Robustness of norm-driven cooperation in the commons
Open accessAbstract
Sustainable use of common-pool resources such as fish, water or forests depends on the cooperation of resource users that restrain their individual extraction to socially optimal levels. Empirical evidence has shown that under certain social and biophysical conditions, self-organized cooperation in the commons can evolve. Global change, however, may drastically alter these conditions. We assess the robustness of cooperation to environmental variability in a stylized model of a community that harvests a shared resource. Community members follow a norm of socially optimal resource extraction, which is enforced through social sanctioning. Our results indicate that both resource abundance and a small increase in resource variability can lead to collapse of cooperation observed in the no-variability case, while either scarcity or large variability have the potential to stabilize it. The combined effects of changes in amount and variability can reinforce or counteract each other depending on their size and the initial level of cooperation in the community. If two socially separate groups are ecologically connected through resource leakage, cooperation in one can destabilize the other. These findings provide insights into possible effects of global change and spatial connectivity, indicating that there is no simple answer as to their effects on cooperation and sustainable resource use.
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Other applications of ABMs include modeling how behavior under social norms changes with external pressures, how the economy and climate may evolve given a diversity of political and economic beliefs, and how individuals may migrate in response to environmental changes.
“Other applications of ABMs include modeling how behavior under social norms changes with external pressures [715], how the economy and climate may evolve given a diversity of political and economic beliefs [272], and how individuals may migrate in response to environmental changes [787].”
From Tackling climate change with machine learning (2023) · cites this paper as [715]
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- Authors
- Schlüter, M., Tavoni, A., & Levin, S.
- Affiliations
- Stockholm Resilience Centre, Stockholm University, Stockholm 10691, Sweden
- London School of Economics and Political Science (GB)
- Princeton University (US)
- Venue
- Proceedings of the Royal Society B: Biological Sciences
- Publisher
- The Royal Society
- Published
- 2016-01-13
- Volume
- 283
- Issue
- 1822
- Pages
- 20152431
- ISSN
- 0080-4649 (ISSN-L)
- 0962-8452
- 1471-2954
- PMC
- PMC4721099
- OpenAlex
- W2264743790
- Cited by
- 68
- References
- 48
- Citations by year
- 2026: 5 · 2025: 9 · 2024: 8 · 2023: 11 · 2022: 5 · 2021: 3 · 2020: 7 · 2019: 4 · 2018: 8 · 2017: 8
- FWCI
- 19.01
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of the Royal Society B Biological Sciences (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- London School of Economics and Political Science Research Online (London School of Economics and Political Science) (acceptedVersion)
- Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion)
- Funders
- European Research Council (283950 SES-LINK)
- Directorate for Biological Sciences (DMS/1260/0955699, EF- 1137894, FQEB RFP-12-14, GEO-1211972)
- Topics
- Social Sciences › Sociology and Political Science › Evolutionary Game Theory and Cooperation
- Social Sciences › Safety Research › Experimental Behavioral Economics Studies
- Social Sciences › Decision Sciences › Management Science and Operations Research › Game Theory and Applications
- Keywords
- common-pool resources, cooperation, social norms, environmental variability, natural resource wealth, resource constraints, global change, spatial connectivity, resource leakage, sustainable resource use
- MeSH terms
- Conservation of Natural Resources, Cooperative Behavior, Game Theory, Humans, Models, Theoretical, Population Dynamics
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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