Hostname: page-component-76fb5796d-zzh7m Total loading time: 0 Render date: 2024-04-26T17:46:28.689Z Has data issue: false hasContentIssue false
  • English
  • Français

Predicting Interstate Conflict Outcomes Using a Bootstrapped ID3 Algorithm

Published online by Cambridge University Press:  04 January 2017

Philip A. Schrodt
Get access Rights & Permissions [Opens in a new window]

Abstract

The ID3 algorithm is an inductive artificial intelligence technique that generates classification trees. These trees are similar to those used in simple expert systems; with ID3 they are generated by machine rather than using human experts. This article applies a bootstrapped ID3 to the Butterworth data set on interstate conflict management. By generating a number of classification trees from randomly selected subsets of the complete data set, the variables that most effectively predict the outcome of the conflict management effort are identified, and the degree of unpredictability in the data is estimated from the accuracy of the classification tree in predicting cases not in the training set. The original set of 38 independent variables can be reduced to 5 or less with almost no loss of accuracy; classification trees using these variables have 95–100 percent accuracy when fitted to the entire data set and an average accuracy of 50–60 percent in predicting new cases in split-sample tests. Unlike many existing statistical techniques, the classification tree is a plausible model of human inductive knowledge representation since it is compatible with the cognitive constraints of the human brain.

Type
Research Article
Information
Political Analysis , Volume 2 , 1990 , pp. 31 - 56
Copyright
Copyright © by the University of Michigan 1991 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

BIBLIOGRAPHY

Alker, Hayward J., and Christensen, C. 1972. “From Causal Modeling to Artificial Intelligence: The Evolving of a UN Peace-Making Simulation.” In Experimentation and Simulation in Political Science, ed. Laponce, J. A. and Smoker, Paul. Toronto: University of Toronto Press.Google Scholar
Alker, Hayward J., and Greenberg, W. 1976. “On Simulating Collective Security Regime Alternatives.” In Thought and Action in Foreign Policy, ed. Bonham, G. M. and Shapiro, M. Basel: Birkhauser Verlag.Google Scholar
Allison, Graham T. 1971. The Essence of Decision. Boston: Little, Brown.Google Scholar
Butterworth, Robert Lyle. 1976. Managing Interstate Conflict, 1945-74: Data with Synopses. Pittsburgh: University of Pittsburgh Center for International Studies.Google Scholar
Cimbala, Stephen, ed. 1987. Artificial Intelligence and National Security. Lexington, Mass.: Lexington Books.Google Scholar
Cohen, Paul R., and Feigenbaum, Edward A. 1982. The Handbook of Artificial Intelligence, vol. 3. Los Altos, Calif.: William Kaufmann.Google Scholar
Diaconis, Persi, and Efron, Bradley. 1983. “Computer-Intensive Methods in Statistics.” Scientific American 248 (5): 116–30.Google Scholar
Forsyth, Richard, and Rada, Roy. 1986. Machine Learning: Applications in Expert Systems and Information Retrieval. New York: Wiley/Halstead.Google Scholar
Garson, G. David. 1987. “The Role of Inductive Expert Systems Generators in the Social Science Research Process.” Social Science Microcomputer Review 5 (1): 11–24.CrossRefGoogle Scholar
George, Alexander L. 1969. “The ‘Operational Code’: A Neglected Approach to the Study of Political Leaders and Decision Making.” International Studies Quarterly 13: 190–222.Google Scholar
Grunbaum, Werner F. 1986. “Using Artificial Intelligence to Predict Supreme Court Decision Making.” Paper presented at the annual meeting of the American Political Science Association, Washington, D.C.Google Scholar
Hudson, Valerie. 1987. “Using a Rule-Based Production System to Estimate Foreign Policy Behavior.” In Artificial Intelligence and National Security, ed. Cimbala, Stephen. Lexington, Mass.: Lexington Books.Google Scholar
Hudson, Valerie, ed. 1991. Artificial Intelligence and International Politics. Boulder: Westview.Google Scholar
Hunt, Earl B., Marin, J., and Stone, P. J. 1966. Experiments in Induction. New York: Academic Press.Google Scholar
Job, Brian L., Johnson, Douglas, and Selbin, Eric. 1987. “A Multi-Agent, Script-based Model of U.S. Foreign Policy Toward Central America.” Paper presented at the annual meeting of the American Political Science Association, Chicago.Google Scholar
Kaw, Marita. 1989. “Predicting Soviet Military Intervention.” Journal of Conflict Resolution 33: 402–29.Google Scholar
Kaw, Marita. 1990. “Choosing Sides: Testing a Political Proximity Model.” American Journal of Political Science 34: 441–70.CrossRefGoogle Scholar
Levine, Robert I., Drang, Diane E., and Edelson, Barry. 1990. AI and Expert Systems. New York: McGraw-Hill.Google Scholar
Majeski, Stephen J. 1989. “A Rule Based Model of the United States Military Expenditures Decision-Making Process.” International Interactions 15 (2): 129–54.Google Scholar
Michalski, Ryszard S., Carbonell, Jaime, and Mitchell, T., eds. 1983. Machine Learning: An Artificial Intelligence Approach. Palo Alto: Tioga Press.Google Scholar
Pedersen, Ken. 1989. Expert Systems Programming. New York: Wiley.Google Scholar
Quinlan, J. Ross. 1979. “Induction over Large Data Bases.” Heuristic Programming Project report HPP-79-14. Computer Science Department, Stanford University.Google Scholar
Quinlan, J. Ross. 1983. “Learning Efficient Classification Procedures and Their Application to Chess End Games.” In Machine Learning, ed. Michalski, R. S., Carbonell, J., and Mitchell, T. Palo Alto: Tioga Press.Google Scholar
Schrodt, Philip A. 1985. “Precedent-Based Logic and Rational Choice: A Comparison.” In Dynamic Models of International Conflict, ed. Luterbacher, Urs and Don Ward, Michael. Boulder: Lynn Rienner Publishing.Google Scholar
Schrodt, Philip A. 1987. “Classification of Interstate Conflict Outcomes using a Bootstrapped CLS Algorithm.” Paper presented at the annual meeting of the International Studies Association, Washington, D.C.Google Scholar
Schrodt, Philip A. 1988. “Artificial Intelligence Models of International Behavior.” American Sociologist 19: 71–85.Google Scholar
Schrodt, Philip A. 1989. “Short-Term Prediction of International Events Using a Holland Classifier.” Mathematical and Computer Modelling 12: 589–600.CrossRefGoogle Scholar
Sylvan, Donald A., and Chan, Steve, eds. 1984. Foreign Policy Decision Making: Perception, Cognition, and Artificial Intelligence. New York: Praeger.Google Scholar
Sylvan, Donald A., Goel, Ashok, and Chandrasekran, B. 1990. “Analyzing Political Decision Making from an Information Processing Perspective: JESSE.” American Journal of Political Science 34: 74–123.Google Scholar
Tanaka, Akihiko. 1984. “China, China Watching, and CHINA-WATCHER.” In Foreign Policy Decision Making, ed. Sylvan, Donald A. and Chan, Steve. New York: Praeger.Google Scholar
Thompson, Beverly, and Thompson, William. 1986. “Finding Rules in Data.” Byte, December.Google Scholar