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Multistage bilateral bargaining model with incomplete information—A fuzzy approach
Authors:Francesco Costantino  Giulio Di Gravio
Affiliation:1. Technical University of Denmark, Department of Management Engineering, Kgs. Lyngby, Copenhagen, Denmark;2. Technical University of Munich, TUM School of Management, Munich, Germany;3. Wageningen University, Operations Research and Logistics Group, Hollandseweg 1, 6706 KN, Wageningen, The Netherlands;1. University of Hagen, Department of Economics, Universitaetsstrasse 41, 58097 Hagen, Germany;2. University of Hannover, School of Economics and Management, Koenigsworther Platz 1, 30167 Hannover, Germany
Abstract:The study proposes the implementation of an intermediation model in supply chains, integrating game theory and fuzzy logic, to represent the characteristic aspects of a bilateral bargaining with incomplete information where supplier–customer relationships are indirectly managed by a third party agent.The choice of combining these theories comes out from the necessity of smoothing the peculiar elements of the two analysis tools that, in describing real situations, present many potentialities of reciprocal adaptation. The scope is to combine a formal structure that could figure out the interrelations among actors involved in a strategic decisional context, with a mathematical elaboration of natural imprecision, uncertainty and incompleteness of data and information.The model derives from the theoretic foundation of Spulber [1999. Market Microstructure. Cambridge University Press, Cambridge, UK] and Rubinstein [1982. Perfect equilibrium in a bargaining model. Econometrica 50(1), 97–109] that, compared to the classical framework of asymmetric information and bid-spread problem by Harsanyi [1967. Games with incomplete information played by “Bayesian” players. I. The basic model. Management Science 14, 159–182], describe the process through the definition of new parameters such as bargaining power and breakdown probability. The contribution to the research is enriched by fuzzyfication process of data, considering Qi et al. [2005. Design retrieval technology of fuzzy customer requirements. In: World Congress on Mass Customization and Personalization] experiences, to build a framework that could transform inputs from the transaction, agents and market in an output that could regulate the possible concessions and the opportunity of accepting or refusing an offer.
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