Penalty bidding mechanisms for allocating resources and overcoming present bias

Hongyao Ma, Reshef Meir, David C. Parkes, Elena Wu-Yan

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

Abstract

From skipped exercise classes to last-minute cancellation of dentist appointments, underutilization of reserved resources abounds. Likely reasons include uncertainty about the future, further exacerbated by present bias. In this paper, we unite resource allocation and commitment devices through the design of contingent payment mechanisms, and propose the two-bid penalty- bidding mechanism. This extends an earlier mechanism proposed by Ma et al. [21], assigning the resources based on willingness to accept a no-show penalty, while also allowing each participant to increase her own penalty in order to counter present bias. We establish a simple dominant strategy equilibrium, regardless of an agent's level of present bias or degree of “sophistication”. Via simulations, we show that the proposed mechanism substantially improves utilization and achieves higher welfare and better equity in comparison with mechanisms used in practice and mechanisms that optimize welfare in the absence of present bias.

Original languageEnglish
Title of host publicationProceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020
EditorsBo An, Amal El Fallah Seghrouchni, Gita Sukthankar
Pages807-815
Number of pages9
ISBN (Electronic)9781450375184
StatePublished - 2020
Event19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020 - Virtual, Auckland, New Zealand
Duration: 19 May 2020 → …

Publication series

NameProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
Volume2020-May

Conference

Conference19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020
Country/TerritoryNew Zealand
CityVirtual, Auckland
Period19/05/20 → …

Keywords

  • Contingent payments
  • Mechanism design
  • Present bias

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering

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