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.

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.

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 represent the rare entities with a small number of neighbors, this paper proposes a knowledge graph embedding model based on the nearest neighbors (NNKGE), which uses the K-Nearest Neighbor algorithm to obtain the nearest neighbors of the target entity as extended information. Based on this, the relational nearest neighbors-based knowledge graph embedding model (RNNKGE) is proposed. To generate an enhanced entity representation, the nearest neighbors of the target entity in relation are obtained by the improved K-Nearest Neighbor algorithm and encoded by the graph memory network. Through the analysis of the experimental results on the public datasets, the above two models outperform the benchmark model (CoNE) in the case of using only the nearest neighbor nodes, alleviating the data sparsity problem and improving the knowledge representation performance.

In order to identify the important weighted motifs in the directed weighted networks, the directed weighted networks are transformed into label networks and the simple motifs are expanded to label motifs by defining the edge weights as strong and weak labels. For the label motifs of the three nodes, the time-consuming procedure of subgraph traversal is replaced by the estimated probability of the corresponding number of the motifs appear in the random networks, and the important label motifs in the directed weighted networks are identified by introducing a dynamic indicator associated with the label motif type. It is applied to the passing networks of Guangdong team and Liaoning team in the 2019—2020 finals of China Basketball Association (CBA). The important passing modes of the teams in the games and the important players in the corresponding modes are obtained. The important label motifs play a significant role in mining the important construction patterns and key nodes of the directed weighted networks.

In order to promote innovation diffusion of electric vehicle by harmonizing multi-agent game relationship. The multi-agent game model based on complex network was formed to analyze the influence of critical factors for innovation diffusion. Study results include that critical factors include the profits that adopting different patent technology, patent fees and advantage coefficients. The diffusion rate is affected by the node degree and network rewiring probability, and the lower node degree, the impact of network rewiring probability on diffusion speed is more significant. Also, the higher the network size, the more significant is the impact that rewiring probability posed on the diffusion speed. Finally the managerial and policy recommendations are proposed to promote the innovation diffusion.

In order to study the impact of different types of emergencies including COVID-19, July 20 Heavy rainstorm in Zhengzhou and variant strains of the new coronavirus attacked Shanghai in 2022 on the performance of food network security, the Grainger causal detection method was used to construct four different stages of food stock correlation network. Through the analysis of the network topology characteristics, node importance and anti-destruction performance in the three periods, it is found that in the process of facing the invasion of overall or local emergencies, the internal structure of the food-related network will be effectively adjusted with the degree of impact, and the nodes that occupy the leading role form a fast and effective response mode according to geographical factors, and the maintenance and protection of these important nodes of food supply is often the focus. The attack simulation results show that deliberate attacks are more persecutive than random attacks, and the food related networks in the second stage are disintegrated most rapidly, and the anti-destruction performance of the third and fourth stages is more unstable. Finally, the early warning effect of CUSUM control chart used in the grain stock market is also well reflected.

In order to better reveal the propagation mechanism of urban traffic congestion, this paper proposes a two-layer network congestion propagation model coupled with multiple warning information subnetworks and traffic road subnetworks, and explores the propagation mechanism of urban road congestion risk under multiple warning information. The model establishes a state transfer tree based on propagation dynamics and analyzes the propagation threshold using microscopic Markov chain (MMCA). Finally, the impact of multiple warning messages on the propagation of urban traffic congestion is analyzed through simulation experiments. The experimental results show that promoting the propagation of "fast" warning information and inhibiting the spread of "short" warning information can play a positive role in reducing traffic congestion pressure.

Rapid and correct identification of vulnerable lines is of great significance to maintaining the safety of the power grid and avoiding large-scale power outages. Based on this, this paper proposes an analysis scheme to establish a multi-link accident chain model based on new indicators and generate a chain fault diagram. The model is calculated and analyzed based on the complex network theory, and the line vulnerability is measured based on the system load loss rate. Finally, the IEEE39-bus system is taken as an example to verify the effectiveness of the method. The method breaks through the limitation of a single pair of single lines in the process of establishing an accident chain model by the traditional method and effectively realizes the identification of fragile lines, so as to maintain the safety of the power grid and improve the stability of the power grid.

A quasiperiodically-forced piecewise smooth system is used as the research object to confirm the existence of strange nonchaotic attractors and further analyze several characteristics. Firstly, qualitative methods of phase diagram and power spectrum are used to analyze the fractal characteristics of strange nonchaotic attractors. Some quantitative methods such as the maximum Lyapunov exponent, phase sensitivity exponent, spectral distribution function and finite-time Lyapunov exponent are used to describe the characteristics of strange nonchaotic attractors. The results show that strange nonchaotic attractors can exist in the system under certain parameters, and exhibit a variety of statistical properties different from other types of attractors.

In order to explore the transmission mechanism of Omicron COVID-19 and predict the epidemic trend, a Caputo-conformable fractional-order SAIQR epidemic model with media effects is established. The basic reproduction number, equilibrium points, stability and other dynamic behaviors of the model are analyzed. The model parameters are fitted by nonlinear least squares with real data and we performed sensitivity analysis of parameters. The numerical simulation results indicate that the media effects parameters play an important role in reducing the number of infected individuals. Adjusting two fractional parameters gives the model numerical solution greater freedom compared to adjusting a single parameter.

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.

To explore how to reasonably and effectively select potential partners in the process of constructing innovation consortia, maximizing the strategic role of innovation consortia in achieving high-level scientific and technoligical self-reliance and strength, this paper introduces a method for computing the technical background similarity of innovative entities based on patent heterogeneity networks and the SimRank algorithm. By using visualization techniques, we display potential collaboration networks and leverage the spectral clustering algorithm to categorize potential partners for the innovation consortium. Through empirical analysis in the field of polymer materials, the results effectively showcase potential collaborative relationships and clusters of innovative entities with highly similar technical backgrounds in this field. This confirms the validity of our theoretical approach and methodology, offering guidance for the selection of partners in innovation consortiums.

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.

Based on evolutionary game theory, this paper establishes an evolutionary game model of collaborative ecological innovation of core enterprises and non-core enterprises in the supply chain under government regulation. And then it analyzes the stability of strategy selection and combines numerical simulation to simulate the impact of different parameter changes on system evolution. The results show that: the higher the income of ecological innovation and the lower the cost, which is conducive to collaborative ecological innovation of enterprises; the free-rider income will have a negative impact on the collaborative ecological innovation of enterprises; there is an optimal co-benefit distribution coefficient that makes the two parties have the greatest probability of collaborative ecological innovation; the government′s subsidy and punishment mechanism has a significant role in promoting the collaborative ecological innovation of enterprises.

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.

With the rapid development of the cold chain logistics industry, enterprises are paying more attention to the cost, quality of logistics services and carbon emissions generated by transportation. In order to solve this problem, a multi- depot half-open cold chain logistics routing optimization model is constructed, and various dynamic changes are integrated into the model. An improved NSGA-II algorithm is designed and the standard test function is used to prove that the algorithm has good convergence and diversity. The results show that, compared with the scheme without considering carbon emission, the distribution cost of the scheme with considering carbon emission increases by 19.00%, the transportation distance decreases by 5.16%, and the carbon emission cost decreases by 13.03%. Compared with the initial distribution scheme, the distribution cost of the real-time optimization scheme decreased by 29.78%, the carbon emission cost decreased by 23.19%, and the customer satisfaction increased by 14.49%. Compared with the single distribution center mode, the distribution cost of the multi-depot mode is reduced by 27.30%, the carbon emission cost is reduced by 48.89%, and the customer satisfaction is increased by 13.11%, which brings certain management enlightenment for cold chain logistics enterprises.

In order to investigate the influence of government regulatory mechanisms on the fulfillment of social responsibility by port enterprises, the evolutionary game models of local governments and port enterprises under different regulatory mechanisms are constructed and numerical simulations are conducted respectively.The study shows that there is no evolutionary stability strategy between the two parties under the static punishment,reward and punishment regulatory mechanism.Under the dynamic punishment,reward and punishment regulation mechanism, both parties can reach the only evolutionary stability strategy. The government′s dynamic punishment regulation mechanism is better than the dynamic reward and punishment regulation mechanism. In this case, the maximum strength of punishment and the cost of government regulation affect the probability of port enterprises to fulfill CSR. Corresponding suggestions are provided for local governments to promote the fulfillment of CSR by port enterprises.

In order to extract the oscillations data more accurately, the evolution characteristics of traffic oscillations are further explored. First, the growth characteristics of the oscillation are analyzed by fitting the speed standard deviation of the oscillating vehicle using a single exponential decay function. Secondly, the short-term Fourier transform is used to extract the change of frequency intensity to track the acceleration and deceleration change points of the oscillation head car and the deceleration stage, so as to explore the propagation mechanism of the oscillation. The analysis results show that the standard deviation of the vehicle′s speed increases along the concave convoy during the oscillation. The initial stage of the oscillation shows the forerunner stage, this stage of the speed change is slow, and as the speed fluctuation increases, the oscillation growth is spread upstream. Affected by the randomness and instability of vehicles in the interweaving area, the oscillations in different lanes of the main line vary in duration, amplitude and intensity of the deceleration phase due to different vehicle behaviors.