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The relative likelihood of a particular outcome among all possible outcomes.
Copyright © 2012, Campbell R. Harvey. All Rights Reserved.


the likelihood of a particular uncertain event occurring, measured on a scale from 0.0 (such an event is impossible) to 1.0 (such an event is certain to occur). People generally estimate probabilities on the basis of the relative frequency with which an event has occurred in the past, under given circumstances, and generalize from this past experience. In some circumstances it is easy to estimate the proportion of occasions on which an event occurs; for example, the probability of getting ‘heads’ when flipping a balanced coin is 0.5 because with such a coin in the long run we would get 50% ‘heads’ and 50% ‘tails’. In estimating probabilities in business situations, though, there may be no or only a few previous experiences that can be used to gauge the relative frequency of an event occurring. See also RISK AND UNCERTAINTY.
Collins Dictionary of Economics, 4th ed. © C. Pass, B. Lowes, L. Davies 2005
References in periodicals archive ?
The role of probabilistic reasoning has not yet been recognized in the sentencing context.
(72.) Kahneman also questions experts' ability to avoid fallacies in probabilistic reasoning. See Chapters 21-22.
Instead of looking for signs of probabilistic reasoning in young humans, some scientists are looking for signs in other species.
As with event description alignment, the sample set has played a crucial role in making sense of a certain individual's probabilistic reasoning and, as a result, the sample set now plays a vital role in Chernoff and Zazkis' (2011) alternative approach to assigning probabilities.
VoxGen said that it intends to develop a statistically rigorous probabilistic reasoning system to combine these two incongruent data points.
The chapters of this volume offer a wide range of areas where the technology of soft computing, fuzzy logic, neural networks, evolutionary computation, and probabilistic reasoning are applied, including medicine, bioinformatics, natural resource management, and industry.
Girotto and Gonzalez (Chapter 8) extend MMT to explain naive probabilistic reasoning. The authors suggest that although people are relatively poor at computing formal probabilities (e.g., requiring a formula), they are surprisingly adept at simple probability judgments (e.g., considering simple combinations).
But, as Labov (1994:450) points out, "[t]he Neogrammarian viewpoint must of course be modified to accept stochastic regularities in place of absolute rules", and he speculates that "[the Neogrammarians] would not have been as likely to welcome the tools of statistical analysis and probabilistic reasoning, since they were committed to discrete solutions" (1994:470).
The final section briefly outlines Haenni's 'probabilistic argumentation' framework, which purports to provide a unified model of both logical and probabilistic reasoning. Next up are computer scientists Didier Dubois and Henri Prade, with a comprehensive, if at times somewhat terse, tour of their 'possibilistic' framework.
Indeed, Peter himself saw the limits of probabilistic reasoning when he wrote Against the Gods quoting from G.K.
Probabilistic reasoning is one of the techniques employed in artificial intelligence to handle uncertainty.

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