A mathematical framework for probabilistic choice based on information theory and psychophysics

Medical Hypotheses
Taiki Takahashi

Abstract

Risky decision-making (e.g. reward dependency) has been associated with substance abuse, psychopathy and pathological gambling; conversely, marked sensitivity to risk and uncertainty have been observed in anxiety disorder patients. In economic decision theory, probability and uncertainty have been dissociated. Frank Knight defined uncertainty as loss of information on the probability distribution of outcomes for choices (i.e., unpredictability), which is referred to as Knightian uncertainty (also as ambiguity). However, even when the probability distribution of outcomes is known, there are different degrees of predictability. In information theory, this type of degrees of uncertainty/unpredictability has been parametrized by introducing Shannon entropy. In the present paper, we show: (i) a mathematical framework combining Shannon entropy in information theory and Weber's law in psychophysics is capable of parametrizing subject's level of both aversion to probabilistic uncertainty (exaggerated in anxiety disorder patients) and reward dependency (enhanced in drug addicts and pathological gamblers), and (ii) this framework has an analogue in thermodynamics, therefore this can readily be utilized in studies in the nascent fields of...Continue Reading

References

Aug 29, 2003·Journal of Affective Disorders·Stephan LudewigFranz X Vollenweider
Jan 28, 2004·Behavioural Processes·Brady ReynoldsKatherine Karraker
Sep 15, 2005·Medical Decision Making : an International Journal of the Society for Medical Decision Making·Andrea D GurmankinKevin G M Volpp
Dec 2, 2005·Annual Review of Psychology·Wolfram Schultz

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Citations

Jun 26, 2015·Physiological Reviews·Wolfram Schultz

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