Buyer behavior adaptation based on a fuzzy logic controller and prediction techniques

Kolomvatsos, K. and Hadjiefthymiades, S. (2012) Buyer behavior adaptation based on a fuzzy logic controller and prediction techniques. Fuzzy Sets and Systems, 189(1), pp. 30-52. (doi: 10.1016/j.fss.2011.07.017)

Abstract

The current form of Web provides numerous product resources available to users. Users can rely on intelligent agents for purchase actions. These actions are taken in specific environments such as Electronic Markets (EMs). In this paper, we study the interaction process between buyers and sellers and focus on the buyer side. Each buyer has the opportunity to interact with a number of sellers trying to buy the most appropriate products. This interaction can be modeled as a finite horizon Bargaining Game (BG). In this game, players have opposite goals concerning the product price. We adopt a number of techniques in the buyer side trying to give the appropriate level of efficiency in the buyer decision process. The buyer uses a prediction mechanism in combination with the use of Fuzzy Logic (FL) theory in order to be able to predict the upcoming seller proposal and, thus, understand the seller pricing policy. Based on this, he/she can adapt his/her behavior when trying to purchase products. The buyer adaptation mechanism produces the belief that the buyer has about the seller pricing policy and a parameter that indicates his/her own pricing policy which yields the buyer offers in the upcoming rounds. Moreover, the buyer is based on FL system that derives the appropriate actions at every round of the BG. Our results show that the combination of Fuzzy Logic (FL) with the above-mentioned techniques provides an efficient decision mechanism in the buyer side that in specific scenarios outperforms an optimal stopping model.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Kolomvatsos, Dr Kostas
• Authors: Kolomvatsos, K., and Hadjiefthymiades, S.
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: Fuzzy Sets and Systems
• Publisher: Elsevier
• ISSN: 0165-0114
• ISSN (Online): 1872-6801
• Published Online: 04 August 2011