Abstract
Our decisions reflect uncertainty in various ways. We take account of the uncertainty embodied in the roll of the die; we less often take account of the uncertainty of our belief that the die is fair. We need to take account of both uncertain knowledge and our knowledge of uncertainty.. “Evidence” itself has been regarded as uncertain. We argue that point-valued probabilities are a poor representation of uncertainty; that we need not be concerned with uncertain evidence; that interval-valued probabilities that result from knowledge of convex sets of distribution functions in reference classes (properly) include Shafer's mass functions as a special case; that these probabilities yield a plausible non-monotonic form of inference (uncertain inference, inductive inference, statistical inference); and finally that this framework provides a very nearly classical decision theory— so far as it goes. It is unclear how global the principles (such as minimax) that go beyond the principle of maximizing expected utility are.
Preview
Unable to display preview. Download preview PDF.
References
Carnap, Rudolf (1950): The Logical Foundations of Probability, University of Chicago Press, Chicago.
Cheeseman, Peter (1985): “In Defense of Probability, “IJCAI 1985, II, pp. 1002–1009
Duda, Hart, and Nilsson, (1976): “Subjective Bayesian Methods for Rule-Based Inference Systems, “Prodeedings of the National Computer Conference 45, pp 1075–1082.
Finetti, Bruno (1937): “La Prevision: Ses Lois Logiques, Ses Sources Subjectives, “Annales de L'Institute Henry Poincaré 7, 1937, pp. 1–68.
Garvey, Lowrance, and Fishler, (1981): “An Inference Technique for Integrating Knowledge from Disparate Sources,” Proceedings IJCAI 7, pp. 319–325.
Jaynes, E. T. (1982): “On the Rationale of Maximum Entrophy Methods,” Proceedings of the IEEE 70, pp. 939–952.
Jeffrey, Richard (1965): The Logic of Decision, McGraw-Hill, New York.
Jeffreys, Harold (1939): Theory of Probability, Oxford University Press, Oxford.
Kyburg, Henry E., Jr. (1968): “Bets and Beliefs” American Philosphical Quarterly 5, pp. 54–63.
— (1961): Probability and the Logic of Rational Belief, Wesleyan University Press, Middletown.
— (1974): The Logical Foundations of Statistical Inference, Reidel, Dordrecht.
— (1983): “The Reference class,” Philosophy of Science 50, pp. 374–397.
— (1984): Theory and Measurement, Cambridge University Press, Cambridge.
— (Forthcoming.2) “Full Belief.”
— (Forthcoming.3): “The Basic Bayesian Blunder.”
Levi, Isaac (1968): “Probability Kinematics, “British Journal for the Philosophy of Science 18, pp. 197–209.
— (1980): The Enterprise of Knowledge, MIT Press, Cambridge.
Loui, Ronald P. (Forthcoming.1): “Interval Based Decisions for Reasoning Systems,” Proceedings of the UCLA Workshop on Uncertainty and Probability in Artificial Intelligence, John Lemmon (ed.).
Lowrance, John (1982): “Dependency Graph Models of Evidential Support,” University of Massachusetts, Amherst.
McCarthy, John, and Hayes, Patrick (1969): “Some Philosophical Problems fron the Standpoint of Artificial Intelligence,” Machine Intelligence 4, pp. 463–502.
Pearl, Judea (1985): “Fusion, Propagation, and Structuring in Bayesian Networks,” TR CSD-850022, UCLA, Los Angeles.
Quinlan, (1982): “Inferno: A Cautious Approach to Uncertain INference, A Rand Note,” California.
Ramsey, F.P. (1931): The Foundations of Mathematics and Other Essays, Humanities Press, New York.
Savage, L.J. (1954): The Foundations of Statistics, John Wiley, New York.
Seidenfeld, Teddy (1979): “Statistical Evidence and Belief Functions” PSA 1978, Asquith and Hacking (eds.).
— (Forthcoming): “Entropy and Uncertainty.”
Shafer, Glenn (1976): A Mathematical Theory of Evidence, Princeton University Press, Princeton.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1987 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kyburg, H.E. (1987). Representing knowledge and evidence for decision. In: Bouchon, B., Yager, R.R. (eds) Uncertainty in Knowledge-Based Systems. IPMU 1986. Lecture Notes in Computer Science, vol 286. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-18579-8_2
Download citation
DOI: https://doi.org/10.1007/3-540-18579-8_2
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-18579-6
Online ISBN: 978-3-540-48020-4
eBook Packages: Springer Book Archive