| Grant number: | 15/11985-3 |
| Support type: | Scholarships in Brazil - Post-Doctorate |
| Effective date (Start): | September 01, 2015 |
| Effective date (End): | November 01, 2019 |
| Field of knowledge: | Physical Sciences and Mathematics - Physics - General Physics |
| Principal researcher: | Marcus Aloizio Martinez de Aguiar |
| Grantee: | Flávia Maria Darcie Marquitti |
| Home Institution: | Instituto de Física Gleb Wataghin (IFGW). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil |
| Associated research grant: | 16/01343-7 - ICTP South American Institute for Fundamental Research: a regional center for theoretical physics, AP.TEM |
| Associated scholarship(s): | 16/00635-4 - The role of ploidy and reproduction method in the evolution of cooperation between species, BE.EP.PD |
Abstract The evolution of cooperation study has been developed in many areas of science, such as economy, politics, and climate change agreements. In biology, evolution of cooperation can be studied to understand how mutualistic ecological interactions can evolve. Mutualism often occurs between individuals of different species, such as pollination systems, in the protection against natural enemies, and in the cleaning performed by cleaners in clients of reef environments. Different species, here understood as different populations, evolve in response to selective pressures imposed by the environment, by individuals of the same population, or as a consequence of interactions established with individuals from other populations. Spatial distribution, different interaction patterns, and coevolution between individuals can have great importance in establishing cooperation (mutualism) in the studied populations. In this project, I aim to understand how to describe the evolution of different strategies in populations of different species that interact mutualistically, given the spatial structure, the structure of interactions between individuals from different populations, and also the evolution of interactions between populations. To accomplish this, I will use the evolutionary game theory approach in mean field scenarios as well as in static and dynamic networks. (AU) | |