Mechanism Design with Partial Revelation

Abstract

Classic direct mechanisms require full utility revelation from agents, which can be very difficult in practical multi-attribute settings. In this work, we study partial revelation within the framework of one-shot mechanisms. Each agent's type space is partitioned into a finite set of partial types and agents (should) report the partial type within which their full type lies. A classic result implies that implementation in dominant strategies is impossible in this model. We first show that a relaxation to Bayes-Nash implementation does not circumvent the problem. We then propose a class of partial revelation mechanisms that achieve approximate dominant strategy implementation, and describe a computationally tractable algorithm for myopically optimizing the partitioning of each agent's type space to reduce manipulability and social welfare loss. This allows for the automated design of one-shot partial revelation mechanisms with worst-case guarantees on both manipulability and efficiency.