Robust mechanisms for information elicitation

Aviv Zohar*, Jeffrey S. Rosenschein

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

We study information elicitation mechanisms in which a principal agent attempts to elicit the private information of other agents using a carefully selected payment scheme based on proper scoring rules. Scoring rules, like many other mechanisms set in a probabilistic environment, assume that all participating agents share some common belief about the underlying probability of events. In real-life situations however, underlying distributions are not known precisely, and small differences in beliefs about these distributions may alter agent behavior under the prescribed mechanism. We propose designing elicitation mechanisms in a manner that will be robust to small changes in belief. We show how to algorithmically design such mechanisms in polynomial time using tools of stochastic programming and convex programming, and discuss implementation issues for multiagent scenarios.

Original languageAmerican English
Title of host publicationProceedings of the Fifth International Joint Conference on Autonomous Agents and Multiagent Systems
Pages1202-1204
Number of pages3
DOIs
StatePublished - 2006
EventFifth International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS - Hakodate, Japan
Duration: 8 May 200612 May 2006

Publication series

NameProceedings of the International Conference on Autonomous Agents
Volume2006

Conference

ConferenceFifth International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
Country/TerritoryJapan
CityHakodate
Period8/05/0612/05/06

Keywords

  • Computational mechanism design
  • Information elicitation
  • Robust mechanisms
  • Scoring rules
  • Stochastic programming

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