An Agent-Based Simulation Model for Analysis on Marketing Strategy Considering Promotion Activities

摘要

This paper proposes a new agent-based model for simulation analysis on firms' marketing strategy in competitive markets environments. In the previous model proposed by Tay and Lasch, firm agents determine their marketing strategies such as price and product attribute at every period so as to maximize their profit. This paper shows that the firm agents constructed based on the Tay-Lasch model do not learn the preferences of consumer agents under a very simple situation. This article proposes a novel agent-based model based on a learning classifier system so that the firm agents have abilities to learn the preferences of consumer agents. Moreover, in order to construct a more realistic simulation model, promotion activity of firm agents is incorporated in the strategy of firm agents. Some relationship between the strategy the firm and profitable firm are clarified through simulation analysis.