Evolutionary dynamics of tax-based strong altruistic reward andpunishment in a public goods game

In public goods games, punishments and rewards have been shown to be effective mechanisms for maintaining individualcooperation. However, punishments and rewards are costly to incentivize cooperation. Therefore, the generation ofcostly penalties and rewards has been a complex problem in promoting the development of cooperation. In real society,specialized institutions exist to punish evil people or reward good people by collecting taxes. We propose a strong altruisticpunishment or reward strategy in the public goods game through this phenomenon. Through theoretical analysis and numericalcalculation, we can get that tax-based strong altruistic punishment (reward) has more evolutionary advantages thantraditional strong altruistic punishment (reward) in maintaining cooperation and tax-based strong altruistic reward leads toa higher level of cooperation than tax-based strong altruistic punishment.

Bioinformatic Game Theory and Its Application to Biological Affinity Networks

The exact evolutionary history of any set of biological taxa is unknown, and all phylogenetic reconstructions are approximations. The problem becomes harder when one must consider a mix of vertical and lateral phylogenetic signals. In this paper we propose a game theoretic approach to constructing biological networks. The key hypothesis is that evolution is driven by distinct mechanisms that seek to maximize two competing objectives, taxonomic conservation and diversity. One branch of the mathematical theory of games is brought to bear. It translates this evolutionary game hypothesis into a mathematical model in two-player zero-sum games, with the zero-sum assumption conforming to one of the fundamental constraints in nature in mass and energy conservation. We demonstrate why and how a mechanistic and localized adaptation to seek out greater information for conservation and diversity may always lead to a global Nash equilibrium in phylogenetic affinity. Our game theoretic method, referred to as bioinformatic game theory, is used to construct network clusters. As an example, we applied this method to clustering of a multidomain protein family. The protein clusters identified were consistent with known protein subfamilies, indicating that this game-theoretic approach provides a new framework in biological sequence analysis, especially in studying gene-genome and domain-protein relationships.

Evolutionary game analysis of problem processing mechanism in new collaboration

This paper analyzes a problem processing mechanism in a new collaboration system between the main manufacturer and the supplier in the"main manufacturer-supplier"mode,which has been widely applied in the collaborative development management of the complex product.This paper adopts the collaboration theory,the evolutionary game theory and numerical simulation to analyze the decision-making mechanism where one upstream supplier and one downstream manufacturer must process an unpredicted problem without any advance contract in common.Results show that both players'decision-makings are in some correlation with the initial state,income impact coefficients,and dealing cost.It is worth noting that only the initial state influences the final decision,while income impact coefficients and dealing cost just influence the decision process.This paper shows reasonable and practical suggestions for the manufacturer and supplier in a new collaboration system for the first time and is dedicated to the managerial implications on reducing risks of processing problems.

Exploring the dynamic evolutionary mechanism of game model on the protection of traditional villages

With the rapid improvement of urbanization and industrialization in countries around the world,how to effectively solve the rapid demise of traditional villages is a social dilemma faced by all countries,which is why a series of relevant protection regulations have been promulgated in different historical periods.However,the formulation of relevant policies is still not scientific,universal,and long-term.In this study,we constructed an evolutionary game model of local governments and residents based on the evolutionary game theory(EGT),which is used to explore the evolutionary stability strategy(ESS)and stability conditions of stakeholders under the premise of mutual influence and restriction.Besides,the study also included the analysis about the impacts of different influence factors on the evolution tendency of the game model.At the same time,numerical simulation examples were used to verify the theoretical results and three crucial conclusions have been drawn.Firstly,the strategic evolution of stakeholders is a dynamic process of continuous adjustment and optimization,and its results and speed show consistent interdependence.Secondly,the decision-making of stakeholders mainly depends on the basic cost,and the high cost of investment is not conducive to the protection of traditional villages.Thirdly,the dynamic evolutionary mechanism composed of different influence factors will have an impact on the direction and speed of decision-making of stakeholders,which provides the basis for them to effectively restrict the decision-making of each other.This study eliminates the weaknesses of existing research approaches and provides scientific and novel ideas for the protection of traditional villages,which can contribute to the formulation and improvement of the relevant laws and regulations.

Analysis of the Choice of Return Mechanism of PPP Model in Rural Human Settlement Improvement under the Rural Revitalization Strat­egy:Research Based on the Perspective of Evolutionary Game

The improvement of rural human settlement environment is a significant direction of the rural revitalization strategy.Based on the finite rational evolutionary game theory,a cooperative behavior evolutionary game model of rural human settlement environment improvement PPP model with local government,social capital and rural residents as the main game players with the reward mechanism of Government Payment and one with the reward mechanism of Viability Gap Funding are constructed.Comparing the total project revenue of two reward mechanisms,the thesis will obtain the effects of choosing the reward mechanism of rural human settlement improvement PPP.Finally,available suggestions are made to the decision of the reward mechanism of PPP project about rural human settlement environment,thus promoting the application and development of PPP in rural environmental management and to promote sustainable improvement of rural habitat improvement.

An evolutionary game theory-based machine learning framework for predicting mandatory lane change decision

Mandatory lane change(MLC)is likely to cause traffic oscillations,which have a negative impact on traffic efficiency and safety.There is a rapid increase in research on mandatory lane change decision(MLCD)prediction,which can be categorized into physics-based models and machine-learning models.Both types of models have their advantages and disadvantages.To obtain a more advanced MLCD prediction method,this study proposes a hybrid architecture,which combines the Evolutionary Game Theory(EGT)based model(considering data efficient and interpretable)and the Machine Learning(ML)based model(considering high prediction accuracy)to model the mandatory lane change decision of multi-style drivers(i.e.EGTML framework).Therefore,EGT is utilized to introduce physical information,which can describe the progressive cooperative interactions between drivers and predict the decision-making of multi-style drivers.The generalization of the EGTML method is further validated using four machine learning models:ANN,RF,LightGBM,and XGBoost.The superiority of EGTML is demonstrated using real-world data(i.e.,Next Generation SIMulation,NGSIM).The results of sensitivity analysis show that the EGTML model outperforms the general ML model,especially when the data is sparse.

Coordinated Route Planning via Nash Equilibrium and Evolutionary Computation

The coordinated route planning problem for multiple unmanned air vehicles （UAVs）, a cooperative optimization problem, also a non-cooperative game, is addressed in the framework of game theory, A Nash equilibrium based route planner is proposed. The rational is that the structure of UAV subteam usually provides some inherent and implicit preference information, which help to find the optimum coordinated routes and the optimum combination of the various objective functions. The route planner combines the concepts of evolutionary computation with problem-specific chromosome structures and evolutionary operators and handles different kinds of mission constraints in hierarchical style. Cooperation and competition among UAVs are reflected by the definition of fitness function. Simulations validate the feasibility and superiority of the game-theoretic coordinated routes planner.

Coordination game model of co-opetition relationship on cluster supply chains

The research of cluster supply chains is a new direction and a hotspot of the industrial cluster theory. On the condition of the coordination game, the enterprises may be stuck on the non-efficient equilibrium status, which becomes an important problem that must be considered on cluster supply chains. A symmetrical coordination game model is constituted to describe the competition and cooperation relationship of the same-quality manufacturers on cluster supply chains. The methods of the non-cooperation game theory and the evolutionary game theory are respectively used to analyze the model, whose parameters＇ influences under each method are then compared. It can be concluded that the analysis of the evolutionary game theory is more realistic and practical. Finally, three approaches are considered to break away from being path-dependence locked-in non-efficient status during this coordination game evolutionary process, which provide the development of cluster supply chains with an effective forecasting and Pareto optimizing method.

RTRC:A Reputation-Based Incentive Game Model for Trustworthy Crowdsourcing Service

The ubiquity of mobile devices have promoted the prosperity of mobile crowd systems, which recruit crowds to contribute their resources for performing tasks. Yet, due to the various resource consumption, the crowds may be reluctant to join and contribute information. Thus, the low participation level of crowds will be a hurdle that prevents the adoption of crowdsourcing. A critical challenge for these systems is how to design a proper mechanism such that the crowds spontaneously act as suppliers to contribute accurate information. Most of existing mechanisms ignore either the honesty of crowds or requesters respectively. In this paper, considering the honesty of both, we propose a game-based incentive mechanism, namely RTRC, to stimulate the crowds to contribute accurate information and to motivate the requesters to return accurate feedbacks. In addition, an evolutionary game is designed to model the dynamic of user-strategy selection. Specially, the replicator dynamic is applied to model the adaptation of strategy interactions taking into account the dynamic nature in time dependence and we also derive the evolutionarily stable strategies(ESSs) for users. Finally, empirical results over the simulations show that all the requesters and suppliers will select honest strategy to maximize their profit.

The evolution of cooperation in public good game with deposit

The emergence of cooperation still remains a fundamental conundrum in the social and behavior sciences.We introduce a new mechanism,deposit mechanism,into theoretical model to explore how this mechanism promotes cooperation in a well-mixed population.Firstly,we extend the common binary-strategy combination of cooperation and defection in public good game by adding a third strategy,namely,deposit cooperation.The players with deposit cooperation strategy pay a deposit in advance to obtain the benefits of public good at a lower contributions compared with the players with cooperation strategy,when the provision of public good is successful.Then,we explore the evolution of cooperation in the public good game with deposit by means of the replicator dynamics.Theoretical computations and stimulations show that the deposit mechanism can promote cooperation in a well-mixed population,and the numbers of equilibrium point are determined by variables of public good game.On the one hand,when the coexistence of cooperators and defectors is the stable equilibrium point in the evolutionary system,increasing the threshold of public good and adopting the weak altruism way for share benefits can enhance the level of cooperation in the population.On the other hand,if the coexistence of deposit cooperators and defectors is the stable equilibrium point,it is effective to promote the deposit cooperation by lowering the values of discount and deposit,and raising the threshold of public good.

A 2-stage strategy updating rule promotes cooperation in the prisoner's dilemma game

In this study,we propose a spatial prisoner＇s dilemma game model with a 2-stage strategy updating rule,and focus on the cooperation behavior of the system.In the first stage,i.e.,the pre-learning stage,a focal player decides whether to update his strategy according to the pre-learning factor β and the payoff difference between himself and the average of his neighbors.If the player makes up his mind to update,he enters into the second stage,i.e.,the learning stage,and adopts a strategy of a randomly selected neighbor according to the standard Fermi updating rule.The simulation results show that the cooperation level has a non-trivial dependence on the pre-learning factor.Generally,the cooperation frequency decreases as the pre-learning factor increases;but a high cooperation level can be obtained in the intermediate region of- 3〈 β 〈-1.We then give some explanations via studying the co-action of pre-learning and learning.Our results may sharpen the understanding of the influence of the strategy updating rule on evolutionary games.

A Novel Dynamic Decision Model in 2-player Symmetric Repeated Games

Considering the dynamic character of repeated games and Markov process, this paper presented a novel dynamic decision model for symmetric repeated games. In this model, players＇ actions were mapped to a Markov decision process with payoffs, and the Boltzmann distribution was intousluced. Our dynamic model is different from others＇ , we used this dynamic model to study the iterated prisoner＇ s dilemma, and the results show that this decision model can successfully be used in symmetric repeated games and has an ability of adaptive learning.

基于演化博弈的拟态防御策略优化

网络空间拟态防御是近些年出现的一种主动防御理论,以异构冗余和动态反馈机制不断调整执行环境来抵抗攻击。然而,面对黑客的多样化攻击手段,仅凭借拟态防御抵抗攻击是不安全的。为了增强系统的安全防御能力,在目前已有的防御系统基础上提出更为合理的防御选取方法。将有限理性的演化博弈引入到拟态防御中,构建了由攻击者、防御者和合法用户组成的三方演化博弈模型,并提出了最优防御策略求解方法。该博弈模型利用复制动态方程得到了演化稳定策略。仿真实验结果表明,系统通过执行推理的演化稳定策略可以降低损失,遏制攻击方的攻击行为,对拟态防御系统中防御策略选取和安全性增强具有一定的借鉴意义。

电厂和政府行为策略演化博弈与仿真研究——基于农林生物质与煤耦合发电产业发展视角

“双碳”背景下,农林生物质与煤耦合发电成为煤电转型的新路径。政府如何制定有效的补贴和监管政策,以及电厂如何进行正确的策略选择是促进农林生物质与煤耦合发电产业发展的关键。通过构建政府和电厂的混合策略博弈模型,分析双方的博弈关系和行为策略动态演变过程,并运用系统动力学进行建模仿真,进一步探析关键因素对政府和电厂行为策略的影响路径。结果表明:政府和电厂的行为策略在较长时期里无法趋于稳定,而是在相互影响中波动变化。政府制定补贴和监管策略、提高耦合发电收益、降低耦合发电成本,都将促进更多电厂选择农林生物质与煤耦合发电,从而进一步促进产业发展。

前景理论视角下废旧动力电池回收监管演化博弈分析

废旧动力电池具有较强的负外部性,违背了新能源汽车设计的初衷。为促进废旧动力电池有效回收,将前景理论与演化博弈论耦合,综合考虑政府、企业(汽车生产)和公众之间的利益,促使政府和公众对企业进行共同监督,构建三方博弈模型。针对初始意愿、罚款组成、风险态度系数和损失规避系数的不同情况,进行模拟数值仿真,并结合现实中废旧动力电池的认识度、奖惩机制和盈利信心进行分析。研究表明:提高公众或政府的初始监督意愿,可以促进企业回收废旧动力电池;当企业的回收策略为亏损时,提高企业对公众的补偿金额、降低企业的风险态度系数和损失规避系数,可以促进企业积极回收;在废旧动力电池回收过程中,共同监督的效果优于单独监督。

基于三方演化博弈的市域产教联合体运行机制构建研究

市域产教联合体是深化现代职业教育体系建设改革的“两翼”之一,是推进产教深度融合,提高职教人才区域适应性的重要载体。基于政府统筹下的学校、企业和产业园区三方演化博弈模型,针对市域产教联合体“联而不合”的风险,探究运行机制的构建目标和策略。研究发现,良好的运行机制能够提高资源配置效率,使校企双方在积极参与产教融合时能获得更高的总收益;合理分配产教融合总收益,恰当地体现学校的贡献,满足企业对经济效益的追求;激励产业园区发挥聚合作用。据此提出,突出产业园区基础地位,构建产教协同机制;明晰合作方式和成果认定,构建分配补偿机制;推进多元主体共同参与,构建考核评价机制等策略。

基于前景理论的绿色建筑供需主体演化博弈分析

为了促进绿色建筑市场供需双方的交易,系统考虑政府激励等客观条件及主体行为特征对其决策的影响,结合前景理论和演化博弈理论构建开发商开发行为和消费者购买行为的两方博弈模型,通过数理推导和数值仿真揭示了各类因素对绿色建筑发展的影响机理。研究表明:随着供应商绿色水平的提高,不同转型能力的开发商开发绿色建筑的概率越大;消费者可接受的绿色建筑溢价会随着消费者绿色偏好的增加而增加;政府应根据供需市场环境灵活调整对开发商和消费者的补贴;主体对选择绿色建筑时带来的增量收益表现得越理性、损失风险偏好越大,越趋向选择绿色建筑。

政府参与下考虑有限注意力的CCUS项目股权投资决策分析

碳捕获、利用与封存(carbon capture, utilization and storage, CCUS)项目的规模化需要风险资本支撑,然而气候政策与项目研发的不确定性造成注意力有限的风险投资机构投资意愿较低。为打破“能源企业不改造,风险投资机构不投资”的“囚徒困境”,本文基于投资方的注意力驱动交易行为构建了能源企业-风险投资机构-政府的演化博弈模型,从政策的“显著性”事件特征入手探讨政府在CCUS项目推广中的激励作用。结果表明:(1)向风险投资机构传递“显眼”信息的政府政策有助于打破当前融资困境;(2)三方参与概率高于30%即可实现CCUS项目股权投资的帕累托最优,且概率越高,演化速度越快;(3)政府实施的补贴和风险分担机制对融资的促进作用均显著,但初期风险分担机制因无法呈现“显著性”特征而难以吸引投资方注意力,效果弱于补贴机制。鉴于此,本文从引导多主体参与,建立信息披露机制,完善政府激励机制和提高碳资产管理效率等方面提出建议,以期为加快CCUS项目商业化提供理论借鉴。

情绪因素影响下的煤矿企业冲突管理演化博弈分析

为了系统地研究非理性情绪对于煤矿企业中矿工和管理者冲突的影响,构建了基于RDEU理论的矿工与管理者冲突博弈演化模型,并对收益赋值,进行模拟仿真分析,得到了情绪对于博弈主体行为策略的影响。研究结果表明,当博弈主体带有悲观情绪时易于采取对抗性的强硬策略,反之当其带有乐观情绪时,倾向于采取让步性的缓和策略。且矿工和管理者的情绪以及对另一方选择策略的概率判断对其自身的行为决策具有显著影响。为煤矿企业有关部门实现冲突的有效管控提出了合理建议。

协同视角下中小型制造企业数字化转型三群体演化博弈分析

为缓解数字化转型背景下中小型制造企业上云难题,促进制造业高质量发展,从多方协同视角构建政府、云平台、企业的演化博弈模型,并考虑金融机构普惠金融对系统的影响作用,分析各博弈主体策略选择及系统均衡点稳定性,通过Matlab对关键影响要素进行数值仿真分析。结果表明:(1)当退税比例在一定区间范围内,政府部门增大退税力度能够有效促进企业转型升级;(2)与其他影响因素相比,云平台增值服务收取比例对于企业策略选择影响更加显著;(3)当政府对平台补贴强度小于临界值时,补贴强度的增大对平台、企业而言均具有良好激励效果;(4)政策引导金融机构推行普惠金融能够促进数字化转型系统的有效运行,研究结论可为推进多层联动的数字化转型体系建设提供参考依据。