Mission of this research is to better capture the complexity of large-scale financial systems and overcome the insufficiency of equilibrium based neoclassical finance and behavioral finance in revealing the mechanism of financial crisis and the emergence of order. This study started from the dilemma of existing theories and the motivation of introducting complexity science. Then advances of multi-agent simulation and complex network analysis are summarized and discussed as two fundimental instruments of complexity science. Research trends of complexity science in financial research, are proposed as the result of the discussions. This study provides methodology reference and analytical tools for the theoretical research and practical applications of complex financial systems in the new era.

The advance-selling mode has prolonged the overall sales duration of products, which brings new problems to the decision-making of product ordering time for e-commerce platforms. In order to study and compare the product deposit, product price and e-commerce platform profit under the influence of two kinds of ordering time strategies, a two-stage leader-follower game model is established to analyze the influence of deposit sensitivity, balance sensitivity and manufacturing cost on ordering time selection and product deposit decision. The results show that the consumer’s sensitivity to the product deposit and the product balance will affect the wholesale price of the products in the delayed order model, while the wholesale price in the early order model is only affected by the sensitivity of the product deposit. Compared with the early order model, the delayed order model can be applied to consumers with more stringent product price sensitivity. The E-commerce platform’s advance-selling coupon/deposit strategy depends on the consumers’ sensitivity to the product balance.

According to the complex correlation characteristics of the actual interdependent network and the important evaluation indicators in the network, five different coupling methods are proposed, and nine interdependent network models are established. Considering the information transmission, redistribution and cascading failures in the network, a cascading failure model based on betweenness artificial flow models is established. We based on the game theory, attack-defense game problems of critical infrastructure are analyzed from the perspective of complex network, and the robustness of various interdependent networks is analyzed. We discovered the preferences of game participants in the interdependent networks, providing decision support for the protection of infrastructure networks.

In order to better understand the network, based on the similarity, let the nodes select multiple similar nodes to form similar node pairs. Through the Monte Carlo simulation results, a pairing algorithm based on the maximum node similarity and degree is proposed to discover the overlapping community structure of the network. Using multi-level most similarity to continue to optimize the community structure, find out the deep overlapping structure and sub-community structure of the network community. The proposed algorithm discovers the overlapping structure of the network based on the reason why the real network forms a community, and further optimizes the community structure, discovering the deep overlapping community structure of the network and its sub-community structure.

In order to ensure that urban emergency supplies can be delivered to the demand point in a timely and accurate manner, especially for special emergency supplies with a short life cycle′ perishable goods, its timeliness requirements are higher. Based on the emergency scenario of sudden public health events, this paper establishes a multi-objective location model of urban perishable goods distribution center with the goal of minimizing transportation time and transportation cost and maximizing relative coverage area. The Harris Hawk optimization algorithm (HHO) is improved to achieve an effective solution to the multi-objective location problem of perishable goods distribution center. In order to verify the effectiveness of the model, a district in Shanghai is selected as a research example. The results show that the improved HHO algorithm can solve the location model of perishable goods distribution center under actual urban road conditions, and can provide an intuitive multi-objective location optimization scheme.

In order to clarify the risk factors of food safety issues in the traditional catering business and find effective measures to address issues from the source. The fault tree is constructed to identify risk factors, and qualitative analysis of risk factors is done by using the minimum cut set and structural importance. Moreover, the fault tree model is transformed into a Bayesian network, and final BN model is finished by structure learning and parameter learning based on case data. The two-way reasoning ability of BN is used to conduct quantitative analysis, and sensitive factors of various types of problems from the perspective of catering operators are finally obtained. The results indicate that personnel management is most likely to cause problems. Insufficient training is an important reason for food safety issues.

In order to explore the dynamic evolution of innovator collaborative innovation network, networks are constructed based on the data of joint invention patent in electronic information industry. The paper reveals the characteristics of networks evolution, and constructs random actor models to identify the influencing factors of networks evolution. The results show that the relationship between innovators and innovation in electronic information industry increases in the early stage and becomes stable in the later stage. The network scale tends to be stable and the connections between nodes become closer. Network evolution is influenced by network structure characteristics, innovator characteristics and proximity mechanism. Among them, cooperation breadth and geographical proximity have the greatest impact.

This paper tries address the problem of regulatory capture in drug safety governance resulting from information asymmetry and hidden behavior of subjects. To achieve this, this paper employs evolutionary game theory to construct a three-party evolutionary game model that includes the State Drug Administration, the local Drug Administration, and the drug manufacturer. Then the Evolutionary Stability Strategies (ESS) are obtained, and a simulation analysis is conducted to analyze the impact of each parameter on the regulatory capture of the drug administrations. In addition, this paper focuses on the effects of the penalty intensity of the State Drug Administration on the local Drug Administration and drug manufacturers. Based on the findings, recommendations are provided for the government.

Most current predation models describe the stochastic nature of the environment in terms of white noise, for the problem that the parameters in the model may satisfy the Ornstein-Uhlenbeck process in a real situation, a stochastic three-species predation model with refuge and Ornstein-Uhlenbeck process is proposed. The dynamic behavior of the model is analyzed by its formula, differential inequality and stochastic analysis theory. The existence and uniqueness of the global positive solution of the model are proved, sufficient conditions for the average persistence and extinction of each population were obtained separately, finally, python is used to carry out numerical simulation to verify the conclusions obtained in the theorem, and further study the impact of refuge on population size.