Mechanism design for public projects via neural networks

Guanhua Wang, Runqi Guo, Yuko Sakurai, Muhammad Ali Babar, Mingyu Guo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

We study mechanism design for nonexcludable and excludable binary public project problems. We aim to maximize the expected number of consumers and the expected agents' welfare. For the nonexcludable public project model, we identify a sufficient condition on the prior distribution for the conservative equal costs mechanism to be the optimal strategy-proof and individually rational mechanism. For general distributions, we propose a dynamic program that solves for the optimal mechanism. For the excludable public project model, we identify a similar sufficient condition for the serial cost sharing mechanism to be optimal for 2 and 3 agents. We derive a numerical upper bound. Experiments show that for several common distributions, the serial cost sharing mechanism is close to optimality. The serial cost sharing mechanism is not optimal in general. We design better performing mechanisms via neural networks. Our approach involves several technical innovations that can be applied to mechanism design in general. We interpret the mechanisms as price-oriented rationing-free (PORF) mechanisms, which enables us to move the mechanism's complex (e.g., iterative) decision making off the neural network, to a separate simulation process.We feed the prior distribution's analytical form into the cost function to provide high-quality gradients for efficient training. We use supervision to manual mechanisms as a systematic way for initialization. Our approach of "supervision and then gradient descent"is effective for improving manual mechanisms' performances. It is also effective for fixing constraint violations for heuristic-based mechanisms that are infeasible.

Original languageEnglish
Title of host publication20th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2021
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Pages1368-1376
Number of pages9
ISBN (Electronic)9781713832621
Publication statusPublished - 2021
Externally publishedYes
Event20th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2021 - Virtual, Online
Duration: May 3 2021May 7 2021

Publication series

NameProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
Volume3
ISSN (Print)1548-8403
ISSN (Electronic)1558-2914

Conference

Conference20th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2021
CityVirtual, Online
Period5/3/215/7/21

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering

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