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Thinking: Psychological Perspectives on Reasoning, Judgment and Decision Making 2003
DOI: 10.1002/047001332x.ch14
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Belief and Preference in Decision Under Uncertainty

Abstract: Most decisions in life are gambles. Should I speed up or slow down as I approach the yellow traffic light ahead? Should I invest in the stock market or in treasury bills? Should I undergo surgery or radiation therapy to treat my tumor? From mundane choices rendered with scarcely a moment's reflection to urgent decisions founded on careful deliberation, we seldom know in advance and with certainty what the consequences of our choices will be. Thus, most decisions require not only an assessment of the attractive… Show more

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Cited by 58 publications
(28 citation statements)
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“…First, a number of studies have shown that when descriptions of events (e.g., ''precipitation next April 1'') are unpacked into a disjunction of constituents (e.g., ''rain or sleet or snow or hail''), judged probability sometimes increases, but not as dramatically as the sum of the probabilities of these constituents when they are judged separately (Rottenstreich & Tversky, 1997;Fox & See, 2003). In fact, unpacking descriptions into a disjunction of constituents and a catch-all sometimes leads to a reduction in judged probability.…”
Section: A Stochastic Model Of Subadditivitymentioning
confidence: 99%
“…First, a number of studies have shown that when descriptions of events (e.g., ''precipitation next April 1'') are unpacked into a disjunction of constituents (e.g., ''rain or sleet or snow or hail''), judged probability sometimes increases, but not as dramatically as the sum of the probabilities of these constituents when they are judged separately (Rottenstreich & Tversky, 1997;Fox & See, 2003). In fact, unpacking descriptions into a disjunction of constituents and a catch-all sometimes leads to a reduction in judged probability.…”
Section: A Stochastic Model Of Subadditivitymentioning
confidence: 99%
“…Naturally, many concepts, such as knowledge, beliefs, and preferences, are not naturally represented with numeric precision (cf., e.g., Refs. [13][14], and the difficulty of eliciting precise decision parameters (utility values, criteria weights, and probabilities on uncertain consequences) have been widely discussed 21 . There are severe inconsistencies with the predictions of the rational model (cf., e.g., Refs.…”
Section: Numerically Imprecise Informationmentioning
confidence: 99%
“…Consider a lottery in which the decision maker wins x if event E occurs. The two-stage model posits that individuals first judge the probability of event E, then transform this subjective probability by the probability weighting function for risk (see also Fox and See 2003;Fox and Tversky 1998;Wu and Gonzalez 1999). The two-stage model proposed by Tversky and Fox, however, does not permit source preference, and thus cannot accommodate the Ellsberg Paradox.…”
Section: Literaturementioning
confidence: 99%