Putting Peer Prediction Under the Micro(economic)scope and Making Truthtelling Focal
Abstract
Peerprediction is a (meta)mechanism which, given any proper scoring rule, produces a mechanism to elicit privatelyheld, nonverifiable information from selfinterested agents. Formally, truthtelling is a strict Nash equilibrium of the mechanism. Unfortunately, there may be other equilibria as well (including uninformative equilibria where all players simply report the same fixed signal, regardless of their true signal) and, typically, the truthtelling equilibrium does not have the highest expected payoff. The main result of this paper is to show that, in the symmetric binary setting, by tweaking peerprediction, in part by carefully selecting the proper scoring rule it is based on, we can make the truthtelling equilibrium focalthat is, truthtelling has higher expected payoff than any other equilibrium. Along the way, we prove the following: in the setting where agents receive binary signals we 1) classify all equilibria of the peerprediction mechanism; 2) introduce a new technical tool for understanding scoring rules, which allows us to make truthtelling pay better than any other informative equilibrium; 3) leverage this tool to provide an optimal version of the previous result; that is, we optimize the gap between the expected payoff of truthtelling and other informative equilibria; and 4) show that with a slight modification to the peer prediction framework, we can, in general, make the truthtelling equilibrium focalthat is, truthtelling pays more than any other equilibrium (including the uninformative equilibria).
 Publication:

arXiv eprints
 Pub Date:
 March 2016
 arXiv:
 arXiv:1603.07319
 Bibcode:
 2016arXiv160307319K
 Keywords:

 Computer Science  Computer Science and Game Theory