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Human-computer Coalition Formation in Weighted Voting Games

Authors:

Yoram Bachrach,

Yair ZickAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 11, Issue 6

Article No.: 73, Pages 1 - 20

https://doi.org/10.1145/3408294

Published: 17 October 2020 Publication History

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Abstract

This article proposes a negotiation game, based on the weighted voting paradigm in cooperative game theory, where agents need to form coalitions and agree on how to share the gains. Despite the prevalence of weighted voting in the real world, there has been little work studying people’s behavior in such settings. This work addresses this gap by combining game-theoretic solution concepts with machine learning models for predicting human behavior in such domains. We present a five-player online version of a weighted voting game in which people negotiate to create coalitions. We provide an equilibrium analysis of this game and collect hundreds of instances of people’s play in the game. We show that a machine learning model with features based on solution concepts from cooperative game theory (in particular, an extension of the Deegan-Packel Index) provide a good prediction of people’s decisions to join coalitions in the game. We designed an agent that uses the prediction model to make offers to people in this game and was able to outperform other people in an extensive empirical study. These results demonstrate the benefit of incorporating concepts from cooperative game theory in the design of agents that interact with people in group decision-making settings.

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Cited By

Jackson M(2024)Modeling of Small Groups in Computational Sciences: A Prospecting ReviewSmall Group Research10.1177/10464964241279164Online publication date: 27-Sep-2024
https://doi.org/10.1177/10464964241279164
González CPupo EIradier EAngueira PMurroni MMontalban J(2024)Network Selection over 5G-Advanced Heterogeneous Networks Based on Federated Learning and Cooperative Game TheoryIEEE Transactions on Vehicular Technology10.1109/TVT.2024.3373638(1-16)Online publication date: 2024
https://doi.org/10.1109/TVT.2024.3373638

Index Terms

Human-computer Coalition Formation in Weighted Voting Games
1. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Algorithmic game theory and mechanism design
      1. Solution concepts in game theory

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 11, Issue 6

Survey Paper and Regular Paper

December 2020

237 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3424135

Editor:
Yu Zheng
JD Digits, China

Issue’s Table of Contents

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 October 2020

Accepted: 01 June 2020

Revised: 01 May 2020

Received: 01 March 2019

Published in TIST Volume 11, Issue 6

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Cited By

Jackson M(2024)Modeling of Small Groups in Computational Sciences: A Prospecting ReviewSmall Group Research10.1177/10464964241279164Online publication date: 27-Sep-2024
https://doi.org/10.1177/10464964241279164
González CPupo EIradier EAngueira PMurroni MMontalban J(2024)Network Selection over 5G-Advanced Heterogeneous Networks Based on Federated Learning and Cooperative Game TheoryIEEE Transactions on Vehicular Technology10.1109/TVT.2024.3373638(1-16)Online publication date: 2024
https://doi.org/10.1109/TVT.2024.3373638

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