Modeling International Negotiation Statistical and Machine Learning Approaches

  • Daniel Druckman
  • Richard Harris
  • Johannes Fürnkranz
Part of the Advances in Group Decision and Negotiation book series (AGDN, volume 2)


An earlier study by Druckman et al. (1999) showed that a variety of cases of international negotiation can be distinguished in terms of their objectives. A set of 16 features of negotiation effectively distinguished—by multidimensional scaling (MDS)—among the types of international negotiation objectives proposed by Iklé (1964). The features include aspects of the parties, issues, process, negotiating environment, and outcomes. The statistical analyses performed in that study showed a distinct profile for each of the Iklé categories: innovation, re-distribution, extension, normalization, and side effects. In addition, a sixth category was identified as being different than the others. This category was referred to as multilateral regime negotiations, a form that became prevalent twenty years after Iklés book appeared. These results were further supported by discriminant analysis classifications. When only information about the 16 features were known, 78 % (or 21 of 27 cases) were placed in the correct a priori category. These are impressive results. They provide empirical validation for this well-known typology of negotiation. In this chapter, an attempt is made to extend these analyses in several directions with the help of sophisticated methodological approaches not previously used to interpret data on negotiation.


Decision Tree Association Rule Rule Learning Delegation Status International Negotiation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., and Verkamo, A.I., 1995, Fast discovery of association rules, in Advances in Knowledge Discovery and Data Mining, Fayyad, U. M., Piatetsky-Shapiro G., Smyth, P., and Uthurusamy, R., eds., AAAI Press, Cambridge, MA.Google Scholar
  2. Beriker, N. and Druckman, D., 1996, Simulating the Lausanne Peace Negotiations, 1922–1923: Power asymmetries in bargaining, Simulation & Gaming 27: 162–183.Google Scholar
  3. Bartos, O. J., 1995, Modeling distributive and integrative negotiations, The Annals of the American Academy of Political and Social Science 542: 48–60.Google Scholar
  4. Breiman, L., 2001, Statistical modeling: The two cultures, Statistical Science 16: 199–231. (With comments by D. R. Cox, B. Efron, B. Hoadley, and E. Parzen, a rejoinder by the author.)CrossRefGoogle Scholar
  5. Breiman, L., Friedman, J., Olshen, R. and Stone, C., 1984, Classification and Regression Trees, Wadsworth & Brooks, Pacific Grove, CA.Google Scholar
  6. Cohen, W. W., 1995, Fast effective rule induction, in Proceedings of the 12th International Conference on Machine Learning (ML-95), Lake Tahoe, CA, Prieditis, A., and Russell, S., eds., Morgan Kaufmann, San Francisco.Google Scholar
  7. Druckman, D., 1997, Dimensions of international negotiations: Structures, processes, and outcomes, Group Decision and Negotiation 6: 395–420.CrossRefGoogle Scholar
  8. Druckman, D., 1994, Determinants of compromising behavior in negotiation: A meta-analysis, Journal of Conflict Resolution 38: 507–556.Google Scholar
  9. Druckman, D., 1993, The situational levers of negotiating flexibility, Journal of Conflict Resolution 37: 236–276.Google Scholar
  10. Druckman, D., and Bonoma, T. V., 1976, Determinants of bargaining behavior in a bilateral monopoly situation II: Opponent concession rate and similarity, Behavioral Science 21: 252–262.Google Scholar
  11. Druckman, D., Harris, R., and Ramberg, B., 2002, Computer-aided international negotiation: A tool for research and practice, Group Decision and Negotiation 11: 231–256.CrossRefGoogle Scholar
  12. Druckman, D., Rozelle, R., and Baxter, J., 1982, Nonverbal Communication: Survey, Theory, and Research, Sage, Beverly Hills, CA.Google Scholar
  13. Druckman, D., Martin, J., Allen Nan, S. and Yagcioglu, D., 1999, Dimensions of international negotiation: A test of Iklé's typology, Group Decision and Negotiation 8: 89–108.CrossRefGoogle Scholar
  14. Friedman, J. H., and Fisher, N. I., 1999, Bump hunting in high-dimensional data, Statistics and Computing 9: 1–20.CrossRefGoogle Scholar
  15. Fürnkranz, J., 1999, Separate-and-conquer rule learning, Artificial Intelligence Review 13: 3-54Google Scholar
  16. Fürnkranz. J., 1997, Pruning algorithms for rule learning, Machine Learning 27: 139–171.Google Scholar
  17. Halpern, J., 1994, The effect of friendship on personal business transactions, Journal of Conflict Resolution 38: 647–664.Google Scholar
  18. Headland, T. N., Pike, K. L., and Harris, M., eds., 1990, Emics and Etics: The Insider/Outsider Debate, Sage , Newbury Park, CA.Google Scholar
  19. Hipp, J., Güntzer, U., and Nakhaeizadeh, G., 2000, Algorithms for association rule mining-a general survey and comparison, SIGKDD Explorations 2: 58–64.Google Scholar
  20. Iklé, F. C., 1964, How Nations Negotiate, Harper, New York.Google Scholar
  21. Irmer, C. G., 2003, The Promise of Process: Evidence on Ending Violent International Conflict, unpublished doctoral dissertation, George Mason University, Fairfax, Virginia.Google Scholar
  22. Kohavi, R., 1995, A study of cross-validation and bootstrap for accuracy estimation and model selection, in Proceedings of the 14th International Joint Conference on Artificial Intelligence (IJCAI-95), Mellish, C. S., ed., Morgan Kaufmann, Montreal.Google Scholar
  23. Quinlan, J. R., 1993, C4.5: Programs for Machine Learning, Morgan Kaufmann, San Mateo, CA.Google Scholar
  24. Quinlan, J. R., 1986, Induction of decision trees, Machine Learning 1:81–106.Google Scholar
  25. Stone, M., 1977, Asymptotics for and against cross-validation, Biometrika 64: 29–35.Google Scholar
  26. Stone, M., 1974, Cross-validatory choice and assessment of statistical predictions, Journal of the Royal Statistical Society B 36:111–147.Google Scholar
  27. Trappl, R., Fürnkranz, J., Bercovitch, J., and Petrak, J., 1997, Machine learning and case-based reasoning: Their potential role in preventing the outbreak of wars or in ending them, in Learning, Networking, and Statistics, Riccia, G. D., Kruse, R., and Lenz, H., eds., Springer, New York.Google Scholar
  28. Webb, G. I., 2000, Efficient search for association rules, in Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2000), Boston, MA, ACM Press, New York.Google Scholar
  29. Witten. I. H., and Frank, E., 2000, Data Mining-Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann, San Mateo, CA.Google Scholar

Copyright information

© Springer 2006

Authors and Affiliations

  • Daniel Druckman
    • 1
  • Richard Harris
    • 2
  • Johannes Fürnkranz
    • 3
  1. 1.George Mason UniversityFairfaxU.S.A.
  2. 2.RA SoftwareChapel HillU.S.A.
  3. 3.Technical University DarmstadtGermany

Personalised recommendations