The field of network science and evolutionary game theory is rapidly advancing, with a focus on developing more resilient social networks and understanding the dynamics of cooperation in complex systems. Researchers are exploring the impact of network topology on the spread of influence and opinions, as well as the role of self-interaction and learning in shaping the evolution of cooperation. New algorithms and frameworks, such as the Marker Gene Method and Team Utility-Constrained Proximal Policy Optimization, are being proposed to improve the stability and robustness of competitive systems. Notable papers in this area include the Marker Gene Method, which provides a theoretically sound and empirically validated framework for enhancing the stability of competitive co-evolutionary algorithms, and TUC-PPO, which achieves faster convergence to cooperative equilibria and greater stability against invasion by defectors in spatial public goods games.
Advances in Network Resilience and Evolutionary Game Theory
Sources
The Effect of Network Topology on the Equilibria of Influence-Opinion Games
Markov Chains of Evolutionary Games with a Small Number of Players
Community-Based Efficient Algorithms for User-Driven Competitive Influence Maximization in Social Networks
Marker Gene Method : Identifying Stable Solutions in a Dynamic Environment
Evolutionary Dynamics with Self-Interaction Learning in Networked Systems
Multi-Agent Coordination under Poisson Observations: A Global Game Approach
Dilution, Diffusion and Symbiosis in Spatial Prisoner's Dilemma with Reinforcement Learning
TUC-PPO: Team Utility-Constrained Proximal Policy Optimization for Spatial Public Goods Games
Imitation and Heterogeneity Shape the Resilience of Community Currency Networks