Decision making in uncertain and changing environments

We consider an agent who has to repeatedly make choices in an uncertain and changing environment, who has full information of the past, who discounts future payoffs, but who has no prior. We provide a learning algorithm that performs almost as well as the best of a given finite number of experts or benchmark strategies and does so at any point in time, provided the agent is sufficiently patient. The key is to find the appropriate degree of forgetting distant past. Standard learning algorithms that treat recent and distant past equally do not have the sequential epsilon optimality property.Adaptive learning, experts, distribution-free, e-optimality, Hannan regret

Job Market Signaling and Job Search

The high cost of searching for employers borne by prospective employees increases friction in the labor market and inhibits formation of efficient employer-employee relationships. It is conventionally agreed that mechanisms that reduce the search costs (e.g., internet portals for job search) lower unemployment and improve overall welfare. We demonstrate that a reduction of the search costs may have the converse effect. We show that in a signaling job market with random matching lower search costs lead to fewer employees willing to exert effort and, in a separating equilibrium, to more individuals opting to stay completely out of the job market and remain unemployed. Furthermore, we show that lower search costs not only deteriorate the market composition, but also impair efficiency by leading to more expensive signaling in a separating equilibrium.Signaling; job market; job search; separating equilibrium; unemployment; moral hazard

On the Impossibility of Regret Minimization in Repeated Games

Regret minimizing strategies for repeated games have been receiving increasing attention in the literature. These are simple adaptive behavior rules that exhibit nice convergence properties. If all players follow regret minimizing strategies, their average joint play converges to the set of correlated equilibria or to the Hannan set (depending on the notion of regret in use), or even to Nash equilibrium on certain classes of games. In this note we raise the question of validity of the regret minimization objective. By example we show that regret minimization can lead to unrealistic behavior, since it fails to take into account the effect of one's actions on subsequent behavior of the opponents. An amended notion of regret that corrects this defect is not very useful either, since achieving a no-regret objective is not guaranteed in that case.Repeated games, Regret minimization, No-regret strategy

Contracts for Experts with Opposing Interests

We study the problem of optimal contract design in an environment with an uninformed decision maker and two perfectly informed experts. We characterize optimal contracts and observe that consulting two experts rather than one is always beneficial; this is so even if the bias of a second expert is arbitrary large and this expert would have no value in a cheap talk environment. We also provide conditions under which these contracts implement the first best outcome; our sufficient condition is weaker than the conditions in the literature on the environments without commitment. In order to derive optimal contracts, we prove a Òconstant-threatÓ result that states that one can restrict attention to contracts in which the action implemented in case of a disagreement among the experts is independent of their reports. A particular implication of this result is that an optimal contract is constant for a large set of expertsÕ preferences and hence is robust to mistakes in their specification.information; optimal contracts; experts; constant-threat principle

Competition of E-Commerce Intermediaries

In e-commerce, where information collection is essentially costless and geographic location of traders matters very little, fierce competition between providers of similar services is expected. We consider a model where two e-commerce intermediaries (internet shops) compete for sellers. We show that two non-identical shops may coexist in equilibrium if the population of sellers is sufficiently differentiated in their time preferences. In such an equilibrium less patient sellers choose the more popular (with a higher rate of arrival of new buyers) and more expensive shop, while more patient sellers prefer the less popular and cheaper one.E-commerce, Intermediary, Competition, Listing fee, Closing fee

Decision Rules for Experts with Opposing Interests

This paper studies optimal decision rules for a decision maker who can consult two experts in an environment without monetary payments. This extends the previous work by Holmstr�m (1984) and Alonso and Matouschek (2008) who consider environments with one expert. In order to derive optimal decision rules, we prove a "constant-threat" result that states that any out-of-equilibrium pair of recommendations by the experts are punished with an action that is independent of their reports. A particular property of an optimal decision rule is that it is simple and constant for a large set of experts' preferences and distribution of their private information. Hence, it is robust in the sense that it is not affected by errors in specifying these features of the environment. By contrast, the constructions of optimal outcomes absent commitment or with only one expert are sensitive to model details.Communication, Information, Noise, Experts, Constant threat

Competing Auction Houses

We consider a model where sellers make repeated attempts to sell an object via two competing auction houses. An auction house that attracts a seller runs a Vickrey auction among a random sample of buyers and collects two fees: a listing fee and, if the object is sold, a closing fee. We characterize equilibria and show that two equilibrium outcomes are possible: a (contestable) monopoly, and a market segmentation between the two competitors.Competing auctions, mediator, listing fee, closing fee