Evidence map›Paper›PMID 28839262›Full record

ArticleScientific reports2017

Evolution of risk preference is determined by reproduction dynamics, life history, and population size.

Oren Kolodny, Caitlin Stern

Abstract read
In one paragraph

Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. The variability of evolvability: Properties of dynamic fitness landscapes determine how phenotypic variability evolves.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Oren KolodnyDepartment of Biology, Stanford University, Stanford, CA, 94305, USA. okolodny@stanford.edu.ORCID 0000-0002-0095-693X
Caitlin SternSanta Fe Institute, 1399 Hyde Park Road, Santa Fe, NM, 87501, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Alternative behavioral strategies typically differ in their associated risks, meaning that a different variance in fitness-related outcomes characterizes each behavior. Understanding how selection acts on risk preference is crucial to interpreting and predicting behavior. Despite much research, most theoretical frameworks have been laid out as optimization problems from the individual's perspective, and the influence of population dynamics has been underappreciated. We use agent-based simulations that implement competition between two simple behavioral strategies to illuminate effects of population dynamics on risk-taking. We explore the effects of inter-generational reproduction dynamics, population size, the number of decisions throughout an individual's life, and simple alternate distributions of risk. We find that these factors, very often ignored in empirical and theoretical studies of behavior, can have significant and non-intuitive impacts on the selection of alternative behavioral strategies. Our results demonstrate that simple rules regarding predicted risk preference do not hold across the complete range of each of the factors we studied; we propose intuitive interpretations for the dynamics within each regime. We suggest that studies of behavioral strategies should explicitly take into account the species' life history and the ecological context in which selection acted on the risk-related behavior of the organism of interest.

Indexed as

Choice BehaviorModels, TheoreticalPopulation DensityPopulation DynamicsReproductionRisk-TakingAlgorithms

Identifiers

PMID28839262
PMCPMC5571215

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.