1 / 162

SFI Summer School 2008

Dive into agent-based models and generative social science with Dr. Joshua M. Epstein from The Brookings Institution and Santa Fe Institute. Learn to model revolutions, epidemics, and adaptive organizations while challenging prevailing theories. Explore how historical reconstruction and modeling can promote humility and illuminate uncertainties. Understand the importance of modeling approaches and the concept of generative explanations in social sciences.

thogue
Download Presentation

SFI Summer School 2008

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related