Environment Updating and Agent Scheduling Policies in Agent-based Simulators

Abstract

Since Schelling’s segregation model, the ability to represent individual behaviours and to execute them to produce emergent collective behaviour has enabled interesting studies in diverse domains, like artificial financial markets, crowd simulation or biological simulations. Nevertheless, the description of such experiments are focused on the agents behaviours, and seldom clarify the exact process used to execute the simulation. In other words, little details are known on the assumptions, the choices and the design that have been done on the simulator on fundamental notions like time, simultaneity, agent scheduling or sequential/parallel execution. Though, these choices are crucial because they impact simulation results. This paper is focused on parameter sensitivity of agent-based simulators implementations, specifically on environment updating and agent scheduling policies. We highlight concepts that simulator designers have to define and presents several possible implementations and their impact.