2007
DOI: 10.1186/1744-9081-3-20
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Comparison of probabilistic choice models in humans

Abstract: Background: Probabilistic choice has been attracting attention in psychopharmacology and neuroeconomics. Several parametric models have been proposed for probabilistic choice; entropy model, Prelec's probability weight function, and hyperbola-like probability discounting functions.

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Cited by 18 publications
(37 citation statements)
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“…First, our study confirmed that both time discounting and probability discounting with physical time and objective odds against (:= 1/p − 1, proportional to waiting time in virtual repeated gambles) were both in non-exponential forms, consistent with previous studies [19,21,22]. Namely, we fitted exponential, hyperbolic and q-exponential function to both discounting behaviors.…”
Section: Resultssupporting
confidence: 76%
“…First, our study confirmed that both time discounting and probability discounting with physical time and objective odds against (:= 1/p − 1, proportional to waiting time in virtual repeated gambles) were both in non-exponential forms, consistent with previous studies [19,21,22]. Namely, we fitted exponential, hyperbolic and q-exponential function to both discounting behaviors.…”
Section: Resultssupporting
confidence: 76%
“…The analytical strategy of statistical procedures in the present study was similar to that in our previous studies on intertemporal choice models [3][4][5][6][7]10,13,14]. We fitted the three types of the social discount model equations (i.e.…”
Section: Discussionmentioning
confidence: 99%
“…We fitted the three types of the social discount model equations (i.e. Equations (1)-(3)) to the behavioral data of indifference points with the Gauss-Newton algorithm (R statistical language, non-linear modeling package), and the fitness of each equation was estimated with AIC (Akaike Information Criterion) values, which is the most standard criterion for the fitness of mathematical model to observed data, following previous studies [3][4][5][6][7]10,14]. It is to be noted that smaller AIC values correspond to better fitting (better fitting in terms of smaller AIC indicates a better tradeoff between overfitting and poor fitting).…”
Section: Discussionmentioning
confidence: 99%
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“…A probability discount rate of k ϭ 1 entails riskneutral behavior. For comparison, we also fit our data with a different risky choice model, Prelec's probability weighting function (Prelec, 1998;Paulus and Frank, 2006;Takahashi et al, 2007Takahashi et al, , 2010Hsu et al, 2009) in its dualparameter implementation:…”
Section: Methodsmentioning
confidence: 99%