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Conflict, Competition, Cooperation and Complexity: Using Evolutionary Game Theory to model realistic populations

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Game theory meets biology for the sake of medicine

Studying the behaviour of populations of agents who frequently engage in strategic interactions and focusing on the themes of conflict, competition, cooperation and complexity, an EU project uses evolutionary game theory to model real populations.

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Evolutionary game theory has proven to be a valuable set of tools to understand and analyse complex biological systems. The EU-funded project FourCmodelling used this theory in research considering four complementary concepts. The Cs refer to conflict, competition, cooperation and complexity. The project has led to the publication of 78 research papers involving 95 authors. Collaborations across the subprojects led to the development of joint work efforts, “a significant challenge,” as Mark Broom, professor of Mathematics at City, University of London and project coordinator, points out. “Such cross-cutting papers are harder to put together than equivalent ones within the subprojects, where the work is done by more established teams.” Partners hosted four annual workshops, conducted in Prague, 2016, London, 2017, Torino, 2018 and Maastricht, 2019.

Subprojects in support of the whole

Four subprojects, modelling structured populations, time constraint models, complex time series data analysis and cancer modelling, were developed in parallel and fostered by frequent research visits, each involving a team of EU and North American researchers and resulting in regular interactions and meetings. The aim was to strengthen and develop research collaborations and to start developing a rich, varied but consistent theory with wide applicability. Both senior and early-stage researchers have benefited from the development of existing collaborations, including extensive periods of research. Researchers from the first two subprojects have developed a significant research interaction through several joint papers. This has led to the unification of two different modelling methodologies, involving time constraints and spatial factors. Researchers in the third subproject developed methodology and software to analyse time series and detect evidence of significant events. The studied techniques have applicability in a wide range of domains, including the spread of epidemics. Research in the fourth subproject has provided evidence of the need for a paradigm shift in the treatment of metastatic cancers, towards the use of evolutionary therapies, as a result of game-theoretic ideas developed within this project and the success of game theory-based clinical trials. A related paper, ‘Optimizing Cancer Treatment Using Game Theory – A Review’, has been published in the journal JAMA Oncology.

From collaboration to innovation and networking

One of the most important aims of the grant was continuation of the teams’ joint project by developing further research and building the network of research institutions working in this area. The consortium thus developed a successful Marie Skłodowska-Curie Innovative Training Network research grant bid building upon the existing network. This grant commences in March 2021, involving almost all organisations from this original MSCA-RISE grant together with the addition of 15 more organisations. The grant is centred upon the training of 15 early-stage researchers, one based at each of the 15 hosting sites, strengthening the project’s original academic core by now including much more non-academic involvement by companies and hospitals.


FourCmodelling, research, game theory, evolution, populations, conflict, competition, cooperation, complexity, modelling, biology

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