MULTIPLE ADVERSE SELECTION

We study an adverse selection model, with a principal and several agents, wherecontracting is under asymmetric information. The number of agents is finite and types are "continuous" and independent. We analyze two settings. In the first one, the performance functions of mechanisms may depend on all the reported types. In the second one, each performance function depends only on the respective announced type.Under the standard hypotheses in the basic one-agent adverse selection model and theindependence assumption, there is not loss of generality if the principal considers onlymechanisms for which every agent reports his true type as a dominant strategy. We consider also the relaxation of the monotonicity hypothesis about the agents' welfare and we will prove that the former "equivalente" behveen the Bayesian implementation and the dominant strategy one stands firm in some cases. We examine the properties of the optimal mechanism, supposing that the principal's "virtual income" depends on the agents' performances only through the aggregate total performance (which is natural in the context of regulation of a good produced by an oligopoly), and also, assuming the frame of regulation of a monopolist with several independent divisions (or the one of a group of firms), each one producing a different good. Unlike the standard properties of the optimal mechmisiiis in the basic one-agent adverse selection model, in our model the optimal mechanism may ask very efficient agents for an individual performance higher than the one of complete information. We show also that if agents are symmetrical, the principal may prefer ex ante to hire more than one agent.Adverse Selection; Independent Types; Optimal Mechanisms.

Mechanism Design with Limited Commitment

We develop a tool akin to the revelation principle for mechanism design with limited commitment. We identify a canonical class of mechanisms rich enough to replicate the payoffs of any equilibrium in a mechanism-selection game between an uninformed designer and a privately informed agent. A cornerstone of our methodology is the idea that a mechanism should encode not only the rules that determine the allocation, but also the information the designer obtains from the interaction with the agent. Therefore, how much the designer learns, which is the key tension in design with limited commitment, becomes an explicit part of the design. We show how this insight can be used to transform the designer's problem into a constrained optimization one: To the usual truthtelling and participation constraints, one must add the designer's sequential rationality constraint.Comment: Added an omitted assumption in Section 4 (see footnote 21 and the proof of Proposition 4.1

Mechanisms with evidence: commitment and robustness

We show that in a class of I‐agent mechanism design problems with evidence, commitment is unnecessary, randomization has no value, and robust incentive compatibility has no cost. In particular, for each agent i, we construct a simple disclosure game between the principal and agent i where the equilibrium strategies of the agents in these disclosure games give their equilibrium strategies in the game corresponding to the mechanism but where the principal is not committed to his response. In this equilibrium, the principal obtains the same payoff as in the optimal mechanism with commitment. As an application, we show that certain costly verification models can be characterized using equilibrium analysis of an associated model of evidence.Accepted manuscrip

Deterministic versus Stochastic Mechanisms in Principal–Agent Models

This paper shows that, contrary to what is generally believed, decreasing concavity of the agent’s utility function with respect to the screening variable is not sufficient to ensure that stochastic mechanisms are suboptimal. The paper demonstrates, however, that they are suboptimal whenever the optimal deterministic mechanism exhibits no bunching. This is the case for most applications of the theory and therefore validates the literature’s usual focus on deterministic mechanisms