Human–computer negotiation in a three player market setting

This paper proposes a novel agent-design for a three-player game involving human players and computer agents. The game is analogous to settings in which participants repeatedly negotiate over contracts, such as cell-phones and credit card plans. The game comprises three players, two service provider...

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Main Authors: Haim, Galit, Gal, Ya'akov (Kobi), An, Bo, Kraus, Sarit
Other Authors: School of Computer Science and Engineering
Format: Journal Article
Language:English
Published: 2018
Subjects:
Online Access:https://hdl.handle.net/10356/87090
http://hdl.handle.net/10220/44316
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author Haim, Galit
Gal, Ya'akov (Kobi)
An, Bo
Kraus, Sarit
author2 School of Computer Science and Engineering
author_facet School of Computer Science and Engineering
Haim, Galit
Gal, Ya'akov (Kobi)
An, Bo
Kraus, Sarit
author_sort Haim, Galit
collection NTU
description This paper proposes a novel agent-design for a three-player game involving human players and computer agents. The game is analogous to settings in which participants repeatedly negotiate over contracts, such as cell-phones and credit card plans. The game comprises three players, two service providers who compete to sign contracts with a single customer player. The service providers compete to make repeated contract offers to the customer consisting of resource exchanges in the game. Customers can join and leave contracts at will. We computed sub-game perfect equilibrium strategies for all players that were based on making contracts involving commitments between the customer player and one of the service provider players. We conducted extensive empirical studies (spanning over 500 participants) comparing the performance of computer agents using different types of equilibrium strategies with that of people in three different countries, the U.S., Israel and China, that are characterized by cultural differences in how people make contracts in the game. Two human participants played a single computer agent in various role configurations in the game. For the customer role, agents using equilibrium strategies were able to obtain a higher score than people playing the same role in three countries. For the service provider role, agents using equilibrium strategies that reasoned about possibly irrational behavior were able to obtain higher scores than people (as well as agents that did not reason about irrational behavior). This work shows that for particular market settings involving competition between service providers, equilibrium strategies can be a successful design paradigm for computer agents without relying on data driven approaches.
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spelling ntu-10356/870902020-03-07T11:48:53Z Human–computer negotiation in a three player market setting Haim, Galit Gal, Ya'akov (Kobi) An, Bo Kraus, Sarit School of Computer Science and Engineering Human–computer Decision-making Multi-agent Negotiation This paper proposes a novel agent-design for a three-player game involving human players and computer agents. The game is analogous to settings in which participants repeatedly negotiate over contracts, such as cell-phones and credit card plans. The game comprises three players, two service providers who compete to sign contracts with a single customer player. The service providers compete to make repeated contract offers to the customer consisting of resource exchanges in the game. Customers can join and leave contracts at will. We computed sub-game perfect equilibrium strategies for all players that were based on making contracts involving commitments between the customer player and one of the service provider players. We conducted extensive empirical studies (spanning over 500 participants) comparing the performance of computer agents using different types of equilibrium strategies with that of people in three different countries, the U.S., Israel and China, that are characterized by cultural differences in how people make contracts in the game. Two human participants played a single computer agent in various role configurations in the game. For the customer role, agents using equilibrium strategies were able to obtain a higher score than people playing the same role in three countries. For the service provider role, agents using equilibrium strategies that reasoned about possibly irrational behavior were able to obtain higher scores than people (as well as agents that did not reason about irrational behavior). This work shows that for particular market settings involving competition between service providers, equilibrium strategies can be a successful design paradigm for computer agents without relying on data driven approaches. Accepted version 2018-01-16T02:29:31Z 2019-12-06T16:34:56Z 2018-01-16T02:29:31Z 2019-12-06T16:34:56Z 2017 Journal Article Haim, G., Gal, Y. (K.), An, B., & Kraus, S. (2017). Human–computer negotiation in a three player market setting. Artificial Intelligence, 246, 34-52. 0004-3702 https://hdl.handle.net/10356/87090 http://hdl.handle.net/10220/44316 10.1016/j.artint.2017.01.003 en Artificial Intelligence © 2017 Elsevier B.V. This is the author created version of a work that has been peer reviewed and accepted for publication by Artificial Intelligence, Elsevier B.V. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.1016/j.artint.2017.01.003]. 36 p. application/pdf
spellingShingle Human–computer Decision-making
Multi-agent Negotiation
Haim, Galit
Gal, Ya'akov (Kobi)
An, Bo
Kraus, Sarit
Human–computer negotiation in a three player market setting
title Human–computer negotiation in a three player market setting
title_full Human–computer negotiation in a three player market setting
title_fullStr Human–computer negotiation in a three player market setting
title_full_unstemmed Human–computer negotiation in a three player market setting
title_short Human–computer negotiation in a three player market setting
title_sort human computer negotiation in a three player market setting
topic Human–computer Decision-making
Multi-agent Negotiation
url https://hdl.handle.net/10356/87090
http://hdl.handle.net/10220/44316
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