Konfliktforschung I Kriegsursachen im historischen Kontext

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1 Konfliktforschung I Kriegsursachen im historischen Kontext Computersimulation und Konfliktforschung am Beispiel von GeoSim Prof. Dr. Lars-Erik Cederman Eidgenössische Technische Hochschule Zürich Center for Comparative and International Studies (CIS) lcederman@ethz.ch

2 Agenda Einführung in die agenten-basierte Modellierung Schellings Segregations-Model Einführung in Geosim Anwendungen in der Konfliktforschung

3 Definition ABM ist eine computergestützte Forschungsmethode, die es dem Forscher erlaubt, künstliche Welten zu kreieren, diese zu analysieren und damit zu experimentieren. Bottom-up Basiert auf zellularen Automata und verteilter künstlicher Intelligenz

4 Disaggregierte Modellierung Wenn <Kondition> dann <Handlung 1> sonst <Handlung 2> Bewegte Agenten Unbewegte Agenten Beobachter Organisationen von Agenten Künstliche Welt Daten

5 Blick vom Berliner Fernsehturm

6 Ethnische Stadtteile Klein-Italien, San Diego Chinatown, New York

7 Nachbarschafts-Segregation Die Spielregeln Ein Agent bleibt, wenn min. 1/3 der Nachbarn seiner Art ist < 1/3 Ansonsten zieht er auf eine freie, angenehmere Position um Thomas C. Schelling Nobelpreis für Wirtschaft 2005

8 Vorführung 1 Schelling (Converted).mov

9 Ergebnisse des Models Zahl der Nachbarschaften Zufriedenheit Zeit Zeit

10 Europa um 1500

11 Europa um 1900

12 States made war and war made the state Charles Tilly

13 GeoSim GeoSim nutzt Repast, ein Java-Werkzeug Staaten sind hierarchische, begrenzte Akteure, die in einem dynamischen, gitter-basierten Netzwerk interagieren

14 Vorführung 2 GeoSim.mov

15 Emergente Ergebnisse Anzahl der Staaten Anteil der sicheren Gebiete Zeit Zeit

16 Mögliche Equilibria Multipolarität mit 15 Staaten (Vorführung) Multipolarität mit 7 Staaten Bipolarität Unipolarität

17 Modeling conflict with ABM 17 Configurations Processes Qualitative properties Distributional properties Example 3. Democratic peace Example 2. State-size distributions Example 4. Emergence of the territorial state Example 1. War-size distributions Cederman "Modeling the Size of Wars. American Political Science Review 97:

18 Cumulative war-size plot, log P(S>s) (cumulative frequency) log P(S>s) = log s 2 R = N = Data Source: Correlates of War Project (COW) 0.01 WWII WWI log s (severity)

19 Theory: Self-organized criticality log f 1 9 Input Output f s -a log s Complex System s Slowly driven systems that fluctuate around state of marginal stability while generating non-linear output according to a power law. Examples: sandpiles, semi-conductors, earthquakes, extinction of species, forest fires, epidemics, traffic jams, city populations, stock market fluctuations, firm size

20 Self-organized criticality 2 0 Per Bak s sand pile Power-law distributed avalanches in a rice pile

21 War clusters in Geosim 2 1 t = 3,326 t = 10,000

22 log P(S > s) (cumulative frequency) Simulated cumulative war-size plot log P(S > s) = log s N = 218 R 2 = log s (severity) See Modeling the Size of Wars American Political Science Review Feb. 2003

23 Modeling conflict with ABM 23 Configurations Processes Qualitative properties Distributional properties Example 3. Democratic peace Example 2. State-size distributions Example 4. Emergence of the territorial state Example 1. War-size distributions Cederman Explaining State Sizes: A Geopolitical Model. Proceedings of Agent 2003, eds. Macal, North & Sallach. Argonne.

24 2. Modeling state sizes: Empirical data 2 4 log Pr (S > s) (cumulative frequency) log S ~ N(5.31, 0.79) MAE = Data: Lake et al log s (state size)

25 Simulating state size with terrain 2 5

26 log Pr (S > s) (cumulative frequency) Simulated state-size distribution 2 6 log S ~ N(1.47, 0.53) MAE = log s (state size)

27 Modeling conflict with ABM 27 Configurations Processes Qualitative properties Distributional properties Example 3. Democratic peace Example 2. State-size distributions Example 4. Emergence of the territorial state Example 1. War-size distributions Cederman, L-E & K S Gleditsch "Conquest and Regime Change" International Studies Quarterly 48:

28 Simulating global democratization 2 8 Proportion of democracies Source: Cederman & Gleditsch 2004 Proportion of democracies Proportion at war Year

29 A simulated democratic outcome 2 9 t = 0 t = 10,000

30 Replications with regime change 3 0 Democratic share of territory Without collective security Democratic share of territory With collective security e e e e e+04Time e e e e e+04Time

31 Modeling conflict with ABM 31 Configurations Processes Qualitative properties Distributional properties Example 3. Democratic peace Example 2. State-size distributions Example 4. Emergence of the territorial state Example 1. War-size distributions Cederman & Girardin Growing Sovereignty International Studies Quarterly 54:

32 Taxation in a linear state 3 2 Tax: k= Discount: d =

33 Results from the linear model Proposition 1. Assuming exponential decay, direct rule is always more efficient Proposition 2. Assuming a step function, indirect rule is more efficient for low threshold values x* p(n) p DR (n) k(3-k) 1-k p IDR (n) p DR (n) 3 3 x* x 1/(1-k) n

34 The initial state of OrgForms 3 4

35 Modeling technological change 3 5 Discouting Distance t = 0 t = 500 t = 1000

36 OrgForms: A dynamic network model 3 6 Conquest Technological Progress Systems Change Organizational Bypass

37 Indirect rule in the Middle Ages 3 7

38 Direct rule in the modern system 3 8

39 Replications with moving threshold and slope time

40 Exploring geopolitics using agent-based modeling 4 0 GeoSim 0 OrgForms GeoSim 4 GeoContest

41 Toward more realistic models of civil wars 4 1 Our strategy: Step I: extending Geosim framework Step II: conducting empirical research Step III: back to computational modeling

42 Use agentbased modeling to articulate identity-based mechanisms of insurgency Step I: Nationalist insurgency model 4 2 In Order, Conflict, and Violence, eds. Kalyvas, Shapiro & Masoud. CUP, 2008.