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Agent Reasoning in Negotiation

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Handbook of Group Decision and Negotiation

Part of the book series: Advances in Group Decision and Negotiation ((AGDN,volume 4))

Abstract

Negotiation has been studied in different communities both scientific and communities of practice. The social sciences (see chapters by Martinovski, Albin and Druckman, Koeszegi and Vetschera, this volume for example) and the mathematical sciences have investigated different aspects of negotiation with different goals: the goals of the social sciences are to understand the factors and reasoning processes that underlie human negotiation behavior. The goal of the mathematical sciences is to formulate mathematical models that capture elements of negotiation. Further, the mathematical models can be divided into analytic models (economic, operations research etc) and computational models. The aim of the analytic models is to provide guarantees of their behavior, characterizations of optimality, or provide managerial guidance to optimize negotiation activity. The computational models aim to provide computational tractability through approximation algorithms and heuristics. Most crucially, the computational research aims to have the models implemented in autonomous processes, called agents, that are able to incorporate realistic factors of negotiation (e.g. argumentation, information seeking, and cognitive factors) and engage in negotiations in a decentralized manner. Such agent models promise to contribute to our understanding of human information processing in negotiation. Additionally, they could be used for decision support of human decision makers. In the long run, such models can even become substitutes for human negotiators. In this chapter we will provide a selective review of the most important works in the analytic and computational negotiation literature, point out some differences and synergies and provide pointers to open questions and future research.

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Notes

  1. 1.

    Many authors, e.g., (Bichler, 2000), refer to auctions as a form of negotiation, which results in confusion in terminology.

  2. 2.

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

  3. 3.

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

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Acknowledgements

The current research has been supported by the ARO Multi University Research Initiative grant W911-NF-0810301.

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Authors and Affiliations

  1. Robotics Institute, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, 15213, USA

    Katia Sycara

  2. Tepper School of Business, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, 15213, USA

    Tinglong Dai

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  1. Katia Sycara
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Correspondence to Katia Sycara .

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Editors and Affiliations

  1. Dept. Mathematics, Wilfrid Laurier University, University Ave. W. 75, Waterloo, N2L 3C5, Canada

    D. Marc Kilgour

  2. Strathclyde Business School, University of Strathclyde, Glasgow, G4 0QU, United Kingdom

    Colin Eden

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Sycara, K., Dai, T. (2010). Agent Reasoning in Negotiation. In: Kilgour, D., Eden, C. (eds) Handbook of Group Decision and Negotiation. Advances in Group Decision and Negotiation, vol 4. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-9097-3_26

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