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Agentenbasierte Simulation in der Politikwissenschaft

  • Martin NeumannEmail author
  • Jan Lorenz
Living reference work entry
Part of the Springer Reference Sozialwissenschaften book series (SRS)

Zusammenfassung

Dieses Kapitel vermittelt einen Einblick in Methoden und politikwissenschaftliche Anwendungsfelder agentenbasierter Simulation. Hierfür wird zunächst die Methode der Simulation charakterisiert. Anschließend werden Grundprinzipien agentenbasierter Simulation und die besonderen Stärken dieser Forschungsmethode erläutert. Agentenbasierte Simulation ist insbesondere geeignet, um die Mikro-Makro Schnittstelle, sowie die Auswirkungen der Interaktionen sozial eingebundener Akteure zu untersuchen. Im Vergleich zu anderen Methoden lassen sich Heterogenität und begrenzte Rationalität von Akteuren mit dieser Methode abbilden. In einem nächsten Abschnitt wird ein Überblick über den Forschungsstand agentenbasierter Simulation in der Politikwissenschaft vermittelt. Exemplarisch für innenpolitische Anwendungen wird die Modellierung des Parteienwettbewerbs sowie der Dynamik öffentlicher Meinung diskutiert. Aus dem Bereich der internationalen Beziehungen werden Modelle zwischenstaatlicher Machtpolitik sowie der Konfliktforschung dargestellt.

Schlüsselwörter

Simulation Agentenbasierte Modellierung Parteienwettbewerb Meinungsdynamik Machtpolitik Konfliktforschung 

Literatur

  1. Abelson, Robert. 1964. Mathematical models of the distribution of attitudes under controversy. In Contributions to mathematical psychology, Hrsg. N. Frederiksen und H. Gulliksen, 141–160. New York: Holt, Rinehart and Winston.Google Scholar
  2. Axelrod, Robert. 1984. The evolution of cooperation. New York: Basic books.Google Scholar
  3. Axelrod, Robert. 1995. A model of the emergence of new political actors. In Artificial societies: The computer simulation of social life, Hrsg. N. Gilbert und R. Conte, 19–39. London: UCL Press.Google Scholar
  4. Axelrod, Robert. 1997. The complexity of cooperation: Agent-based models of competition and cooperation. Princeton: Princeton University Press.Google Scholar
  5. Axtell, Robert. 2001. Why agents? On the varied motivations for agent computing in the social sciences. Center on social and economic dynamics working paper 17.Google Scholar
  6. Bennet, D. Scott. 2008. Governments, civilians, and the evolution of insurgency: Modeling the early dynamics of insurgencies. Journal for Artificial Societies and Social Simulation 11(4). http://jasss.soc.surrey.ac.uk/11/4/7.html. Zugegriffen am 26.12.2018.
  7. Bhavnani, Ravi. 2006. Ethnic norms and interethnic violence: Accounting for mass participation in the Rwandan genocide. Journal of Peace Research 43(6): 651–669.CrossRefGoogle Scholar
  8. Bhavnani, Ravi, Dan Miodownik, und Jonas Nart. 2008. REsCape: An agent-based framework for modeling resources, ethnicity, and conflict. Journal for Artificial Societies and Social Simulation 11(2). http://jasss.soc.surrey.ac.uk/11/2/7.html. Zugegriffen am 26.12.2018.
  9. Bhavnani, Ravi, Karsten Donnay, Dan Miodownik, Maayan Mor, und Dirk Helbing. 2014. Group segregation and urban violence. American Journal of Political Science 58(1): 226–245.CrossRefGoogle Scholar
  10. Bianchi, Federico, und Flaminio Squazzoni. 2015. Agent-based models in sociology. Wiley Interdisciplinary Reviews 7(4): 284–306.CrossRefGoogle Scholar
  11. Brousmiche, Kei-Leo, Jean-Daniel Kant, Nicolas Sabouret, und Francois Prenot-Guinard. 2016. From beliefs to attitudes: Polias, a model of attitude dynamics based on cognitive modeling and field data. Journal of Artificial Societies and Social Simulation 19(4). http://jasss.soc.surrey.ac.uk/19/4/2.html. Zugegriffen am 26.12.2018.
  12. Cederman L. E. 1997. Emergent Actors in World Politics. Princeton, NJ: Princeton University Press.Google Scholar
  13. Cederman L. E. 2003. Modeling the Size of Wars: From Billiard Balls to Sandpiles, American Political Science Review 97(1): 135–150.CrossRefGoogle Scholar
  14. Cioffi-Revilla, Claudio, und Mark Rouleau. 2010. MASON RebeLand: An agent-based model of politics, environment, and insurgency. International Studies Review 12(1): 31–52.CrossRefGoogle Scholar
  15. Coleman, James. 1990. Foundations of social theory. Belknap: Harvard.Google Scholar
  16. Conte, Rosaria, Giulia Andrighetto, und Marco Campenni. 2014. Minding norms. Mechanisms and dynamics of social order in agent societies. Oxford: Oxford University Press.Google Scholar
  17. Cusack, Thomas R., und Richard J. Stoll. 1990. Exploring Realpolitik: Probing international relations theory with computer Simulation. Cambridge: Cambridge University Press.Google Scholar
  18. Deffuant, Guillaume. 2006. Comparing extremism propagation patterns in continuous opinion models. Journal of Artificial Societies and Social Simulation 9(3). http://jasss.soc.surrey.ac.uk/9/3/8.html. Zugegriffen am 26.12.2018.
  19. Deffuant, Guillaume, David Neau, Frederic Amblard, und Gérard Weisbuch. 2000. Mixing beliefs among interacting agents. Advances in Complex Systems 3(1): 87–98.CrossRefGoogle Scholar
  20. Deffuant, Guillaume, Frederic Amblard, Gérard Weisbuch, und Thierry Faure. 2002. How can extremism prevail? A study based on the relative agreement interaction model. Journal of Artificial Societies and Social Simulation 5(4). http://jasss.soc.surrey.ac.uk/5/4/1.html. Zugegriffen am 26.12.2018.
  21. Downs, Anthony. 1957. An economic theory of democracy. New York: Harper & Row Publishers.Google Scholar
  22. Duggins, Peter. 2017. A psychologically-motivated model of opinion change with applications to American politics. Journal of Artificial Societies and Social Simulation 20(1). http://jasss.soc.surrey.ac.uk/20/1/13.html. Zugegriffen am 26.12.2018.
  23. Edmonds, Bruce. 2015. Using qualitative evidence to inform the specification of agent-based models. Journal of Artificial Societies and Social Simulation 18(1): 18. http://jasss.soc.surrey.ac.uk/18/1/18.html. Zugegriffen am 26.12.2018.
  24. Edmonds, Bruce. 2017. Different modelling purposes. In Simulating social complexity. A handbook, Hrsg. Bruce Edmonds und Ruth Meyer, 39–58. Cham: Springer.CrossRefGoogle Scholar
  25. Edmonds, Bruce, und Ruth Meyer. 2017. Simulating social complexity. A handbook, 2. Aufl. New York: Springer.CrossRefGoogle Scholar
  26. Edmonds, Bruce, und Scott Moss. 2005. From KISS to KIDS. An anti-simplistic modeling approach. In Multi-agent based simulation 2004, Lecture notes in artificial intelligence 3446, Hrsg. Paul Davidsson, 130–144. New York: Springer.Google Scholar
  27. Epstein, Joshua M. 2002. Modelling civil violence. An agent-based computational approach. PNAS 99(3): 7243–7250.CrossRefGoogle Scholar
  28. Epstein, Joshua M. 2006. Generative social science. Studies in agent-based computational modeling. Princeton: Princeton University Press.Google Scholar
  29. Epstein, Joshua M. 2008. Why model? Journal of Artificial Societies and Social Simulation 11(4). http://jasss.soc.surrey.ac.uk/11/4/12.html. Zugegriffen am 23.03.2017.
  30. Epstein, Joshua M., und Robert Axtell. 1996. Growing artificial societies: Social science from the bottom up. Cambridge: MIT Press.CrossRefGoogle Scholar
  31. Esser, Hartmut. 2002. Soziologie: Sinn und Kultur, Bd. 6. Campus.Google Scholar
  32. Fiorina, Morris P., und Samuel J. Abrams. 2008. Political polarization in the American public. Annual Review of Political Science 11:563–588.CrossRefGoogle Scholar
  33. Fishbein, Martin, und Icek Ajzen. 1975. Belief, attitude, intention and behavior: An introduction to theory and research. Reading: Addison-Wesley.Google Scholar
  34. Florea, A. 2012. Where do we go from here? Conceptual, theoreticall, and methodological gaps in the large-N civil war research programme. International studies review 14(1): 78–98.CrossRefGoogle Scholar
  35. Fowler, James H., und Michael Laver. 2008. A tournament of party decision rules. Journal of Conflict Resolution 52(1): 68–92.CrossRefGoogle Scholar
  36. Galan, Jose Manuel, und Luis R. Izquierdo. 2005. Appearances can be deceiving: Lessons learned re-implementing Axelrod’s ‚Evolutionary Approach to Norms‘. Journal of Artificial Societies and Social Simulation 8(3): 2. http://jasss.soc.surrey.ac.uk/8/3/2.html. Zugegriffen am 26.12.2018.
  37. Geller, Armando, und Shah Jamal Alam. 2010. A socio-political and – Cultural model of the war in Afghanistan. International Studies Review 12(1): 8–30.CrossRefGoogle Scholar
  38. Gilbert, Nigel, und Klaus G. Troitzsch. 2005. Simulation for the social scientist, 2. Aufl. London: Open University Press.Google Scholar
  39. Granovetter, Mark. 1985. Economic action and social structure. The problem of embeddedness. American Journal of Sociology 91(3): 481–510.CrossRefGoogle Scholar
  40. Grimm, Volker, Uta Berger, Finn Bastiansen, Sigrunn Eliassen, Vincen Ginot, Jarl Giske, John Goss-Custard, Tamara Grand, Simone K. Heinz, und Geir Huse. 2006. A standard protocol for describing individual-based and agent-based models. Ecological Modelling 198:115–126.CrossRefGoogle Scholar
  41. Gurr, Ted Robert. 1970. Why men rebel. Princeton: Princeton University Press.Google Scholar
  42. Hegselmann, Rainer, und Ulrich Krause. 2002. Opinion dynamics and bounded confidence models, analysis and simulation. Journal of Artificial Societies and Social Simulation 5(3). http://jasss.soc.surrey.ac.uk/5/3/2.html. Zugegriffen am 26.12.2018.
  43. Jager, Wander. 2017. Enhancing the realism of simulation (EROS): On implementing and develoing psychological theory in social simulation. Journal of Artificial Societies and Social Simulation 20(3). http://jasss.soc.surrey.ac.uk/20/3/14.html. Zugegriffen am 26.12.2018.
  44. Jager, Wander, und Frederic Amblard. 2005. Uniformity, bipolarization and pluriformity captured as generic stylized behavior with an agent-based simulation model of attitude change. Computational and Mathematical Organization Theory 10(4): 295–303.CrossRefGoogle Scholar
  45. Kennedy, Paul. 1987. The rise and fall of the great powers. New York: Random House.Google Scholar
  46. King, Gary, und Langche Zeng. 2001. Improving forecasts of state failure. World Politics 53(4): 623–658.CrossRefGoogle Scholar
  47. Kollman, Ken, John H. Miller, und Scott E. Page. 1992. Adaptive parties in spatial elections. American Political Science Review 86(4): 929–937.CrossRefGoogle Scholar
  48. Kurahasi-Nakamura, Takasumi, Michael Mäs, und Jan Lorenz. 2016. Robust clustering in generalized bounded confidence models. Journal of Artificial Societies and Social Simulation 19(4). http://jasss.soc.surrey.ac.uk/19/4/7.html. Zugegriffen am 26.12.2018.
  49. Laver, Michael. 2005. Policy and the dynamics of political competition. American Political Science Review 99(2): 263–281.CrossRefGoogle Scholar
  50. Laver, Michael, und Michel Schilperoord. 2007. Spatial models of political competition with endogenous political parties. Philosophical Transactions of the Royal Society B: Biological Sciences 362:1711–1721.CrossRefGoogle Scholar
  51. Limpert, Eckhard, Werner A. Stahel, und Markus Abbt. 2001. Log-normal distributions across the sciences: Keys and clues. BioScience 51(5): 341–352.CrossRefGoogle Scholar
  52. Lim M., Metzler R., Bar-Yam Y. 2007. Global Pattern Formation and Ethnic/Cultural Violence. Science 317:1540–1544.CrossRefGoogle Scholar
  53. Lorenz, Jan. 2012. Zur Methode der agenten-basierten Simulation in der Politikwissenschaft am Beispiel von Meinungsdynamik und Parteienwettstreit. In Jahrbuch für Handlungs- und Entscheidungstheorie. Band 7: Experiment und Simulation, Hrsg. T. Bräuninger, A. Bächtiger und S. Shikano, 31–58. Wiesbaden: VS Verlag für Sozialwissenschaften.CrossRefGoogle Scholar
  54. Lustick, Ian. 1996. History, historiography, and political science: Multiple historical records and the problem of selection bias. American Political Science Review 90(3): 605–6018.CrossRefGoogle Scholar
  55. Lustick, Ian. 2000. Agent-based modelling of collective identity: Testing constructivist theory. Journal of Artificial Societies and Social Simulation 3(1). http://jasss.soc.surrey.ac.uk/3/1/1.html. Zugegriffen am 26.12.2018.
  56. Lustick, Ian, Dan Miodownik, und Roy Eidelson. 2004. Secessionism in multicultural states: Does power sharing prevent or encourage it? American Political Science Review 98(2): 209–229.CrossRefGoogle Scholar
  57. Macy, Michael W., und Robert Willer. 2002. From factors to actors: Computational sociology and agent-based modeling. Annual Review of Sociology 28:143–166.CrossRefGoogle Scholar
  58. Maynard Smith, John. 1982. Evolution and the theory of games. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  59. McKeown, Gary, und Noel Sheehy. 2006. Mass media and polarisation processes in the bounded confidence model of opinion dynamics. Journal of Artificial Societies and Social Simulation 9(1). http://jasss.soc.surrey.ac.uk/9/1/11.html. Zugegriffen am 26.12.2018.
  60. Merton, Robert K. 1957. Social theory and social structure, 2. Aufl. Glencoe: The Free Press.Google Scholar
  61. Nye, Joseph. 1990. Bound to lead, the changing nature of American power. New York: Basic books.Google Scholar
  62. O’Sullivan, David, Tom Evans, Steven Manson, Sara Metcalf, Arika Ligmann-Zielinska, und Cris Bone. 2016. Strategic directions for agent-based modeling: Avoiding the YAAWN syndrome. Journal of land use science 11(2): 177–187.CrossRefGoogle Scholar
  63. Pulick, Erick, Patrick Korth, Patrick Grim, und und Jiin Jung. 2015. Modelling interaction effects in polarization: Individual media influence and the impact of town meetings. Journal of Artificial Societies and Social Simulation 19(2). http://jasss.soc.surrey.ac.uk/19/2/1.html. Zugegriffen am 26.12.2018.
  64. Richardson, L.F. 1948. Variation of the Frequency of Fatal Quarrels with Magnitude. American Statistical Association 43:523–546.CrossRefGoogle Scholar
  65. Rosenberg, Milton J., und Carl I. Hovland. 1960. Cognitive, affective, and behavioral components of attitudes. In Attitude organization and change: An analysis of consistency among attitude components, Hrsg. Milton J. Rosenberg, 1–14. New Haven: Yale University Press.Google Scholar
  66. Ross, Michael. 2006. A closer look at oil, diamonds, and civil war. Annual Review of Political Science 9:265–300.CrossRefGoogle Scholar
  67. Rutherford, Alex, Dion Harmon, Justin Werfel, Alexander Gard-Murray, Shlomiya Bar-Yam, Andreas Gros, Ramon Xulvi-Brunet, und Yaneer Bar-Yam. 2014. Good fences. The importance of setting boundaries for peaceful coexistence. PlosOne.  https://doi.org/10.1371/journal.pone.0095660. Zugegriffen am 26.12.2018.CrossRefGoogle Scholar
  68. Salzarulo, Laurent. 2006. A continuous opinion dynamics model based on the principle of meta-contrast. Journal of Artificial Societies and Social Simulation 9(1). http://jasss.soc.surrey.ac.uk/9/1/13.html. Zugegriffen am 26.12.2018.
  69. Scharpf, Fritz W. 2000. Interaktionsformen: akteurzentrierter Institutionalismus in der Politikforschung. Opladen: Leske+Budrich.Google Scholar
  70. Schelling, Thomas C. 1971. Dynamic models of segregation. Journal of Mathematical Sociology 1(2): 143–186.CrossRefGoogle Scholar
  71. Sherif, Muzafer, und Carl I. Hovland. 1961. Social judgement. New Haven: Yale University Press.Google Scholar
  72. Smith, Eliot R., und Frederica Conrey. 2007. Agent-based modeling: A new approach for theory building in social psychology. Personality and Social Psychology Review 11(1): 87–104.CrossRefGoogle Scholar
  73. Squazzoni, Flaminio, Wander Jager, und Bruce Edmonds. 2014. Social simulation in the social sciences: A brief overview. Social Science Computer Review 32(3): 279–294.CrossRefGoogle Scholar
  74. Srbljinovic, Armano, Drazen Penzar, Petra Rodik, und Kruno Kardov. 2003. An agent-based model of ethnic mobilization. Journal of Artificial Societies and Social Simulation 6(1). http://jasss.soc.surrey.ac.uk/6/1/1.html. Zugegriffen am 26.12.2018.
  75. Troitzsch, Klaus G. 2017. Can agent-based simulation replicate organized crime? Trends in Organized Crime 20(1–2): 100–119.CrossRefGoogle Scholar
  76. Turner, John C., Michael A. Hogg, Penelope J. Oakes, Stephen D. Reicher, und Margaret S. Wetherell. 1987. Rediscovering the social group: A self-categorization theory. Oxford: Blackwell.Google Scholar
  77. Waltz, Kenneth. 1979. Theory of international politics. New York: McGraw-Hill.Google Scholar
  78. Weidmann, N., Cedermann, L.E. 2008. GeoContest. Modelling strategic competition in Geopolitical Systems. Social Science Computer Review 26(4): 510–518.CrossRefGoogle Scholar
  79. Weidmann, N., Salehyan, I. (2013). Violence and Ethnic Segregation: A Computational Model Applied to Baghdad. International Studies Quarterly 57(1): 52–64.CrossRefGoogle Scholar
  80. Wooldrige, Michael J. 1999. Intelligent agents. In Multi agent systems:A modern approach to distributed artificial intelligence, Hrsg. G. Weiß, 27–77. Cambridge, MA: MIT Press.Google Scholar
  81. Zeigler, Bernard P. 1985. Theory of modeling and simulation. Malabar: Krieger.Google Scholar

Copyright information

© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2019

Authors and Affiliations

  1. 1.Jacobs University BremenBremenDeutschland
  2. 2.Universität KoblenzKoblenzDeutschland

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