USING EVOLUTIONARY GAMES THEORY FOR PLAYERS BEHAVIOR ANALYSIS IN COMPETITIVE SHOOTING GAMES: A LITERATURE REVIEW

The paper is dedicated to the overview of current state of the evolutionary games approach to the building of environments to analyze players behavior. The evolutionary game theory applications differ from the orthodox game theory. Initially, it was used to address problems in evolutionary biology and later was suited for broader range of problems.We will oversee the development of the evolutionary games theory in finance and its applications in behavior analysis in competitive gaming. The paper is focused on replicator dynamics, learning model based on it and its possible application to behavior analysis based on fuzzy algorithms and approaches used in economics to be applied to the new emerging field of cybersports.

Research on the Low-Carbon Evolutionary Model of Chinese Traditional Industrial Clusters Based on Evolutionary Games Theory under Low-Carbon Constraints

The low-carbon evolution of traditional industry cluster is the key to a low-carbon economy, and also a frontier of industry cluster theory research. The paper uses evolutionary game theory to construct a low-carbon evolutionary model of Chinese traditional industrial clusters, which considers uncertain factors such as political, economic, cultural, etc. Through the analysis of the cluster low-carbon evolutionary paths and stable equilibrium strategies, the model reflects the inherent law of clusters low-carbon evolution. Finally, the paper gives advices to promote industrial cluster agents to adopt the low-carbon cooperation strategy.

Algorithm and Simulation of Chinese Traditional Industrial Clusters Low Carbon Evolution Based on Evolutionary Games Theory on Complex Networks

Industrial clusters are complex networks formed by numerous agents who continuously imitate, learn from each other and make optimal choice accordingly. The paper uses random learning game and multi-agent system models to construct a Chinese traditional industrial clusters low carbon evolution model and introduce an algorithm based on the network external effect and characteristics of agents adaptive behavior. Then the simulation of low-carbon competition, emergence and evolution was conducted, which produced some valuable conclusions.