boundedly rational expectations

I study a market model in which profit-maximizing firms compete in multi-dimensional pricing strategies over a consumer, who is limited in his ability to grasp such complicated objects and therefore uses a sampling procedure to evaluate them. Firms respond to increased competition with an increased effort to obfuscate, rather than with more competitive pricing. As a result, consumer welfare is not enhanced and may even deteriorate. Specifically, when firms control both the price and the quality of each dimension, and there are diminishing returns to quality, increased competition implies an efficiency loss which is entirely borne by consumers.Bounded rationality, industrial organization, multi-dimensional pricing, law of small numbers, market exploitation, obfuscation

The Model Selection Curse

A "statistician" takes an action on behalf of an agent, based on the agent's self-reported personal data and a sample involving other people. The action that he takes is an estimated function of the agent's report. The estimation procedure involves model selection. We ask the following question: Is truth-telling optimal for the agent given the statistician's procedure? We analyze this question in the context of a simple example that highlights the role of model selection. We suggest that our simple exercise may have implications for the broader issue of human interaction with "machine learning" algorithms