Convex Optimization
Non-myopic strategies in prediction markets
Proceedings of the 9th ACM conference on Electronic commerce
Prediction Mechanisms That Do Not Incentivize Undesirable Actions
WINE '09 Proceedings of the 5th International Workshop on Internet and Network Economics
Prediction markets, mechanism design, and cooperative game theory
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
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
Decision markets with good incentives
WINE'11 Proceedings of the 7th international conference on Internet and Network Economics
Eliciting high quality feedback from crowdsourced tree networks using continuous scoring rules
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
A consensual linear opinion pool
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Smart pricing scheme: A multi-layered scoring rule application
Expert Systems with Applications: An International Journal
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Scoring rules for eliciting expert predictions of random variables are usually developed assuming that experts derive utility only from the quality of their predictions. We study more realistic settings in which (a) the principal is a decision maker who takes a decision based on the expert's prediction; and (b) the expert has an inherent interest in the decision. Not surprisingly, in such situations, the expert usually has an incentive to misreport her forecast to influence the choice of the decision maker. We develop a general model for this setting and introduce the concept of a compensation rule. When combined with the expert's inherent utility for decisions, a compensation rule induces a net scoring rule that behaves like a traditional scoring rule. Assuming full knowledge of expert utility, we provide a complete characterization of all (strictly) proper compensation rules. We then analyze the case when the expert's utility function is not fully known to the decision maker. We show bounds on: (a) expert incentive to misreport; (b) the degree to which an expert will misreport; and (c) decision maker loss in utility due to such uncertainty. These bounds depend in natural ways on the degree of uncertainty, the local degree of convexity of net scoring function, and properties of the decision maker's utility function. Finally, we briefly discuss the use of compensation rules in prediction markets.