Evidence map›Paper›PMID 40854001›Full record

ArticlePLoS computational biology2025

Age polyethism can emerge from social learning: A game-theoretic investigation.

Moein Khajehnejad, Julian García, Bernd Meyer

Abstract read
In one paragraph

Article in PLoS computational biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Moein KhajehnejadDepartment of Data Science and Artificial Intelligence, Faculty of Information Technology, Monash University, Clayton, Victoria, Australia.ORCID 0000-0002-2185-4596
Julian GarcíaDepartment of Data Science and Artificial Intelligence, Faculty of Information Technology, Monash University, Clayton, Victoria, Australia.
Bernd MeyerDepartment of Data Science and Artificial Intelligence, Faculty of Information Technology, Monash University, Clayton, Victoria, Australia.

Funding

Australian Research Council
6 · The paper itself

Abstract

Age-polyethism-the age-based allocation of tasks in social insect colonies-is a key feature of division of labour. While its hormonal underpinnings have been studied extensively, the behavioural and environmental mechanisms driving age-polyethism remain poorly understood, especially under ecological stress. We present a novel modelling framework that integrates social learning with task-related environmental feedback to explain the emergence and breakdown of age-polyethism. We develop two models: a Social Learning (SL) model, in which individuals adapt task preferences by copying similar peers, and a Stimulus-Response Threshold Social Learning (SRT-SL) model, which extends this framework by incorporating task-related dynamic stimuli and response thresholds that regulate collective task demand. Our models demonstrate that age-polyethism can emerge from simple social imitation processes, without the need for fixed hormonal schedules. We show that under increasing environmental pressure (e.g., resource scarcity), age-polyethism collapses as younger individuals are forced into tasks typically handled by older workers. Importantly, we find that age-polyethism does not necessarily optimize immediate colony efficiency; instead, it appears to reflect a trade-off between environmental constraints and behavioural coordination. These findings provide a mechanistic and ecologically grounded explanation for empirical observations linking environmental stress to dysfunctional division of labour and colony collapse.

Indexed as

Game TheorySocial LearningAnimalsBehavior, AnimalComputational BiologyModels, BiologicalSocial Behavior

Identifiers

PMID40854001
PMCPMC12396761

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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.