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.

We provide an organizational economics foundation for commitment to information structures in persuasion. An uninformed principal faces a joint screening-and-persuasion problem: she wants to influence a receiver’s beliefs about a payoff-relevant state using information elicited from a privately informed agent. The principal cannot act as an intermediary that commits to an optimal garbling of the agent’s private communications; instead, the agent’s messages are all publicly observed by the receiver. We show that the principal can still (indirectly) implement the optimal unconstrained intermediation scheme. Commitment only to an employment contract with the agent suffices for optimal persuasion of the receiver. We apply our result to the context of a brokerage contracting with a sell-side analyst, where private communication is constrained by conflict-of-interest regulations. We show that a public communication scheme—which closely corresponds to the investment ratings schemes observed in practice—can sidestep these regulations.

Different agents need to make a prediction. They observe identical data, but have different models: they predict using different explanatory variables. We study which agent believes they have the best predictive ability—as measured by the smallest subjective posterior mean squared prediction error—and show how it depends on the sample size. With small samples, we present results suggesting it is an agent using a low-dimensional model. With large samples, it is generally an agent with a high-dimensional model, possibly including irrelevant variables, but never excluding relevant ones. We apply our results to characterize the winning model in an auction of productive assets, to argue that entrepreneurs and investors with simple models will be overrepresented in new sectors, and to understand the proliferation of “factors” that explain the cross-sectional variation of expected stock returns in the asset-pricing literature.

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.