It is important to note that most simulation methods function best within the domain of predictable, limited conditions. Consumers, however, are not content to be restrained. They evolve, migrate, associate, cooperate and interfere with one another in complex ways. The same can be said of retail stores. Techniques that allow evolution of a system's component parts are valuable.
Adaptive agents provide just such a platform.
If a system is defined as a collection of entities, the entities can be termed agents, and the agents can be modeled in such a way as to allow evolution, thereby changing behavior under new circumstances. Agent-based methods improve the ability of mathematical simulators to address the dynamic, nonlinear nature of retail in that the output is a set of emergent properties new, unexpected behavior resulting from the changing complexity of the retail system. One of the most useful applications is the ability to create new demand for products or services not previously offered in the marketplace. The technique predicts the degree of demand, varying from situation to situation. Within reason, there is no such thing as a bad analysis method, simply bad applications of good techniques.