Evidence map›Paper›PMID 37204305›Full record

ArticleeLife2023

Coevolutionary dynamics via adaptive feedback in collective-risk social dilemma game.

Linjie Liu, Xiaojie Chen, Attila Szolnoki

Abstract read
In one paragraph

Article in eLife, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

18 citing papers in PubMed.

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  16. Managing ecological thresholds of a risky commons.Royal Society open science · 2023
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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.

Linjie LiuCollege of Science, Northwest A & F University, Yangling, China.ORCID 0000-0003-0286-8885
Xiaojie ChenSchool of Mathematical Sciences, University of Electronic Science and Technology of China, Chengdu, China.ORCID 0000-0002-9129-2197
Attila SzolnokiInstitute of Technical Physics and Materials Science, Centre for Energy Research, Budapest, Hungary.ORCID 0000-0002-0907-0406

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Human society and natural environment form a complex giant ecosystem, where human activities not only lead to the change in environmental states, but also react to them. By using collective-risk social dilemma game, some studies have already revealed that individual contributions and the risk of future losses are inextricably linked. These works, however, often use an idealistic assumption that the risk is constant and not affected by individual behaviors. Here, we develop a coevolutionary game approach that captures the coupled dynamics of cooperation and risk. In particular, the level of contributions in a population affects the state of risk, while the risk in turn influences individuals' behavioral decision-making. Importantly, we explore two representative feedback forms describing the possible effect of strategy on risk, namely, linear and exponential feedbacks. We find that cooperation can be maintained in the population by keeping at a certain fraction or forming an evolutionary oscillation with risk, independently of the feedback type. However, such evolutionary outcome depends on the initial state. Taken together, a two-way coupling between collective actions and risk is essential to avoid the tragedy of the commons. More importantly, a critical starting portion of cooperators and risk level is what we really need for guiding the evolution toward a desired direction.

Indexed as

Cooperative BehaviorGame TheoryBiological EvolutionEcosystemFeedbackHumansadaptive feedbackcoevolutionary dynamicscollective-risk social dilemmacomputational biologydecision-makingevolutionary biologygame theorynonesystems biology

Identifiers

PMID37204305
PMCPMC10198723

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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.