Chapter

Internet and Network Economics

Volume 7090 of the series Lecture Notes in Computer Science pp 72-83

Decision Markets with Good Incentives

  • Yiling ChenAffiliated withHarvard University
  • , Ian KashAffiliated withHarvard University
  • , Mike RuberryAffiliated withHarvard University
  • , Victor ShnayderAffiliated withHarvard University

* Final gross prices may vary according to local VAT.

Get Access

Abstract

Decision markets both predict and decide the future. They allow experts to predict the effects of each of a set of possible actions, and after reviewing these predictions a decision maker selects an action to perform. When the future is independent of the market, strictly proper scoring rules myopically incentivize experts to predict consistent with their beliefs, but this is not generally true when a decision is to be made. When deciding, only predictions for the chosen action can be evaluated for their accuracy since the other predictions become counterfactuals. This limitation can make some actions more valuable than others for an expert, incentivizing the expert to mislead the decision maker. We construct and characterize decision markets that are – like prediction markets using strictly proper scoring rules – myopic incentive compatible. These markets require the decision maker always risk taking every available action, and reducing this risk increases the decision maker’s worst-case loss. We also show a correspondence between strictly proper decision markets and strictly proper sets of prediction markets, creating a formal connection between the incentives of prediction and decision markets.