Behavioural decision theory and it's implication for knowledge engineering* | The Knowledge Engineering Review | Cambridge Core (original) (raw)

Abstract

This paper explores the implications of research results in behavioural decision theory on knowledge engineering. Behavioural decision theory, with its performance (versus process) orientation, can tell us a great deal about the validity of human expert knowledge, and when it should be modelled. A brief history of behavioural decision theory is provided. Implications for knowledge elicitation and representation are discussed. An approach to knowledge engineering is proposed that takes into account these implications.

References

Adelman, L, Donnell, M, Phelps, R and Patterson, J, 1982, “An iterative Bayesian decision aid: Toward improving the user-aid and user-organization interface” IEEE Trans. Systems, Man, and Cybernetics SMC–12 733–742.Google Scholar

Christenses-Szalanski, J and Beach, L, 1984, “The citation bias: Fad and fashion in the judgement and decision literature” American Psychologist 39 75–78.CrossRefGoogle Scholar

Dawes, R, 1979, “The robust beauty of improper linear models in decision making” American Psychologist 34(7) 571–582.CrossRefGoogle Scholar

Dawes, R and Corrigan, B, 1974, “Linear models in decision making” Psychological Bulletin 81 95–106.CrossRefGoogle Scholar

Edwards, W, 1968, “Conservatism in human information processing” in: Kleinmuntz, B, ed., Formal Representation of Human Judgment Wiley.Google Scholar

Edwards, W, Phillips, L, Hays, W and Goodman, B, 1968, “Probabilistic information processing systems: Design and evaluation” IEEE Transactions on Systems Science and Cybernetics SCC–4 248–265.CrossRefGoogle Scholar

Edwards, W, Schum, D and Winkler, R, in press, “Murder and (of?) the likelihood principle: A trialogue” Journal of Behavioral Decision Making, to appear.Google Scholar

Einhorn, H and Hogarth, R, 1978, “Confidence in judgment: Persistence of the illusion of validity” Psychological Review 85 395–416.CrossRefGoogle Scholar

Einhorn, H and Hogarth, R, 1986, “Judging probable cause” Psychological Bulletin 99 3–19.CrossRefGoogle Scholar

Ericson, K and Simon, H, 1984, Protocol Analysis: Verbal Reports as Data MIT Press.Google Scholar

Fishchhoff, B, 1975, “Hindsight ≠ foresight: The effect of outcome knowledge on judgment under uncertainty” Journal of Experimental Psychology: Human Perception and Performance 4 330–344.Google Scholar

Fischhoff, B, 1989, “Eliciting knowledge for analytical representation” IEEE Transactions on Systems, Man and Cybernetics 19(3) 448–461.Google Scholar

Fishburn, P, 1988, “Normative theories of decision making under risk and under uncertainty” in: Bell, D, Raiffa, H and Tversky, A, eds., Decision Making: Descriptive, Normative, and Prescriptive Interactions Cambridge University Press.Google Scholar

Ford, J, Schmitt, N, Schechtman, S, Hulls, B and Doherty, M, 1989, “Process tracing methods: Contributions, problems and neglected research questions” Organizational Behavior and Human Decision Making 43 75–117.CrossRefGoogle Scholar

Hammond, K, 1976, Facilitation of Interpersonal Learning and Conflict Reduction by One-line Communication University of Colorado.Google Scholar

Hammond, K, 1987, “Toward a unified approach to the study of expert judgment” in: Mumpower, J, Phillips, L, Renn, O and Uppuluri, V, Expert Judgment and Expert Systems Springer-Verlag.Google Scholar

Hogarth, RM, 1975, “Cognitive processes and the assessment of subjective probability distributions” Journal of the American Statistical Association 70 272–289.Google Scholar

Hogarth, R, 1986, “Generalization in decision research: The role of formal models” IEEE Transactions on Systems, Man, and Cybernetics SMC–16(3) 439–449.Google Scholar

Horvitz, E, Brese, J and Henrion, M, 1988, “Decision theory in expert systems and artificial intelligence” International Journal of Approximation Reasoning 2 247–302.CrossRefGoogle Scholar

Johnson, E, 1988, “Expertise and decision under uncertainty: Performance and process” in: Chi, M, Glaser, R and Farr, M, eds., The Nature of Expertise Lawrence Erlbaum Associates.Google Scholar

Kahneman, D and Tversky, A, 1973, “On the psychology of prediction” Psychological Review 80 237–251.CrossRefGoogle Scholar

Kahneman, D and Tversky, A, 1979, “Prospect theory: An analysis of decision under risk” Econometric 47 263–289.CrossRefGoogle Scholar

Kelly, C and Barkley, S, 1973, “A generalized Bayesian model for hierarchical inference” Organizational Behavior and Human Performance 10 388–403.CrossRefGoogle Scholar

Klein, G, Calderwood, R and MacGregor, D, 1989, “Critical decision method for eliciting knowledge” IEEE Transactions on Systems, Man and Cybernetics 19(3) 462–472.Google Scholar

Lehner, P and Adelman, L, 1988, “Senior battle staff decision aiding: A case study” in: Andriole, S and Hopple, G, eds., Defense Applications of Artificial Intelligence Lexington Books.Google Scholar

Lehner, PE, Probus, MR and Donnell, M, 1985, “Building decision aids: Exploiting the synergy between decision analysis and artificial intelligence” IEEE Transactions on Systems, Man and Cybernetics SMC–14(4), 409–414.Google Scholar

Levi, K, 1989, “Expert systems should be more expert than human experts: Evaluation procedures from human judgment and decision making” IEEE Transactions on Systems, Man and Cybernetics 19(3) 647–657.Google Scholar

Mullin, T, 1989, “Expert's estimation of uncertain quantities and its implications for knowledge acquisition” IEEE Transactions on Systems, Man and Cybernetics 19(3) 616–625.Google Scholar

Newell, A and Simon, H, 1972, Human Problem Solving Prentice Hall.Google Scholar

Nisbett, R and Ross, L, 1980, Human Inference: Strategies and Shortcomings of Social Judgment Prentice-Hall.Google Scholar

Nisbett, R and Wilson, T, 1977, “Telling more than we can know: verbal reports on mental processes” Psychological Review 84 231–259.CrossRefGoogle Scholar

Schank, R and Abelson, R, 1977, Scripts, Plans, Goals, and Understanding Erlbaum Associates.Google Scholar

Slovic, P, Fischhoff, B and Lischtenstein, S, 1977, “Behavioral decision theory” Annual Review of Psychology 28 1–39.CrossRefGoogle Scholar

Slovic, P, Fischhoff, B and Lichtenstein, S, 1988, “Response mode, framing, and information-processing effects in risk assessment” in: Bell, D, Raiffa, H and Tversky, A, eds., Decision Making: Descriptive, Normative, and Prescriptive Interactions Cambridge University Press.Google Scholar

Tolcott, MA, Marvin, FF and Lehner, PE, 1989, “Expert decision making in evolving situations” IEEE Transactions on Systems, Man and Cybernetics 19(3) 606–615.Google Scholar

Tversky, A and Kahneman, D, 1971, “The belief in the ‘Law of Small Numbers’” Psychological Bulletin 76 105–110.CrossRefGoogle Scholar

Tversky, A and Kahneman, D, 1973, “Availability: A heuristic for judging frequency and probability” Cognitive Psychology 5 207–232.CrossRefGoogle Scholar

Tversky, A and Kahneman, D, 1980, “Causal schemas in judgement under uncertainty” in: Fishbein, M, ed., Progress in Social Psychology Erlbaum.Google Scholar

von Neumann, J and Morgenstern, O, 1944, Theory of Games and Economic behavior Princeton University Press.Google Scholar

von Winterfeldt, D, 1988, “Expert systems and behavioral decision research” Decision Support Systems 4 461–471.CrossRefGoogle Scholar

Wason, P, 1960, “On the failure to eliminate hypotheses in a conceptual task” Quarterly Journal of Experimental Psychology 12 129–140.CrossRefGoogle Scholar