Mechanism design with unstructured beliefs: Doctoral consortium

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

Mechanism design is the task to design algorithms, toward desired objectives, that is robust to potential manipulation by strategic players. Traditionally, it is assumed that the mechanism designer and the players in the economy share some common knowledge. However, as pointed out by Wilson, such common knowledge is "rarely present in experiments and never in practice", and "only by repeated weakening of common knowledge assumptions will the theory approximate reality." In the work, we mainly focus on designing resilient mechanisms that work properly even in such a less foreseeable environment. Bayesian auction design is a very flourishing topic in the field of mechanism design, where an important simplifying assumption is both the seller and the players know the exact distributions of all players' valuations. In this work we first consider the query complexity of Bayesian mechanisms, where we only allow the seller to have limited oracle accesses to the players' value distributions via simple queries. Then we further weaken the assumption by considering an information structure where the knowledge about the distributions can be arbitrarily scattered among the players. In both of these two unstructured information settings, we design mechanisms that are constant approximations to the optimal Bayesian mechanisms with full information. Finally, we study an envy-free allocation problem that the unstructured beliefs need to be taken into consideration. In particular, we model an environment where each player is unaware of the bundles (or allocated items) of other players, but still knows he does not receive the worst bundle. We present both conceptual and algorithmic results for this new envy-free allocation domain.

Original languageEnglish
Title of host publication18th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Pages2429-2431
Number of pages3
ISBN (Electronic)9781510892002
Publication statusPublished - 2019
Externally publishedYes
Event18th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019 - Montreal, Canada
Duration: 13 May 201917 May 2019

Publication series

NameProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
Volume4
ISSN (Print)1548-8403
ISSN (Electronic)1558-2914

Conference

Conference18th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019
Country/TerritoryCanada
CityMontreal
Period13/05/1917/05/19

Keywords

  • Bayesian auction
  • Fair allocation
  • Information elicitation
  • Maximin-aware allocation
  • Query complexity

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering

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