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Bilateral learning model in construction claim negotiations

Bilateral learning model in construction claim negotiations Purpose– The learning ability on critical bargaining information contributes to accelerating construction claim negotiations in the win-win situation. The purpose of this paper is to study how to apply Zeuthen strategy and Bayesian learning to simulate the dynamic bargaining process of claim negotiations with the consideration of discount factor and risk attitude. Design/methodology/approach– The authors first adopted certainty equivalent method and curve fitting to build a party’s own curve utility function. Taking the opponent’s bottom line as the learning goal, the authors introduced Bayesian learning to refine former predicted linear utility function of the opponent according to every new counteroffer. Both parties’ utility functions were revised by taking discount factors into consideration. Accordingly, the authors developed a bilateral learning model in construction claim negotiations based on Zeuthen strategy. Findings– The consistency of Zeuthen strategy and the Nash bargaining solution model guarantees the effectiveness of the bilateral learning model. Moreover, the illustrative example verifies the feasibility of this model. Research limitations/implications– As the authors developed the bilateral learning model by mathematical deduction, scholars are expected to collect empirical cases and compare actual solutions and model solutions in order to modify the model in future studies. Practical implications– Negotiators could refer to this model to make offers dynamically, which is favorable for the parties to reach an agreement quickly and to avoid the escalation of claims into disputes. Originality/value– The proposed model provides a supplement to the existing studies on dynamic construction claim negotiations. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Engineering, Construction and Architectural Management Emerald Publishing

Bilateral learning model in construction claim negotiations

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Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
0969-9988
DOI
10.1108/ECAM-04-2014-0062
Publisher site
See Article on Publisher Site

Abstract

Purpose– The learning ability on critical bargaining information contributes to accelerating construction claim negotiations in the win-win situation. The purpose of this paper is to study how to apply Zeuthen strategy and Bayesian learning to simulate the dynamic bargaining process of claim negotiations with the consideration of discount factor and risk attitude. Design/methodology/approach– The authors first adopted certainty equivalent method and curve fitting to build a party’s own curve utility function. Taking the opponent’s bottom line as the learning goal, the authors introduced Bayesian learning to refine former predicted linear utility function of the opponent according to every new counteroffer. Both parties’ utility functions were revised by taking discount factors into consideration. Accordingly, the authors developed a bilateral learning model in construction claim negotiations based on Zeuthen strategy. Findings– The consistency of Zeuthen strategy and the Nash bargaining solution model guarantees the effectiveness of the bilateral learning model. Moreover, the illustrative example verifies the feasibility of this model. Research limitations/implications– As the authors developed the bilateral learning model by mathematical deduction, scholars are expected to collect empirical cases and compare actual solutions and model solutions in order to modify the model in future studies. Practical implications– Negotiators could refer to this model to make offers dynamically, which is favorable for the parties to reach an agreement quickly and to avoid the escalation of claims into disputes. Originality/value– The proposed model provides a supplement to the existing studies on dynamic construction claim negotiations.

Journal

Engineering, Construction and Architectural ManagementEmerald Publishing

Published: Jul 18, 2016

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