Betting on the outcomes of measurements: a Bayesian theory of quantum probability

https://doi.org/10.1016/S1355-2198(03)00035-2Get rights and content

Abstract

We develop a systematic approach to quantum probability as a theory of rational betting in quantum gambles. In these games of chance, the agent is betting in advance on the outcomes of several (finitely many) incompatible measurements. One of the measurements is subsequently chosen and performed and the money placed on the other measurements is returned to the agent. We show how the rules of rational betting imply all the interesting features of quantum probability, even in such finite gambles. These include the uncertainty principle and the violation of Bell's inequality among others. Quantum gambles are closely related to quantum logic and provide a new semantics for it. We conclude with a philosophical discussion on the interpretation of quantum mechanics.

Section snippets

The gamble

The Bayesian approach takes probability to be a measure of ignorance, reflecting our state of knowledge and not merely the state of the world. It follows Ramsey's contention that “we have the authority both of ordinary language and of many great thinkers for discussing under the heading of probability … the logic of partial belief” (Ramsey, 1926, p. 55). Here we shall assume, furthermore, that probabilistic beliefs are expressed in rational betting behavior: “The old-established way of

Uncertainty relations

Consider the following quantum gamble M consisting of seven incompatible measurements (Boolean algebras), each generated by its three possible outcomes: 〈E1,E2,F2〉, 〈E1,E3,F3〉, 〈E2,E4,E6〉, 〈E3,E5,E7〉, 〈E6,E7,F〉, 〈E4,E8,F4〉, 〈E5,E8,F5. Note that some of the outcomes are shared by two measurements; these are denoted by the letter E. The other outcomes each belong to a single algebra and are denoted by F. As before, when two algebras share an event, they also share its complement so that, for

Semantics for quantum logic and structural realism

The line we have taken has some affinity with Bohr's approach—or more precisely, with the view often attributed to Bohr4—in that we treat the outcomes of future measurements as mere possibilities and do not associate them with properties that exist prior to the act of

Acknowledgements

I would like to thank Harvey Brown, Jeremy Butterfield, William Demopoulos, Wayne Myrvold, and Simon Saunders for helpful comments and suggestions. This research is supported by an Israel Science Foundation grant number 879/02.

References (36)

  • W. Demopoulos

    The possibility structure of physical systems

  • Demopoulos, W. (2003). Elementary propositions and essentially incomplete knowledge: A framework of the interpretation...
  • A. Einstein et al.

    Can quantum-mechanical description of physical reality be considered complete?

    Physical Review

    (1935)
  • D. Finkelstein

    The logic of quantum physics

    Transactions of the New York Academy of Sciences

    (1962)
  • Fuchs, C.A. (2001). Quantum mechanics as quantum information (and only a little more)...
  • C.A. Fuchs et al.

    Quantum theory needs no interpretation

    Physics Today

    (2000)
  • A.M. Gleason

    Measures on the closed subspaces of a Hilbert space

    Journal of Mathematics and Mechanics

    (1957)
  • D.M. Greenberger et al.

    Going beyond Bell's theorem

  • Cited by (74)

    • Application of quantum-like Bayesian network and belief entropy for interference effect in multi-attribute decision making problem

      2021, Computers and Industrial Engineering
      Citation Excerpt :

      Moreover, Bayesian network is an important approach to analyze the MADM problems (Delcroix et al., 2013). The Bayesian network, a powerful reasoning model, was firstly proposed by Pearl (1988) who successfully applied Bayesian network in literature, major scientific information, genetics, medical decision-making, economics and other fields for many years (Pitowsky, 2003). Bayesian decision-making is to estimate some unknown states with subjective probability under incomplete information, then modify the occurrence probability with Bayesian formula, and finally make the optimal decision with expected value and modified probability.

    • Probabilism for stochastic theories

      2019, Studies in History and Philosophy of Science Part B - Studies in History and Philosophy of Modern Physics
    View all citing articles on Scopus
    View full text