Introduction to the Modeling and Analysis of Complex Systems

19.2. BUILDING AN AGENT-BASED MODEL 437def initialize():global agentsagents = []for i in xrange(n):ag = agent()ag.type = randint(2)ag.x = random()ag.y = random()agents.append(ag)def observe():global agentscla()white = [ag for ag in agents if ag.type == 0]black = [ag for ag in agents if ag.type == 1]plot([ag.x for ag in white], [ag.y for ag in white], ’wo’)plot([ag.x for ag in black], [ag.y for ag in black], ’ko’)axis(’image’)axis([0, 1, 0, 1])def update():global agentsag = agents[randint(n)]neighbors = [nb for nb in agentsif (ag.x - nb.x)**2 + (ag.y - nb.y)**2 < r**2 and nb != ag]if len(neighbors) > 0:q = len([nb for nb in neighbors if nb.type == ag.type]) \/ float(len(neighbors))if q < th:ag.x, ag.y = random(), random()import pycxsimulatorpycxsimulator.GUI().start(func=[initialize, observe, update])When you run this code, you should set the step size to 50 under the “Settings” tab tospeed up the simulations.