Evolutionary dynamics of fitness-driven walkers on a graph

Evolutionary dynamics of fitness-driven walkers on a graph

In this project, we introduce a model that investigates how strategies of interacting individuals in a group evolve as they are allowed to interact with other agents and migrate to other neighboring groups through a biased random walk. Here, we construct varying complex network models to create different grouping topologies. Each node in a network represents a group of players in a well mixed state. Agents in a group/class then interact with each other playing either one of two strategies, $A$ and $B$. The population of players using strategies $A$ and $B$ are then evaluated, respectively, allowing us to calculate the average payoff of a group. Each agent then migrates to a particular neighboring node or group through a biased random walk by evaluating and contrasting the different fitness values of its group's neighbors.

This model is reminiscent of social systems and other ecological systems where agents migrate to a neighboring environment depending on the fitness of the said region. In social systems, this may describe how and why individuals would move from one neighboring city to another. This may also tell of why employees of a certain company would want to move to other companies.

(Details to be uploaded soon.)