Human, social, and international conflicts are inescapable facts of life. This article elaborates on how advances in simulation theory and methodology can improve exploring and studying the dynamics of conflicts. The premise of the proposed strategy is based on the observation that agent-based social simulation, which enables high-level and powerful problem-solving capabilities, needs to be significantly enhanced to model complex, multilevel, and multistaged conflicts. To this end, the issues, challenges, and rationale underlying a novel simulation methodology, called multisimulation, are explained. The conceptual foundations needed for the realization of multisimulation are presented to contribute to the development of advanced simulation-based computational laboratories for conflict studies. Finally, by using the Bloomfield-Leiss dynamic phase model of conflict, the author argues for the utility of multisimulation.
Arquilla, J., & Ronfeldt, D. (1997). A new epoch and spectrum of conflict. Santa Monica, CA: RAND.
2.
Axelrod, R. (1987). Evolution of strategies in the iterated prisoner’s dilemma. In L. Davis (Ed.), Genetic algorithms and simulated annealing: Research notes in artificial intelligence (Vol. 24, pp. 32-41). London: Pitman.
3.
Bankes, S. (1993). Exploratory modeling for policy analysis. Operations Research, 4(3), 435-449.
4.
Bankes, S. (1999). Tools and techniques for developing policies for complex and uncertain systems. Proceedings of the National Academy of Sciences, Colloquium, 99(Suppl. 3), 7263-7266.
5.
Banks, J. S., & Sundaram, R. K. (1990). Repeated games, finite automata, and complexity. Games and Economic Behavior, 2, 97-117.
6.
Binmore, K. G. (1998). Game theory and the social contract volume 2: Just playing. Cambridge, MA: MIT Press.
7.
Bloomfield, L. P., & Moulton, A. (1997). Managing international conflict: From theory to policy. New York: St. Martin’s.
8.
Cheldelin, S., Druckman, D., & Fast, L. (2003). Conflict: From analysis to intervention. London: Continuum.
9.
Conte, R., & Castelfranchi, C. (1995). Cognitive and social action. London: UCL Press.
10.
Conte, R., Sichman, J. S., & Gilbert, N. (1998). MAS and social simulation: A suitable commitment. In J. S. Sichman, R. Conte, & N. Gilbert (Eds.), Multi-agent systems and agent-based simulation: First international workshop MABS’98 (pp. 1-9). Berlin, Germany: Springer.
11.
Davis, K. P. (1988). The role of uncertainty in assessing the NATO-PACT central region balance (N-2839). Santa Monica, CA: RAND.
12.
Davis, K. P. (2000). Exploratory analysis enabled by multiresolution, multiperspective modeling. In J. A. Joines, R. R. Barton, K. Kang, & P. A. Fishwick (Eds.), Proceedings of the 2000 Winter Simulation Conference (pp. 293-302). Santa Monica, CA: RAND.
13.
Davis, K. P. (2001). Analytical architecture for capabilities-based planning, mission-system analysis, and transformation. Santa Monica, CA: RAND.
14.
Davis, K. P., & Anderson, A. R. (2003). Improving the composability of department of defense models and simulations. Santa Monica, CA: RAND.
15.
Davis, K. P., Bankes, S. C., & Kahan, J. P. (1986). A new methodology for modeling national command level decision making in war games and simulations. Santa Monica, CA: RAND.
16.
Davis, K. P., & Hall, E. H. (1989). Overview of system software in the RAND strategy assessment system. Santa Monica, CA: RAND.
17.
Davis, K. P., Kulick, J., & Egner, M. (2005). Implications of modern decision science for military decision-support systems. Santa Monica, CA: RAND.
18.
Druckman, D. (1995). The educational effectiveness of interactive games. In D. Crookall & K. Arai (Eds.), Simulation/gaming across disciplines and cultures: ISAGA at a watershed (pp. 178-187). Thousand Oaks, CA: Sage.
19.
Druckman, D., Love, R., & Rozelle, R. (1983). Resolving conflicts of interest and ideologies: A simulation of political decision-making. Social Behavior and Personality: An International Journal, 11, 23-28.
20.
Ferber, J. (1998). Multi-agent systems: An introduction to distributed artificial intelligence. Harlow, UK: Addison-Wesley.
21.
Fishwick, P., & Zeigler, B. E. (1992). A multimodel methodology for qualitative model engineering. ACM Transactions on Modeling and Simulation, 2(1), 52-81.
22.
Fraser, M. N., & Hipel, K. W. (1984). Conflict analysis models and resolutions. New York: North-Holland.
23.
Fürnkranz, K. K., Petrak, J., Fahringer, B. P., Trappl, R., & Widmer, G. (2000). Searching for patterns in political event sequences: Experiments with the KEDS database. Cybernetics and Systems, 31, 649-668.
24.
Grant, N. (1992). Illustrated history of 20th century conflict. London: Reed Consumer.
25.
Guetzkow, H. (1995). Recollections about the inter-nation simulation (INS) and some derivatives in global modeling. Simulation & Gaming: An International Journal, 26, 453-470.
26.
Guetzkow, H. C., Alger, F., Brody, R. A., Noel, R. C., & Snyder, R. C. (1963). Simulation in international relations: Developments for teaching and research. Englewood Cliffs, NJ: Prentice Hall.
27.
Guetzkow, H., & Valadez, J. J. (1981). Simulation and “reality”: Validity research. In H. Guetzkow & J. J. Valadez (Eds.), Simulated international processes: Theories and research in global modeling (pp. 253-330). Beverly Hills, CA: Sage.
28.
Herzig, M., & Skidmore, D. (1995). Nations: A simulation game in international politics. Pew case studies in international affairs (169). Washington, DC: Institute for the Study of Diplomacy.
29.
Holland, J. H. (1992). Adaptation in natural and artificial systems (2nd ed.). Cambridge, MA: MIT Press.
30.
Internet Encyclopedia of Philosophy. (n.d.). Francis Bacon. Retrieved August 31, 2006, from http://www.utm.edu/research/iep/b/bacon.htm
31.
Isard, W., & Smith, C. (1982). Conflict analysis and practical conflict management procedures: An introduction to peace science. Cambridge, MA: Ballinger.
32.
Kirchkamp, O. (1999). Simultaneous evolution of learning rules and strategies. Journal of Economic Behavior and Organization, 40, 295-312.
33.
Lantis, J. S. (1998). Simulations and experiential learning in the international relations classroom. International Negotiation: A Journal of Theory and Practice, 3(1), 39-57.
34.
Lempert, R., Popper, S., & Bankes, S. (2003). Shaping the next one hundred years: New methods for quantitative long-term policy analysis. Santa Monica, CA: RAND.
35.
Lewis, R. D. (1999). When cultures collide—Managing successfully across cultures. London: Nicholas Brealey.
36.
Lindgren, K. (1997). Evolutionary dynamics in game-theoretic models. Reading, MA: Addison-Wesley.
37.
Marshall, I., & Zohar, D. (1997). Who’s afraid of Schrödinger’s cat?New York: Quill-William Morrow.
38.
Popper, K. (2002). The logic of scientific discovery. London: Routledge. (Original work published 1935)
39.
Saunders, D. M., & Lewicki, R. J. (1995). Teaching negotiation with computer simulations: Pedagogical and practical considerations. Negotiation Journal, 11(2), 157-167.
40.
Schelling, T. (1960). The strategy of conflict. Cambridge, MA: Harvard University Press.
41.
Shubik, M. (Ed.). (1964). Game theory and related approaches to social behavior. New York: John Wiley.
42.
Skidmore, D. (1993). Group projects and teaching international relations. International Studies Notes, 18(3), 49-53.
43.
Smith, R. (2001). Aristotle’s logic. Retrieved August 31, 2006, from http://plato.stanford.edu/archives/win2001/entries/aristotle-logic
44.
Stoll, R., & Subramanian, D. (2002). Events, patterns, and analysis: Forecasting international conflict in the twenty-first century. In R. Trappl (Ed.), Computer-aided methods for international conflict resolution and prevention (pp. 145-160). New York: Springer Verlag.
45.
Trappl, R., Fürnkranz, J., & Petrak, J. (1996). Digging for peace: Using machine learning methods for assessing international conflict databases. In Proceedings of the 12th European Conference on Artificial Intelligence (ECAI-96) (pp. 453-457). Budapest, Hungary: John Wiley.
46.
Trappl, R., Fürnkranz, J., Petrak, J., & Bercovitch, J. (1997). Machine learning and case-based reasoning: Their potential role in preventing the outbreak of war or in ending them. In G. Della Riccia, R. Kruse, & H.-J. Lenz (Eds.), Learning, networks and statistics (pp. 209-225). New York: Springer-Verlag.
47.
Weiss, G. (1999). Multiagent systems: A modern approach to distributed artificial intelligence. Cambridge, MA: MIT Press.
48.
Yilmaz, L., & Ören, T. (2004). Dynamic model updating in simulation with multimodels: A taxonomy and generic agent-based architecture. Proceedings of the SCSC, 4, 3-8.
49.
Zeigler, P. B. (1990). Object oriented simulation with hierarchical modular models: Intelligent agents and endomorphic systems. San Diego, CA: Academic Press.
