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Agent Reasoning in Negotiation

Chapter
Part of the Advances in Group Decision and Negotiation book series (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.

Keywords

Negotiation Process Reservation Price Negotiation Model Negotiation Outcome Automate Negotiation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

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

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Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.Robotics InstituteCarnegie Mellon UniversityPittsburghUSA
  2. 2.Tepper School of BusinessCarnegie Mellon UniversityPittsburghUSA

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