Suchergebnisse

Government procurement and allocation programs often use subsidies and setasides favoring small businesses and other target groups to address fairness concerns. These concerns are in addition to standard objectives such as efficiency and revenue. We study the design of the optimal mechanism for a seller concerned with efficiency, subject to a constraint to favor a target group. In our model, buyers' private values are determined by costly pre-auction investment. If the constraint is distributional, i.e. to guarantee that the target group wins sufficiently often, then the constrained efficient mechanism is a flat subsidy. This is consistent with findings in the empirical literature. In contrast, if the constraint is to ensure a certain investment level by the target group, the optimal mechanism is a type dependent subsidy. In this case a set aside may be better than a flat or percentage subsidy.

Motivated by markets for "expertise," we study a bandit model where a principal chooses between a safe and risky arm. A strategic agent controls the risky arm and privately knows whether its type is high or low. Irrespective of type, the agent wants to maximize duration of experimentation with the risky arm. However, only the high type arm can generate value for the principal. Our main insight is that reputational incentives can be exceedingly strong unless both players coordinate on maximally inefficient strategies on path. We discuss implications for online content markets, term limits for politicians, and experts in organizations.

Motivated by the question of how one should evaluate professional election forecasters, we study a novel dynamic mechanism design problem without transfers. A principal who wishes to hire only high-quality forecasters is faced with an agent of unknown quality. The agent privately observes signals about a publicly observable future event, and may strategically misrepresent information to inflate the principal's perception of his quality. We show that the optimal deterministic mechanism is simple and easy to implement in practice: it evaluates a single, optimally timed prediction. We study the generality of this result and its robustness to randomization and noncommitment. (JEL C53, D72, D82)

We study the design of mechanisms satisfying a novel desideratum: privacy. This requires the mechanism not reveal 'much' about any agent's type to other agents. We propose the notion of joint differential privacy: a variant of differential privacy used in the privacy literature. We show by construction that mechanisms satisfying our desiderata exist when there are a large number of players, and any player's action affects any other's payoff by at most a small amount. Our results imply that in large economies, privacy concerns of agents can be accommodated at no additional 'cost' to standard incentive concerns.