Prediction Markets as Decision Support Systems
Information Systems Frontiers
Combinatorial Information Market Design
Information Systems Frontiers
Information incorporation in online in-Game sports betting markets
Proceedings of the 4th ACM conference on Electronic commerce
Information aggregation in dynamic markets with strategic traders
Proceedings of the 10th ACM conference on Electronic commerce
Composition of markets with conflicting incentives
Proceedings of the 11th ACM conference on Electronic commerce
Decision rules and decision markets
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Information elicitation for decision making
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Eliciting forecasts from self-interested experts: scoring rules for decision makers
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
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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.