Non-Bayesian Decision Theory: Beliefs and Desires as Reasons for Action

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Springer Science & Business Media, 2008 M06 6 - 170 pages
For quite some time, philosophers, economists, and statisticians have endorsed a view on rational choice known as Bayesianism. The work on this book has grown out of a feeling that the Bayesian view has come to dominate the academic com- nitytosuchanextentthatalternative,non-Bayesianpositionsareseldomextensively researched. Needless to say, I think this is a pity. Non-Bayesian positions deserve to be examined with much greater care, and the present work is an attempt to defend what I believe to be a coherent and reasonably detailed non-Bayesian account of decision theory. The main thesis I defend can be summarised as follows. Rational agents m- imise subjective expected utility, but contrary to what is claimed by Bayesians, ut- ity and subjective probability should not be de?ned in terms of preferences over uncertain prospects. On the contrary, rational decision makers need only consider preferences over certain outcomes. It will be shown that utility and probability fu- tions derived in a non-Bayesian manner can be used for generating preferences over uncertain prospects, that support the principle of maximising subjective expected utility. To some extent, this non-Bayesian view gives an account of what modern - cision theory could have been like, had decision theorists not entered the Bayesian path discovered by Ramsey, de Finetti, Savage, and others. I will not discuss all previous non-Bayesian positions presented in the literature.

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Contents

Introduction
1
Bayesian decision theory
13
Choosing what to decide
31
Indeterminate preferences
61
6
95
8
125
Proofs
143
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Page 13 - The probability of any event is the ratio between the value at which an expectation depending on the happening of the event ought to be computed, and the value of the thing expected upon its happening...
Page 13 - If a person has an expectation depending on the happening of an event, the probability of the event is to the probability of its failure as his loss if it fails to his gain if it happens
Page 132 - ... the sense that the payoff depends only upon the state and not upon the act adopted. In other words, in the array representing problem 2, all entries in the same column are the same. If the decision maker knows only that he is playing problem 1 with probability p and problem 2 with probability 1 — p when he has to adopt an act, then he should adopt an act which is optimal for problem 1 , since problem 2, which enters with probability 1 — p, is irrelevant as far as his choice is concerned....
Page 13 - The aim of this chapter is to give an overview of the main TA methods and their applications, with sufficient references to provide reader access to more detailed information.
Page 65 - In words this means that if an individual selects batch one over batch two, he does not at the same time select two over one.
Page 130 - DDP) whose worst possible outcome is at least as good as the worst possible outcome of any other option (DDP).
Page 85 - M, if a >~ b, then there is a positive integer n such that (na oc) > (nb od), where na is defined inductively as la = a, (n + l)a = (ao no).

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