LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Elliott, E (2017)
Publisher: Springer Verlag
Languages: English
Types: Article
Subjects:
The standard representation theorem for expected utility theory tells us that if a subject’s preferences conform to certain axioms, then she can be represented as maximising her expected utility given a particular set of credences and utilities—and, moreover, that having those credences and utilities is the only way that she could be maximising her expected utility (given her preferences). However, the kinds of agents these theorems seem apt to tell us anything about are highly idealised, being (amongst other things) always probabilistically coherent with infinitely precise degrees of belief and full knowledge of all a priori truths. Ordinary subjects do not look very rational when compared to the kinds of agents usually talked about in decision theory. In this paper, I will develop an expected utility representation theorem aimed at the representation of those who are neither probabilistically coherent, logically omniscient, nor expected utility maximisers across the board—that is, agents who are frequently irrational. The agents in question may be deductively fallible, have incoherent credences, limited representational capacities, and fail to maximise expected utility for all but a limited class of gambles.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • 1. Alon S, Schmeidler D (2014) Purely subjective maxmin expected utility. Journal of Economic Theory 152:382-412
    • 2. Anscombe FJ, Aumann RJ (1963) A definition of subjective probability. The Annals of Mathematical Statistics 34(2):199-205
    • 3. Aumann RJ (1962) Utility theory without the completeness axiom. Econometrica 30(3):445-462
    • 4. Bradley R (2001) Ramsey and the Measurement of Belief, Kluwer Academic Publishers, pp 261-275
    • 5. Buchak L (2023) Risk and Rationality. Oxford University Press, Oxford
    • 6. Chalmers D (2011) Frege's puzzle and the objects of credence. Mind 120(479):587-635
    • 7. Chalmers D (2011) The Nature of Epistemic Space, Oxford University Press, Oxford, pp 60-107
    • 8. Cozic M, Hill B (2015) Representation theorems and the semantics of decisiontheoretic concepts. Journal of Economic Methodology 22:292-311
    • 9. Davidson D (1980) Toward a unified theory of meaning and action. Grazer Philosophische Studien 11:1-12
    • 10. Davidson D (1990) The structure and content of truth. The Journal of Philosophy 87(6):279-328
    • 11. Dogramaci S (forthcoming) Knowing our degrees of belief. Episteme
    • 12. Easwaran K (2014) Decision theory without representation theorems. Philosopher's Imprint 14(27):1-30
    • 13. Eells E (1982) Rational Decision and Causality. Cambridge University Press, Cambridge
    • 14. Elliott E (forthcoming) Probabilism, representation theorems, and whether deliberation crowds out prediction. Erkenntnis
    • 15. Elliott E (forthcoming) Ramsey without ethical neutrality: A new representation theorem. Mind
    • 16. Fishburn PC (1981) Subjective expected utility: A review of normative theories. Theory and Decision 13:139-199
    • 17. Gilboa A I, Postlewaite A, Schmeidler D (2012) Rationality of belief or: why savage's axioms are neither necessary nor sufficient for rationality. Synthese 187:11-31
    • 18. Harsanyi J (1977) On the rationale of the Bayesian approach: comments of Professor Watkins's paper, D. Reidel, Dordrecht, pp 381-392
    • 19. Jeffrey R (1986) Bayesianism with a human face. Minnesota Studies in the Philosophy of Science 10:133-156
    • 20. Jeffrey RC (1968) Probable knowledge. Studies in Logic and the Foundations of Mathematics 51:166-190
    • 21. Jeffrey RC (1990) The logic of decision. University of Chicago Press, Chicago
    • 22. Joyce J (2015) The value of truth: A reply to howson. Analysis 75:413-424
    • 23. Kahneman D, Tversky A (1979) Prospect theory: An analysis of decision under risk. Econometrica 47:263-291
    • 24. Krantz DH, Luce RD, Suppes P, Tversky A (1971) Foundations of measurement, Vol. I: Additive and polynomial representations. Academic Press
    • 25. Levin IP, Schneider SL, Gaeth GJ (1998) All frames are not equal: A typology and critical analysis of framing effects. Organizational Behavior and Human Decision Processes 76:149-188
    • 26. Lewis D (1974) Radical interpretation. Synthese 27(3):331-344
    • 27. Luce RD (1992) Where does subjective expected utility fail descriptively? Journal of Risk and Uncertainty 5:5-27
    • 28. Luce RD, Krantz DH (1971) Conditional expected utility. Econometrica 39(2):253-271
    • 29. Maher P (1993) Betting on Theories. Cambridge University Press, Cambridge
    • 30. Maher P (1997) Depragmatized dutch book arguments. Philosophy of Science 64(2):291-305
    • 31. Meacham CJG, Weisberg J (2011) Representation theorems and the foundations of decision theory. Australasian Journal of Philosophy 89(4):641-663, DOI 10. 1080/00048402.2010.510529
    • 32. von Neumann J, Morgenstern O (1944) Theory of Games and Economic Behavior. Princeton University Press, Princeton
    • 33. Pettit P (1991) Decision theory and folk psychology, Basil Blackwater, Oxford, pp 147-175
    • 34. Rabinowicz W (2012) Value relations revisited. Economics and Philosophy 28:133-164
    • 35. Ramsey FP (1931) Truth and probability, Routledge, Oxon, pp 156-198
    • 36. Rinard S (2015) A decision theory for imprecise credences. Philosopher's Imprint 15:1-16
    • 37. Savage LJ (1954) The Foundations of Statistics. Dover, New York
    • 38. Schervish MJ, Seidenfeld T, Kadane JB (1990) State-dependent utilities. Journal of the American Statistical Association 85:840-847
    • 39. Seidenfeld T (2004) A contrast between two decision rules for use with (convex) sets of probabilities: Gamma-maximin versus e-admissibility. International Journal of Approximate Reasoning 140:69-88
    • 40. Troffaes MCM (2007) Decision making under uncertainty using imprecise probabilities. International Journal of Approximate Reasoning 45:17-29
    • 41. Tversky A, Kahneman D (1981) The framing of decisions and the psychology of choice. Science 211:453-458
    • 42. Van Schie ECM, Van Der Pligt J (1995) Influencing risk preference in decision making: The effects of framing and salience. Organizational Behavior and Human Decision Processes 63:264-275
    • 43. Wakker PP (2004) On the composition of risk preference and belief. Psychological Review 111:236-241
    • 44. Walley P (1999) Towards a unified theory of imprecise probability. International Journal of Approximate Reasoning 24:125-148
    • 45. Weirich P (2004) Realistic Decision Theory: Rules for nonideal agents in nonideal circumstances. Oxford University Press, Oxford
    • 46. Zynda L (2000) Representation theorems and realism about degrees of belief. Philosophy of Science 67(1):45-69
  • No related research data.
  • No similar publications.

Share - Bookmark

Funded by projects

  • EC | NATREP

Cite this article