Punishment Mechanism Research Articles

Evolutionary game and simulation analysis of tripartite subjects in public health emergencies under government reward and punishment mechanisms

Public health emergencies are critical to people’s lives and health, economic development and social stability. Understanding how to respond correctly to public health emergencies is the focus of societal attention. This paper focuses on the tripartite entities of public health emergencies: local governments, pharmaceutical enterprises and the public. On the basis of the assumption of finite rationality, it delves into the game-theoretic interaction among these groups during such crises. By constructing an evolutionary game model, this paper analyses the dynamic adjustment process of the decision-making behaviors of these three parties, leading to the identification of evolutionarily stable strategies for the local government, pharmaceutical enterprises, and the public under different conditions. Moreover, MATLAB is used to carry out simulation experiments to analyse the influence of the local government’s reward and punishment mechanism on the strategic choices of the involved parties in the game. The research findings indicate that (1) For the tripartite entities of public health emergencies, the key for strategy choices is to reduce the gain obtained from illegal production and non-cooperation with prevention and control. (2) The strength of the initial willingness to participate has a significant effect on the evolution strategies of each subject. (3) For pharmaceutical companies and the public, the incentives and penalties of local governments can promote the former’s compliance and the latter’s cooperation in prevention and control. Based on these results, countermeasure suggestions to promote mutual collaboration among local governments, pharmaceutical enterprises, and the public to jointly respond to public health emergencies are proposed.

An Evolutionary Game and Simulation Study of Work Safety Governance and Its Impact on Long-Term Sustainability Under the Supervisory System

Work safety governance is a critical component of corporate ESG (Environmental, Social, and Governance) performance, particularly in high-risk industries. Effective safety supervision systems not only protect workers’ wellbeing, a key social metric in ESG frameworks, but also enhance corporate governance through improved risk management and regulatory compliance. The supervisory system represents a major institutional innovation in China’s approach to addressing increasingly complex work safety governance challenges. This study constructs an evolutionary game model involving the central government, local government, and high-risk enterprises to analyze the evolutionary characteristics of stakeholder behaviors. Through system simulation, we examine how key parameter changes affect the stability of system equilibrium points. Our findings reveal that (1) the current supervisory system effectively incentivizes both local governments to conduct safety supervision and high-risk enterprises to comply with safety investment requirements. (2) While government penalty levels do not affect strategy combinations, both insufficient and excessive penalties slow the system’s evolution toward optimal states. (3) Local governments tend to choose non-regulatory strategies when transfer payments and enterprise subsidies are inadequate. (4) Insufficient supervision intensity from the central government leads to local government non-regulation, and although this can be addressed by increasing supervision intensity, excessive supervision reduces the system’s evolution speed toward ideal states. Based on these findings, we propose policy recommendations for rational supervision intensity control, scientific reward–punishment mechanisms, and enhanced safety information transparency. This framework provides insights into the relationship between governance mechanisms and corporate long-term sustainability, which has been shown to improve ESG standards.

Effects of punishment driven by inequity aversion on promoting cooperation in public goods games

Unlike traditional punishment mechanisms, which target individuals adopting specific strategies, punishers driven by inequity aversion focus on individuals with higher payoffs. The intensity of punishment varies based on the difference in payoffs. This paper examines the cooperation-promoting effect of this unique punishment mechanism within a structured population. Specifically, we study the evolutionary dynamics of two three-strategy systems in public goods games (PGGs), comprising cooperators, inequity-averse individuals, and defectors. Furthermore, we analyze the spatial dynamics and the evolution of characteristic spatial distributions of strategies to uncover the mechanisms underlying various phases and phase transitions. Our findings indicate that cooperation is enhanced regardless of whether the punishment is enforced by cooperators or defectors. When inequity-averse individuals cooperate in PGGs, punishing defectors at the cluster boundary fosters cooperation. However, mutual punishment among inequity-averse individuals diminishes the efficiency of cooperation promotion. For inequity-averse defectors, low punishment intensity safeguards cooperators from defector invasion, while high punishment intensity against defectors facilitates the spread of cooperation among them.

What Kind of Institutional Configuration Incentivizes Farmers’ Behavior in Ecological Value Co-Creation of Cultivated Land?

Innovating institutional incentives and promoting multiple-subject interaction and cooperation to create ecological value of cultivated land are practical requirements for the sustainable protection of cultivated land and high-quality development of agriculture and rural areas. We performed a survey questionnaire of 862 farmers in the Wuhan Metropolitan Area and used the fuzzy set qualitative comparative analysis (fsQCA) method to explore the institutional configuration effect of farmers’ behavior in the ecological value co-creation of cultivated land (FBEVCCL). The institutional configuration characteristics of the ecological value co-creation of cultivated land was analyzed, and we propose institutional optimization suggestions for the ecological value co-creation of cultivated land. The research results demonstrated the following: (1) A single institutional element does not constitute a necessary condition for the FBEVCCL, and it is necessary to analyze the institutional configuration effect. (2) Four institutional configurations lead to high-level FBEVCCL. Both formal and informal configurations have important impacts on the FBEVCCL, but the guidance, subsidies, and constraints of formal institutions are often the core driving factors, complemented by the shaping of farmers’ values to jointly enhance the FBEVCCL. The formal institutions of publicity and guidance, rewards and subsidies, and disciplinary constraints can to some extent replace each other. (3) The three institutional configurations lead to low-level FBEVCCL, and the lack of informal and formal institutions for incentives and punishments is the main reason for low-level FBEVCCL. In the future, we should further standardize the reward and punishment mechanism, enhance farmers’ green production skills, and sustainably enhance the behavior of farmers in the ecological value co-creation of cultivated land.

Supervision mechanism of integrated construction consultancy services in China: tripartite game analysis

PurposeMegaprojects contribute greatly to China’s socio-economic development. However, the diverse stakeholders of megaprojects tend to be prone to conflict and plunge the project into lower performance than expected. This study aims to investigate (1) the optimal supervision mechanism under direct supervision of the owner and (2) the optimal indirect supervision mechanism of the owner, with involvement of integrated construction consultancy (ICC) in supervision and considerations into the impact of collusion between the ICC and the general contractor on supervision efficiency.Design/methodology/approachThis study applies the principal–agent theory and game theory to design effective incentive and punishment mechanisms for direct and indirect supervision by the owner. Through the theoretical framework of game theory, it analyses the strategic interactions between the owner, the ICC and the general contractor, and evaluates the effectiveness of various supervision mechanisms based on Nash equilibrium theory.FindingsThe results showed adopting the direct supervision mechanism when the probability of the general contractor’s opportunistic behavior being detected was higher or the occurrence had a greater impact on the owner. However, the increase in supervision cost would reduce the motivation of owner’s direct supervision and prompt the owner to choose the indirect supervision mechanism. Additionally, the indirect supervision mechanism was more likely to inhibit the collusion between the ICC and the general contractor. Thus, increasing penalties for ICC would result in improved supervision of ICC, thereby improving the benefits to the owner.Originality/valueThis study examines the dynamic interaction between the owner, the general contractor and the ICC. The study establishes a game tree and game matrix based on principal–agent theory, and analyses direct and indirect supervision models to determine the Nash equilibrium and optimal supervision strategy that is in the best interest of the owner. The study advocates for improved management and supervision mechanisms and argues that tailored supervision mechanisms can significantly improve the performance of megaprojects, which is well validated in megaprojects in China.

Multi-Agent System Anti-Interference Algorithm Based on Deep Reinforcement Learning

As multi-agent systems are increasingly applied in various environments, achieving stable control under communication interference has become a key challenge. This paper proposes a stability control algorithm based on a Markov model and reinforcement learning, which enables the multi-agent system to achieve its goals through a reward and punishment mechanism under interference. The results demonstrate that the algorithm effectively handles communication interference, converges well, and ultimately completes control tasks, showing promising application prospects for the proposed solution.

Application of Reinforcement Learning in Controlling Quadrotor UAV Flight Actions

Most literature has extensively discussed reinforcement learning (RL) for controlling rotorcraft drones during flight for traversal tasks. However, most studies lack adequate details regarding the design of reward and punishment mechanisms, and there is a limited exploration of the feasibility of applying reinforcement learning in actual flight control following simulation experiments. Consequently, this study focuses on the exploration of reward and punishment design and state input for RL. The simulation environment is constructed using AirSim and Unreal Engine, with onboard camera footage serving as the state input for reinforcement learning. The research investigates three RL algorithms suitable for discrete action training. The Deep Q Network (DQN), Advantage Actor–Critic (A2C), and Proximal Policy Optimization (PPO) were combined with three different reward and punishment design mechanisms for training and testing. The results indicate that employing the PPO algorithm along with a continuous return method as the reward mechanism allows for effective convergence during the training process, achieving a target traversal rate of 71% in the testing environment. Furthermore, this study proposes integrating the YOLOv7-tiny object detection (OD) system to assess the applicability of reinforcement learning in real-world settings. Unifying the state inputs of simulated and OD environments and replacing the original simulated image inputs with a maximum dual-target approach, the experimental simulation achieved a target traversal rate of 52% ultimately. In summary, this research formulates a set of logical frameworks for an RL reward and punishment design deployed with real-time Yolo’s OD implementation synergized as a useful aid for related RL studies.

Revolutionizing automated pear picking using Mamba architecture

With the emergence of the new generation vision architecture Vmamba and the further demand for agricultural yield and efficiency, we propose an efficient and high-accuracy target detection network for automated pear picking tasks based on Vmamba, aiming to address the issue of low efficiency in current Transformer architectures. The proposed network, named SRSMamba, employs a Reward and Punishment Mechanism (RPM) to focus on important information while minimizing redundancy interference. It utilizes 3D Selective Scan (SS3D) to extend scanning dimensions and integrates global information across channel dimensions, thereby enhancing the model's robustness in complex agricultural environments and effectively adapting to the extraction of complex features in pear orchards and farmlands. Additionally, a Stacked Feature Pyramid Network (SFPN) is introduced to enhance semantic information during the feature fusion stage, particularly improving the detection capability for small targets. Experimental results show that SRSMamba has a low parameter count of 21.1 M, GFLOPs of 50.4, mAP of 72.0%, mAP50 reaching 94.8%, mAP75 at 68.1%, and FPS at 26.9. Compared with other state-of-the-art (SOTA) object detection methods, it achieves a good trade-off between model efficiency and detection accuracy.

How to Promote Data Sharing of Financial Service Providers Based on Federated Learning Technology

Data sharing between financial service providers (FSPs) can promote the reduction of financial risks, but the reality is that the FSPs may be unwilling to share data with each other due to data security problems. The emergence of federated data technology provides a convenient way to address this issue. In this article, the author proposes using a federated data sharing model for FSPs and two dynamic evolutionary games for evaluating influencing factors. Cooperation results when both FSPs reach a certain level of mutual payoff. The reward and punishment mechanisms are important means by which FSPs actively implement real data sharing. The interaction between the FSP and the federated technology service provider (FTSP) demonstrates the practicality of this new technology. The results show that the data sharing improved the data trust and supported regulatory mechanisms.

FAKTOR YANG BERHUBUNGAN PADA MOTIVASI PENGGUNA DAN IMPLEMENTASI REKAM MEDIS ELEKTRONIK DENGAN EFISIENSI PELAYANAN DI INSTALASI RAWAT JALAN RSUD KEBAYORAN LAMA

Electronic Medical Records (EMR) have emerged as an innovative solution in healthcare because EMR offers several advantages and provides a comprehensive solution to improve health information management in today’s digital era. This study applies the Technology Acceptance Model (TAM) as its theoretical foundation, aiming to evaluate the relationship between user perceptions of key factors influencing EMR implementation and service efficiency in the Kebayoran Lama Regional Hospital outpatient unit. The research design is non-experimental with a quantitative approach, utilizing numerical data analyzed through Structural Equation Modeling (SEM). The study collected data on user perceptions regarding EMR implementation and service efficiency through questionnaires filled out by 72 users as respondents, interviews with four stakeholders, and document reviews related to EMR implementation. Findings indicated significant relationships between the perceived usefulness of EMR and motivation, motivation and EMR implementation, and EMR implementation and service efficiency. These findings offer valuable insights for developing more effective EMR implementation strategies. Management can apply a fair reward and punishment mechanism, offering incentives for users consistently utilizing EMR and sanctions, such as reduced honoraria, for non-compliant users. Additionally, regular training tailored to individual needs, particularly for specialists, can improve comfort and proficiency in EMR use. Direct, responsive management support for user challenges can further boost motivation. Routine evaluations and active user involvement in feedback processes strengthen their sense of ownership toward the system, increasing motivation and commitment to EMR use.

Evolution of corporate carbon information disclosure considering the reward and punishment mechanism and new media environment

This study examines the internal logic and dynamic evolution of corporate carbon information disclosure decision making. Drawing on the game-theoretical relationship among enterprises, third-party carbon verification agencies, and local governments, we analyze the intrinsic mechanisms by which rewards, punishment, and media supervision influence corporate carbon information disclosure behavior. We further examine each participant's strategy selection process in the corporate carbon information disclosure decision making under different conditions and consider the impact of key factors on the process. The findings show that the best evolutionary stabilization strategy consists of true disclosure, compliance, and negative support. A stronger reward and punishment mechanism of carbon information disclosure helps achieve greater self-regulation and reduce false disclosure. Enterprises are increasingly choosing true disclosure as the probability of media exposure increases. Decision makers involved in the carbon information disclosure process are influenced by image loss, disclosure costs, and government regulatory costs.

Taxation Can Significantly Enhance The Effectiveness of Reward and Punishment Mechanisms in The N-Person Snowdrift Game

We extend the reward and punishment mechanisms in the N-person evolutionary snowdrift game (NSG) model by constructing a tax-based pure punishment NSG model and a tax-based pure reward NSG model. Using replicator dynamics, we established two sets of dynamic equations that describe the evolution of the frequencies of the three strategies. Over longer time scales, both systems exhibit stable states involving two strategies, with the exclusion of the third. Introducing tax factors into the reward and punishment mechanisms significantly increases the proportion of cooperators in the steady state and eliminates defectors, with the time to reach the steady state depending on the tax rate. Moreover, we found that tax-based pure reward strategies are more conducive to fostering cooperative behavior within the system compared to tax-based pure punishment strategies. We employed numerical algorithms to simulate the replicator dynamics, and the results of the dynamic equations were in perfect agreement with the numerical simulations.

Threshold incentive mechanisms for the sustainable management of public resources.

Maintaining global ecological balance is inextricably linked to the effective governance of public resources. Previous studies have proposed the introduction of incentive mechanisms to mitigate the defection behavior within the scope of public resource management to facilitate sustainable utilization. However, these incentive strategies often present a considerable financial burden. Here, we first propose a two-threshold incentive scheme based on group state objectives, where the reward mechanism is triggered when the number of cooperators is below the threshold M, and the punishment mechanism is triggered when the number of cooperators is above the threshold Q. We further introduce an incentive mechanism by considering both resource and group thresholds, where incentives are implemented separately when the number of cooperators and the current quantity of resources meet thresholds. Our theoretical results reveal that introducing these two new mechanisms can effectively maintain cooperative behavior and sustainable resources while reducing management costs. However, if the initial resource level is low, the tragedy of the commons will still occur. It is recommended that the number of developers be reduced, the allocation be lowered, or the development simply suspended and the natural regeneration of resources be waited for.

Evolutionary game and simulation study of public transport under government incentive and punishment mechanism.

Public transport plays an indispensable role in the whole public transport system. This paper makes an in-depth study on how public transport can provide passengers with higher service quality while meeting the needs of passengers. In order to achieve this research goal, this paper organically incorporates the three key subjects of government supervision, public transport and passengers into the research framework. Evolutionary game theory is used to construct the corresponding research model. It has been found that the decision-making behaviours of government regulators, public transport and passengers are intricately intertwined to influence each other in the evolutionary process. It is particularly noteworthy that the incentive or punishment measures adopted by the government have a great impact on the quality of public transport services. In addition, timely supervision and inspection of government regulatory authorities by higher authorities proved to be crucial for buses to provide stable and high-quality services. This study reveals the mechanisms of interaction between different subjects in the public transport system, particularly the government-guided incentive measures and supervision mechanism to promote the overall service level. To further support the research conclusions, this paper carries on the simulation analysis, and puts forward the countermeasures and suggestions for the bus to provide high-quality service according to the simulation results. These recommendations will help guide the government, public transport and passengers to make better decisions in the synergistic development process, thereby improving the overall level of service.

Governance structure, organizational form, and business performance: A study of the Shanxi piaohao (banks), 1820s–1930s

This paper studies Shanxi piaohao, China’s first and largest private remittance banking group, established by a group of merchants from Shanxi province. Drawing on extensive primary sources, our study offers the first empirical analysis of the piaohao’s business organisation and performance. We show that piaohao thrived but were also constrained by their own private rules. In their earlier development, they successfully overcame the commitment problem by implementing a townsmen-guarantor-based recruitment system, a centralised M-form structure, and a multilateral punishment mechanism. To align the interests of shareholders and employees, they created employment shareholders and offered a competitive compensation scheme. However, piaohao’s governance structure became obsolete as shareholders increasingly failed to monitor head managers in the later period. A drastic increase in business volume and a changing business climate also exposed the defects of a centralised structure. As modern Chinese banks rose to prominence, the piaohao were gradually outcompeted and declined.

An evolutionary game analysis of the prefabricated building supply side under dynamic government reward and punishment mechanisms

From the standpoint of the supply side of prefabricated buildings, an evolutionary game model between the government and developers is constructed under the static and dynamic reward and punishment mechanisms in order to promote the development of prefabricated buildings and realize the transformation and upgrading of the construction industry. The simulation study of the example is conducted, and the impact of policy cost, subsidy upper limit, and tax upper limit on the system’s evolutionary stability strategy is examined. The findings demonstrate that when the government chooses static subsidy and static tax policies, there is no evolutionary stable strategy in the game system. The flaws of the static reward and punishment system can be adequately compensated for by the dynamic subsidy and dynamic tax policy, leading to an evolutionary stable state. The likelihood of developers producing prefabricated structures is negatively connected with policy cost and subsidy upper limits and favorably correlated with tax upper limits, according to the dynamic reward and punishment mechanism. The findings of the study have significant implications for government policy that will encourage the widespread use of prefabricated structures in China.

Tripartite evolutionary game analysis of government reward and punishment mechanisms and agricultural resilience under a dual carbon context

PurposeUnder the “dual carbon” framework, the article explores the equilibrium points among the government, agricultural enterprises and village committees, and uses sensitivity analysis to reveal the dynamic factors affecting these stakeholders, thereby proposing methods to enhance agricultural disaster resilience.Design/methodology/approachThe article uses MATLAB to construct a game model for the three parties with interests: agribusiness, government and village council. It examines the stability of strategies among these entities. Through graphical simulation, the paper analyzes the sensitivity of agricultural enterprises carbon emissions and village committees’ rent-seeking behaviors in the decision-making process, focusing on significant factors such as government carbon tax and regulatory policies.FindingsA single government reward and punishment mechanism is insufficient to influence the strategic choices of enterprises and village committees. The cost of rent-seeking does not affect the strategic choices of enterprises and village committees. A key factor influencing whether the village committee engages in rent-seeking is the level of labor income of the village committee as an “intermediary”.Originality/valueThis paper focuses on the dynamic game between three stakeholders (the government, agricultural enterprises and village committees), seeking dynamic equilibrium and conducting sensitivity analysis through visualization to provide the government with optimal policy recommendations.

Render help or stand by? The effect of group size on third-party punishment and its neural mechanisms

Third-party punishment (TPP) is the punishment that an individual executes on a violator as a third party or observer to maintain social norms. Many studies have provided insights into the neural mechanisms of third-party punishment in group environments. Still, only some studies have focused on the neural mechanisms of third-party punishment in different group sizes. This study used EEG analysis to explore the effects of group size on third-party punishment and its neural activity characteristics from the context of gain and loss. The results show that the punishment rate and amount of the third party in the small group size and loss context were significantly higher than that in the large group size and gain context. EEG results showed that third-party punishment in small groups induced greater P2 than in large groups. In the loss context, the third-party punishment in the large group size induced more negative LNP and activated more theta band activation than in the small group. The results showed that the motivation of the third party to seek a positive reputation in the small size exceeds the balance of its economic interests and tends to punish the violator for maintaining fair norms. The loss context plays a promoting role in this process. However, in the large size, the third-party consideration of its interests was stronger than the willingness to maintain social norms. This study provided neuroscientific evidence for third-party punishment to maintain fair norms in a group environment and further explanations from neuroscience for understanding Indirect Reciprocity Theory.

Evolutionary Game and Simulation Analysis of New-Energy Vehicle Promotion in China Based on Reward and Punishment Mechanisms

In China, new-energy vehicles are viewed as the ultimate goal for the automobile industry, given the current focus on the “dual-carbon” target. Therefore, it is important to promote the sustainable development of this new-energy market and ensure a smooth transition from fuel-driven vehicles to new-energy vehicles. This study constructs a tripartite evolutionary game model involving vehicle enterprises, consumers, and the government. It improves the tripartite evolutionary game through the mechanisms of dynamic and static rewards and punishments, respectively, using real-world data. The results show the following. (1) A fluctuation is present in the sales of new-energy vehicles by enterprises and the active promotional behavior of the government. This fluctuation leads to instability, and the behavior is difficult to accurately predict, which is not conducive new-energy vehicles’ promotion and sales. (2) A static reward and punishment mechanism can change the fluctuation threshold or peak value. Nevertheless, the stability of the system’s strategy is not the main reason that the government has been actively promoting it for a long time. However, enterprises are still wavering between new-energy and fuel vehicles. (3) The linear dynamic reward and punishment mechanism also has its defects. Although they are considered the stability control strategy of the system, they are still not conducive to stability. (4) The nonlinear dynamic reward and punishment mechanism can help the system to achieve the ideal stabilization strategy.

Research on the Green Transition Path of Airport Development under the Mechanism of Tripartite Evolutionary Game Model

Since existing studies primarily explore green development measures from the static perspective of a single airport stakeholder, this paper constructs an evolutionary game model to analyze the strategic choices of three key stakeholders: airport authorities, third-party organizations, and government departments, based on evolutionary game theory. By solving the stable strategy of the tripartite evolution using the Jacobian matrix, the green transition of airport development can be divided into three stages: “initiation”, “development”, and “maturity”, allowing for the exploration of key factors influencing the green transition of airport development. A simulation analysis is conducted based on real Guangzhou Baiyun International Airport data. The results indicate that the tripartite evolutionary game strategy is stable at E4(0,0,1) and the green transition of Baiyun Airport remains in the development stage. By improving the reward and punishment mechanisms of government departments, the evolutionary game strategy can be stabilized at E8(1,1,1), promoting the green transition of airport development toward the mature stage. By adjusting the game parameters, the dynamic process of green transition in airports at different levels of development and under varying regulatory environments can be effectively captured, supporting the precise formulation of corresponding policies.