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PMID: 9386907 Published · ppublish English Journal Article Research Support, Non-U.S. Gov't Review

Normative and descriptive models of decision making: time discounting and risk sensitivity.

Ciba Foundation symposium ·Vol. 208 ·1997-00-00 ·Pages 51-67; discussion 67-70

Kacelnik A

Abstract

The task of evolutionary psychologists is to produce precise predictions about psychological mechanisms using adaptationist thinking. This can be done combining normative models derived from evolutionary hypotheses with descriptive regularities across species found by experimental psychologists and behavioural ecologists. I discuss two examples. In temporal discounting, a normative model (exponential) fails while a descriptive one (hyperbolic) fits both human and non-human data. In non-humans hyperbolic discounting coincides with rate of gain maximization in repetitive choices. Humans may discount hyperbolically in non-repetitive choices because they treat them as a repetitive rate-maximizing problem. In risk sensitivity, a theory derived from fitness considerations produces inconclusive results in non-humans, but succeeds in predicting human risk proneness and risk aversion for both the amount and delay of reward in a computer game. Strikingly, and in contrast with the existing literature, risk aversion for delay occurs as predicted. The predictions of risk aversion for delay may fail in many animal experiments because the manipulations of the utility function are not appropriate. In temporal discounting animal experiments help the interpretation of human results, while in risk sensitivity studies human results help the analysis of non-human data.

MeSH Terms
Decision Making Humans Models, Psychological Risk Time
Authors & Affiliations
1 authors, click to expand affiliations / ORCID
Kacelnik A
Department of Zoology, Oxford University, UK.
Article Info
Journal
Ciba Foundation symposium
Abbr.
Ciba Found Symp
ISSN
0300-5208
Published
1997-00-00
Pages
51-67; discussion 67-70
Language
English
Region
Netherlands
NLM ID
0356636
Subset
IM
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