Study on Multi-Agent Evolutionary Game of Emergency Management of Public Health Emergencies Based on Dynamic Rewards and Punishments

Evolutionary game analysis of multiple subjects in the management of major public health emergencies

The frequent occurrence of major public health emergencies (MPHEs) significantly challenges national security, economic stability, social operation and the safety of people's lives and property worldwide. Consequently, enhancing the emergency management of MPHEs is critically urgent. This paper constructs a game model involving local government, social organisations, and the public for MPHE management, exploring strategy combinations and influencing factors across various scenarios. Several results were obtained. (1) Local government, social organisations, and the public each have positive and negative strategy choices based on cost-benefit analysis, leading to eight different strategy combinations. Furthermore, all three take positive strategies as the optimal way to achieve the game equilibrium. (2) The transformation of strategy combinations is primarily influenced by the cost-benefit gap and the strategic decisions of local government. (3) Altering a subject's initial strategy value doesn't change its final choice but impacts the time to achieve a stable strategy equilibrium. The severity of local government punishments on social organisations influences their strategic choices and the time to optimal strategy, whereas rewards to the public or social organisations only affect the time to achieve this strategy. The findings of this study can not only help improve the collaborative governance system of MPHEs but also provide scientific guidance on how governments can manage MPHEs.

A tripartite evolutionary game study of low-carbon innovation system from the perspective of dynamic subsidies and taxes

Traditional manufacturing industry is in the early stages of transition to low-carbon innovative production, and is in urgent need of a low-carbon innovation system to achieve the goal of carbon neutrality. In order to realize the effective supervision of enterprise carbon emissions, this paper constructs a tripartite evolutionary game model among the corporate, government and public from the perspective of dynamic subsidies and taxes. The main results are as follows. First, the increase in government subsidies to a certain extent will help encourage companies to choose low-carbon innovative production strategies, but more subsidies are not always better. Excessive subsidies will increase the cost of government regulation and reduce the probability of government regulation. Second, the tripartite evolutionary game system does not converge under the static subsidies and taxes mechanism. But the system could quickly converges to the stable condition under dynamic subsidies and taxes. The stable point is the situation of corporate low-carbon innovation, government regulation, and public supervision. Third, the public intervention and supervision can effectively prevent the phenomenon of government misconduct and enterprises over-emission production. And the influence of public reward and punishment is more effective for the government than for enterprises.

A study on the evolution of tripartite collaborative prevention and control under public health emergencies using COVID-19 as an example

The problem of repeated epidemic fluctuations in the normalized prevention and control stage is revealed by data from January 20, 2020, to January 30, 2023. In order to improve the collaborative response of the public and government departments to public health emergencies and avoid repeated fluctuations of the epidemic, a tripartite evolutionary game model of the public, local government, and central government departments is constructed, focusing on the evolutionary paths and evolutionary stabilization strategies of the three subjects, and the influence of each element on the evolutionary results is simulated by numerical simulation in Matlab, and based on the inadequacy of the static reward and punishment mechanism, a dynamic Based on the shortcomings of static reward and punishment mechanism, dynamic reward and punishment mechanism is introduced to control the stability of the evolving system. The study shows that (1) with the increase of the initial willingness of the three parties, the rate of the public choosing the discretionary flow strategy slows down, and the collaborative prevention and control process can be accelerated. (2) The reward and punishment mechanism of central government departments has a positive incentive effect on the local government's strict prevention and control and the public's conscious isolation. Appropriately increasing rewards, formulating reasonable subsidy strategies, and increasing penalties for violations are conducive to the overall optimization of the system, and the punishment mechanism is most sensitive to the regulation of the public's discretionary mobility behavior. (3) Government departments' prevention and control costs can influence their enthusiasm for strict prevention and control and real-time supervision. Reducing the human resources cost, time cost, and financial cost of prevention and control is conducive to government departments performing their duties more responsibly. (4) The static punishment mechanism fails to make timely adjustments according to the strategy choice of each actor. It cannot control the stability of the evolving system. In contrast, the dynamic punishment mechanism considers the punishment parameters to link the casual isolation rate with the lenient prevention and control rate, which can effectively control the system's fluctuating instability and is the system's stability control strategy. Finally, combining theoretical and simulation analysis, management suggestions are made for controlling repeated fluctuations of the epidemic in practice, and the research limitations of this paper are explained.

Network evolution of diffusion in enterprise digitalization and intellectualization transformation: A technology-organization-environment framework perspective

Enterprise digitalization and intellectualization (EDI) is a crucial aspect of China's modernization process. However, uncertainty in market and business decisions hinders the EDI diffusion process in China. Therefore, this research aims to solve the uncertainty problem of EDI diffusion by examining market demand and government policy coordination. First, we utilize complex network game theory and establish a technology-organization-environment framework for the factors that influence the elements of EDI transformation by combining evidence from existing studies. Second, the network game model is constructed to analyze and optimize the updating rules in the network as a diffusion strategy that enterprises under the uncertain market can adopt. Finally, the impact of adjusting government subsidies and different premiums on the diffusion of EDI transformation strategies is examined. The degree of market diffusion and average revenue of EDI are higher after the optimization of network node strategy updating rules compared to before optimization. Further analysis reveals that only the premium effect of product pricing and inverted U-shaped subsidy support from the government affect the degree of market diffusion and the average revenue of EDI, while the other premium effects are not significant. These findings enrich research related to complex networks and nonlinear dynamic strategies. They also indicate recommendations for government policies to enhance diffusion efficiency and reasonable pricing for enterprises to promote returns.

Allocation strategy of medical supplies during a public health emergency: a tripartite evolutionary game perspective

Ensuring the rational and orderly circulation of medical supplies during a public health emergency is crucial to quickly containing the further spread of the epidemic and restoring the order of rescue and treatment. However, due to the shortage of medical supplies, there are challenges to rationalizing the allocation of critical medical supplies among multiple parties with conflicting interests. In this paper, a tripartite evolutionary game model is constructed to study the allocation of medical supplies in the rescue environment of public health emergencies under conditions of incomplete information. The game's players include Government-owned Nonprofit Organizations (GNPOs), hospitals, and the government. By analyzing the equilibrium of the tripartite evolutionary game, this paper makes an in-depth study on the optimal allocation strategy of medical supplies. The findings indicate that: (1) the hospital should reasonably increase its willingness to accept the allocation plan of medical supplies, which can help medical supplies allocate more scientifically. (2) The government should design a reasonable reward and punishment mechanism to ensure the rational and orderly circulation of medical supplies, which can reduce the interference of GNPOs and hospitals in the allocation process of medical supplies. (3) Higher authorities should strengthen the supervision of the government and the accountability for loose supervision. The findings of this research can guide the government in promoting better circulation of medical supplies during public health emergencies by formulating more reasonable allocation schemes of emergency medical supplies, as well as incentives and penalties. At the same time, for GNPOs with limited emergency medical supplies, the equal allocation of emergency supplies is not the optimal solution to improve the efficiency of emergency relief, and it is simpler to achieve the goal of maximizing social benefits by allocating limited emergency resources to the demand points that match the degree of urgency. For example, in Corona Virus Disease 2019, emergency medical supplies should be prioritized for allocation to government-designated fever hospitals that are have a greater need for medical supplies and greater treatment capacity.

Resource Support for "Mobilization-Participation" in Public Health Emergencies Based on a Complex Network Evolutionary Game

The organized system of emergency resources with the participation of social subjects features a network, which puts forward new requirements for mobilization policies for public health emergencies. Considering the "mobilization-participation" action of the relationship between the government and social resource subjects and revealing the mechanism of governance measures represent the foundation of developing effective mobilization strategies. To analyze the behavior of subjects in an emergency resource network, this study proposes a framework for the emergency actions of government and social resource subjects, as well as clarifies the functions of relational mechanisms and interorganizational learning in decision making. The game model and its rules of evolution in the network were developed by considering the interventions of rewards and penalties. An emergency resource network was constructed on the basis of a response to the COVID-19 epidemic in a city in China, and a simulation of the "mobilization-participation" game was designed and conducted. We propose a path to promote emergency resource actions by analyzing the initial situations and the interventions' effects. This article suggests that guiding and improving the initial selection of subjects under a certain reward system would be an effective path to facilitate resource support actions during public health emergencies.

Study on population behavior under home quarantine policies of COVID-19 in China based on double-layer network evolutionary games

In the practice of COVID-19 prevention and control in China, the home quarantine policy directly connects and manages the residents, which plays a significant role in preventing the spread of the epi-demic in the community. We evaluate the effectiveness of current home quarantine policy in the actual execution process based on the evolutionary game relationship between the community and res-idents. This paper establishes a double-layer coupled complex network game model, and uses the multi-agent modeling method to study the game relationship between the community and residents in the context of home quarantine policies. The results show that initial strategy of the community with strict supervision and reasonable government reward allocation will increase the proportion of the residents complying with the quarantine rule. When 80% of the communities chose to supervise strictly at the beginning, people are more likely to follow the rules. While when the residents can only get 20% of the government’s reward, the proportion of choosing to violate the quarantine rules is much higher than that when they can get 80% of the reward. Besides, the structure of small-world network and environmental noise will also affect the residents’ strategy. As the probability of reconnection of the small-world network rises from 0.2 to 0.8, the proportion of residents who choose to comply with the strategy becomes much higher. When the environmental noise reaches 0.5, the ratio of residents who choose to violate the strategy is higher than the ratio of complianc. The study is helpful to provide the basis for the government to formulate the quarantine policy and propose an optimization for making effective quarantine measures. In this way, the government can adjust the parameters to make residents achieve the possible level of compliance with quarantine policies as high as possible to contain the spread of the epidemic.

Regulating the product quality of COVID-19 antigen testing reagents: A tripartite evolutionary game analysis under China's legal framework

Personal purchases of novel coronavirus antigen detection reagents (ADRs) for self-detection have contributed to the optimization of medical resources and containment of the COVID-19 pandemic. The recurring occurrence of false testing results in China has generated concerns regarding the quality of ADRs and the testing mechanism for medical devices. Academic viewpoints and remarks on the sensitivity, application possibilities, and product innovation of ADRs may be found in the extant scientific literature. However, the current research does not explore the microscopic product quality concerns that emerge throughout the production and marketing of ADRs. To explore strategic equilibrium circumstances and behavioral evolution processes, an evolutionary game model was developed to include ADR manufacturers, third-party medical device inspection agencies, and regulatory authorities. The results reveal that the quantity of illegal incentives, the cost of regulation, and the loss of government credibility have a major impact on the decisions of regulatory authorities and determine three potential systemic equilibrium states. To maximize social welfare, ADRs should be incorporated into China's medication price monitoring system in order to manage market prices. To cut regulatory expenses, the government should employ blockchain technology for traceable network regulation of ADR product quality. The government should also protect the people's right to free speech and encourage online reporting of adverse incidents caused by ADRs. The conclusions of this article can provide many developing nations with important insights for regulating the quality of ADR products.

How to promote the hierarchical diagnosis and treatment system: A tripartite evolutionary game theory perspective

Due to the disorderly access to medical care and inefficient use of health resources, the advancement of the hierarchical diagnosis and treatment is more valued in promoting health system reform. Hence, this article integrates prospect theory into an evolutionary game model of the local government health departments, the medical institutions, and the patients in the system promotion of the hierarchical diagnosis and treatment. The simulation shows the specific influencing mechanism of the psychological perceived value of game subjects. Then by introducing the stochastic evolutionary game model, the system promotion under different medical cultures is also discussed in detail. The results indicate that for local government health departments, the amount and duration of financial subsidies are the key factors influencing the game system's evolution. For medical institutions, participating in the hierarchical diagnosis and treatment system is relatively beneficial. For patients, the recovery rate in primary hospitals matters more than the cost of treatment. Changes in the risk sensitivity coefficient will cause the equilibrium of the game system to change. However, changes in the loss avoidance factor do not change the equilibrium and only have an impact on the speed of convergence. With the health departments' intervention, patients in rural medical culture are more inclined to support the hierarchical diagnosis and treatment system than those in urban or town medical culture. Therefore, in order to promote the hierarchical diagnosis and treatment system, this article recommends that more attention should be paid to the regulatory role of health departments and the participation improvement of medical institutions and patients.

Incentive Mechanism Design in Collaborative Management of Public Health Emergencies

Against the background of the worldwide spread of the COVID-19 pandemic, due to the lack of effective collaborative incentive mechanisms, issues of dislocation and incoordination have emerged in the public health emergency management system (PES) globally. Nevertheless, the available research rarely touches upon the incentive mechanism design for the collaborative management of PES. To alleviate these inefficiency operations problems with PES, three game-theoretical decision models, including a decentralized decision model without public governance policy incentive (GPI), a decentralized decision model with GPI, and a collaborative decision model with GPI, were developed and analyzed to explore and design the incentive mechanism of PES. Furthermore, the corresponding numerical and sensitivity analyses were conducted to validate the modelling results in the article. The research results show that: (1) the collaborative decision scenario with GPI performs best, and the decentralized decision scenario with GPI performs second-best regarding the equilibrium emergency management efforts (EMEs) and utilities in all the decision scenarios; (2) an incentive mechanism of the “carrot + Stick” can effectively enhance the collaborative management of public health emergencies and its governance-driven operational performance/efficiency; (3) strengthening the performance assessment of emergency management for relevant government departments is beneficial in improving the overall emergency management efficiency of the PES; (4) reducing the cost of EMEs and enhancing the effectiveness of EMEs are conducive to improving the overall emergency management efficiency of the PES. This research provides a novel framework for designing an effective incentive mechanism to overcome the incoordination impacts and achieve collaborative operations across the PES.

Promotion Policies for Electric Vehicle Diffusion in China Considering Dynamic Consumer Preferences: A Network-Based Evolutionary Analysis

An improved understanding of how policies can promote the diffusion of electric vehicles (EVs) is critical to achieving sustainable development. Previous studies of EV diffusion dynamics have paid insufficient attention to consumer preferences. In this paper, a network-based evolutionary game model considering dynamic consumer preference is constructed to study EV diffusion. Through numerical experiments, the evolutionary processes and results of various promotion policies, including carbon taxes, production subsidies, purchase subsidies, and information policy on EV diffusion, are simulated. In particular, this paper explores the differentiated effects of supply-side policies and demand-side policies. The simulation results indicate that: (1) The effectiveness of promotion policies is sensitive to the size of the manufacturer network, and large networks can dampen periodical fluctuations in diffusion rates. (2) Supply-side carbon taxes and subsidies facilitate a steady diffusion of EVs. However, compared with the sustained effectiveness of subsidies, carbon taxes may inhibit the rapid penetration of EVs. (3) Implementing purchase subsidies in the early stages of diffusion is more effective than production subsidies, but the potential uncertainty of demand-side subsidies should be noted. (4) The impact of information policy on the evolutionary trend of EV diffusion is pronounced but is a longer-term impact, requiring a long enough implementation horizon.

Agricultural Openness and the Risk of COVID-19 Incidence: Evidence from China

At present, there are large number of articles on the impact of COVID-19, but there are only a few articles on the impact of COVID-19 and international agriculture. Agriculture product is different from other industrial products. If domestic food cannot be self-sufficient, it must be resolved through imports. This will inevitably face the dilemma between the opening up agriculture and the risk of importing COVID-19. This paper pioneered the use of entropy method, TOPSIS method and grey correlation analysis to predict the correlation between agricultural opening to the outside world and the input and spread of COVID-19. We use the correlation matrix quantifying the number of confirmed COVID-19 cases and agricultural openness to deduce that there is a significant positive correlation between the flow of agricultural products caused by China’s agricultural opening-up and the spread of COVID-19, and use the proposed matrix to predict the spread risk of COVID-19 in China. The results of the empirical analysis can provide strong evidence for decision-makers to balance the risk of COVID-19 transmission with the opening of agricultural markets, and they can take this evidence into full consideration to formulate reasonable policies. This has great implications both for preventing the spread of COVID-19 and for agricultural opening-up.

How to Improve the Cooperation Mechanism of Emergency Rescue and Optimize the Cooperation Strategy in China: A Tripartite Evolutionary Game Model

To reveal the interaction and influence mechanism between emergency rescue entities, and to explore and optimize a cooperation mechanism of emergency rescue entities, a tripartite evolutionary game model of emergency rescue cooperation based on government rescue teams, social emergency organizations, and government support institutions was constructed. The stability of each game subject’s strategy choice was explored. Simulation analysis was applied to investigate the influence mechanism of key parameters on the evolution of the game subject’s strategy combination. The research results show that government rescue teams, social emergency organizations, and government support institutions have consistent political demands and rescue targets in emergency rescue cooperation. The game subjects are driving forces for each other to choose positive strategies. The game evolution process of the emergency cooperation model shows a “mobilization-coordination” feature. At the same time, the emergency capital stock formed based on trust relationships, information matching, and institutional norms between game subjects can promote the evolution of the game system toward (1,1,1). In addition, for government organizations with limited emergency resources, the average allocation of emergency resources is not the optimal solution for emergency rescue efficiency. However, it is easier to achieve the overall target of emergency rescue cooperation by investing limited emergency resources in key variables that match the on-site situation. On this basis, combined with the practice of emergency rescues in emergencies, countermeasures and solutions are proposed to optimize the mechanism and improve the efficiency of emergency rescue cooperation.

Hybrid Model-Based Simulation Analysis on the Effects of Social Distancing Policy of the COVID-19 Epidemic

This study utilizes modeling and simulation to analyze coronavirus (COVID-19) infection trends depending on government policies. Two modeling requirements are considered for infection simulation: (1) the implementation of social distancing policies and (2) the representation of population movements. To this end, we propose an extended infection model to combine analytical models with discrete event-based simulation models in a hybrid form. Simulation parameters for social distancing policies are identified and embedded in the analytical models. Administrative districts are modeled as a fundamental simulation agent, which facilitates representing the population movements between the cities. The proposed infection model utilizes real-world data regarding suspected, infected, recovered, and deceased people in South Korea. As an application, we simulate the COVID-19 epidemic in South Korea. We use real-world data for 160 days, containing meaningful days that begin the distancing policy and adjust the distancing policy to the next stage. We expect that the proposed work plays a principal role in analyzing how social distancing effectively affects virus prevention and provides a simulation environment for the biochemical field.