Growing Artificial Societies: Conclusions Mark Madrilejo November 22, 2005

Emergent society Separate disciplines, one science Link models from the top down? Aggregated; fixed coefficients “… discrediting interdisciplinary inquiry” Bottom-up modeling Rules, interaction: “turn on, turn off” Aspects “emerge - and merge” Multifaceted, dynamic individuals

Artificial society as instrument Policy: social change Given a desirable outcome, find small local reforms that generate it (Un)intended consequences Given regulatory/technological change, find range of potential outcomes China example Growth => pollution (sewage) => disease Environmental laws => enforcement issues (corruption)

Rule ecologies Instead of one behavioral rule, a population of rules Arthur: “Inductive reasoning and bounded rationality” (sound familiar?) Arthur et al: Artificial stock market Psychology, technical trading, bubbles, crashes When agents can explore different expectations rapidly

Sustainability What not to do … Optimal rule transforms environment in sudden, irreversible way New environment requires new rule for survival Agents cannot change to new rule quickly enough Catastrophe, perhaps extinction Examples: fish stocks “… our rules create the cliffs we drive off.” Dinosaurs, Romans, Mayans

Other models, extensions Models Schelling’s segregation Ringworld; Anasazi Extensions Hybrid models: artificial agents, realistic landscapes New math? “It is our hope … artificial societies will inspire … new formal methods.”

Generative social science Holistic: “a new, more unified and evolutionary social science” Not deductive, not inductive … generative “Sufficient to generate”: find agents, environments, and rules Phenomena emerge from the bottom up ”If you didn't grow it, you didn't explain it." - Joshua Epstein