Evaluating probabilities: asymmetric scoring rules
Management Science
Prediction Markets as Decision Support Systems
Information Systems Frontiers
Combinatorial Information Market Design
Information Systems Frontiers
Optimal false-name-proof voting rules with costly voting
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Prediction Mechanisms That Do Not Incentivize Undesirable Actions
WINE '09 Proceedings of the 5th International Workshop on Internet and Network Economics
Bluffing and strategic reticence in prediction markets
WINE'07 Proceedings of the 3rd international conference on Internet and network economics
Information elicitation for decision making
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Decision markets with good incentives
WINE'11 Proceedings of the 7th international conference on Internet and Network Economics
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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We explore settings where a principal must make a decision about which action to take to achieve a desired outcome. The principal elicits the probability of achieving the outcome by following each action from a self-interested (but decision-agnostic) expert. We prove results about the relation between the principal's decision rule and the scoring rules that determine the expert's payoffs. For the most natural decision rule (where the principal takes the action with highest success probability), we prove that no symmetric scoring rule, nor any of Winkler's asymmetric scoring rules, have desirable incentive properties. We characterize the set of differentiable scoring rules with desirable incentive properties and construct one. We then consider decision markets, where the role of a single expert is replaced by multiple agents that interact by trading with an automated market maker. We prove a surprising impossibility for this setting: an agent can always benefit from exaggerating the success probability of a suboptimal action. To mitigate this, we construct automated market makers that minimize manipulability. Finally, we consider two alternative decision market designs. We prove the first, in which all outcomes live in the same probability universe, has poor incentive properties. The second, in which the experts trade in the probability of the outcome occurring unconditionally, exhibits a new kind of no-trade phenomenon.