Introduction to the Modeling and Analysis of Complex Systems

440 CHAPTER 19. AGENT-BASED MODELSYou can also simulate the collective behavior of a population in which multipletypes of agents are mixed together. It is known that interactions among kineticallydistinct types of swarming agents can produce various nontrivial dynamic patterns[87].(a) (b) (c)Figure 19.3: Three essential behavioral rules of Boids. (a) Cohesion. (b) Alignment.(c) Separation.19.3 Agent-Environment InteractionOne important component you should consider adding to your ABM is the interactionbetween agents and their environment. The environmental state is still part of the system’soverall state, but it is defined over space, and not associated with specific agents.The environmental state dynamically changes either spontaneously or by agents’ actions(or both). The examples discussed so far (Schelling’s segregation model, DLA, Boids)did not include such an environment, but many ABMs explicitly represent environmentsthat agents act on and interact with. The importance of agent-environment interaction iswell illustrated by the fact that NetLogo [13], a popular ABM platform, uses “turtles” and“patches” by default, to represent agents and the environment, respectively. We can dothe same in Python.A good example of such agent-environment interaction is in the Keller-Segel slimemold aggregation model we discussed in Section 13.4, where slime mold cells behave asagents and interact with an environment made of cAMP molecules. The concentration of