David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
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Erkenntnis 14 (2):159 - 181 (1979)
In the traditional decision theories the role of forecasts is to a large extent swept under the carpet. I believe that a recognition of the connections between forecasts and decisions will be of benefit both for decision theory and for the art of forecasting.In this paper I have tried to analyse which factors, apart from the utilities of the outcomes of the decision alternatives, determine the value of a decision. I have outlined two answers to the question why a decision which is made on the basis of a forecast is better than a decision which is based on a guess. Neither of these answers is universally valid. An assumption which is necessary for the first answer, i.e. Good's result, is that Bayes' rule is accepted as a correct and generally applicable decision principle. The second answer, which was given with the aid of probability intervals, departed from a more general decision principle, the maximin criterion for expected utilities, which was formulated in order to evade some of the criticism against Bayes' rule. However, the argument leading to the ansser is based on the assumption that the probability intervals associated with the states of nature represent certain knowledge. For this reason this answer is only approximatively valid.As a number of quotations in the section on “the weight of evidence” show, it is not sufficient to describe the knowledge about the states of nature by a single number, representing the (subjective) probability of the state, but something else has to be invoked which measures the amount of information on which a decision is based. Several authors have tried to characterize this mysterious quantity, which here was called the weight of evidence. However, there seems to be little agreement as to how this quantity should be measured.
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Citations of this work BETA
Peter Gärdenfors & Nils-Eric Sahlin (1982). Unreliable Probabilities, Risk Taking, and Decision Making. Synthese 53 (3):361-386.
George Gargov (1999). Knowledge, Uncertainty and Ignorance in Logic: Bilattices and Beyond. Journal of Applied Non-Classical Logics 9 (2-3):195-283.
Donald B. Davis (1994). A Challenge to the Compound Lottery Axiom: A Two-Stage Normative Structure and Comparison to Other Theories. Theory and Decision 37 (3):267.
Sven Ove Hansson (2009). Measuring Uncertainty. Studia Logica 93 (1):21 - 40.
Sven Ove Hansson (2009). Measuring Uncertainty. Studia Logica 93 (1):21-40.
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