Evidence map›Paper›PMID 39531436›Full record

ArticlePloS one2024

Evolutionary origins of temporal discounting: Modeling how time and uncertainty constrain optimal decision-making strategies across taxa.

Brian Villmoare, David Klein, Pierre Liénard, Timothy S McHale

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Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Brian VillmoareDepartment of Anthropology, University of Nevada Las Vegas, Las Vegas, Nevada, United States of America.
David KleinUniversity of California, San Luis Obispo, California, United States of America.
Pierre LiénardDepartment of Anthropology, University of Nevada Las Vegas, Las Vegas, Nevada, United States of America.
Timothy S McHaleSocial Sciences Department, California Polytechnic State University, San Luis Obispo, California, United States of America.ORCID 0000-0002-9715-5062

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The propensity of humans and non-human animals to discount future returns for short-term benefits is well established. This contrasts with the ability of organisms to unfold complex developmental sequences over months or years efficiently. Research has focused on various descriptive and predictive parameters of 'temporal discounting' in behavior, and researchers have proposed models to explain temporal preference in terms of fitness-maximizing outcomes. Still, the underlying ultimate cause of this phenomenon has not been deeply explored across taxa. Here, we propose an ultimate (i.e., evolutionary) causal explanation for the selection of temporal discounting largely conserved across taxa. We propose that preference for a short-term reward (e.g., heightened impulsivity) often is less than optimal and likely is the product of constraints imposed on natural selection with respect to predicting events in a temporal framework in the context of future uncertainty. Using a simple Newtonian model for time across a fitness landscape in which movement by organisms is only possible in one direction, we examine several factors that influence the ability of an organism to choose a distant reward over a more temporally proximate reward: including the temporal distance of the far reward, the relative value of the distant reward, and the effect of uncertainty about the value and presence of the distant reward. Our results indicate that an organism may choose a more distant reward, but only if it is not too far into the future and has a substantially higher-value fitness payoff relative to the short-term reward. Notably, any uncertainty about the distant reward made it extremely unlikely for an organism to choose the delayed reward strategy compared to choosing a closer reward, even if the distant reward had a much higher payoff because events that are uncertain are only partially visible to natural selection pressures. The results help explain why natural selection is constrained to promote more optimal behavioral strategies and why it has difficulty selecting a distant reward over a lower-value short-term reward. The degree of uncertainty is an especially salient ecological variable in promoting and preferencing short-term behavioral strategies across taxa. These results further help illustrate why, from an ultimate causal perspective, human and non-human taxa have difficulty making more optimal long-term decisions.

Indexed as

Decision MakingDelay DiscountingRewardAnimalsBiological EvolutionHumansTime FactorsUncertainty

Identifiers

PMID39531436
PMCPMC11556739

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.