The interplay between the formation of cooperative groups and the evolution of cooperation

Henrik Sjödin, of the Evolution and Ecology Program, is using mathematical models to show how migration between groups can transform simple, non-cooperative communities into highly cooperative ones. The results may help improve governance of common goods.

Henrik Sjödin

Henrik Sjödin

Introduction

The ubiquity of cooperation among non-relatives is one of the most puzzling facts in science, as it appears to contradict Darwinian evolutionary theory. While this is interesting and challenging to understand from a theoretical point of view, an advancement of our knowledge about the nature of cooperation, for instance around public goods and common-pool resources such as global climate, forests, and fish stocks, will be central in safeguarding a sustainable future. Previous and recent works have highlighted the importance of understanding the emergence of cooperation in large populations subdivided into groups (e.g.[1], [2], [3]). The mechanistic underpinnings of group formation in such systems, however, have yet to be explored. Likewise, the effects of the resulting group structures on the emergence of cooperation are not yet well understood. Therefore, in this project I study the emergence of group structures resulting from migration behaviors in large communities, and the resulting effects on cooperation.

Methods

Through analytical and numerical exploration of mathematical formulations of stochastic individual-based Markovian processes of (a) inter-group migration, (b) social learning, and (c) exploration of migration strategies, I aim to show how these three types of processes interact and to what levels cooperation becomes established within the emerging group-structured communities.

Preliminary results

There is an intricate dynamical interaction between the group-structure of a community, the residents’ inter-group migration behaviors and the levels of cooperation that establish. Results show novel bottom-up effects on cooperation in large communities, which provide new insights in the emergence and maintenance of cooperation and sociality. A universal between-groups migration behavior inevitably develops over time in populations, which also transforms simple non-cooperative communities into highly cooperative communities. It is principally a very simple migration behavior built upon free choice. The new insight from the results is interesting in and of itself, and are anticipated in addition to facilitate the understanding of top-down regulation of cooperative communities and governance of common goods. The compiled results of the project are expected to be published during 2016. 

References

[1] Garcia T, De Monte S (2013). Group formation and the evolution of sociality. Evolution 67(1):131–41.

[2] Hauert C, Monte SD, Hofbauer J, Sigmund K. (2002). Volunteering as Red Queen mechanism for cooperation in public goods games. Science 296(5570):1129–32.

[3] Sasaki T, Okada I, Unemi T (2007). Probabilistic participation in public goods games. Proc. R. Soc. B. 274(1625):2639–42.


Note

Henrik Sjödin is a Swedish citizen, and a Kempe Postdoctoral Scholar (Aug 2014 – Aug 2016).


Print this page

Last edited: 02 March 2016

CONTACT DETAILS

Tanja Huber

YSSP Coordinator & Team Leader

Young Scientists Summer Program

T +43(0) 2236 807 344

PUBLICATIONS

Davis KF, Yu K, Herrero M, Havlik P, Carr JA, & D’Odorico P (2015). Historical trade-offs of livestock’s environmental impacts. Environmental Research Letters 10 (12): p. 125013. DOI:10.1088/1748-9326/10/12/125013.

Wilson C & Grubler A (2015). Historical Characteristics and Scenario Analysis of Technological Change in the Energy System. In: Technology and Innovation for Sustainable Development. Eds. Vos, R. & Alarcon, D., pp. 45-80 Norwich, UK: Bloomsbury Academic. ISBN 978-1-4725-8079-5 DOI:10.5040/9781472580795.ch-003.

Duarte R, Feng K, Hubacek K, Sanchez-Choliz J, Sarasa C, & Sun L (2015). Modeling the carbon consequences of pro-environmental consumer behavior. Applied Energy 184: 1207-1216. DOI:10.1016/j.apenergy.2015.09.101.

International Institute for Applied Systems Analysis (IIASA)
Schlossplatz 1, A-2361 Laxenburg, Austria
Phone: (+43 2236) 807 0 Fax:(+43 2236) 71 313