We examine an adverse selection relationship in which the principal is unaware of the ex ante distribution of the agent's types. We show that the minimax regret mechanism, which is an incentive compatible and individually rational mechanism that minimizes the maximal principal's regret, requires the efficient agent to realize the corresponding first-best action and demands an action lower than the first-best one from the inefficient type. We prove also that the value of the minimal informational rent affects both, the optimal regrets and the distortion induced by the minimax regret mechanism.Principal-agent problem, adverse selection, minimax Regret Criterion
We consider an agent interacting with an environment in a single stream of actions, observations, an...
Abstract. In the paper we analyze a contractual relationship between two economic agents using a sta...
We study the regret of optimal strategies for online convex optimization games. Using von Neumann's ...
We consider decision problems under complete ignorance and extend the minimax regret principle to si...
In this paper, we examine the behavior of the agent who envies his principal’s wealth, and character...
We provide general conditions under which principal-agent problems admit mechanisms that are optimal...
We study a principal-agent model with moral hazard and adverse selection. Agents have private inform...
Due to information asymmetry, adverse selection exists largely in the multiagent market. Aiming at t...
We study an adverse selection model, with a principal and several agents, wherecontracting is under ...
In his discussion of minimax decision rules, Savage (1954, p. 170) presents an example purporting to...
Classic direct mechanisms suffer from the drawback of requiring full type (or utility function) reve...
In the standard mechanism design setting, the type (e.g., utility function) of an agent is not known...
International audienceWe prove an existence result for the principal-agent problem with adverse sele...
This paper studies the interaction between a single long-lived principal and a series of short-lived...
This paper studies the interaction between a single long-lived principal and a series of short-lived...
We consider an agent interacting with an environment in a single stream of actions, observations, an...
Abstract. In the paper we analyze a contractual relationship between two economic agents using a sta...
We study the regret of optimal strategies for online convex optimization games. Using von Neumann's ...
We consider decision problems under complete ignorance and extend the minimax regret principle to si...
In this paper, we examine the behavior of the agent who envies his principal’s wealth, and character...
We provide general conditions under which principal-agent problems admit mechanisms that are optimal...
We study a principal-agent model with moral hazard and adverse selection. Agents have private inform...
Due to information asymmetry, adverse selection exists largely in the multiagent market. Aiming at t...
We study an adverse selection model, with a principal and several agents, wherecontracting is under ...
In his discussion of minimax decision rules, Savage (1954, p. 170) presents an example purporting to...
Classic direct mechanisms suffer from the drawback of requiring full type (or utility function) reve...
In the standard mechanism design setting, the type (e.g., utility function) of an agent is not known...
International audienceWe prove an existence result for the principal-agent problem with adverse sele...
This paper studies the interaction between a single long-lived principal and a series of short-lived...
This paper studies the interaction between a single long-lived principal and a series of short-lived...
We consider an agent interacting with an environment in a single stream of actions, observations, an...
Abstract. In the paper we analyze a contractual relationship between two economic agents using a sta...
We study the regret of optimal strategies for online convex optimization games. Using von Neumann's ...