---
title: "Mechanism Design with Partial Revelation"
authors:
  - "Nathanael Hyafil"
  - "Craig Boutilier"
doi: ""
url: "https://ijcai.org/proceedings/2007/215"
pdf: "https://ijcai.org/proceedings/2007/0215.pdf"
venue: "Proceedings of the Twentieth International Joint Conference on Artificial Intelligence"
year: 2007
pages: "1333-1339"
track: ""
keywords:
---

# Mechanism Design with Partial Revelation

Nathanael Hyafil, Craig Boutilier

*Proceedings of the Twentieth International Joint Conference on Artificial Intelligence*, pages 1333-1339.

## 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.