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Peer-prediction is a (meta-)mechanism which, given any proper scoring rule,produces a mechanism to elicit privately-held, non-verifiable information fromself-interested agents. Formally, truth-telling is a strict Nash equilibrium ofthe mechanism. Unfortunately, there may be other equilibria as well (includinguninformative equilibria where all players simply report the same fixed signal,regardless of their true signal) and, typically, the truth-telling equilibriumdoes not have the highest expected payoff. The main result of this paper is toshow that, in the symmetric binary setting, by tweaking peer-prediction, inpart by carefully selecting the proper scoring rule it is based on, we can makethe truth-telling equilibrium focal---that is, truth-telling has higherexpected payoff than any other equilibrium.  Along the way, we prove the following: in the setting where agents receivebinary signals we 1) classify all equilibria of the peer-prediction mechanism;2) introduce a new technical tool for understanding scoring rules, which allowsus to make truth-telling pay better than any other informative equilibrium; 3)leverage this tool to provide an optimal version of the previous result; thatis, we optimize the gap between the expected payoff of truth-telling and otherinformative equilibria; and 4) show that with a slight modification to the peerprediction framework, we can, in general, make the truth-telling equilibriumfocal---that is, truth-telling pays more than any other equilibrium (includingthe uninformative equilibria).

Putting Peer Prediction Under the Micro(economic)scope and Making Truth-telling Focal