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

Abstract

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.

Citation

Turan, N., Dudik, M., Gordon, G. and Weingart, L.R. (2011), "Chapter 8 Modeling Group Negotiation: Three Computational Approaches that can Inform Behavioral Sciences", Mannix, E.A., Neale, M.A. and Overbeck, J.R. (Ed.) Negotiation and Groups (Research on Managing Groups and Teams, Vol. 14), Emerald Group Publishing Limited, Leeds, pp. 189-205. https://doi.org/10.1108/S1534-0856(2011)0000014011