2D-3D MultiAgent GeoSimulation with Knowledge-Based Agents of Customers' Shopping Behavior in a Shopping Mall

In this paper we present a simulation prototype of the customers’ shopping behavior in a mall using a knowledge-based multiagent geosimulation approach. The shopping behavior in a shopping mall is performed in a geographic environment (a shopping mall) and is influenced by several shopper’s characteristics (internal factors) and factors which are related to the shopping mall (external or situational factors). After identifying these factors from a large literature review we grouped them in what we called “dimensions”. Then we used these dimensions to design the knowledge-based agents’ models for the shopping behavior simulation. These models are created from empirical data and implemented in the MAGS geosimulation platform. The empirical data have been collected from questionnaires in the Square One shopping mall in Toronto (Canada). After presenting the main characteristics of our prototype, we discuss how mall’s managers of the Square One can use the Mall_MAGS prototype to make decisions about the mall spatial configuration by comparing different simulation scenarios. The simulation results are presented to mall’s managers through a user-friendly tool that we developped to carry out data