Having information about an uncertain event is crucial for informed decision-making. This paper introduces a simple framework in which 1) a principal uses the reported beliefs of multiple agents to make a decision and 2) the agents reporting a belief are affected by the decision. Naturally, the...

Assume in a 2-person game that one player, Predictor (P), does not have a dominant strategy but can predict with probability p 1/2 the strategy choice of an opponent, Predictee (Q). Q chooses a strategy that maximizes her expected payoff, given that she knows p—but not P's prediction—and...

We describe a new mechanism that induces accurate forecasts within an organization while reducing moral hazards and the stigma associated with negative opinions. It is based on the notion of identity escrow, whereby the identity of a forecaster is kept anonymous and only revealed when a number...

Information aggregation mechanisms are designed explicitly for collecting and aggregating dispersed information. An excellent example of the use of this "wisdom of crowds" is a prediction market. The purpose of our Twitter-based prediction market is to suggest that carefully designed market...

We analyze experimental evidence on whether untrained subjects can predict how trustworthy an individual is. Two players on a TV show play a high stakes prisoner's dilemma with pre-play communication. Our subjects report probabilistic beliefs that each player cooperates, before and after...

We describe a methodology for making counterfactual predictions when the information held by strategic agents is a latent parameter. The analyst observes behavior which is rationalized by a Bayesian model in which agents maximize expected utility, given partial and differential information...

We describe a methodology for making counterfactual predictions when the information held by strategic agents is a latent parameter. The analyst observes behavior which is rationalized by a Bayesian model in which agents maximize expected utility given partial and differential information about...

Classic artificial intelligence (Q-learning) algorithms have been capable of consistently learning supra-competitive pricing strategies in infinitely repeated Nash-Bertrand pricing games without human communication. Such algorithms have been able to converge due to the temporal correlation of...

We use machine learning to uncover regularities in the initial play of matrix games. We first train a prediction algorithm on data from past experiments. Examining the games where our algorithm predicts correctly, but existing economic models don't, leads us to add a parameter to the best...

A principal acquires information about a shock and then discloses it to an agent. After the disclosure, the principal and agent each decide whether to take costly preparatory actions that yield mutual benefits but only when the shock strikes. The principal maximizes his expected payoff by ex...