Detecting the Collapse of Cooperation in Evolving Networks
- Submitting institution
-
Manchester Metropolitan University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2347
- Type
- D - Journal article
- DOI
-
10.1038/srep30845
- Title of journal
- Scientific Reports
- Article number
- 30845
- First page
- -
- Volume
- 6
- Issue
- 1
- ISSN
- 2045-2322
- Open access status
- Compliant
- Month of publication
- August
- Year of publication
- 2016
- URL
-
https://e-space.mmu.ac.uk/620000/
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
3
- Research group(s)
-
C - Machine Intelligence
- Citation count
- 9
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The paper describes a computational methodology to forecast the collapse of cooperation in communities. It is the result of an interdisciplinary collaboration with Vasilis Dakos (Centre National de la Recherce Scientifique) one of the world’s leading ecologists in the quantification of early warnings. It is one of the first papers to propose the use of ecological early warnings to forecast the collapse of cooperation in dynamical networks. The approach has influenced research in other different disciplines such as evolutionary dynamics e.g. (The Swain Lab, Edinburgh University 2017) and energy transition and simulation (Oscar Kraan et al, 2019)
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
