� We survey the literature on prediction mechanisms, including prediction markets and peer prediction systems. We pay particular attention to the design process, highlighting the objectives and properties that are important in the design of good prediction mechanisms. Mechanism design has been described as “inverse game theory. ” Whereas game theorists ask what outcome results from a game, mechanism designers ask what game produces a desired outcome. In this sense, game theorists act like scientists and mechanism designers like engineers. In this article, we survey a number of mechanisms created to elicit predictions, many newly proposed within the last decade. We focus on the engineering questions: How do they work and why? What factors and goals are most important in their

Abstract. Decision and prediction markets are designed to determine the likelihood of future events; prediction markets predict what will happen, and decision markets predict the results of a choice, or what would happen. Both allow multiple participants to review and make predictions, and participants are typically scored for improving the accuracy of the market’s prediction. Previous work has demonstrated prediction markets can reward accuracy improvements, as can a single participant informing a decision. We construct and characterize decision markets where all participants are scored for improving the market’s accuracy. These markets require the decision maker always risk taking an action at random, and reducing this risk increases its potential loss. We also relate these decision markets to sets of prediction markets, demonstrating a correspondence between their perfect Bayesian equilibria. 1