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Agent Reasoning in AI-Powered Negotiation

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Handbook of Group Decision and Negotiation

Abstract

Negotiation, a fast-developing application area of artificial intelligence, has been studied by social and mathematical scientists with starkly different goals. Whereas social scientists have sought to understand various factors and reasoning processes underlying human negotiation behavior, mathematical scientists have developed theoretic models that formalize various elements of negotiation. We focus on mathematical models of agent reasoning in a negotiation, which can be either analytical or computational by nature: Analytical models offer structural predictions of agent behavior and provide managerial insights into negotiation strategy, whereas computational models offer optimization algorithms and heuristics to function as building blocks of negotiation tools powered by artificial intelligence (AI). Together, mathematical models of negotiation can often be implemented in autonomous processes, referred to as negotiation agents that are able to incorporate realistic factors of negotiation and engage in negotiations in a decentralized manner. Such agent models promise to contribute to our understanding of human information processing in negotiation and can be used for decision support of human decision-makers. In the long run, they may even become substitutes for human negotiators. In this chapter, we review the analytical and computational negotiation literature, reveal areas of differences and synergies, and provide pointers to open questions and future research.

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Notes

  1. 1.

    The words cooperative and competitive here are not to be confused with the notions of cooperative and noncooperative game theory.

  2. 2.

    Interested readers are referred to Klamler, “Fair Division” in this volume for a comprehensive survey of various approaches to fair division.

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Acknowledgments

This chapter is an updated version of “Agent Reasoning in Negotiation,” a chapter of the first edition of the same handbook. The earlier version was made possible by the generous support from the ARO Multi University Research Initiative (Grant No.: W911-NF-0810301).

Author information

Authors and Affiliations

  1. Carey Business School, Johns Hopkins University, Baltimore, MD, USA

    Tinglong Dai

  2. Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA

    Katia Sycara

  3. McCombs School of Business, The University of Texas at Austin, Austin, TX, USA

    Ronghuo Zheng

Authors
  1. Tinglong Dai
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  2. Katia Sycara
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  3. Ronghuo Zheng
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Correspondence to Katia Sycara .

Editor information

Editors and Affiliations

  1. Wilfrid Laurier University Dept. Mathematics, Waterloo, ON, Canada

    D. Marc Kilgour

  2. University of Strathclyde Strathclyde Business School, Glasgow, UK

    Colin Eden

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Dai, T., Sycara, K., Zheng, R. (2020). Agent Reasoning in AI-Powered Negotiation. In: Kilgour, D.M., Eden, C. (eds) Handbook of Group Decision and Negotiation. Springer, Cham. https://doi.org/10.1007/978-3-030-12051-1_26-1

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  • DOI: https://doi.org/10.1007/978-3-030-12051-1_26-1

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  • Print ISBN: 978-3-030-12051-1

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