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SFI Summer School 2008

SFI Summer School 2008. Dr. Joshua M. Epstein The Brookings Institution and Santa Fe Institute. LECTURES. Agent-Based Models and Generative Social Science Applications: Revolutions and Ethnic Violence Epidemics and Public Health Adaptive Organizations. Outline. Models

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SFI Summer School 2008

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  1. SFI Summer School 2008 Dr. Joshua M. Epstein The Brookings Institution and Santa Fe Institute

  2. LECTURES • Agent-Based Models and Generative Social Science • Applications: • Revolutions and Ethnic Violence • Epidemics and Public Health • Adaptive Organizations

  3. Outline • Models • Agent-Based Models and Generative Explanation • Agent-Based Civil Violence Model

  4. Possible Modeling Goals • Prediction; • Explanation (very distinct from Prediction); • Historical reconstruction; • Illumination of core uncertainties; • To suggest what new data should be collected; • Promote humility; • To bound outcomes to plausible ranges; • To offer crisis options in real time; • To demonstrate tradeoffs/ help set budget priorities • To simply explore and discover new questions; • To challenge prevailing theory; • To expose prevailing wisdom as logically inconsistent; • To expose prevailing wisdom as incompatible with available data; • To train practitioners (exercises); • To educate the general public; • To show presumably simple (complex) things to be complex (simple)

  5. Explanation ≠ Prediction • Explanation does not imply prediction • Plate tectonics explains earthquakes, but we can’t predict • Electrostatics  lightning, can’t predict where • Evolution  speciation, can’t predict next year’s flu strain

  6. Guide Data Collection • Naive view of science: Collect lots of data, and then run regressions on it. This can be very productive. • Not the rule, in fact: • Theory often precedes data. • Maxwell’s EM theory predicted the existence of radio waves • Einstein predicted that light should bend in a gravitational field

  7. Historical Reconstruction • We can reconstruct historical cases with high fidelity • Lends credibility to recommendations: • Smallpox models replicated history • Size distribution • Distribution of transmissions by social unit (schools, hospitals, workplaces, homes) • Longini, et al, IJID, 2006 • …did same for 1968 global flu

  8. Most Important: From Ignorant Militance to Militant Ignorance • Modeling enforces scientific habit of mind: militant ignorance. • Commitment to “I don’t know.” All scientific knowledge is uncertain, contingent, subject to revision, falsifiable in principle. • Don’t base beliefs on authority, but on evidence. • Levels the playing field. • Why science (as a mode of inquiry) is antithetical to monarchy, theocracy, and authoritarianism • Feynman: the hard-won “Freedom to doubt” • Long and brutal struggle. • Essential to functioning democracy • Intellectuals have a solemn duty to doubt and to teach doubt • Education is not about “a saleable skill set”…it’s about freedom

  9. Modeling Approaches • Aggregate Statistical • Compartmental ODEs • Game Theoretic • Agent-based

  10. Features of Agent-BasedComputational Models • Heterogeneity • No representative agent; no homogeneous pools, no aggregation • Every agent explicitly represented, and differ by: • Wealth, network, immunocompetence, memory, genetics, culture, ... • Autonomy • Bounded Rationality • Bounded Information • Bounded Computing • Explicit Space • Local Interactions • Non-Equilibrium Dynamics • Tipping Phenomena

  11. Generative Social Science: Studies in Agent-Based Computational ModelingPrinceton 2006 • Overarching Chapters: “Agent-Based Computational Models and Generative Social Science.” Complexity, 1999, “Remarks on the Foundations Of Agent-Based Generative Social Science,” Handbook. • Subsequent chapters illustrate core points • Non-Explanatory Equilibria • Epidemic Dynamics • Civil Violence: Revolutions and Ethnic Conflict (PNAS) • Histories:The Anasazi (PNAS) • Adaptive Organizations • Retirement Dynamics • Economic Classes • Demographic Games • Spatial zones of cooperation in demographic PD Game • Spatial norm maps in demographic Coordination Game • Thoughtless Conformity to Social Norms • CD containing 40 movies/Programs • Aspires to be > collection. An argument, about ABM generally:

  12. Stakes: The Future of Explanation • What’s centrally at stake in the advent of agent-based modeling is the notion of an explanation in the social sciences. • To explain macroscopic social patterns, we “grow” them in agent models. • It does not suffice to demonstrate that, if society is placed in the pattern, no individual would unilaterally depart (Nash equilibrium/refinements). • Rather, one must show how a population of plausible agents, interacting under plausible rules, could actually arrive at the pattern—be it a segregation pattern, a wealth distribution, or a pattern of violence. • Motto: “If You Didn’t Grow It, You Didn’t Explain It”

  13. Generative Explanation • To explain a macroscopic phenomenon is to furnish a microscopic (i.e., agent) specification that suffices to generate it. • Canonical experiment is to situate an initial population of autonomous heterogeneous agents in the relevant spatial environment; allow them to interact according to simple local rules and thereby generate--or “grow”--the macroscopic phenomenon from the bottom up. • Generative sufficiency is the core explanatory notion:

  14. Generating and Explaining The Rise and Fall of a Civilization: The Artificial Anasazi (PNAS, 2002) • Kayenta Anasazi of Longhouse Valley: 800-1350 • Digitize Actual Environmental and Demographic History • Hydrology, Top Soil, Drought Severity, Maize Potential • Household Sizes and Locations • Use an Agent-Based Model to Test Whether Various Microspecifications (movement, farming, reproduction rules) Suffice to Generate--or “Grow”--the Actual History. • Phase I focused on sufficiency of purely environmental factors

  15. The Artificial Anasazi

  16. Population Dynamics:Simulated vs. Historical Can we do this for other phenomena?

  17. Generative Social Science: An Agent Model of Civil Violence (PNAS 2002) Dr. Joshua M. Epstein The Brookings Institution-Johns Hopkins Center on Social and Economic Dynamics and Santa Fe Institute Nuffield

  18. Civil Violence:Two Model Variants PNAS 2002 • Civil Authority Seeks to Suppress Rebellion • Civil Authority Seeks to Suppress Communal Violence

  19. Civilians: To Rebel or Not Rebel • What is my level of grievance? • G = H (1-L) • Britain during the blitz. L = 1, so High H ≠ G • Kurds in Iraq L = 0 , so High H = G • How likely am I to get arrested if I rebel? • Estimated P = F(C/A within my vision). • What’s my risk aversion, R? • Simple local rule: If Grievance Exceeds Risk, Rebel. If G-RP> T, then Rebel; Otherwise Don’t.

  20. Civil Violence Model IDecentralized Rebellion AgainstCentral Authority • Agents • V = vision • R = risk aversion U(0,1) • H = hardship U(0,1). Exogenous for now • L = Legitimacy. Exogenous for now • G = grievance: H(1-L). • Estimated Arrest Probability • P = 1- exp[-k (C/A)v)] • Net Risk N= RP • State (Q,A) • Jail Term (0, )

  21. Agent State Transition State (G-N) State Transition Q >T Q A Q <T Q Q A >T A A A <T A Q Simple Local Rule:If G-N>T, Be Active; Otherwise, Be Quiet.

  22. Actual Arrest and Detention • Cops • Vision = V • Movement (Random) • Rule: Inspect all sites within V. Arrest a Random Active Agent. • Jail Terms are Random U(0, Max_Term). Exogenous for now. • No deterrent or behavioral effects for now

  23. Graphics • Left Screen: Action • Blue if Quiescent • Red if Active • Right Screen: Emotion • The Brighter the Red, the Higher the Grievance • Both Screens: Cops • Black on Both Screens

  24. Expectations? • Frozen into place, so can’t spread the revolution. Now set them in motion: • Might expect a slow take-off and then explosion, or • An S-curve familiar from diffusion and epidemiology modeling, • Or maybe oscillations like predator-prey cycles….

  25. Core Dynamics • Local Conformity, Global Diversity, Punctuated Equilibrium (Young, 1998)

  26. Punctuated Equilibria

  27. Punctuated Equilibrium Persists

  28. Waiting Time Distribution

  29. OLS on Logged Data (Truncated)

  30. Some Regularities • Spatially: localized/blotchy • Temporally: punk-eek/spiky • Qualitative features of civil violence data • Encouraging for empirical calibration of model variants • Will show current econometrics

  31. Guatemalan Civil War Source: Gulden (2004)

  32. Punctuated Equilibrium Source: Gulden (2004)

  33. Rwanda

  34. Rwanda

  35. French Revolution

  36. Outburst Size Distribution

  37. Tension

  38. A Game • Large, but slow, legitimacy reductions • Small, but immediate, legitimacy reductions • Which is more destabilizing? Why?

  39. Large Legitimacy Reduction in Small Increments: Salami Tactics

  40. SmallOne-Shot Legitimacy Reduction

  41. Cascades • This is why “isolated” events (e.g., assassinations, massacres, rape) loom so large: Not that everyone gets equally angry. It’s that the core grows, which reduces the risk of joining for the marginal actor. • Russia 1917 • Abu Grahib, Desecration of the Koran,…

  42. Cop Reductions

  43. Stylized Facts Generated • Individual Deception • Social Tipping Points (Blue on Left, Red on Right) • Endogenous Cycles of Violence/Punctuated Equilibria. • Explains Standard Repressive Tactics (Restrictions on Freedom of Assembly). • Salami Tactics:Rate of Change of Legitimacy Highly Salient • DeTocqueville: “Liberalization is the Most Difficult of Political Arts.” • Lab: Explore Peacekeeping

  44. Civil Violence Model II: Inter-Group Violence • A new social group--Greens--is added. Now Blues and Greens. • Agents are as in Model I, and turn Red when active. But now, “Going Active” means attacking (i.e., killing) an agent of the other group. • Cops are as before, and arrest Red agents within their vision. • Add Population Dynamics. • Birth=Clone onto neighboring site with probability p. • Offspring Inherit Parent’s Grievance. • Death=Random Age from U(0,max_age).

  45. Peace and Genocide High L Low L

  46. Interventions Safe Havens Through Peacekeeping Reversion to Genocide

  47. Waiting Time to GenocideAnd Initial Cop Density

  48. Mean Time to Monochrome:Linear Fit

  49. Standard Deviation

  50. Coefficient of Variation

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