Before purchase, a buyer of an experience good learns about the product's fit using various information sources, including some of which the seller may be unaware of. The buyer, however, can conclusively learn the fit only after purchasing and trying out the product. We show that the seller can use a simple mechanism to best take advantage of the buyer's post-purchase learning to maximize his guaranteed-profit. We show that this mechanism combines a generous refund, which performs well when the buyer is relatively informed, with non-refundable random discounts, which work well when the buyer is relatively uninformed. JEL: D82, C79, D42

We study information acquisition in dealer markets. We first identify a one-sided strategic complementarity in information acquisition: the more informed traders are, the larger market makers' gain from becoming informed. When quotes are observable, this effect in turn induces a strategic complementarity in information acquisition amongst market makers. We then derive the equilibrium pattern of information acquisition and examine the implications of our analysis for market liquidity and price discovery. We show that increasing the cost of information can decrease market liquidity and improve price discovery.

We use a simple cost-benefit analysis to derive optimal similarity judgments – addressing the question: when should we expect a decision maker to distinguish between different time periods or different prizes? Our key premise is that cognitive resources are costly and are to be deployed only where they really matter. We show that this simple insight can explain a number of observed anomalies, such as: (i) time inconsistency, (ii) magnitude effects, (iii) interval length effects. For each of these phenomena, our approach allows to identify the direction of the bias relative to the benchmark case where cognitive resources are costless. Finally, we show that, when applied to choice under risk, the same insights predict anomalies such as the ratio and certainty effects, and rationalize Rabin's risk aversion paradox. This suggests that the theory may provide a parsimonious explanation of behavioral anomalies in different contexts.

We study career concerns in Italian academia. We mould our empirical analysis on the standard model of contests, formalised in the multi-unit all-pay auction. The number of posts, the number of applicants, and the relative importance of the criteria for promotion determine academics' effort and output. In Italian universities incentives operate only through promotion, and all appointment panels are drawn from strictly separated and relatively narrow scientific sectors: thus the parameters affecting payoffs can be measured quite precisely, and we take the model to a newly constructed dataset which collects the journal publications of all Italian university professors. Our identification strategy is based on a reform introduced in 1999, parts of which affected different academics differently. We find that individual researchers respond to incentives in the manner described by the theoretical model: roughly, more capable researchers respond to increases in the importance of the publications for promotion and in the competitiveness of the scientific sector by exerting more effort; less able researchers are discouraged by competition and do the opposite.

We compare two cooperation mechanisms for consumer/producers of a public good: the Nash Bargaining Solution (NBS) and the Exchange-Matching-Lindahl (EML) solution, where each agent specifies her demand for and supply of the public good according to her personal exchange rate. Both mechanisms are Pareto-efficient. EML is equivalent to matching. In our specific model with linear or quadratic benefits and quadratic costs, EML and NBS are equivalent when there are two agents. With more than two agents, the high-benefit/low-cost agents are better off under EML. We also analyze outsourcing, where agent i can pay agent j to produce the amount that agent i promised to contribute. In our specific model, payments from high-cost to low-cost agents (and from high-benefit to low-benefit agents) are (usually) lower in EML than in NBS.