Scartozzi, Cesare M. (2018): A New Taxonomy for International Relations: Rethinking the International System as a Complex Adaptive System. Published in: Journal on Policy and Complex Systems , Vol. 4, No. 1 (2018): pp. 109-133.
PDF
MPRA_paper_95496.pdf Download (261kB) |
Abstract
Abstract: The international system is a complex adaptive system with emergent properties and dynamics of self-organization and information processing. As such, it is better understood with a multidisciplinary approach that borrows methodologies from the field of complexity science and integrates them to the theoretical perspectives offered by the field of international relations (IR). This study is set to formalize a complex systems theory approach to the study of international affairs and introduce a new taxonomy for IR with the two-pronged aim of improving interoperability between different epistemological communities and outlining a formal grammar that set the basis for modeling international politics as a complex adaptive system.
Item Type: | MPRA Paper |
---|---|
Original Title: | A New Taxonomy for International Relations: Rethinking the International System as a Complex Adaptive System |
Language: | English |
Keywords: | international politics; international relations theory; complex systems theory; taxonomy; adaptation; fitness; self-organization |
Subjects: | N - Economic History > N4 - Government, War, Law, International Relations, and Regulation > N40 - General, International, or Comparative Y - Miscellaneous Categories > Y8 - Related Disciplines > Y80 - Related Disciplines |
Item ID: | 95496 |
Depositing User: | Cesare M. Scartozzi |
Date Deposited: | 12 Aug 2019 10:50 |
Last Modified: | 29 Sep 2019 11:58 |
References: | Atran, S. (1990). Cognitive Foundations of Natural History. Cambridge. Axelrod, R. (1997). The complexity of cooperation: Agent-Based Models of Competition and Collaboration. Princeton, NJ: Princeton University Press. Axelrod, R. (2006). Alternative Uses of Simulation. In N. E. Harrison (Ed.), Complexity in World Politics: Concepts and Methods of a New Paradigm (pp. 137–142). New York: State University of New York Press. Axtell, R. L., Epstein, J. M., & Young, H. P. (2000). The Emergence of Classes in a Multi ¬Agent Bargaining Model (No. 9). Social Dynamics. Bak, P., Tang, C., & Wiesenfeld, K. (1987). Self-organized criticality: An explanation of the 1/f noise. Physical Review Letters, 59(4), 381–384. Bhavnani, R. (2006). Agent-Based Models in the Study of Ethnic Norms and Violence. In N. E. Harrison (Ed.), Complexity in World Politics: Concepts and Methods of a New Paradigm (pp. 121–136). New York: State University of New York Press. Boccara, N. (2004). Modeling Complex Systems. New York: Springer. Byrne, D. (1998). Complexity theory and the social sciences an introduction. London: Routledge. Cederman, L.-E. (1997). Emergent Actors in World Politics: How States and Nations Develop and Dissolve. Princeton, NJ: Princeton University Press. Clemens, W. C. J. (2006). Understanding and Coping with Ethnic Conflict and Development Issues in Post-Soviet Eurasia. In N. E. Harrison (Ed.), Complexity in World Politics: Concepts and Methods of a New Paradigm (pp. 73–94). New York: State University of New York Press. Descartes, R. (1637). A Discourse on the Method. (I. Maclean, Trans.) (2006th ed.). Oxford: Oxford University Press. Downey, A. B. (2012). Think Complexity. Green Tea Press (2nd ed.). Needham, Mass.: Green Tea Press. Earnest, D. C., & Rosenau, J. N. (2006). Signifying Nothing? What Complex Systems Theory Can and Cannot Tell Us about Global Politics. In N. E. Harrison (Ed.), Complexity in World Politics: Concepts and Methods of a New Paradigm (pp. 143–164). New York: State University of New York Press. Epstein, J. M. (1996). Remarks on the Foundations of Agent-Based Generative Social Science. In K. L. J. Leigh Tesfatsion (Ed.), Handbook of Computational Economics (2nd ed., pp. 1585–1605). Amsterdam: North-Holland. Epstein, J. M. (2007). Generative Social Science: Studies in Agent-Based Computational Modeling. Princeton, NJ: Princeton University Press. Epstein, J. M. (2014). Agent_Zero. Princeton: Princeton University Press. Epstein, J. M., & Axtell, R. (1996). Growing Artificial Societies. Crawfordsville, Indiana: The Brookings Institution. Gros, C. (2011). Complex and Adaptive Dynamical Systems. London: Springer. Harrison, N. E. (2006). Complexity in World Politics: Concepts and Methods of a New Paradigm. (N. E. Harrison, Ed.). New York: State University of New York Press. Harrison, N. E. (2006). Thinking About the World We Make. In N. E. Harrison (Ed.), Complexity in World Politics: Concepts and Methods of a New Paradigm (pp. 1–24). New York: State University of New York Press. Harrison, N. E., & Singer, J. D. (2006). Complexity Is More Than Systems Theory. In N. E. Harrison (Ed.), Complexity in World Politics: Concepts and Methods of a New Paradigm (pp. 35–43). New York: State University of New York Press. Hoffmann, M. (2006). Beyond Regime Theory: Complex Adaptation and the Ozone Depletion Regime. In N. E. Harrison (Ed.), Complexity in World Politics: Concepts and Methods of a New Paradigm (pp. 95–121). New York: State University of New York Press. Holland, J. H. (1995). Hidden Order: How Adaptation Builds Complexity. New York: Addison-Wesley. Holland, J. H. (2013). Complexity: A Very Short Introduction (e-book). London: Oxford University Press. Kauffman, S. A. (1993). The Origins of Order: Self-Organization and Selection in Evolution. New York: Oxford University Press. Kavalski, E. (2007). The fifth debate and the emergence of complex international relations theory: notes on the application of complexity theory to the study of international life. Cambridge Review of International Affairs, 20(3), 435–454. Latane, B., & Nowak, A. (1994). Measuring emergent social phenomena: Dynamism, polarization, and clustering as order parameters of Social Systems. Behavioral Science, 39(1), 1–12. Lloyd, S. (n.d.). Measures of Complexity a non-exhaustive list. Retrieved November 1, 2015, from http://web.mit.edu/esd.83/www/notebook/Complexity.PDF McKelvey, B. (2001). Foundations of "New" Social Science: Institutional Legitimacy from Philosophy, Complexity Science, Postmodernism, and Agent-based Modeling. In University of California (Ed.), Sackler Colloquium on Agents, Intelligence, and Emergent Human Organization: Capturing Complexity Through Agent-Based Modeling. Irvine: The Anderson School at UCLA. Meadows, D. H. (2009). Thinking in Systems. (D. Wright, Ed.). London: Earthscan. Mitchell, M. (2005). Self-awareness and control in decentralized systems (Working Papers of the AAAI 2005 Spring Symposium on Metacognition in Computation). Working Papers of the AAAI 2005 Spring Symposium on Metacognition in Computation. Menlo Park, CA. Mitchell, M. (2009). Complexity: A Guided Tour. New York: Oxford University Press. Mitchell, M. (2015). Core Disciplines, Goals, and Methodologies of the Sciences of Complexity. Retrieved February 1, 2016, from https://www.complexityexplorer.org/courses/27-introduction-to-complexity-summer-2015/segments/2944 Mitchell, M. (2015). Models of Biological Self-Organization, Introduction. Retrieved December 31, 2015, from https://www.complexityexplorer.org/courses/27-introduction-to-complexity-summer-2015/segments/3083 Mitchell, M. (2015). What are Complex Systems? The Experts Weigh In. Retrieved October 25, 2015, from https://www.complexityexplorer.org/courses/27-introduction-to-complexity-summer-2015/segments/2946 Mitchell, M. (2016). Information Processing in Biological Systems. Retrieved March 19, 2016, from https://www.complexityexplorer.org/courses/27-introduction-to-complexity-summer-2015/segments/3089 NoraMueller, E., Parsons, J., J., W. A., & Turnbull, L. (Eds.). (2014). Patterns of Land Degradation in Drylands: Understanding Self-Organised Ecogeomorphic Systems. London: Springer. Nowak, A., & Lewenstein, M. (1996). Modeling social change with cellular automata. In R. Hegselmann, U. Mueller, & K. G. Troitzsch (Eds.), Modelling and Simulation in the Social Sciences from the Philosophy of Science Point of View (pp. 249–285). Dordrecht: Springer. Richards, D. (2000). Nonlinear Dynamics in Games: Convergence and Stability in International Environmental Agreements. In D. Richards (Ed.), Political Complexity: Nonlinear Models of Politics (pp. 174–208). Ann Arbor, MI: The University of Michigan Press. Richards, D. (2000). Nonlinear Modeling: All Things Suffer Change. In D. Richards (Ed.), Political Complexity: Nonlinear Models of Politics (pp. 1–22). Ann Arbor, MI: The University of Michigan Press. Richards, D. (2000). Political Complexity: Nonlinear Models of Politics. (D. Richards, Ed.). Ann Arbor, MI: The University of Michigan Press. Rockefeller Foundation. (2016). Warren Weaver. Retrieved May 3, 2016, from http://www.rockefeller100.org/biography/show/warren-weaver Sandole, D. J. D. (2006). Complexity and Conflict Resolution. In N. E. Harrison (Ed.), Complexity in World Politics: Concepts and Methods of a New Paradigm (pp. 43–72). New York: State University of New York Press. Santa Fe Institute. (2015). Glossary. Retrieved November 6, 2015, from https://www.complexityexplorer.org/explore/glossary Saunders-Newton, D. (2006). When Worlds Collide: Reflections on the Credible Uses of Agent-Based Models in International and Global Studies. In N. E. Harrison (Ed.), (pp. 165–182). New York: State University of New York Press. Schrodt, P. A. (2000). Pattern Recognition of International Crises Using Hidden Markov Models. In D. Richards (Ed.), Political Complexity: Nonlinear Models of Politics (pp. 296–331). Ann Arbor, MI: The University of Michigan Press. Schweller, R. L. (2010). Entropy and the trajectory of world politics: why polarity has become less meaningful. Cambridge Review of International Affairs, 23(1), 145–163. Scott, A. (2006). Encyclopedia of nonlinear science. New York: Routledge. Sim, Y. (n.d.). International Relations and Complex Systems Theory, 1–12. Toole, J., & Page, S. E. (2011). Predicting cellular automata. Complex Systems, 19(4), 343. Waldrop, M. (1992). Complexity: The Emerging Science at the Edge of Order and Chaos. London: Touchstone, Simon & Schuster. Weaver, W. (1948). Science and complexity. American Scientist, 36(4), 1–11. Wilensky, U., & Resnick, M. (1999). Thinking in Levels: A Dynamic Systems Approach to Making Sense of the World. Journal of Science Education and Technology, 8(1), 3–19. Zhong, N., Liu, J., Jinglon, W., Yao, Y., Lu, S., & Li, K. (2005). Web Intelligence Meets Brain Informatics: First WICI International Workshop, WImBI 2006 Beijing, China, December 15-16, 2006 Revised Selected and Invited Papers. (J. G. C. Siekmann & J. Subseries, Eds.) Lecture Notes in Computer Science. Berlin: Springer. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/95496 |