Skip to main content
SpringerLink
Log in

Agentenbasierte Simulation in der Politikwissenschaft

  • Chapter
  • First Online:
Handbuch Methoden der Politikwissenschaft

  • 19k Accesses

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.

Ein Teil der Arbeit an diesem Kapitel wurde im Rahmen des von der Deutschen Forschungsgemeinschaft (DFG) geförderten Forschungsprojekts „Meinungsdynamik und kollektive Entscheidungen“ (265108307) erbracht.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 129.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Wir möchten an dieser Stelle dem Gutachter Florian Bader, Anne-Kathrin Fischer und auch den Herausgebern ganz herzlich danken für wertvolle Hinweise zur Verbesserung dieses Beitrages.

Literatur

  • 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 

  • Axelrod, Robert. 1984. The evolution of cooperation. New York: Basic books.

    Google Scholar 

  • 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 

  • Axelrod, Robert. 1997. The complexity of cooperation: Agent-based models of competition and cooperation. Princeton: Princeton University Press.

    Book  Google Scholar 

  • 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 

  • 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.

  • Bhavnani, Ravi. 2006. Ethnic norms and interethnic violence: Accounting for mass participation in the Rwandan genocide. Journal of Peace Research 43(6): 651–669.

    Article  Google Scholar 

  • 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.

  • 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.

    Article  Google Scholar 

  • Bianchi, Federico, und Flaminio Squazzoni. 2015. Agent-based models in sociology. Wiley Interdisciplinary Reviews 7(4): 284–306.

    Article  Google Scholar 

  • 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.

  • Cederman L. E. 1997. Emergent Actors in World Politics. Princeton, NJ: Princeton University Press.

    Google Scholar 

  • Cederman L. E. 2003. Modeling the Size of Wars: From Billiard Balls to Sandpiles, American Political Science Review 97(1): 135–150.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Coleman, James. 1990. Foundations of social theory. Belknap: Harvard.

    Google Scholar 

  • 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 

  • Cusack, Thomas R., und Richard J. Stoll. 1990. Exploring Realpolitik: Probing international relations theory with computer Simulation. Cambridge: Cambridge University Press.

    Google Scholar 

  • 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.

  • Deffuant, Guillaume, David Neau, Frederic Amblard, und Gérard Weisbuch. 2000. Mixing beliefs among interacting agents. Advances in Complex Systems 3(1): 87–98.

    Article  Google Scholar 

  • 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.

  • Downs, Anthony. 1957. An economic theory of democracy. New York: Harper & Row Publishers.

    Google Scholar 

  • 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.

  • 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.

  • Edmonds, Bruce. 2017. Different modelling purposes. In Simulating social complexity. A handbook, Hrsg. Bruce Edmonds und Ruth Meyer, 39–58. Cham: Springer.

    Chapter  Google Scholar 

  • Edmonds, Bruce, und Ruth Meyer. 2017. Simulating social complexity. A handbook, 2. Aufl. New York: Springer.

    Book  Google Scholar 

  • 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 

  • Epstein, Joshua M. 2002. Modelling civil violence. An agent-based computational approach. PNAS 99(3): 7243–7250.

    Article  Google Scholar 

  • Epstein, Joshua M. 2006. Generative social science. Studies in agent-based computational modeling. Princeton: Princeton University Press.

    Google Scholar 

  • 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.

  • Epstein, Joshua M., und Robert Axtell. 1996. Growing artificial societies: Social science from the bottom up. Cambridge: MIT Press.

    Book  Google Scholar 

  • Esser, Hartmut. 2002. Soziologie: Sinn und Kultur, Bd. 6. Campus.

    Google Scholar 

  • Fiorina, Morris P., und Samuel J. Abrams. 2008. Political polarization in the American public. Annual Review of Political Science 11:563–588.

    Article  Google Scholar 

  • Fishbein, Martin, und Icek Ajzen. 1975. Belief, attitude, intention and behavior: An introduction to theory and research. Reading: Addison-Wesley.

    Google Scholar 

  • 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.

    Google Scholar 

  • Fowler, James H., und Michael Laver. 2008. A tournament of party decision rules. Journal of Conflict Resolution 52(1): 68–92.

    Article  Google Scholar 

  • 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.

  • 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.

    Article  Google Scholar 

  • Gilbert, Nigel, und Klaus G. Troitzsch. 2005. Simulation for the social scientist, 2. Aufl. London: Open University Press.

    Google Scholar 

  • Granovetter, Mark. 1985. Economic action and social structure. The problem of embeddedness. American Journal of Sociology 91(3): 481–510.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Gurr, Ted Robert. 1970. Why men rebel. Princeton: Princeton University Press.

    Google Scholar 

  • 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.

  • 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.

  • 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.

    Article  Google Scholar 

  • Kennedy, Paul. 1987. The rise and fall of the great powers. New York: Random House.

    Google Scholar 

  • King, Gary, und Langche Zeng. 2001. Improving forecasts of state failure. World Politics 53(4): 623–658.

    Article  Google Scholar 

  • Kollman, Ken, John H. Miller, und Scott E. Page. 1992. Adaptive parties in spatial elections. American Political Science Review 86(4): 929–937.

    Article  Google Scholar 

  • 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.

  • Laver, Michael. 2005. Policy and the dynamics of political competition. American Political Science Review 99(2): 263–281.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Limpert, Eckhard, Werner A. Stahel, und Markus Abbt. 2001. Log-normal distributions across the sciences: Keys and clues. BioScience 51(5): 341–352.

    Article  Google Scholar 

  • Lim M., Metzler R., Bar-Yam Y. 2007. Global Pattern Formation and Ethnic/Cultural Violence. Science 317:1540–1544.

    Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

  • 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.

    Article  Google Scholar 

  • Macy, Michael W., und Robert Willer. 2002. From factors to actors: Computational sociology and agent-based modeling. Annual Review of Sociology 28:143–166.

    Article  Google Scholar 

  • Maynard Smith, John. 1982. Evolution and the theory of games. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • 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.

  • Merton, Robert K. 1957. Social theory and social structure, 2. Aufl. Glencoe: The Free Press.

    Google Scholar 

  • Nye, Joseph. 1990. Bound to lead, the changing nature of American power. New York: Basic books.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

  • Richardson, L. F. 1948. Variation of the Frequency of Fatal Quarrels with Magnitude. American Statistical Association 43:523–546.

    Google Scholar 

  • 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 

  • Ross, Michael. 2006. A closer look at oil, diamonds, and civil war. Annual Review of Political Science 9:265–300.

    Article  Google Scholar 

  • 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.

  • 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.

  • Scharpf, Fritz W. 2000. Interaktionsformen: akteurzentrierter Institutionalismus in der Politikforschung. Opladen: Leske+Budrich.

    Google Scholar 

  • Schelling, Thomas C. 1971. Dynamic models of segregation. Journal of Mathematical Sociology 1(2): 143–186.

    Article  Google Scholar 

  • Sherif, Muzafer, und Carl I. Hovland. 1961. Social judgement. New Haven: Yale University Press.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

  • Troitzsch, Klaus G. 2017. Can agent-based simulation replicate organized crime? Trends in Organized Crime 20(1–2): 100–119.

    Article  Google Scholar 

  • 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 

  • Waltz, Kenneth. 1979. Theory of international politics. New York: McGraw-Hill.

    Google Scholar 

  • Weidmann, N., Cedermann, L. E. 2008. GeoContest. Modelling strategic competition in Geopolitical Systems. Social Science Computer Review 26(4): 510–518.

    Google Scholar 

  • Weidmann, N., Salehyan, I. (2013). Violence and Ethnic Segregation: A Computational Model Applied to Baghdad. International Studies Quarterly 57(1): 52–64.

    Google Scholar 

  • 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 

  • Zeigler, Bernard P. 1985. Theory of modeling and simulation. Malabar: Krieger.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Psychology & Methods, Jacobs University Bremen, Bremen, Deutschland

    Jan Lorenz

  2. Institut für Soziologie, Technik- und Innovationssoziologie, Simulationsmethoden, Johannes Gutenberg-Universität Mainz, Mainz, Deutschland

    Martin Neumann

Authors
  1. Martin Neumann
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jan Lorenz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Martin Neumann .

Editor information

Editors and Affiliations

  1. Goethe-Universität Frankfurt, Frankfurt am Main, Germany

    Claudius Wagemann

  2. Institut für Politikwissenschaft (IfP), Universität Duisburg-Essen, Duisburg, Germany

    Achim Goerres

  3. Hochschule für Politik München, TU München, München, Germany

    Markus B. Siewert

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Neumann, M., Lorenz, J. (2020). Agentenbasierte Simulation in der Politikwissenschaft. In: Wagemann, C., Goerres, A., Siewert, M.B. (eds) Handbuch Methoden der Politikwissenschaft. Springer VS, Wiesbaden. https://doi.org/10.1007/978-3-658-16936-7_34

Download citation

  • DOI: https://doi.org/10.1007/978-3-658-16936-7_34

  • Published:

  • Publisher Name: Springer VS, Wiesbaden

  • Print ISBN: 978-3-658-16935-0

  • Online ISBN: 978-3-658-16936-7

  • eBook Packages: Social Science and Law (German Language)

Publish with us

Policies and ethics

Search

Navigation