Equilibrium Selection in Information Elicitation without Verification via Information Monotonicity
@inproceedings{Kong2018EquilibriumSI, title={Equilibrium Selection in Information Elicitation without Verification via Information Monotonicity}, author={Yuqing Kong and Grant Schoenebeck}, booktitle={ITCS}, year={2018} }
Peer-prediction is a mechanism which elicits privately-held, non-variable information from self-interested agents---formally, truth-telling is a strict Bayes Nash equilibrium of the mechanism. The original Peer-prediction mechanism suffers from two main limitations: (1) the mechanism must know the "common prior" of agents' signals; (2) additional undesirable and non-truthful equilibria exist which often have a greater expected payoff than the truth-telling equilibrium. A series of results has… CONTINUE READING
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References
SHOWING 1-10 OF 34 REFERENCES
Putting Peer Prediction Under the Micro(economic)scope and Making Truth-Telling Focal
- Mathematics, Computer Science
- WINE
- 2016
- 37
- PDF
A Framework For Designing Information Elicitation Mechanisms That Reward Truth-telling
- Computer Science, Mathematics
- ArXiv
- 2016
- 18
- PDF