Uncertainty, belief, and probability

We introduce a new probabilistic approach to dealing with uncertainty, based on the observation that probability theory does not require that every event be assigned a probability. For a nonmeasurable event (one to which we do not assign a probability), we can talk about only the inner measure and outer measure of the event. Thus, the measure of belief in an event can be represented by an interval (defined by the inner and outer measure), rather than by a single number. Further, this approach allows us to assign a belief (inner measure) to an event E without committing to a belief about its negation E (since the inner measure of an event plus the inner measure of its negation is not necessarily one). Interestingly enough, inner measures induced by probability measures turn out to correspond in a precise sense to Dempster-Shafer belief functions. Hence, in addition to providing promising new conceptual tools for dealing with uncertainty, our approach shows that a key part of the important Dempster-Shafer theory of evidence is firmly rooted in classical probability theory.

[1]  B. O. Koopman The bases of probability , 1940 .

[2]  B. O. Koopman The Axioms and Algebra of Intuitive Probability , 1940 .

[3]  J. Neumann,et al.  Theory of Games and Economic Behavior. , 1945 .

[4]  Rudolf Carnap,et al.  Logical foundations of probability , 1951 .

[5]  William Feller,et al.  An Introduction to Probability Theory and Its Applications , 1951 .

[6]  A. Tarski A Decision Method for Elementary Algebra and Geometry , 2023 .

[7]  G. Choquet Theory of capacities , 1954 .

[8]  Howard Raiffa,et al.  Games And Decisions , 1958 .

[9]  Cedric A. B. Smith,et al.  Consistency in Statistical Inference and Decision , 1961 .

[10]  R. Jeffrey The Logic of Decision , 1984 .

[11]  Joseph R. Shoenfield,et al.  Mathematical logic , 1967 .

[12]  Henry E. Kyburg,et al.  Probability and the logic of rational belief , 1970 .

[13]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[14]  Hung T. Nguyen,et al.  On Random Sets and Belief Functions , 1978, Classic Works of the Dempster-Shafer Theory of Belief Functions.

[15]  Elliott Mendelson,et al.  Introduction to Mathematical Logic , 1979 .

[16]  T. Fine,et al.  Towards a Frequentist Theory of Upper and Lower Probability , 1982 .

[17]  Sandy L. Zabell,et al.  Some Alternatives to Bayes' Rule. , 1983 .

[18]  Lotfi A. Zadeh,et al.  Review of A Mathematical Theory of Evidence , 1984 .

[19]  Paul R. Cohen,et al.  Heuristic reasoning about uncertainty: an artificial intelligence approach , 1984 .

[20]  Joseph Y. Halpern,et al.  A Guide to the Modal Logics of Knowledge and Belief: Preliminary Draft , 1985, IJCAI.

[21]  Peter C. Cheeseman,et al.  In Defense of Probability , 1985, IJCAI.

[22]  John F. Lemmer,et al.  Confidence Factors, Empiricism and the Dempster-Shafer Theory of Evidence , 1985, UAI.

[23]  Glenn Shafer,et al.  The combination of evidence , 1986, Int. J. Intell. Syst..

[24]  John H. Reif,et al.  The complexity of elementary algebra and geometry , 1984, STOC '84.

[25]  Nils J. Nilsson,et al.  Probabilistic Logic * , 2022 .

[26]  Leslie Pack Kaelbling,et al.  The Synthesis of Digital Machines With Provable Epistemic Properties , 1986, TARK.

[27]  Alessandro Saffiotti,et al.  An AI view of the treatment of uncertainty , 1987, The Knowledge Engineering Review.

[28]  Henry E. Kyburg,et al.  Bayesian and Non-Bayesian Evidential Updating , 1987, Artificial Intelligence.

[29]  Daniel Hunter,et al.  Dempster-Shafer vs. Probabilistic Logic , 1987, UAI 1987.

[30]  D. G. Rees,et al.  Foundations of Statistics , 1989 .

[31]  Joseph Y. Halpern,et al.  A Logic to Reason about Likelihood , 1987, Artif. Intell..

[32]  Ronald Fagin,et al.  A logic for reasoning about probabilities , 1988, [1988] Proceedings. Third Annual Information Symposium on Logic in Computer Science.

[33]  J. Kyburg Higher order probability and intervals , 1988 .

[34]  Christos H. Papadimitriou,et al.  Probabilistic satisfiability , 1988, J. Complex..

[35]  John F. Canny,et al.  Some algebraic and geometric computations in PSPACE , 1988, STOC '88.

[36]  Paul K. Black Is shafer general bayes? , 1988, Int. J. Approx. Reason..

[37]  Henry E. Kyburg,et al.  Higher order probabilities and intervals , 1988, Int. J. Approx. Reason..

[38]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[39]  Ronald Fagin,et al.  A new approach to updating beliefs , 1990, UAI.

[40]  Ronald Fagin,et al.  Two Views of Belief: Belief as Generalized Probability and Belief as Evidence , 1992, Artif. Intell..