Conflict, Competition, Cooperation and Complexity: Using Evolutionary Game Theory to Model Realistic Populations
Real animals and human populations are complex, involving structural relationships depending upon space and time and varied interactions between potentially many individuals. Human societies feature family units, communities, companies and nations. Some animal also have complex societies, such as primate groups and social insect colonies. Single organisms themselves can be thought of as complex ecosystems, host to many interacting life forms.
Models of populations are necessarily idealised, and most involve either simple pairwise interactions or "well-mixed" structureless populations, or both. In this project we shall develop game-theoretical models, both general and focused on specific real population scenarios, which incorporate population structure and within population interactions which are both complex in character. We will focus on the themes of Conflict, Competition, Cooperation and Complexity inherent in the majority of real populations.
There will be four complementary sub-projects within the overall project. The first will focus on developing a general theory of modelling multiplayer evolutionary games in structured populations, and will feed into each of the other three sub-projects. The second will consider complex foraging games, in particular games under time constraints and involving sequential decisions relating to patch choice. The third will involve modelling human social behaviours, a particular example being epidemic cascades on social networks. The final sub-project will model cancer as a complex adaptive system, where a population of tumour, normal and immune cells evolve within a human ecosystem.
The four sub-projects will be developed in parallel fostered by frequent research visits and interactions, each involving a team comprising of EU and North American researchers, and will feed into each other through regular interactions and meetings. The aim is to develop a rich, varied but consistent theory with wide applicability.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 690817.
Project Consortium Members and Partners
The following EU universities are members of the project consortium:
Biology Center, Ceske Budejovice webpage
City University London webpage
Eotvos Lorand University webpage
Maastricht University webpage
University of Torino webpage
The following North American universities are partners in the project:
University of Illinois at Chicago webpage
University of North Carolina at Greensboro webpage
Wilfird Laurier University webpage
There will be an annual series of workshops associated with the project. The project was launched at a workshop in Plon, Germany, January 13-15, 2016 at the Max Planck Institute for Evolutionary Biology. The first annual workshop then took place in Prague from June 28-July 1 2016 at Vila Lanna. A short report on these events together with programmes for both can be found here.
The second annual workshop will take place at City, University of London from July 3-7, 2017. As in the previous workshop, this will consist of talks, discussion, and break out sessions focusing on the four main themes of the project. This event is open to researchers whose areas of expertise complement these themes. A limited number of positions is reserved for PhD students and junior researchers working/interested in evolutionary game theory and their applications within the scope of this project. The workshop fee is £300 and the reduced fee is £125 including lunch and coffee/tea during the day. If you are interested in participating in this event, please contact Mark Broom.
The following publications are associated with the project:
Broom, M., Krivan, V. (in press) Biology: Application of evolutionary game theory. Pages xx-xx in Tamer Basar, Georges Zaccour, eds. Handbook of Dynamic Game Theory. Springer.
Broom,M., Rychtar,J. (2016). Evolutionary games with sequential decisions and dollar auctions. Dynamic Games and its applications.doi:10.1007/s13235-016-0212-4
Broom,M., Rychtar,J. (2016) Ideal cost-free distributions in structured populations for general payoff functions. Dynamic Games and its applications. doi:10.1007/s13235-016-0204-4
Brown,J.S. (2016) Why Darwin would have loved evolutionary game theory. Proceedings of the Royal Society B 283: 20160847
Brown,J.S., Cunningham,J.J, Gatenby,R.A. (2016) Aggregation Eects and Population-based dynamics as a source of therapy resistance in cancer. Accepted by Transactions of Biomedical Engineering.
Cressman, R., Apaloo, J. (in press). Evolutionary game theory. Pages xxxx in Tamer Basar, Georges Zaccour, eds. Handbook of Dynamic Game Theory. Springer.
Garay, J., Csiszar, V., Mori, T.F. (2016) Evolutionary stability for matrix games under time constraints. Accepted by the Journal of Theoretical Biology.
Guo, R. Shakarian, P. (2016) A Comparison of Methods for Cascade Prediction, 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. ASONAM-2016.
Krivan,V, Cressman,R (2017). Interaction times change evolutionary outcomes: Two-player matrxi games. Journal of Theoretical Biology 416 199-207.
Kumar, N., Guo, R., Aleali, A. Shakarian, P. (2016) An Empirical Evaluation of Social Influence Metrics, ASONAM Workshop on Social Influence-2016.
Li, A., Broom, M., Du, J. & Wang, L. (2016) Evolutionary dynamics of general group interactions in structured populations. Phys. Rev. E 93, 022407.
Poccia, S.R., Sapino M.L., Liu, S., Chen, X., Garg, Y., Huang, S., Kim,J.H., Li, X., Nagarkar, P. Candan, K.S. (2017) SIMDMS: Data Management and Analysis to Support Decision Making through Large Simulation Ensembles. EDBT 2017: 582-585
Revilla, T. A., Krivan, V. (2016) Pollinator foraging exibility and the coexistence of competing plants. Plus One 11: e0160076. 10.1371/journal. pone.0160076