Introduction to the Modeling and Analysis of Complex Systems

460 CHAPTER 19. AGENT-BASED MODELSwithm = mr if ag.type == ’r’ else mfCode 19.21:m = ag.min the update function. These changes make the magnitude of movement a heritableattribute of individual agents. Finally, to introduce variation of this attribute, we should addsome small random number to it when an offspring is born. This can be implemented byreplacingCode 19.22:with something like:Code 19.23:agents.append(cp.copy(ag))newborn = cp.copy(ag)newborn.m += uniform(-0.01, 0.01)agents.append(newborn)There are two places in the code where this replacement is needed, one for prey andanother for predators. Note that, with the mutations implemented above, the diffusionrates of agents (m) could become arbitrarily large or small (they could even become negative).This is fine for our purpose, because m is used in the update function in the formof uniform(-m, m) (which works whether m is positive or negative). But in general, youshould carefully check your code to make sure the agents’ evolvable attributes stay withinmeaningful bounds.For completeness, here is the revised code for the evolutionary predator-prey ABM(with the revised parts indicated by ###):Code 19.24: predator-prey-abm-evolvable.pyimport matplotlibmatplotlib.use(’TkAgg’)from pylab import *import copy as cpnr = 500. # carrying capacity of rabbits