基于演化博弈的领先用户知识共享行为激励机制

针对用户创新社区中未考虑企业激励机制对领先用户知识共享行为影响的问题,提出一种基于演化博弈的领先用户知识共享行为激励机制。首先,将企业和领先用户作为博弈主体,分别构建企业未采取激励措施和企业采取激励措施条件下的演化博弈模型;其次,分别对两个模型进行局部稳定性分析,以探讨系统的动态演化过程与演化稳定策略;最后,通过计算机模拟仿真,对比两种条件下领先用户知识共享行为的演化结果,分析领先用户知识共享行为的影响因素及最佳激励策略。实验结果表明,企业采取激励措施可以有效促进领先用户的知识共享行为,并且将激励分配系数控制在一定范围内时系统将达到最佳的稳定状态;最佳激励分配系数大小由知识共享成本、知识搜索成本及额外成本共同决定;知识共享成本、知识搜索成本以及激励分配系数会显著影响领先用户知识共享行为的水平。     

基于多智能体建模的知识转移激励机制的演化博弈模型与仿真

虚拟企业盟员间的知识转移,能够增加盟员企业收益;盟主企业对盟员企业知识转移的激励行为,能够适当降低盟员企业知识转移的风险和成本,提高整个虚拟企业的知识收益。依据演化博弈理论及虚拟企业知识转移基本理论,采用复杂适应系统多智能(Multi-Agent)体的整体建模仿真方法,在NetLogo仿真平台上建立知识转移激励行为的演化博弈仿真模型,在不同的收益参数下对盟主企业的知识转移激励行为与盟员企业间知识转移进行演化博弈分析,得出盟主企业在针对盟员企业知识转移行为时应采取的策略。通过对虚拟企业知识转移激励机理的演化博弈分析,将有助于虚拟企业知识转移激励机制的建立。     

Evolutionary game-based incentive models for sustainable trust enhancement in a blockchained shared manufacturing network

Shared manufacturing (SM) is an advanced manufacturing mode to solve the problems of information asymmetry and resource imbalance in the complex supply and demand relationship under the background of mass personalization. Blockchain can provide technical solutions for trust issues between manufacturing service providers and demanders, but less research has focused on the stable maintenance of a blockchained shared manufacturing network (BSMN). Industrial knowledge graph and cognitive intelligence play an important role in designing incentive mechanisms for BSMN. Based on the evolutionary game theory, this paper designs various incentive models and develops the corresponding smart contracts to encourage different enterprises to participate in the “accounting” operations, to enhance the trust and maintain the operational stability of BSMN. Firstly, the evolution mechanism of BSMN was analyzed. Then, an evolutionary game model between shared manufacturing enterprise nodes was established to deduce the stable state of BSMN. Based on the analysis of the evolutionarily stable state, an incentive model with three strategies to encourage different enterprise nodes to participate in “accounting” operations was determined, and smart contracts of the incentive models were developed. Finally, the effectiveness of the proposed incentive models was verified by simulation experiments.     

时间尺度与选择倾向性协同作用下的演化博弈模型

针对合作行为的涌现与维持问题，基于演化博弈理论和网络理论，提出了一种促进合作的演化博弈模型。该模型同时将时间尺度、选择倾向性引入到演化博弈中。在初始化阶段，根据持有策略的时间尺度将个体分为两种类型：一种个体在每个时间步都进行策略更新；另一种个体在每一轮博弈后，以某种概率来决定是否进行策略更新。在策略更新阶段，模型用个体对周围邻居的贡献来表征他的声誉，并假设参与博弈的个体倾向于学习具有较好声誉邻居的策略。仿真实验结果表明，所提出的时间尺度与选择倾向性协同作用下的演化博弈模型中，合作行为能够在群体中维持；惰性个体的存在不利于合作的涌现，但是个体的非理性行为反而能够促进合作。     

An incentive game based evolutionary model for crowd sensing networks

Crowd sensing networks can be used for large scale sensing of the physical world or other information service by leveraging the available sensors on the phones. The collector hopes to collect as much as sensed data at relatively low cost. However, the sensing participants want to earn much money at low cost. This paper examines the evolutionary process among participants sensing networks and proposes an evolutionary game model to depict collaborative game phenomenon in the crowd sensing networks based on the principles of game theory in economics. A effectively incentive mechanism is established through corrected the penalty function of the game model accordance with the cooperation rates of the participant, and corrected the game times in accordance with it’s payoff. The collector controls the process of game by adjusting the price function. We find that the proposed incentive game based evolutionary model can help decision makers simulate evolutionary process under various scenarios. The crowd sensing networks structure significantly influence cooperation ratio and the total number of participant involved in the game, and the distribution of population with different game strategy. Through evolutionary game model, the manager can select an optimal price to facilitate the system reach equilibrium state quickly, and get the number of participants involved in the game. The incentive game based evolutionary model in crowd sensing networks provides valuable decision-making support to managers.     

资源共享P2P网络的进化博弈激励模型

根据进化博弈的观点,提出一种资源共享型P2P网络博弈激励模型。对P2P网络进行描述,并对节点的行为进行量化分析,建立节点资源访问的概率模型,给出共享型P2P网络中的个体模拟动态方程。在随机博弈收益矩阵的基础上,通过调整相关参数引导P2P网络向动态平衡的状态演化。仿真实验结果验证了该模型的可行性和灵活性。     

政府补贴和成本共担如何影响平台和企业策略选择-----基于三方演化博弈

基于地方政府对工业互联网平台和加入平台的制造业企业的补贴,以及工业互联网平台与制造业企业间的成本共担,构建"政府-平台-企业"3个主体之间的非对称演化博弈模型,运用微分方程的稳定性定理分析各博弈主体的策略演化路径以及影响其策略演化的因素,并通过雅克比矩阵探讨系统的演化稳定策略.通过数值仿真分析政府补贴力度和平台成本分担比例对系统演化稳定策略的影响,界定可以促使制造业企业加入平台、工业互联网平台进行优化服务的政府补贴力度和成本分担比例的有效区间,为地方政府、工业互联网平台和制造业企业的行为决策提供理论参考.     

校企合作创新网络信任的动态演进分析

从动态角度研究校企合作创新网络中知识转移中信任的演进,构建校企合作创新网络知识转移的非对称演化博弈模型,分析了参数变化对合作创新网络信任的影响,研究表明,收益分配系数、投机收益、知识转移成本、知识转移量等因素影响了校企合作创新网络知识转移中信任的动态演化路径及结果,并通过netlogo仿真,验证了企业和高校知识转移中不同得益情况下策略的选择及信任的演进趋势。     

基于量子反应均衡的政府与共享单车企业停车管理博弈分析

共享单车停车问题是城市交通治理中最主要的问题之一,其表面为共享单车用户停车行为不规范、共享单车企业管理调度不及时所致,究其本质实为共享单车各利益主体缺乏有效协同治理机制、停车权责划分不清晰、成本分担失衡的结果.考虑政府与共享单车企业的有限理性,建立扩展式博弈的量子反应均衡(QRE)模型分析双方停车管理博弈机理,并利用Gambit软件进行算例仿真分析.研究表明:双方博弈结果随理性程度增加逐渐趋向于基于完全理性的子博弈精炼纳什均衡,政府掌握更多企业运营信息有利于达到社会效用最大化的博弈结果;政府引入有效处罚措施或主导负责停车管理可以降低自身的财政支出和社会总成本,建立量化的处罚标准能够促进企业主动承担停车管理责任;有限理性程度、违停数量、激励机制和科技手段运行效率会影响企业的停车管理策略选择.     

基于激励机制的网络攻防演化博弈模型研究

随着网络信息系统的日益复杂化,网络的安全性和用户隐私性引起了人们的高度重视,寻找能够维护网络安全、分析和预判网络攻防形式的新技术尤为重要.由于演化博弈理论的特性与网络攻防的特性较为契合,因此,本文对网络环境进行了分析,构建网络攻防场景,并在惩罚机制的基础上引入激励机制,提出了基于激励机制的攻防演化博弈模型.通过给出群体不同的问题情境,利用复制动态方程对局中人的策略选取进行演化分析.另外,在第三方监管部门对局中人管理的基础上,分析不同攻击时长时攻击群体的演化规律,证明攻击具有时效性.通过激励机制对防御群体策略选取的影响以及引入防御投资回报,来进一步证明增加激励机制的可行性.根据实验验证表明,本文提出的攻防演化博弈模型在不同的问题情境下均可达到稳定状态并获得最优防御策略,从而有效减少防御方的损失,遏制攻击方的攻击行为.     

A game theoretical perspective on the quantum probabilities associated with a GHZ state

In the standard approach to quantum games, players’ strategic moves are local unitary transformations on an entangled state that is subsequently measured. Players’ payoffs are then obtained as expected values of the entries in the payoff matrix of the classical game on a set of quantum probabilities obtained from the quantum measurement. In this paper, we approach quantum games from a diametrically opposite perspective. We consider a classical three-player symmetric game along with a known expression for a set of quantum probabilities relevant to a tripartite Einstein–Podolsky–Rosen (EPR) experiment that depends on three players’ directional choices in the experiment. We define the players’ strategic moves as their directional choices in an EPR setting and then express their payoff relations in the resulting quantum game in terms of their directional choices, the entries of the payoff matrix, and the quantum probability distribution relevant to the tripartite EPR experiment.     

有限理性下知识团队知识共享的演化博弈分析

为提高知识团队成员共享知识的意愿,通过运用演化博弈论,对知识团队知识共享的内在机理与动态演化过程进行了分析.结果表明,影响知识团队知识共享的因素有知识员工的知识拥有量差距、风险系数、知识共享度、互补性知识比例、激励系数、协同性影响程度等,通过调整这些参数的大小,可以有效提高知识团队成员选择知识共享策略的概率.     

Play-centric games education

Major universities around the world now offer degree programs in game design in response to student demand; more than 80 such programs exist in North America alone. Recognizing the overwhelming interest in guidelines for teaching game design, the International Game Developers Association established a committee to help educators craft a curriculum that reflects the real-world creative process of professional game designers. The interactive media program at the USC School of Cinema-Television combines a broad liberal arts education with the technical expertise needed to create games that provide players with a narratively rich and emotionally immersive experience.     

网构软件的Wright-Fisher 多策略信任演化模型

网构软件是开放网络环境中软件系统基本形态的一种抽象,其信任关系本质上是最复杂的社会关系之一.为了增强信任演化模型的自适应性,提高预测的准确性以及有效地抑制自私节点的产生,结合差异化服务和演化博弈理论,提出了一种符合开放网络特征的信任演化模型:(1)建立基于差异化服务的实体全局收益函数,以增强信任演化模型的自适应性;(2)以演化博弈理论作为分析工具,借助Wright-Fisher模型的特点,提出了一种Wright-Fisher多策略信任演化模型,以增强对信任演化预测的准确性;(3)根据“公平规范”原则建立了基于博弈的激励机制,激励信任策略的演化,从而有效地抑制自私节点的产生,实验结果表明:该模型能够更准确地反映开放网络中实体信任行为的复杂性特点;在激励机制的作用下,网构软件的信任演化能够更快地达到稳定状态,从而有效地提高网络的效率,使网构软件系统的信任收益达到最优.     

A fuzzy approach to strategic games

A game is a decision-making situation with many players, each having objectives that conflict with each other. The players involved in the game usually make their decisions under conditions of risk or uncertainty. In the paper, a fuzzy approach is proposed to solve the strategic game problem in which the pure strategy set for each player is already defined. Based on the concepts of fuzzy set theory, the approach uses a multicriteria decision-making method to obtain the optimal strategy in the game, a method which shows more advantages than the classical game methods. Moreover, with this approach, some useful conclusions are reached concerning the famous “prisoner's dilemma” problem in game theory     

A simulation framework for measuring robustness of incentive mechanisms and its implementation in reputation systems

In game theoretical analysis of incentive mechanisms, all players are assumed to be rational. Since it is likely that mechanism participants in the real world may not be fully rational, such mechanisms may not work as effectively as in the idealized settings for which they were designed. Therefore, it is important to evaluate the robustness of incentive mechanisms against various types of agents with bounded rational behaviors. Such evaluations would provide us with the information needed to choose mechanisms with desired properties in real environments. In this article, we first propose a general robustness measure, inspired by research in evolutionary game theory, as the maximal percentage of invaders taking non-equilibrium strategies such that the agents sustain the desired equilibrium strategy. We then propose a simulation framework based on evolutionary dynamics to empirically evaluate the equilibrium robustness. The proposed simulation framework is validated by comparing the simulated results with the analytical predictions based on a modified simplex analysis approach. Finally, we implement the proposed simulation framework for evaluating the robustness of incentive mechanisms in reputation systems for electronic marketplaces. The results from the implementation show that the evaluated mechanisms have high robustness against a certain non-equilibrium strategy, but is vulnerable to another strategy, indicating the need for designing more robust incentive mechanisms for reputation management in e-marketplaces.     

Evolutionarily stable sets in quantum penny flip games

In game theory, an Evolutionarily Stable Set (ES set) is a set of Nash Equilibrium (NE) strategies that give the same payoffs. Similar to an Evolutionarily Stable Strategy (ES strategy), an ES set is also a strict NE. This work investigates the evolutionary stability of classical and quantum strategies in the quantum penny flip games. In particular, we developed an evolutionary game theory model to conduct a series of simulations where a population of mixed classical strategies from the ES set of the game were invaded by quantum strategies. We found that when only one of the two players’ mixed classical strategies were invaded, the results were different. In one case, due to the interference phenomenon of superposition, quantum strategies provided more payoff, hence successfully replaced the mixed classical strategies in the ES set. In the other case, the mixed classical strategies were able to sustain the invasion of quantum strategies and remained in the ES set. Moreover, when both players’ mixed classical strategies were invaded by quantum strategies, a new quantum ES set was emerged. The strategies in the quantum ES set give both players payoff 0, which is the same as the payoff of the strategies in the mixed classical ES set of this game.     

基于演化博弈论的WSNs 信任决策模型与动力学分析

针对无线传感器网络（WSNs）节点间信任关系建立时的信任决策和动态演化问题,引入与节点信任度绑定的激励机制,建立WSNs节点信任博弈模型以反映信任建立过程中表现出的有限理性和每次博弈过程的收益.基于演化博弈论研究节点信任策略选择的演化过程,给出WSNs节点信任演化的复制动态方程,提出并证明在不同参数条件下达到演化稳定策略的定理,为WSNs信任机制设计提供了理论基础.实验表明了定理结论和激励机制的效果.     

A differential game of retailer promotions

The paper identifies equilibrium marketing strategies over time in a marketing channel of distribution. A number of retailers promote locally a manufacturer's brand in order to increase their sales. However, sustained and substantial retailer promotions will damage the image of the manufacturer's brand. The manufacturer advertises nationally to improve the brand image. Demand at the retail outlets increase both by the local promotions and by the strength of brand image. First, we identify a noncooperative equilibrium of a differential game played with Markovian strategies. Next, we study a cooperative game where the players make coordinated marketing decisions and address the question whether the manufacturer can design an incentive strategy such that the retailers will stick to their parts of the agreed solution.     

复杂系统多智能体建模的博弈进化仿真

采用基于复杂系统多智能（Multi-Agent）体的整体建模仿真方法,利用Swarm仿真软件平台建立进化博弈仿真模型（EGM）,对该模型进行了描述和定义,运用EGM对各类对称博弈模型进行仿真运行,并对结果进行分析。仿真结果表明在不同类型的对称博弈中初始策略选择概率和收益矩阵对动态稳定策略的获得有不同的影响,在给定不同的收益矩阵和初始策略选择概率下可以获得不同的进化稳定策略。