"Walking Ant in the Rain" model: Microscopic complexity in a general decision-based system
Abstract
We show a model that represents a general decision-based complex system (DBCS). DBCS can be found in many different fields of study from the natural sciences to the social sciences. The model uses a "fight or flight" mechanism where the agent is exposed to the same obstacles at random times and at random places. Memory of previous encounters is provided to the agent to allow for different "strategies" in agent response. Simulation of the model showed that for choices with equal probabilities ofsuccess, agents segregate into groups were decisions are made using extreme interpretation of memory. This shows that complexity in DBCS is not limited to system behavior (macroscopic complexity) but also in agent interaction (microscopic complexity).