Punishment Mechanism Research Articles

Promotion, Disintegration and Remediation of group cooperation under heterogeneous distribution system based on peer rating

Collective action, like public goods game, often requires mechanism intervention to avoid exploitation by free-riders. Reward and punishment are two widely studied solutions, but the balance of budgets and diverse behavioral motivations need further emphasis. Here, we introduce a heterogeneous distribution system based on peer rating (PRHDS), a disguised reward and punishment mechanism under budget balancing, and consider a comprehensive range of behavioral motivations. Preliminary studies show that PRHDS has the potential to promote cooperation, but is too sensitive to random exploration. This vulnerability is attributed to the exploitation of prosocial cooperators by egoistic cooperators. Afterward, we propose three punishments for minority raters and find that two monetary punishments can compensate for the above limitation. A notable finding is that the cooperation potential at medium multipliers seems to exceed that at high multipliers. In addition, our further analysis suggests that PRHDS may perform better when strategy differentiation is considered. This study reveals the possible limitations of PRHDS as a solution to collective social dilemma, and may have some enlightening significances for seeking the countermeasures.

Analysis of collaborative urban public crisis governance in complex system: A multi-agent stochastic evolutionary game approach

As the world economy is leaping forward, countries have been exposed to the constant threat of public crises. Accelerated urbanization has currently led to a proliferation of urban public crisis events. Conventional governance methods become ineffective under the effect of the highly uncertain and dynamic nature of urban public crisis events. Accordingly, this study primarily aims to facilitate the construction of a collaborative multi-agent alliance in urban public crisis governance. Thus, evolutionary game theory is combined with system dynamics, Gaussian white noise stochastic disturbance terms are introduced to model the complex characteristics of urban public crisis events, and evolutionary game models and stochastic evolutionary game models are built for three types of groups (i.e., local governments, enterprises, and the public) to study the evolutionary process of group strategy choice behavior with or without central government supervision. The result of this study suggests that under the effect of the complexity factor, the central government's dynamic reward and punishment mechanism plays a role of in reaching a facilitative and stable alliance for collaborative urban public crisis governance. Furthermore, contributions, unresolved issues and vital challenges are identified. In this study, a solid basis is laid for future research and widespread use of collaborative urban public crisis governance, which provides a reference for stakeholders in the field of urban public crisis governance.

An efficient caching security approach for content‐centric mobile networks in internet of things systems

AbstractWith the expansion of internet of things (IoT), the IP‐based network architecture has been difficult to support the development of IoT. Content‐centric mobile networking (CCMN) models are based on naming the content to get rid of address‐space scarcity, caching the content at intermediate nodes to provide efficient data delivery, which can solve the development bottleneck of IoT. The in‐network caching is a key factor to enable practical deployments of CCMN. And it is also subject to serious security threats of cache pollution attacks (CPA), which can tamper the distribution of content and reduce the advantages of built‐in cache. In addition, the identity and trust mechanism of mobile devices are also important factors hindering the deployment of CCMN. However, existing caching security mechanisms cannot be applied directly, because the features of the built‐in cache, including ubiquity, mobility, and heterogeneity, bring new challenges on designing the caching security mechanism against CPA. In this article, we propose a lightweight CPA detection and defense approach for CCMN. We first model the attack and defense process of CPA as a dynamic game, which can optimize the performance of the attack detection. Then, we adopt popularity analysis to detect CPA, and accurately estimate the current network and attack state by a logical regression algorithm. Our design can also intelligently maintain the identity and trust mechanism. Finally, we design a punishment mechanism to deal with the attackers. Extensive simulations demonstrate that our approach can detect CPA efficiently, mitigate the impact of CPA effectively. Our game model can reduce the impact of locality‐disruption attack and false‐locality attack by 60% and 30%, respectively, compared with the state‐of‐the‐art methods. When the game reaches Nash equilibrium, our model can reduce the impact of CPA on cache hit ratio by 55% on average.

Research on Evolutionary Game of Collaborative Innovation Among High - Tech Industries - From the Perspective of Trust Relationship

In the era of 'Internet+' background, large flow, strong liquidity and short shelf life become the characteristics of information, technology specialization, increasing complexity, it is difficult for individual enterprises to grasp all the elements needed for successful innovation. In order to study the problem of collaborative innovation among high-tech inadustries, from the perspective of trust relationship between subjects, an evolutionary game model of collaborative innovation is constructed based on the technology intensity and spillover of high-tech industries, and the strategy evolution of subjects under market mechanism and government regulation is analyzed. The results show that innovation output and technology transformation ability promote collaborative innovation; r & D costs, 'free rider' income and so on hinder the collaborative innovation between industries; the influence of trust relationship and distribution coefficient on collaborative innovation depends on the situation. In addition, through simulation, it is found that the reward and punishment mechanisms of policies have their own advantages and disadvantages and complement each other, and the impact of supervision on collaborative innovation is diminishing marginal benefits. It is found that a reasonable policy combination can stimulate innovation potential more effectively. It is proposed that the strong technology spillover and mutual solubility between high-tech industries also lead to technology imitation. In the process of collaborative R & D, it is necessary to establish a solid strategic partnership, adhere to trust rationality, grasp the scale, and promote collaborative innovation. Under the principle of trust rationality, information sharing, cost sharing, and efforts to increase technology and information output can increase innovation benefits; the government should follow the economic law when making policies, rationally combine policies, make good use of the 'push' and 'pull' of the policy reward and punishment mechanism, promote collaborative innovation among industries, and enhance the driving force of innovation for economic development.

A multi-class teaching–learning-based optimization for multi-objective distributed hybrid flow shop scheduling

Distributed hybrid flow shop scheduling problem (DHFSP) has attracted some attention. In this study, DHFSP with sequence-dependent setup times is studied and a multi-class teaching–learning-based optimization (MTLBO) is proposed to minimize makespan and maximum tardiness simultaneously. A two-string representation is adopted. s classes are formed to improve search efficiency by implementing reward and punishment mechanism among them. Class evaluation is introduced and two teacher phases and one learner phase are applied in the evolution of each class. Elimination process acts on the worst class to avoid the waste of computing resource. A number of experiments are conducted and the computational results demonstrate that MTLBO is a very competitive method for DHFSP.

Resource booms and group punishment in a coupled social-ecological system

Climate change is altering the dynamics of common pool resources around the world. We design an experimental game that varies both the flow of natural resources and the group punishment mechanisms available to resource users. Our experiments show that subjects are more likely to over-harvest when resource flows are high. But group punishment mechanisms that rely on social ostracism (as opposed to fines or fees) are more effective at mediating the desire to over-harvest when resources are booming. This suggests that collective management systems based on social norms, exclusion, and ostracism may be more resilient to unexpected changes in resource dynamics and supports bottom-up, as opposed to top-down, management of common pool resources.

Research on digital transformation strategy and subsidy mechanism of manufacturing supply chain based on differential game

<abstract> <p>In the current research on the impact of the government on the digital transformation of enterprises, scholars have neglected the synergies of the supply chain and the mechanisms for regulating and assessing the subsequent behaviour of enterprises. Therefore, this paper uses differential games to study the optimal strategy of cost sharing in manufacturing supply chains during the digital transformation of upstream and downstream firms with government subsidies. The equilibrium game strategy is used with the government and parts of the supply chain as players under different models of cooperation, independent operation and government awards and punishments. For each model, the equilibrium results are solved, compared and simulated. The results show that the government reward and punishment mechanism can effectively suppress the "double marginal effect" and improve digitalization and benefits of enterprises. Meanwhile, the benefit level of enterprises under the government reward and punishment mechanism is related to the target digitalization level and the strength of the reward and punishment. When the target digitization level is relatively low at the early stage of digital transformation, the government reward and punishment mechanism is more likely to promote the level of benefits of enterprises. When the target digitalization level is relatively high in the middle and late stages of digital transformation, the government needs to reasonably set the strength of the reward and punishment in order to effectively promote digitalization and economic development.</p> </abstract>

Research on the Impact of Internet Finance on Human Resource Management in Banks

In the process of continuously improving the level of information technology, internet finance information technology is gradually maturing, bringing development opportunities to banks, while human resource management also faces severe challenges. This article will provide an overview of the impact of internet finance on banks in various aspects, including trading platforms, product homogenization, and management concepts. It is believed that the human resource management of banks in the context of internet finance has problems such as weak management awareness, single talent recruitment, incomplete assessment, reward and punishment mechanisms, salary mechanisms, and incomplete employee training systems. Propose optimization strategies for bank human resource management, which require innovative thinking in internet finance management, enhancing the rationality of talent employment, constructing a human resource management model, improving performance reward and punishment mechanisms, and increasing training to enhance professional abilities.

Research and experiment on global path planning for indoor AGV via improved ACO and fuzzy DWA.

In order to obtain an optimal trajectory for indoor AGV, this paper combined an improved ACO and fuzzy DWA (IACO-DWA) algorithm, which can provide an optimal path with collision-free under higher optimization efficiency. The highlights of this paper are detailed as follows: Firstly, an improved adaptive pseudo-random transition strategy is adopted in the state transition probability with an angle factor. A reward and punishment mechanism is introduced in the pheromone updating strategy, then a path optimization strategy called IACO is proposed for the more optimized path. Secondly, IDWA adopted three fuzzy controllers of direction, security and adjustment coefficients through evaluating directional and safety principles, then improving the angular velocity by processing the linear velocity with linear normalization. By adapting to the changes of the environment, the IDWA parameters can be dynamically adjusted to ensure the optimal running speed and reasonable path of AGV. Thirdly, aiming to deal with the path-planning problem in complex environments, we combined IACO with IDWA, the hybrid algorithm involves dividing the globally optimal path obtained from IACO planning into multiple virtual sub-target points. IDWA completes the path planning by switching between these local target points, thereby improving the efficiency of the path planning. Finally, simulations is verified by Matlab and experiment results on the QBot2e platform are given to verify IACO-DWA algorithm's effectiveness and high performance.

The impact of regulatory mechanisms on vulnerability disclosure behavior during crowdsourcing cybersecurity testing.

There are various regulatory mechanisms to coordinate vulnerability disclosure behaviors during crowdsourcing cybersecurity testing. However, in the case of unclear regulatory effectiveness, enterprises cannot obtain sufficient vulnerability information, third-party crowdsourcing cybersecurity testing platforms fail to provide trusted services, and the government lacks strong credibility. We have constructed a tripartite evolutionary game model to analyze the evolutionary process of the equilibrium of {legal disclosure, active operation, strict regulation}, and the paper reveals the impact of three regulatory mechanisms. We find that these participants' positive behaviors are in a stable state. Higher initial willingness accelerates the speed of reaching the evolutionary stability of the system, and this equilibrium is satisfied only if the governmental regulatory benefits are sufficiently high. Regarding the punishment mechanism, increased punishment for enterprises causes them to adopt positive behaviors faster, while the opposite occurs for platforms; increased punishment for platforms drives both participants to adopt positive behaviors faster. Concerning the subsidy mechanism, increased subsidy to enterprises causes them to adopt legal disclosure behaviors faster, while platforms remain unresponsive; increased subsidy to platforms motivates both players to choose their own positive behaviors. In terms of the collaborative disclosure mechanism, excessive collaborative costs reduce the platforms' willingness to operate actively, which decreases the enterprises' incentives to disclose vulnerability legally. These findings guide the government to establish suitable mechanisms to regulate the participants' behavior and promote the healthy development of the cybersecurity crowdsourcing industry.

The supervision game of economically motivated adulteration behavior of fresh agricultural product suppliers

<p style='text-indent:20px;'>Economically motivated adulteration (EMA) of fresh agricultural product suppliers is a serious threat to public health. In this paper, considering the deliberate and concealed nature of EMA, the social benefits brought by the effective supervision and the "free riding" behavior of suppliers, we build a three-party regulatory game model composed of local governments, dealers, and suppliers to study how to reduce the EMA behavior of fresh agricultural product suppliers. The results show that (a) Since local governments cannot effectively reduce EMA of suppliers only by increasing the incentives, it is necessary to establish a complete reward and punishment mechanism. (b) The government and dealers can increase the cost of EMA by increasing adulteration penalties and improving detection methods so that reduce suppliers' EMA. (c) Compared with the inspection cost, the local governments' penalties for inspection failures are a decisive factor affecting dealers' inspection methods. When the local governments' penalties are strong, even if the cost of improving inspection is higher, the dealers will actively improve the inspection level. Our study shows that the tripartite coordinated supervision mechanism can effectively curb the EMA behavior of suppliers, thereby ensuring the quality safety of the fresh agricultural product supply chain.</p>

Multiagent Game of Intelligent Building Detection and Its Harm Rumor Analysis

The “14th Five‐Year” development plan of the construction industry of the Chinese government makes it clear that intelligent buildings are the general direction of future construction development. However, the occurrence of construction accidents in recent years has brought huge casualties. With the rapid development of third‐party inspection institutions, their judgment has become an important threshold for the access to smart buildings. The failure of inspection institutions to detect the renewal of leases may lead to the failure of intelligent systems, leading to the failure of construction equipment and causing major safety accidents such as the uncontrolled fall of construction elevators and the fall of tower crane items. As the supervisor of third‐party inspection institutions, government functional departments are responsible for the failure of intelligent systems. All of them undertake the important role of intelligent building security. This paper builds an evolutionary game model with government regulatory authorities, third‐party testing institutions, and intelligent construction enterprises as the three game parties, based on variables that affect the factors of the players, and discusses the possible scenarios of the stabilization strategy, which draws the following points: (1) the increase of incentives and penalties by the government is conducive to promoting the construction of high‐quality intelligent buildings and the normative behavior of the third party without intention to seek rent, but the increase of incentives will not be conducive to the government fulfilling its regulatory responsibilities. (2) The reasonable reward and punishment mechanism set by the government must meet the condition that the sum of rewards and punishments for all parties is greater than their speculative returns so as to ensure the safety of intelligent buildings in the evolving and stable market environment. (3) The accountability of the superior government for the dereliction of duty of the regulatory authorities is of great significance to enhancing the stability of enterprises in building high‐quality intelligent buildings. (4) It is also an effective way to avoid the construction of low‐quality smart buildings by improving the listed sales income of enterprises and increasing the rent‐seeking cost of enterprises. Combined with the improved SIR model, it is calculated that after the occurrence of a construction accident, when the rumor first appears, the rumor will quickly spread out of control without timely management and supervision. The conclusion of the game model provides a great reference value for the government’s regulatory behavior. The quality of the government’s regulatory behavior is directly related to the future of high‐quality development of intelligent construction and also expands the application field of evolutionary game theory and SIR theory. The government should actively manage third‐party testing institutions and intelligent building enterprises before construction accidents happen. After the accident, strict management and supervision should be carried out on well‐known bloggers before the emergence of rumors so as to reduce the possibility of standardizing individuals’ reprinting of rumors and avoid the uncontrollable negative impact in the later period, thus seriously affecting the credibility of government supervision.

The governance of manufacturers’ greenwashing behaviors: A tripartite evolutionary game analysis of electric vehicles

Greenwashing seriously undermines the efforts toward a low carbon economy, and distorts quality information for green products including electric vehicles (EVs). However, it remains unclear how greenwashing behaviors (GWBs) can effectively be governed. Considering the formation mechanism of GWBs and collusion in EV certification processes, we constructed a tripartite evolutionary game model based on strategic interactions among manufacturers, certification authorities, and governments. Subsequently, we investigated the evolutionarily stable strategies (ESSs) and corresponding conditions of each game player and then conducted a simulation to explore the participants’ system equilibrium interactions. We also evaluated vital factor changes in three stakeholders’ decision-making, including initial strategy selection, the related cost, penalty, and reward. The results demonstrate that 1) the initial strategy selection is critical to the evolution of equilibrium strategies; 2) appropriate reward and punishment mechanisms can affect decision-making speed, which takes less time to reach stable evolution, and 3) related costs are acritical factors for every player in strategy selection. High supervision costs are severe impediments to governments aiming to enforce regulations, but lower certification and production costs could prompt manufacturers and certification authorities to choose appropriate strategies. This paper provides a theoretical explanation for GWBs, makes constructive suggestions for preventing manufacturers’ greenwashing, and sheds new light on an effective governance model for EVs.

The effectiveness of prosocial policies: Gender differences arising from social norms.

We study policies aimed at discouraging behavior that produces negative externalities, and their differential gender impact. Using driving as an application, we carry out an experiment where slowest vehicles are the safest choice, whereas faster driving speeds lead to higher potential payoffs but higher probabilities of accidents. Faster speeds have a personal benefit but create a negative externality. We consider four experimental policy conditions: a baseline situation, a framing condition in which drivers are suggested that driving fast violates a social norm, and two punishment conditions, one exogenous and one endogenous. We find that the most effective policies use different framing and endogenously determined punishment mechanisms (to fast drivers by other drivers). These policies are only effective for female drivers which leads to substantial gender payoff differences. Our data suggest that these results arise from differences in social norms across genders, thus opening the way to designing more effective policies.

Inequality impacts of ETS penalties: A case study on the recent Chinese nationwide ETS market

Climate change induces inequality, and climate policies to cope with climate change may unexpectedly further increase inequality. Within the domains of climate policies, punishment mechanisms are vital to ensure policy effects; nevertheless, very few studies have focused on inequality impacts of punishment mechanisms. In this paper, we attempt to narrow the research gap by using a dynamic recursive Computable General Equilibrium (CGE) model to analyze how the designed ETS penalties affect inequality in China. The CGE model results show that ETS penalties induce uneven distribution of mitigation costs, output loss, and emission abatement among the sectors. ETS penalties slightly decrease household income and consumption inequality, denoted by the Gini coefficients; however, people's negative feelings about inequality are positively related to ETS penalties. The results imply that the current Chinese nationwide ETS market is not stringent enough to effectively achieve mitigation targets. In such a loose ETS policy, ETS penalties cannot give full play to regulate emission non-compliance and thus have minimal impacts on inequality. Hence, worldwide policymakers should not design too loose ETS policies to avoid undermining ETS punishment mechanisms.

Research on Decision-Making Based on the Three-Party Evolutionary Game of Tourists, Scenic Spots, and Government

The continuous upsurge of tourism consumption activities has promoted economic development, but at the same time, it has also produced numerous problems, such as low-quality service and high admission prices at scenic spots, which are not conducive to the sustainable development of tourism. In this paper, in view of the phenomenon of low-quality service of scenic spots, a three-party evolutionary game model of scenic spots, tourists, and government is constructed under the participation of tourists and the reward-subsidy mechanism and punishment mechanism, and a simulation analysis is performed using the NetLogo platform. The results show that, under the reward-subsidy and punishment mechanisms, the service strategy selection of scenic spots will eventually evolve to provide high-quality services, tourists will eventually choose the no-complaint strategy, and the government will eventually evolve to provide active supervision.

How to Promote Quality and Equity of Early Childhood Education for Sustainable Development in Undeveloped Rural Areas of China: An Evolutionary Game Study

To promote the sustainable development of early childhood education (ECE) in undeveloped rural areas of China, it is vital to guarantee high-quality equity for all children from disadvantaged backgrounds. Focusing on analyzing the efficacy of the Chinese governments’ policy support to promote quality and equity, the study constructs a tripartite evolutionary game model of ECE providers in undeveloped rural areas, local governments, and central government, and correlates the governments’ financial investment with the regulation and supervision in ECE as strategy combination via a reward-punishment mechanism. Through the evolution process of “change-adjustment-convergence” of behavioral decisions, each party seeks optimization to achieve the ideal stable equilibrium state. The numerical simulation is used to verify the dynamic evolutionary process. The simulation results presented the governments’ subsidies and reward, regulation, and supervision have a substantive effect on the improvement of quality and equity. Consequently, the governments should strengthen the regulation and supervision of rural ECE, formulate a more reasonable reward and punishment mechanism, and adjust and optimize the policies and measures to improve the efficiency of educational funding. This study can provide reference value for the optimization of relevant policies and the practical operation of new policy-making.

Study on policy synergy strategy of the central government and local governments in the process of coal de-capacity: Based on a two-stage evolutionary game method

In recent years, the Chinese government has intensively introduced a series of measures to solve the problem of overcapacity in the coal industry, but the effect is not ideal. Policy synergy between the central government (CG) and local governments (LGs) can effectively reduce overcapacity and promote energy transition. Based on China's fragmented administrative management system and public policy process attributes, this study investigates the CG-LGs’ policy synergy strategy choice and influencing factors in the process of coal de-capacity in China using a two-stage evolutionary game model. The results show that: first, in the policy formulation stage, reducing policy formulation costs and increasing public credit losses can prompt the CG to consider provincial heterogeneity, while reducing policy formulation costs and increasing political gains and administrative penalties can prompt LGs to actively negotiate with the CG to develop policies. Second, in the policy implementation stage, the positive net income of LGs can result in an ideal situation (i.e., the CG chooses the loose supervision strategy, but LGs still adopt the strict enforcement strategy). Reducing implementation costs, enhancing environmental quality, and establishing reasonable reward and punishment mechanisms prompt LGs to implement a de-capacity policy. Third, the CG's strategic choice in the policy formulation stage impacts the evolution of the strategies of the two subjects in the policy implementation stage. The above results promote policy synergy between CG and LGs and provide insights for policymakers for designing an effective coal production capacity governance model.

Efficient CP-ABE Scheme Resistant to Key Leakage for Secure Cloud-Fog Computing

<p>With the evolution of the Internet, people’s lives are growing increasingly intelligent, and massive amounts of data are also generated. The combination of cloud computing and fog computing can store and process it efficiently. However, a new and urgent challenge has emerged about how to ensure that data in cloud and fog nodes are accessed securely. In order to address this issue, we propose an efficient anti-key leakage CP-ABE (EAL-CP-ABE) scheme based on cloud-fog computing. In our scheme, we achieve the identity tracing of the authorized user who leaked the private key by means of white-box tracing and the maximum number of user accesses. Meanwhile, we design a reward and punishment mechanism which rewards those with a good reputation and revokes authorized users who have exceeded the access limit or sold private keys to further reduce the possibility of key leakage. Due to limited local resources, we use fog nodes to achieve outsourced decryption of ciphertext. In addition, the detailed security analysis is provided to prove that the EAL-CP-ABE scheme is IND-CPA secure and traceable based on the hardness assumptions. Finally, we demonstrate that the EAL-CP-ABE scheme is efficient through the performance analysis.</p> <p> </p>

Traffic Police Punishment Mechanism Promotes Cooperation in Snowdrift Game on Lattice

Traffic Police Punishment Mechanism Promotes Cooperation in Snowdrift Game on Lattice