Modelling of consumer goods markets: An agent-based computational approach

An agent-based behavioral model incorporating utility-based rational choice enhanced with psychological drivers is presented to study a typical consumer market. The psychological drivers incorporate purchase strategies of loyalty and change-of-pace, using agent-specific memory of past purchases. Attribute-specific preferences and prices drive the utility-based choice function. Transactions data is used to calibrate and test the model. Results indicate that prediction accuracy at both macro and micro levels can be significantly improved with the incorporation of purchase strategies. Moreover, increased agent memory does not improve predictions in the model beyond a threshold, indicating that consumer memory of past shopping instances is finite and recent purchase history is more relevant to current decision making than the distant past. The chapter illustrates the use of agent-based simulations to model changes or interventions in the market, such as new product introductions, for which no history exists.