Abstract

In recent years, there is a large amount of literature that studies the theory of business ecosystem, but there is rarely literature on the financial system which plays a critical role in the good running of the enterprise. To fill this gap, the purpose of this paper is to address the evolution of financial ecosystem from an ecological and dynamic perspective. In order to provide a better presentation of the evolutionary process, based on complex adaptive system (CAS) theory and evolutionary game theory (EGT), this paper analyzed the adaptability of financial ecosystem and built an evolutionary game model of financial ecosystem to confirm the point of the view. The results show that the evolution of financial ecosystem is a dynamic adaptive process. Under the assumption of limited rationality, the financial ecosystem gradually finds the optimal strategy through adaptive learning, and finally the evolution reaches an equilibrium stage.

Highlights

  • This paper will use complex adaptive system (CAS) theory to explore the evolution mechanism of financial ecosystem strategy by evolutionary game theory

  • Biological analogy is one of the important methods used in the research of enterprise growth theory

  • What’s more, he structures financial management theory system from the unit of life, movement form, equilibrium, thinks that the financial ecosystem is composed of enterprise financial life and financial environment, expound a detailed description of the financial ecology system of dynamic balance and openness characteristics

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Summary

Introduction

This paper will use complex adaptive system (CAS) theory to explore the evolution mechanism of financial ecosystem strategy by evolutionary game theory. The remainder of this paper is organized as follows: Sect. analyzes theoretical foundations framework; Sect. discusses the modeling process; Section 4 describes the analysis of the evolutionary results; Sect. 5 presents the conclusions

Financial ecosystem basic concept
Evolutionary game theory
Assumptions of the model
Setting the learning algorithm of agents
New strategy 0 Old strategy
Analyzing game result
Conclusion
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