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  1. Updating beliefs in light of uncertain evidence: Descriptive assessment of Jeffrey's rule.Daniel Osherson & Jiaying Zhao - 2010 - Thinking and Reasoning 16 (4):288-307.
    Jeffrey (1983) proposed a generalization of conditioning as a means of updating probability distributions when new evidence drives no event to certainty. His rule requires the stability of certain conditional probabilities through time. We tested this assumption (“invariance”) from the psychological point of view. In Experiment 1 participants offered probability estimates for events in Jeffrey’s candlelight example. Two further scenarios were investigated in Experiment 2, one in which invariance seems justified, the other in which it does not. Results were in (...)
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  • On the provenance of judgments of conditional probability.Jiaying Zhao, Anuj Shah & Daniel Osherson - 2009 - Cognition 113 (1):26-36.
  • Uncertain premises and Jeffrey's rule.David E. Over & Constantinos Hadjichristidis - 2009 - Behavioral and Brain Sciences 32 (1):97-98.
    Oaksford & Chater (O&C) begin in the halfway Bayesian house of assuming that minor premises in conditional inferences are certain. We demonstrate that this assumption is a serious limitation. They additionally suggest that appealing to Jeffrey's rule could make their approach more general. We present evidence that this rule is not limited enough to account for actual probability judgements.
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  • Subjective probabilities inferred from decisions.Ward Edwards - 1962 - Psychological Review 69 (2):109-135.
  • Against Conditionalization.Fahiem Bacchus, Henry E. Kyburg & Mariam Thalos - 1990 - Synthese 85 (3):475-506.
  • Some Problems for Conditionalization and Reflection.Frank Arntzenius - 2003 - Journal of Philosophy 100 (7):356-370.
  • Some Problems for Conditionalization and Reflection.Frank Arntzenius - 2003 - Journal of Philosophy 100 (7):356-370.
  • Availability: A heuristic for judging frequency and probability.Amos Tversky & Daniel Kahneman - 1973 - Cognitive Psychology 5 (2):207-232.
  • Probabilistic coherence and proper scoring rules.Joel Predd, Robert Seiringer, Elliott Lieb, Daniel Osherson, H. Vincent Poor & Sanjeev Kulkarni - 2009 - IEEE Transactions on Information Theory 55 (10):4786-4792.
    We provide self-contained proof of a theorem relating probabilistic coherence of forecasts to their non-domination by rival forecasts with respect to any proper scoring rule. The theorem recapitulates insights achieved by other investigators, and clarifi es the connection of coherence and proper scoring rules to Bregman divergence.
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