@inproceedings{5325c06efb0a4895bc950e4a3f4f1100,
title = "Penalty bidding mechanisms for allocating resources and overcoming present bias",
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.",
keywords = "Contingent payments, Mechanism design, Present bias",
author = "Hongyao Ma and Reshef Meir and Parkes, {David C.} and Elena Wu-Yan",
note = "Publisher Copyright: {\textcopyright} 2020 International Foundation for Autonomous.; 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020 ; Conference date: 19-05-2020",
year = "2020",
language = "الإنجليزيّة",
series = "Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS",
pages = "807--815",
editor = "Bo An and {El Fallah Seghrouchni}, Amal and Gita Sukthankar",
booktitle = "Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020",
}