, Volume 156, Issue 3, pp 513–535 | Cite as

The kinematics of belief and desire

  • Richard Bradley
Original Paper


Richard Jeffrey regarded the version of Bayesian decision theory he floated in ‘The Logic of Decision’ and the idea of a probability kinematics—a generalisation of Bayesian conditioning to contexts in which the evidence is ‘uncertain’—as his two most important contributions to philosophy. This paper aims to connect them by developing kinematical models for the study of preference change and practical deliberation. Preference change is treated in a manner analogous to Jeffrey’s handling of belief change: not as mechanical outputs of combinations of intrinsic desires plus information, but as a matter of judgement and of making up one’s mind. In the first section Jeffrey’s probability kinematics is motivated and extended to the treatment of changes in conditional belief. In the second, analogous kinematical models are developed for preference change and in particular belief-induced change that depends on an invariance condition for conditional preference. The two are the brought together in the last section in a tentative model of pratical deliberation.


Preference revision Belief revision Kinematics Bayesian conditioning 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Applebaum D. (1996). Probability and information. Cambridge, Cambridge University PressGoogle Scholar
  2. Armendt B. (1980). Is there a Dutch book theorem for probability kinematics?. Philosophy of Science 47, 563–588CrossRefGoogle Scholar
  3. Bolker E. (1966). Functions resembling quotients of measures. Transactions of the American Mathematical Society 124, 292–312CrossRefGoogle Scholar
  4. Bradley R. (1999). Conditional desirability. Theory and Decision 47, 23–55CrossRefGoogle Scholar
  5. Bradley R. (2005). Radical probabilism and Bayesian conditioning. Philosophy of Science 72, 342–364CrossRefGoogle Scholar
  6. Diaconis P., Zabell S. (1982). Updating subjective probability. Journal of the American Statistical Association 77, 822–308CrossRefGoogle Scholar
  7. Earman J. (1992). Bayes or Bust: A critical examination of Bayesian confirmation theory. Cambridge, Mass, MIT PressGoogle Scholar
  8. Howson C. (1996). Bayesian rules of updating. Erkenntnis 45, 195–208Google Scholar
  9. Jeffrey R.C. (1983). The logic of decision (2nd ed). Chicago, University of Chicago PressGoogle Scholar
  10. Jeffrey R. (1992). Probability and the art of judgement. Cambridge, Cambridge University PressGoogle Scholar
  11. Joyce J. (1999). The foundations of causal decision theory. Cambridge, Cambridge University PressGoogle Scholar
  12. Maher P. (1993). Betting on theories. Cambridge, Cambridge University PressGoogle Scholar
  13. Ramsey F.P. (1926). Truth and probability. In: Mellor D.H. (Ed), Philosophical papers. Cambridge, Cambridge University Press 1990.Google Scholar
  14. Skyrms B. (1987). Dynamic coherence and probability kinematics. Philosophy of Science 54, 1–20CrossRefGoogle Scholar
  15. Skyrms B. (1990). The dynamics of rational deliberation. Cambridge, Mass, Harvard University PressGoogle Scholar
  16. Sobel J.H. (1989). Partition-theorems for Causal decision theories. Philosophy of Science 56, 70–93CrossRefGoogle Scholar
  17. Teller P. (1973). Conditionalization and observation. Synthese 26, 218–258Google Scholar
  18. van Fraassen B. (1980). Rational belief and probability kinematics. Philosophy of Science 47, 165–187CrossRefGoogle Scholar
  19. van Fraassen B. (1989). Laws and symmetry. Oxford, ClarendonGoogle Scholar

Copyright information

© Springer Science+Business Media, Inc. 2007

Authors and Affiliations

  1. 1.Department of Philosophy, Logic and Scientific MethodLondon School of EconomicsLondonUK

Personalised recommendations