Stochastic Evolutionary Game Research Articles

A comprehensive analysis of package tour quality: A stochastic evolutionary game

This study sheds light on the shaping and evolution of package tour supply strategies by examining package tour quality variance under various information conditions using evolutionary game and stochastic processes. The findings indicate that demand elasticity is critical in package tours' quality when the market information is symmetric. In an asymmetric information tourism market, adverse selection and moral hazard are the primary causes of degraded package tour quality. This study develops a compound relief mechanism comprised of stratification, reputation, and punishment mechanisms to address issues with the quality of package tours. The compound relief mechanism can assist policymakers in developing practical market regulations and policies by providing a theoretical basis for package tour quality control.

Stochastic Evolutionary Game in Food Takeaway Management with Improved Replication Dynamic Equations in Postepidemic Era

The rapid growth of the food takeaway industry during COVID-19 raises a claim to its quality improvement, especially the hygiene condition. In the takeaway chain, the food takeaway platforms, the restaurants, and the distributors are responsible for food hygiene. Accordingly, this study employs an evolutionary game to study the takeaway management among them. It constructs their traditional replication dynamic equations after parameter design. Then, it improves the equations by introducing the incentive relationships and interference factors. Through the equations, it analyzes and simulates the stochastic evolution process of the three parties, exploring the influence of some parameters’ numerical changes. After that, it concludes that the proportional expenditure coefficient for illegal rectification, the incentive coefficient, the interference intensity coefficient, and the step size can impact the efficiency, the results, or the fluctuations in the evolution, obtaining methods to theoretically control the evolution. Based on that, it puts forward targeted suggestions for constructing the contract mechanism of the takeaway, which provides a reference for the management of food takeaways practically in the postepidemic era.

Theoretical analyses of stochastic evolutionary game systems

Evolutionary game theory is a powerful tool for studying the frequency-dependent selection, where the success of one strategy depends on the frequency of all strategies. The classic theoretical approach is the replicator equation, where the strategy evolution is deterministic for infinitely large populations. However for the stochastic evolutionary systems of finite populations, one of the most difficult obstacles lies in making qualitative or quantitative analytical deductions about evolutionary outcomes. In this paper, we present a brief yet important report about quantitative analytical methods for finite-size evolutionary game systems, from the two perspectives of how to calculate fixation probabilities and stationary frequencies. Importantly, we not only review some crucial developments about theoretical approaches which were achieved in this field but also put forward two remarkable prospects for further research.

Stochastic Evolutionary Game Analysis on the Strategy Selection of Green Building Stakeholders from the Perspective of Supply-Demand Subject Populations

In construction industry, the research and development (R&D) and application of green building technologies (GBTs) are crucial to promote the development of green buildings. From the perspective of supply-demand subject populations, this paper discusses the dynamic evolution process of strategy between individuals within construction enterprise populations and between construction enterprise populations and consumer populations. Firstly, based on Moran process, the stochastic evolutionary game model of construction enterprises adopting independent innovation strategy was constructed to obtain the conditions for the dominance of independent innovation strategy. Next, the bimatrix game model of construction enterprise populations and consumer populations was constructed to obtain the equilibrium frequency of the joint strategy. Then, the influence laws of the change of parameters on game were discussed through numerical simulations. The results show that (1) reducing the cost of independent R&D cost, reducing the spillover effect coefficient, increasing the loss of technology introduction, and increasing R&D subsidies for independent innovation construction enterprises are all conducive to IIS becoming an evolutionary stable strategy. (2) The marginal effect of IIS increases with the decrease in the spillover effect coefficient, the increase in the loss of technology introduction of construction enterprises, and the increase R&D subsidies. (3) The smaller the mutation rate is, the greater the cross-price sensitivity coefficient is, the greater the green sensitivity coefficient is, the greater the probability of government active encouragement is, and the more dominant (production of green buildings, purchase of green buildings) is. Finally, relevant measures and suggestions are proposed.

Implementation of Construction Waste Recycling under Construction Sustainability Incentives: A Multi-Agent Stochastic Evolutionary Game Approach

Because of the rapid development of the economy and the process of urbanization, construction waste recycling is becoming increasingly important and should be considered. Motivated by effectively managing the construction waste recycling under sustainability incentives, the multi-agent stochastic game model is used to evaluate the evolutionary behavior of the government agencies, waste recyclers, and waste producers. To capture the uncertainty existing in the external environment, the replicator dynamic formula is integrated with Gaussian noise, and the Lyapunov exponent diagram is analyzed to illustrate the nonlinear dynamic behavior. The numerical approximations are then solved by utilizing the random Taylor expansion formula. Finally, a numerical simulation is performed to evaluate the evolutionary trajectories of the participants involved. The findings revealed that: (1) the government agency should adopt a positive supervision approach, which can encourage waste producers and recyclers to collaborate around each other; (2) lower sorting and disposal costs can enhance construction waste recycling; and (3) the existence of uncertainty in the environment around different participants will influence one’s strategy selection.

The stochastic evolutionary game analysis of public prevention and control strategies in public health emergencies

PurposeThe COVID-19 epidemic is still spreading globally and will not be completely over in a short time. Wearing a mask is an effective means to combat the spread of COVID-19. However, whether the public wear a mask for epidemic prevention and control will be affected by stochastic factors such as vaccination, cultural differences and irrational emotions, which bring a high degree of uncertainty to the prevention and control of the epidemic. The purpose of this study is to explore and analyze the epidemic prevention and control strategies of the public in an uncertain environment.Design/methodology/approachBased on the stochastic evolutionary game model of the Moran process, the study discusses the epidemic prevention and control strategies of the public under the conditions of the dominance of stochastic factors, expected benefits and super-expected benefits.FindingsThe research shows that the strategic evolution of the public mainly depends on stochastic factors, cost-benefit and the number of the public. When the stochastic factors are dominant, the greater the perceived benefit, the lower the cost and the greater the penalty for not wearing masks, the public will choose to wear a mask. Under the dominance of expected benefits and super-expected benefits, when the number of the public is greater than a certain threshold, the mask-wearing strategy will become an evolutionary stable strategy. From the evolutionary process, the government’s punishment measures will slow down the speed of the public choosing the strategy of not wearing masks. The speed of the public evolving to the stable strategy under the dominance of super-expected benefits is faster than that under the dominance of expected benefits.Practical implicationsThe study considers the impact of stochastic factors on public prevention and control strategies and provides decision-making support and theoretical guidance for the scientific prevention of the normalized public.Originality/valueTo the best of the authors’ knowledge, no research has considered the impact of different stochastic interference intensities on public prevention and control strategies. Therefore, this paper can be seen as a valuable resource in this field.

Research on Risk Management of Digital Transformation Based on Stochastic Evolutionary Game

This paper studies the risk cooperation and management problem in the process of digital transformation of Internet enterprises to help small and medium-sized enterprises. First of all, build the payment benefit matrix of both Internet enterprises and small and medium-sized enterprises. Secondly, the stochastic evolution game model between Internet enterprises and small and medium-sized enterprises is constructed based on the stochastic evolution game theory. Finally, we solve the exponential stability condition based on the determination theorem of stochastic differential equations. The research shows that (i) When the proportion of SMEs (small and medium-sized enterprises) choose not to conduct cooperative management is below a certain threshold, the Internet platform will choose the cooperative management strategy. (ii) When the proportion of the Internet enterprises choose not to cooperative management is below a certain threshold, SMEs will choose cooperative management strategy.

Large deviations and Stochastic stability in Population Games

<p style='text-indent:20px;'>In this article we review a model of stochastic evolution under general noisy best-response protocols, allowing the probabilities of suboptimal choices to depend on their payoff consequences. We survey the methods developed by the authors which allow for a quantitative analysis of these stochastic evolutionary game dynamics. We start with a compact survey of techniques designed to study the long run behavior in the small noise double limit (SNDL). In this regime we let the noise level in agents' decision rules to approach zero, and then the population size is formally taken to infinity. This iterated limit strategy yields a family of deterministic optimal control problems which admit an explicit analysis in many instances. We then move in by describing the main steps to analyze stochastic evolutionary game dynamics in the large population double limit (LPDL). This regime refers to the iterated limit in which first the population size is taken to infinity and then the noise level in agents' decisions vanishes. The mathematical analysis of LPDL relies on a sample-path large deviations principle for a family of Markov chains on compact polyhedra. In this setting we formulate a set of conjectures and open problems which give a clear direction for future research activities.</p>

Modeling the industry perspective of university-industry collaborative innovation alliances: Player behavior and stability issues

Many firms find it challenging to develop innovations, evidenced by the ever-mounting number of university-industry research alliances. This study examines the strategic choices of actors who participate in collaborative innovation alliances involving partnerships between industry and universities (U-I) based on a stochastic evolutionary game model. White noise was introduced to reflect uncertainty and the stochastic interferences caused by the differences between actors. Using the Itô stochastic differential equation theory, we analyze stability issues of player behaviors in the evolution of a collaborative innovation alliance. The results illustrate that improvements in innovation efficiency can contribute to U-I collaborative innovation alliances. High knowledge complementarity appears to be unbeneficial to the stability of these alliances, and controlling knowledge spillovers may suppress free-rider problems from both sides of the game. Our study contributes to innovation research by providing a decision-making reference for the design of U-I cooperation.

A Tripartite Stochastic Evolutionary Game Model of Complex Technological Products in a Transnational Supply Chain

A Tripartite Stochastic Evolutionary Game Model of Complex Technological Products in a Transnational Supply Chain

Distributed Resource Allocation in RF-Powered Cognitive Ambient Backscatter Networks

Ambient backscatter communications has shown the great potentials to achieve spectrum and energy efficient communications for future wireless networks (e.g., 6G). Besides, to fully exploit the ambient RF environment, backscatter communications can be combined with RF-powered cognitive networks which brings more reliable and flexible communications. In such networks, a cognitive user pair could communicate through active transmission or backscattering existing signals. In this work, we investigate the distributed resource allocation in terms of channel selection and backscatter power allocation in such setting. A unified framework integrating stochastic geometry and evolutionary game is proposed to model and analyze the distributed resource allocation. Specifically, stochastic geometry is used to analyze the average performance for users considering different communication modes. Then with the average performance obtained, evolutionary game theory is used to model the competitive resource allocation, and replicator dynamics is used to capture the dynamic change of user selections. Based on the formulated evolutionary game, a distributed channel selection and backscatter power allocation algorithm is proposed. Simulations have been performed which validate the theoretical analysis. Also the effectiveness of the proposed scheme is verified.

Network Attack and Defense Modeling and System Security Analysis: A Novel Approach Using Stochastic Evolutionary Game Petri Net

At present, most network security analysis theory assumes that the players are completely rational. However, this is not consistent with the actual situation. In this paper, based on the effectiveness constraints on both sides with network attack and defense, with the help of stochastic Petri net and evolutionary game theory, the Petri net model of network attack and defense stochastic evolutionary game is reconstructed, the specific definition of the model is given, and the modeling method is given through the network connection relationship and attack and defense strategy set. Using this model, a quantitative analysis of network attack events is carried out to solve a series of indicators related to system security, namely, attack success rate, average attack time, and average system repair time. Finally, the proposed model and analysis method are applied to a classic network attack and defense process for experimental analysis, and the results verify the rationality and accuracy of the model and analysis method.

Technical standard competition: An ecosystem-view analysis based on stochastic evolutionary game theory

This research addressed two key questions: Which one of the competing technologies can become the de facto technical standard? Under what conditions might this standard arise? Previous studies either ignored the role of complementary suppliers or adopted perfect rationality assumptions, resulting in limited explanatory powers to reality. This study constructs a technology (hardware) selection model involving software vendors and consumers from an ecosystem view. Applying the analysis method of stochastic evolutionary game, three interesting results were found. First, a single technology may become the de facto technical standard, or multiple technologies may coexist. Which equilibrium will emerge depends on the initial state of the game, the performance-price ratio of the two technologies, and the complementarity between hardware and software. Second, it is easier for hardware manufacturers to achieve market monopoly by adopting the strategy of subsidizing software vendors than by improving their own performance-price ratio. Finally, the equilibrium of coexistence may occur in industries where hardware and software are not strongly complementary to each other, and where the development of software is poor. The core mechanism that leads to the coexistence is the inconsistent decision-making pace between consumers and software vendors.

Almost sure exponential stability of two-strategy evolutionary games with multiplicative noise

In this paper, stochastic two-strategy evolutionary games in which the players are interfered by multiplicative noise are studied. We propose a stochastic replicator dynamic model to investigate the almost sure exponential stability (ASES) of general two-strategy games. Compared with the deterministic games and stochastic games with additive noise, our most interesting results concern the impact of multiplicative noise on the cooperation behavior in large well-mixed populations. We establish sufficient conditions on stochastic noise which can ensure the stable equilibrium points of general two-strategy games are indeed ASES. The stochastic Lyapunov framework is used to prove the stochastic stability, where finding the suitable Lyapunov function is the key and challenging in our case. Three different types of two-strategy game models (snowdrift games, hunt stag games and prisoner’s dilemmas) are simulated to verify our theoretical results.

Stochastic evolutionary game analysis of e-waste recycling in environmental regulation from the perspective of dual governance system

Due to the rapid popularisation and development of electronic products, recycling e-waste has become an issue which cannot be ignored. To facilitate the effective management of the recycling of e-waste, this paper constructed a tripartite Itô stochastic evolutionary game model to perform analysis of the complex game interaction between national government, local government and recyclers. Due to uncertainty in the external environment, Gaussian white noise was introduced into replicator dynamic equations, the random Taylor expansion was applied to solve the numerical approximations, certain critical values were determined so the system exhibited chaotic behaviour and numerical simulation was used to describe the dynamic evolution of tripartite. The results showed that the following: (1) The optimal evolutionary stability strategy indicated that national government should adopt positive supervision, local government should enforce environmental regulations and recyclers should choose qualified disassembly. This behavioural strategy would significantly improve the quality of e-waste recycling. (2) Enforcing environmental regulations is dependent on a subsidy-penalty coordination mechanism, whereby the subsidy is increased for qualified recyclers and there are default penalties for collusion. (3) Local government should cover more of the cost of environmental governance in order to avoid excessive costs which can decrease national government enthusiasm for positive supervision. Avoiding low costs could lead to free-riding behaviour from local government and collusion between local government and recyclers. (4) The uncertainty of the external environment impacts the decision-making of the tripartite participants. The stability of the equilibrium solution is related to the Gaussian white noise intensity and this can cause the equilibrium strategy to deviate from its original trajectory. This study will provide constructive suggestions for dual governments which will enable them to better formulate and implement environmental regulations while encouraging recyclers to participate in qualified disassembly more actively.

Three-Party Stochastic Evolutionary Game Analysis of Reward and Punishment Mechanism for Green Credit

To get rid of the development dilemma of green credit, we constructed a stochastic evolutionary game model of local government, commercial banks, and loan enterprises. We gave sufficient conditions for the stability of strategy based on the stability discriminant theorem of It o ^ ' s stochastic differential equation (SDE). Then, we discussed the impacts of incentive and penalty parameters on green credit. Through the above analysis, we got the following conclusions: (1) rewards and punishments always benefit green production and green credit, but increasing incentives is not conducive to the governments’ performance of regulatory duties; (2) punishments can better improve the convergence rate of players’ strategy than rewards; and (3) both rewards and punishments can exert an obvious effect in improving the changing degree of players’ strategy. Finally, we put forward some suggestions to optimize the green credit mechanism.

How to escape the dilemma of charging infrastructure construction? A multi-sectorial stochastic evolutionary game model

Providing effective charging infrastructure is an essential step in popularizing electric vehicles, which is conductive to improving pollution induced by traditional fuel vehicles and thus improving the environmental sustainability. The most crucial factor in escaping the dilemma of charging infrastructure construction lies in the synergies of multi-sectorial. This paper develops a stochastic evolutionary game model grounded on the strategy adoption of charging infrastructure construction involving governments (public), companies (private), and consumers (civil). We collect the data of Shanghai for simulation to compare the influence of strategy adoption. Our findings suggest that ⅰ) unlike the public and civil sector, in the current situation, the private sector will adopt the defector strategy quickly and lock in the defection state as time goes on; ⅱ) the additional cost effect caused by public-civil co-construction scenario is significant positive to the public sector but almost no impact on the civil sector; ⅲ) civil's strategy adoption has directional differences under two cost sharing scenarios (private-civil and public-civil) of charging infrastructures construction. In the private-civil scenario, if the cost of charging infrastructures construction is transferred to the civil sector, not only will it not change the private-sectorial defection state, but it will also cause civil sector to shift from cooperation to defection. However, at the same sharing ratio of the public-civil scenario, the civil sector is still willing to cooperate. ⅳ) reducing incentives of electric vehicles or increasing costs of internal combustion engine vehicles is necessary, which will not change the lock-in cooperation state of civil sector, and it can improve the cooperation willingness of publicsector.

Machine learning of stochastic automata and evolutionary games

A large categories of time series fluctuate dramatically, for example, prices of agriculture produce. Traditional methods in time series and stochastic prediction may not capture such dynamics. This paper tries to use machine learning to tune the model for a real situation by establishing a price determination mechanism on the model of stochastic automata (SA) and evolutionary game (EG). Time series volatility attributed to the chaotic process can be obtained through the learning algorithm of Markov Chain Monte Carlo (MCMC). Using machine learning through the chaotic analysis of stochastic automata and evolutionary games, we find that a more spatially aggregated distribution (smaller entropy) leads to larger time series fluctuations, regardless of the initial distribution of crops. By integrating the factors discovered in this study, we can develop a better learning algorithm in a highly volatile time series in agriculture prices.

Stochastic Evolutionary Game Analysis Between Special Committees and CEO: Incentive and Supervision

Focusing on the problem of CEO incentive and supervision, this paper constructs a three-party stochastic evolutionary game model of the CEO, compensation committee and audit committee, and analyzes the boundary conditions of the stability based on stochastic replicator dynamics equations. In addition, it carries out the numerical approximations with Taylor expansion and $$It{\hat{o}}$$ -type stochastic differential equation, and further analyzes the influence of key variables on the direction and convergence rate of game players' strategy choice. The results show that: (1) The probability that the CEO takes self-interested behavior falls faster and the probability that audit committee adopts supervisory strategy rises faster when the self-interested behavior of the CEO gets larger. (2) The audit committee has a fast convergence rate, and arrives earlier in the neighborhood of the stochastically stable state where the incentive coefficient adopted by the compensation committee is larger. (3) The increase in supervision intensity increases the convergence rate of the CEO, but decreases the convergence rate of the compensation committee. (4) According to a comparative analysis of noise intensity, the influence of random interference on the decision-making of the audit committee is more volatile.

The Stochastic Evolution Game of Knowledge Sharing in the Infrastructure PPP Supply Chain Network

Due to the complexity and randomness of environmental change, the knowledge-sharing behavior of members in the PPP supply chain will be affected by random disturbance factors. Traditional evolutionary game models cannot describe this effect. In this study, Gaussian white noise is introduced into the stochastic evolutionary game model of PPP supply chain knowledge sharing. The numerical simulation of the model is carried out to analyze the influence of parameter changes on the strategy selection of enterprise groups. The result shows that the strong knowledge power enterprise group is more sensitive with the variation of parameters than that of the weak knowledge power enterprise group. However, the weak enterprise group has a more vital willingness to share knowledge. Incentive measures can increase the knowledge-sharing benefits and promote the strong knowledge powerful enterprise group toward the sharing strategy. Increasing the mutual trust coefficient, reducing the sharing cost, and improving the enterprise’s transformation ability will help both sides to evolve into the sharing strategy more quickly. This study would have significance for PPP project performance and industry innovation.