This project aims to explore the behavior of four evolutionary games, namely weak prisoner’s dilemma, stag hunt, snowdrift, and hawk dove, on different network topologies using various update rules. The analysis focuses on understanding how network structure and update rules influence the evolution of cooperation in complex systems. To investigate the dynamics of these evolutionary games, several update rules are employed. The ”random rule” assigns strategies randomly, while the ”stochastic best response rule” selects the best response to a neighbor’s strategy with a probability determined by the payoff difference. The ”generous tit for tat rule” imitates the strategy of a neighbor with a higher payoff, and the ”replicator rule” and ”multiple replicator rule” replicate the strategy of a neighbor with a higher payoff based on a normalized probability. The ”unconditional imitation rule” adopts the strategy of the neighbor with the highest payoff, while the ”moran rule” and ”fermi rule” introduce stochasticity based on payoffs and temperature, respectively. The analysis encompasses various network topologies, including complete networks, homogeneous random graphs, Erdos Renyi networks, Barabasi Albert networks, community graphs, Watts-Strogatz graphs a ral twork grap. By exploring these diverse network structures, this project aims to gain insights into how network topology shapes the evolution of cooperation in different evolutionary games. The project also focuses on investigating equilibrium states, dynamics, and the impact of varying values of T (temptation to defect) and S (sucker’s payoff) on the evolution of cooperation. Through extensive simulations and analysis, the project aims to uncover the intricate relationship between network topology, update rules, and the emergence of cooperation. To support the development of this project, three key resources will be thoroughly examined, drawing from existing literature and research. These resources will provide valuable insights into complex networks, evolutionary game theory, and the analysis of network dynamics. By leveraging these resources, the project aims to contribute to the understanding of cooperative behaviors in complex systems and advance the field of network science. Overall, this project combines theoretical analysis, computational simulations, and insights from network science to shed light on the dynamics of cooperation in evolutionary games on diverse network topologies.
hajarlachheb/Game-Theory-Complex-Network
This project aims to explore the behavior of four evolutionary games, namely weak prisoner’s dilemma, stag hunt, snowdrift, and hawk dove, on different network topologies using various update rules. The analysis focuses on understanding how network structure and update rules influence the evolution of cooperation in complex systems.
Python