Research Papers

Strategic Design Decisions for Uncertain Market Systems Using an Agent Based Approach

[+] Author and Article Information
Z. Wang

Department of Mechanical Engineering, A. J. Clark School of Engineering,  University of Maryland, College Park, MD 20742zhichao@umd.edu

S. Azarm1

Department of Mechanical Engineering, A. J. Clark School of Engineering,  University of Maryland, College Park, MD 20742azarm@umd.edu

P. K. Kannan

Department of Marketing, R. H. Smith School of Business,  University of Maryland, College Park, MD 20742pkannan@rhsmith.umd.edu


Corresponding author.

J. Mech. Des 133(4), 041003 (May 09, 2011) (11 pages) doi:10.1115/1.4003843 History: Received May 12, 2010; Revised March 11, 2011; Accepted March 16, 2011; Published May 09, 2011; Online May 09, 2011

Market players, such as competing manufacturing firms and retail channels, can significantly influence the demand and profit of a new product. Existing methods in design for market systems use game theoretic models that can maximize a firm’s profit with respect to the product design and price variables given the Nash equilibrium of the market system. However, in the design for uncertain market systems, there is seldom equilibrium with players having fixed strategies in a given time period. In this paper, we propose an agent based approach for design for market systems that accounts for learning behaviors of the market players under uncertainty. By learning behaviors we mean that market players gradually, over time, learn to play with better strategies based on action–reaction behaviors of other players. We model a market system with agents representing competing manufacturers and retailers who possess learning capabilities and based on some prespecified rules are able to react and make decisions on the product design and pricing. The proposed agent based approach provides strategic design and pricing decisions for a manufacturing firm in response to possible reactions from market players in the short and long term horizons. Our example results show that the proposed approach can produce competitive strategies for the firm by simulating market players’ learning behaviors when they react only by setting prices, as compared to a game theoretic approach. Furthermore, it can yield profitable product design decisions and competitive strategies when competing firms react by changing design variables in the short term—case for which no previous method in design for market systems has been reported.

Copyright © 2011 by American Society of Mechanical Engineers
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Grahic Jump Location
Figure 1

Problem definition

Grahic Jump Location
Figure 3

Result of scenario 1: (a)-(d) empirical action profile for manufacturer agent 1-4, (e) agent payoffs, and (f) empirical convergence index

Grahic Jump Location
Figure 4

Results of scenario 3



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