Chapter 8 Modeling Group Negotiation: Three Computational Approaches that can Inform Behavioral Sciences

Purpose – The purpose of this chapter is to introduce new methods to behavioral research on group negotiation. Design/methodology/approach – We describe three techniques from the field of Machine Learning and discuss their possible application to modeling dynamic processes in group negotiation: Markov Models, Hidden Markov Models, and Inverse Reinforcement Learning. Although negotiation research has employed Markov modeling in the past, the latter two methods are even more novel and cutting-edge. They provide the opportunity for researchers to build more comprehensive models and to use data more efficiently. To demonstrate their potential, we use scenarios from group negotiation research and discuss their hypothetical application to these methods. We conclude by suggestions for researchers interested in pursuing this line of work. Originality/value – This chapter introduces methods that have been successfully used in other fields and discusses how these methods can be used in behavioral negotiation research. This chapter can be a valuable guide to researchers that would like to pursue computational modeling of group negotiation.

[1]  Biing-Hwang Juang,et al.  Hidden Markov Models for Speech Recognition , 1991 .

[2]  Wendi L. Adair,et al.  The Negotiation Dance: Time, Culture, and Behavioral Sequences in Negotiation , 2005, Organ. Sci..

[3]  Aaron Hertzmann,et al.  Style machines , 2000, SIGGRAPH 2000.

[4]  L. Baum,et al.  An inequality and associated maximization technique in statistical estimation of probabilistic functions of a Markov process , 1972 .

[5]  Lawrence D. Jackel,et al.  Backpropagation Applied to Handwritten Zip Code Recognition , 1989, Neural Computation.

[6]  G. Loewenstein,et al.  Egocentric Interpretations of Fairness and Interpersonal Conflict , 1992 .

[7]  Anind K. Dey,et al.  Maximum Entropy Inverse Reinforcement Learning , 2008, AAAI.

[8]  L. Thompson,et al.  Relationships, goal incompatibility, and communal orientation in negotiations , 1998 .

[9]  Pieter Abbeel,et al.  Apprenticeship learning via inverse reinforcement learning , 2004, ICML.

[10]  Gang Hua,et al.  Tracking articulated body by dynamic Markov network , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[11]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[12]  Azriel Rosenfeld,et al.  Face recognition: A literature survey , 2003, CSUR.

[13]  Catherine H. Tinsley,et al.  Models of conflict resolution in Japanese, German, and American cultures. , 1998 .

[14]  Philip L. Smith,et al.  Conflicting social motives in negotiating groups. , 2007, Journal of personality and social psychology.

[15]  Eric Moulines,et al.  Inference in hidden Markov models , 2010, Springer series in statistics.

[16]  Daniel Druckman,et al.  Emotions in negotiation , 2008 .

[17]  Karen A. Jehn,et al.  Do friends perform better than acquaintances? the interaction of friendship, conflict, and task , 1993 .

[18]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[19]  Andrew W. Moore,et al.  Fast State Discovery for HMM Model Selection and Learning , 2007, AISTATS.

[20]  Laurie R. Weingart,et al.  BAUBLES, BANGLES, AND BEADS: MODELING THE EVOLUTION OF NEGOTIATING GROUPS OVER TIME , 2004 .

[21]  Igor Kononenko,et al.  Machine learning for medical diagnosis: history, state of the art and perspective , 2001, Artif. Intell. Medicine.

[22]  Byron Boots,et al.  Reduced-Rank Hidden Markov Models , 2009, AISTATS.

[23]  Tom Fawcett,et al.  Adaptive Fraud Detection , 1997, Data Mining and Knowledge Discovery.

[24]  Jitendra Malik,et al.  Robust Multiple Car Tracking with Occlusion Reasoning , 1994, ECCV.

[25]  D. Heckerman,et al.  Toward Normative Expert Systems: Part I The Pathfinder Project , 1992, Methods of Information in Medicine.

[26]  David J. Hand,et al.  Statistical fraud detection: A review , 2002 .