When choice is stochastic, revealed preference analysis often relies on random utility models. However, it is impossible to infer preferences without assumptions on the distribution of utility noise. We show that this difficulty can be overcome by using response time data. A simple condition on response time distributions ensures that choices reveal preferences without distributional assumptions. Standard models from economics and psychology generate data fulfilling this condition. Sharper results are obtained under symmetric or Fechnerian noise, where response times allow uncovering preferences or predicting choice probabilities out of sample. Application of our tools is simple and generates remarkable prediction accuracy.

We study subgame-perfect implementation (SPI) mechanisms that have been proposed as a solution to incomplete contracting problems. We show that these mechanisms, which are based on off-equilibrium arbitration clauses that impose large fines for lying and the inappropriate use of arbitration, have severe behavioral constraints because the fines induce retaliation against legitimate uses of arbitration. Incorporating reciprocity preferences into the theory explains the observed behavioral patterns and helps us develop a new mechanism that is more robust and achieves high rates of truth-telling and efficiency. Our results highlight the importance of tailoring implementation mechanisms to the underlying behavioral environment.