Introduction to the Modeling and Analysis of Complex Systems

19.3. AGENT-ENVIRONMENT INTERACTION 445# simulating diffusion and evaporation of cAMPfor x in xrange(w):for y in xrange(w):C, R, L, U, D = env[x,y], env[(x+1)%w,y], env[(x-1)%w,y], \env[x,(y+1)%w], env[x,(y-1)%w]lap = (R + L + U + D - 4 * C)/(Dh**2)nextenv[x,y] = env[x,y] + (- k * C + Dc * lap) * Dtenv, nextenv = nextenv, env# simulating secretion of cAMP by agentsfor ag in agents:env[ag.x, ag.y] += f * Dt# simulating chemotaxis of agentsfor ag in agents:newx, newy = (ag.x + randint(-1, 2)) % w, (ag.y + randint(-1, 2)) % wdiff = (env[newx, newy] - env[ag.x, ag.y]) / 0.1if random() < exp(diff) / (1 + exp(diff)):ag.x, ag.y = newx, newyimport pycxsimulatorpycxsimulator.GUI().start(func=[initialize, observe, update])As you see, the code is getting longer and more complicated than before, which is atypical consequence when you implement an ABM.Figure 19.4 shows a typical simulation result. The tiny blue dots represent individualcells (agents), while the grayscale shades on the background show the cAMP concentration(environment). As time goes by, the slightly more populated areas produce morecAMP, attracting more cells. Eventually, several distinct peaks of agent populations arespontaneously formed, reproducing self-organizing patterns that are similar to what thePDE-based model produced.What is unique about this ABM version of the Keller-Segel model, compared to itsoriginal PDE-based version, is that the simulation result looks more “natural”—the formationof the population peaks are not simultaneous, but they gradually appear one afteranother, and the spatial arrangements of those peaks are not regular. In contrast, inthe PDE-based version, the spots are formed simultaneously at regular intervals (see