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An Inductive Inference Model to Elicit Noncompensatory Judgment Strategies

  • Jing Yin
  • Ling Rothrock
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6761)

Abstract

The proposed research developed a noncompensatory policy capturing technique to infer judgment rules (represented in disjunctive normal form) from available human data. The rule induction algorithm employs multiobjective Genetic Algorithm (GA) as its central search mechanism to enhance the induction and classification process. The quality of the induced rule set is measured by two criteria, fidelity (the degree to which the rule set reflects the judgment data they have been extracted from) and compactness (the simplicity of the rule set). An experimental study is conducted to demonstrate the effectiveness of the algorithm on a number of benchmark datasets.

Keywords

Disjunctive Normal Form Judgment Data Rule Induction Algorithm Judgment Behavior Noncompensatory Strategy 
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.

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References

  1. 1.
    Neumann, J.v., Morgenstern, O.: Theory of Games and Economic Behavior. Princeton University Press, Princeton (1947)zbMATHGoogle Scholar
  2. 2.
    Mosteller, F., Nogee, P.: An experimental measurement of utility. Journal of Political Economy 59 (1951)Google Scholar
  3. 3.
    Simon, H.A.: A behavior model of rational choice. Quarterly Journal of Economics 69, 99–118 (1955)CrossRefGoogle Scholar
  4. 4.
    Luce, R.D.: Semi-order and a theory of utility discrimination. Econometrica 24, 178–191 (1956)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Simon, H.A.: Models of Man. Wiley, New York (1957)Google Scholar
  6. 6.
    Payne, J.W.: Task complexity and contingent processing in decision making: An information search and protocol analysis. Organizational Behavior and Human Performance 16, 366–387 (1976)CrossRefGoogle Scholar
  7. 7.
    Beach, L.R.: Measures of linear and nonlinear models of decision behavior: Process tracing versus policy capturing. Organizational Behavior and Human Performance 31, 331–351 (1993)Google Scholar
  8. 8.
    Payne, J.W., Bettman, J.R., Johnson, E.J.: The Adaptive Decision Maker. Cambridge University Press, New York (1993)CrossRefGoogle Scholar
  9. 9.
    Payne, J.W., Bettman, J.R., Luce, M.F.: Behavioral decision research: An overview. In: Birnbaum, M.H. (ed.) Measurement, Judgment, and Decision Making, pp. 303–359. Academic Press, New York (1998)CrossRefGoogle Scholar
  10. 10.
    Page, D., Ray, S.: Skewing: An efficient alternative to look ahead for decision tree induction. In: Proceedings of International Joint Conference on Artificial Intelligence, pp. 601–607 (2003)Google Scholar
  11. 11.
    Yin, J., Rothrock, L.: A rule-based lens model. International Journal of Industrial Ergonomics 36, 499–509 (2006)CrossRefGoogle Scholar
  12. 12.
    Elrod, T., Johnson, R.D., White, J.: A new integrated model of noncompensatory and compensatory decision strategies. Organizational Behavior and Human Decision Processes 95, 1–19 (2004)CrossRefGoogle Scholar
  13. 13.
    Rothrock, L., Kirlik, A.: Inferring rule-based strategies in dynamic judgment tasks: Toward a noncompensatory formulation of the Lens Model. IEEE Transactions on Systems, Man, and Cybernetics – Part A: Systems and Humans 33, 58–72 (2003)CrossRefGoogle Scholar
  14. 14.
    Rothrock, L., Ventura, J., Park, S.: An optimization methodology to investigate operator impact on quality of service. In: Interservice/Industry Training, Simulation, and Education Conference (I/ITSEC), Orlando (2003) Google Scholar
  15. 15.
    DeJong, K.A., Spears, W.M., Gordon, D.F.: Using genetic algorithms for concept learning. Machine Learning 13, 161–188 (1993)Google Scholar
  16. 16.
    Holland, J.H., Holyoak, K.F., Nisbett, R.E., Thagard, P.R.: Induction. The MIT Press, Cambridge (1986)Google Scholar
  17. 17.
    Markowsha-Kaczmar, U., Mularczyk, K.: GA-based Pareto optimization for rule extraction from neural networks. Studies in Computational Intelligence 16, 313–338 (2006)Google Scholar
  18. 18.
    Blake, C., Keogh, E., Merz, C.J.: UCI Repository of Machine Learning Databases, Department of Information and Computer Science, University of California, Irvine, CA (1998), http://www.ics.uci.edu/~mlean/MLRepository.htm
  19. 19.
    Schaffer, C.: A conservation law for generalization performance. In: Machine Learning: Proceedings of the Eleventh International Conference, pp. 259–265. Morgan Kaufmann, San Francisco (1994)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jing Yin
    • 1
  • Ling Rothrock
    • 2
  1. 1.MicroStrategy Inc.ViennaUSA
  2. 2.The Pennsylvania State UniversityUSA

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