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Predicting Volatile Consumer Markets using Multi-agent Methods: Theory and Validation

Sengupta, A. and Glavin, S.E. (2012) Predicting Volatile Consumer Markets using Multi-agent Methods: Theory and Validation. In: Alexandrova-Kabadjova, B. and Martinez-Jaramillo, S. and Garcia-Almanza, A.L. and Tsang, E., eds. Simulation in Computational Finance and Economics: Tools and Emerging Applications. IGI Global, pp. 339-358. ISBN 978-1-4666-2011-7. (doi:10.4018/978-1-4666-2011-7.ch016) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:71520)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL:
http://dx.doi.org/10.4018/978-1-4666-2011-7.ch016

Abstract

A behavioral model incorporating utility-based rational choice enhanced with psychological drivers is presented to study a consumer goods market, characterized by repeat purchase incidences by households. The psychological drivers incorporate purchase strategies of loyalty and change-of-pace, which affect the choice set of consumer agents in an agent-based simulation environment. Agent specific memories of past purchases drive these strategies, while attribute specific preferences and prices drive the utility-based choice function. Transactions data from a category in a supermarket is used to initialize, calibrate, and test the accuracy of predictions of the model. Results indicate that prediction accuracy at both macro and micro levels can be significantly improved with the incorporation of purchase strategies. Moreover, increasing the memory length beyond a certain limit does not improve predictions in the model, indicating that consumer memory of past shopping instances is finite and low and recent purchase history is more relevant to current decision making than the distant past.

Item Type: Book section
DOI/Identification number: 10.4018/978-1-4666-2011-7.ch016
Subjects: H Social Sciences
H Social Sciences > H Social Sciences (General)
Divisions: Divisions > Kent Business School - Division > Kent Business School (do not use)
Depositing User: Abhijit Sengupta
Date Deposited: 03 Jan 2019 11:56 UTC
Last Modified: 16 Nov 2021 10:26 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/71520 (The current URI for this page, for reference purposes)

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