research-article

Towards a Repeated Negotiating Agent that Treats People Individually: Cooperation, Social Value Orientation, & Machiavellianism

Authors:

Johnathan Mell,

Jonathan GratchAuthors Info & Claims

IVA '18: Proceedings of the 18th International Conference on Intelligent Virtual Agents

Pages 125 - 132

https://doi.org/10.1145/3267851.3267910

Published: 05 November 2018 Publication History

Abstract

We present the results of a study in which humans negotiate with computerized agents employing varied tactics over a repeated number of economic ultimatum games. We report that certain agents are highly effective against particular classes of humans: several individual difference measures for the human participant are shown to be critical in determining which agents will be successful. Asking for favors works when playing with pro-social people but backfires with more selfish individuals. Further, making poor offers invites punishment from Machiavellian individuals. These factors may be learned once and applied over repeated negotiations, which means user modeling techniques that can detect these differences accurately will be more successful than those that don't. Our work additionally shows that a significant benefit of cooperation is also present in repeated games---after sufficient interaction. These results have deep significance to agent designers who wish to design agents that are effective in negotiating with a broad swath of real human opponents. Furthermore, it demonstrates the effectiveness of techniques which can reason about negotiation over time.

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Cited By

Rosero AKluck MMacKay A(2022)The Case for Negotiation Robots in simulated workplace negotiations A Theoretical ApproachProceedings of the Human Factors and Ergonomics Society Annual Meeting10.1177/107118132266138366:1(1095-1099)Online publication date: 27-Oct-2022
https://doi.org/10.1177/1071181322661383
Aydogan RKeskin OCakan U(2022)Would You Imagine Yourself Negotiating With a Robot, Jennifer? Why Not?IEEE Transactions on Human-Machine Systems10.1109/THMS.2021.312166452:1(41-51)Online publication date: Feb-2022
https://doi.org/10.1109/THMS.2021.3121664
Mell JBeissinger MGratch J(2021)An expert-model and machine learning hybrid approach to predicting human-agent negotiation outcomes in varied dataJournal on Multimodal User Interfaces10.1007/s12193-021-00368-wOnline publication date: 3-Mar-2021
https://doi.org/10.1007/s12193-021-00368-w
Show More Cited By

Index Terms

Towards a Repeated Negotiating Agent that Treats People Individually: Cooperation, Social Value Orientation, & Machiavellianism
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Empirical studies in HCI

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Published In

cover image ACM Conferences

IVA '18: Proceedings of the 18th International Conference on Intelligent Virtual Agents

November 2018

381 pages

ISBN:9781450360135

DOI:10.1145/3267851

Conference Chairs:
Anton Bogdanovych
Western Sydney University
,
Deborah Richards
Macquarie University
,
Simeon Simoff
Western Sydney University
,
Program Chairs:
Catherine Pelachaud
CNRS - ISIR, Université Pierre et Marie Curie
,
Dirk Heylen
University of Twente
,
Tomas Trescak
Western Sydney University

Copyright © 2018 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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New York, NY, United States

Publication History

Published: 05 November 2018

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IVA '18

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SIGAI

IVA '18: International Conference on Intelligent Virtual Agents

November 5 - 8, 2018

NSW, Sydney, Australia

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IVA '18 Paper Acceptance Rate 17 of 82 submissions, 21%;

Overall Acceptance Rate 53 of 196 submissions, 27%

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Cited By

Rosero AKluck MMacKay A(2022)The Case for Negotiation Robots in simulated workplace negotiations A Theoretical ApproachProceedings of the Human Factors and Ergonomics Society Annual Meeting10.1177/107118132266138366:1(1095-1099)Online publication date: 27-Oct-2022
https://doi.org/10.1177/1071181322661383
Aydogan RKeskin OCakan U(2022)Would You Imagine Yourself Negotiating With a Robot, Jennifer? Why Not?IEEE Transactions on Human-Machine Systems10.1109/THMS.2021.312166452:1(41-51)Online publication date: Feb-2022
https://doi.org/10.1109/THMS.2021.3121664
Mell JBeissinger MGratch J(2021)An expert-model and machine learning hybrid approach to predicting human-agent negotiation outcomes in varied dataJournal on Multimodal User Interfaces10.1007/s12193-021-00368-wOnline publication date: 3-Mar-2021
https://doi.org/10.1007/s12193-021-00368-w
Lucas GLehr JKrämer NGratch JPelachaud CMartin JBuschmeier HLucas GKopp S(2019)The Effectiveness of Social Influence Tactics when Used by a Virtual AgentProceedings of the 19th ACM International Conference on Intelligent Virtual Agents10.1145/3308532.3329464(22-29)Online publication date: 1-Jul-2019
https://dl.acm.org/doi/10.1145/3308532.3329464

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