理性背叛的推荐合作激励机制

为了促进协作系统中用户的合作行为,激励机制得到了广泛的使用.然而,现有的激励机制往往存在无条件合作策略占优互惠策略的现象,进而抑制了合作的涌现.为了解决这一问题,本文在推荐激励模型上进一步考虑了用户的理性背叛行为.以演化博弈为框架,研究了理性背叛机制在全局平均学习和当前最优学习两种模式下的策略演化特性.结合实际场景,本文还研究了在非完美推荐下理性背叛机制的鲁棒性问题,并且基于余弦相似度提出了一种策略识别方案.最后,通过大量的数值实验与仿真实验,验证了理性背叛机制的理论特性,也展示了该机制在促进合作方面的有效性能.     

An incentive mechanism to reinforce truthful reports in reputation systems

The success of current trust and reputation systems is on the premise that the truthful feedbacks are obtained. However, without appropriate mechanisms, in most reputation systems, silent and lying strategies usually yield higher payoffs for peers than truthful feedback strategies. Thus, to ensure trustworthiness, incentive mechanisms are highly needed for a reputation system to encourage rational peers to provide truthful feedbacks. In this paper, we model the feedback reporting process in a reputation system as a reporting game. We propose a wage-based incentive mechanism for enforcing truthful report for non-verifiable information in self-interested P2P networks. A set of incentive compatibility constraint rules including participation constraint and self-selection constraints are formulated. We design, implement, and analyze incentive mechanisms and players’ strategies. The extensive simulation results demonstrate that the proposed incentive mechanisms reinforce truthful feedbacks and achieve optimal welfare.     

基于演化博弈的理性拜占庭容错共识算法

拜占庭容错算法(byzantine fault-tolerant)是保证区块链等分布式系统能够达成一致性的重要算法,其性能影响着系统的安全性和稳定性.针对现有共识算法存在效率低下和缺少激励机制等问题,提出了一种基于演化博弈的理性实用拜占庭容错共识算法.首先,通过引入信誉机制来确定节点在共识过程中的可信任度,以信誉值为理...     

基于声誉的移动众包系统的在线激励机制

在大数据环境下,对移动众包系统的研究已经成为移动社会网络（MSN）的研究热点。然而由于网络个体的自私性,容易导致移动众包系统的不可信问题,为了激励个体对可信策略的选取,提出一种基于声誉的移动众包系统的激励机制——RMI。首先,结合演化博弈理论和生物学中的Wright-Fisher模型研究移动众包系统的可信演化趋势;在此基础上,分别针对free-riding问题和false-reporting问题建立相应的声誉更新方法,从而形成一套完整的激励机制,激励感知用户和任务请求者对可信策略的选取;最后通过模拟实验对提出的激励机制的有效性和适应性进行了验证。结果显示,与传统的基于社会规范的声誉更新方法相比,RMI有效地提高了移动众包系统的可信性。     

An Eigentrust dynamic evolutionary model in P2P file-sharing systems

Many reputation systems have been proposed to distinguish malicious peers and to ensure the quality of the service in P2P file sharing systems. Most of those reputation systems implicitly assumed that normal peers are always altruistic and provide their resources unconditionally when requested. However, as independent decision makers in real networks, peers can be completely altruistic (always cooperative, ALLC), purely selfish (always defective, ALLD), or reciprocal (R). In addition, those systems do not provide an effective method to reduce free-riders in P2P networks. To address these two problems, in this paper, we propose an EigenTrust evolutionary game model based on the renowned EigenTrust reputation model. In our model, we use evolutionary game theory to model strategic peers and their transaction behaviors, which is close to the realistic scenario. Many experiments have been designed and performed to study the evolution of strategies and the emergence of cooperation under our proposed EigenTrust evolutionary model. The simulation results showed that rational users are inclined to cooperate (enthusiastically provide resources to other peers) even under some conditions in which malicious peers try to destroy the system.     

电子商务中客户评价策略选择的演化博弈分析

针对电子商务信任评价机制中难以采取有效激励措施使客户进行真实评价的问题,根据客户对商家的评价行为具有不完全信息和有限理性特点,建立演化博弈模型对信任模型的激励措施进行分析和改进。该模型运用复制动态机制模拟客户的策略选择演化,对现有激励措施下演化稳定策略（ESS）的存在性进行论证,并基于系统动力学增加补偿收益进行改进。基于NetLogo系统动力学模块建模实验表明,相较于基于兴趣群组的信任激励模型（IGTrust）,改进后的激励措施得到ESS并成功预测和控制了客户评价策略;同时模型具备鲁棒性,应对7%客户评价策略变异也能够保持演化稳定状态。     

Strong mediated equilibrium

Stability against potential deviations by sets of agents is a most desired property in the design and analysis of multi-agent systems. However, unfortunately, this property is typically not satisfied. In game-theoretic terms, a strong equilibrium, which is a strategy profile immune to deviations by coalition, rarely exists. This paper suggests the use of mediators in order to enrich the set of situations where we can obtain stability against deviations by coalitions. A mediator is defined to be a reliable entity, which can ask the agents for the right to play on their behalf, and is guaranteed to behave in a pre-specified way based on messages received from the agents. However, a mediator cannot enforce behavior; that is, agents can play in the game directly, without the mediator's help. A mediator generates a new game for the players, the mediated game. We prove some general results about mediators, and mainly concentrate on the notion of strong mediated equilibrium, which is just a strong equilibrium at the mediated game. We show that desired behaviors, which are stable against deviations by coalitions, can be obtained using mediators in several classes of settings.     

基于极大熵原理的理性公平交换协议

鉴于理性交换协议是一个动态博弈模型, 在完全不完美动态博弈中, 力图用极大熵原理来解决理性参与者的策略行为推断问题。扩展了一个基于信息熵的理性交换协议模型, 通过引入期望收益函数和期望均衡的方法, 给出理性交换协议的公平性描述; 基于最大熵原理构造了一种新的理性交换协议; 证明该协议的安全性, 利用博弈树的方法对整个交换过程进行分析并给出了理性公平性证明, 结果表明该协议能达到期望均衡。协议交换过程中无须可信第三方的参与, 该协议实现了理性公平性且具有更好的适应性。     

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。     

A Class of Evolutionary Models for Participation Games with Negative Feedback

We introduce a framework to analyze the interaction of boundedly rational heterogeneous agents repeatedly playing a participation game with negative feedback. We assume that agents use different behavioral rules prescribing how to play the game conditionally on the outcome of previous rounds. We update the fraction of the population using each rule by means of a general class of evolutionary dynamics based on imitation, which contains both replicator and logit dynamics. Our model is analyzed by a combination of formal analysis and numerical simulations and is able to replicate results from the experimental and computational literature on these types of games. In particular, irrespective of the specific evolutionary dynamics and of the exact behavioral rules used, the dynamics of the aggregate participation rate is consistent with the symmetric mixed strategy Nash equilibrium, whereas individual behavior clearly departs from it. Moreover, as the number of players or speed of adjustment increase the evolutionary dynamics typically becomes unstable and leads to endogenous fluctuations around the steady state. These fluctuations are robust with respect to behavioral rules that try to exploit them.     

SONR: A reliable reputation system of self-organized network

Due to self-organized network without any basic infrastructure and central equipments, it is scalable and fault-tolerant, and we believe that it must be a main direction of next generation communication network and Internet. However, because of the lack of central authority, building trust among all nodes contained in a self-organized network would become more difficult. In this paper, we propose a universal reputation system and make it reliable, lie-tolerant and DoS immunity through multidimensional reputation model, adapted discounting algorithm, recommendation game, secure reputation protocol and fault-tolerant framework. Furthermore, we demonstrate its robustness and high efficiency through example and simulation.     

External Incentive Mechanism Research on Knowledge Cooperation-Sharing in the Chinese Creative Industry Cluster

The creative industry is a knowledge-based industry, but it is difficult and complex to create knowledge for enterprises. The principle of cooperation-sharing posits that companies’ limited resources prohibit them from gaining a competitive advantage in all business areas. Therefore, cooperation-sharing can help businesses overcome this hurdle. Cooperation-sharing expedites economic development, breaks the barrier of independent knowledge creation, and enhances resource utilization. However, the effectiveness and stability of knowledge cooperation-sharing are key problems facing governments and other regulators. This study can help regulators promote honesty in enterprise cooperation-sharing. Based on the hypothesis of bounded rationality, the evolutionary game theory was used to construct the “Enterprises–Informal Institutions–Government” tripartite game matrix. Next, based on this game matrix, a simulation analysis method was used to analyze the effects of external incentives on the stability of evolutionary strategies. The analysis shows that the strategic choice of the “Enterprises–Informal Institutions–Government” tripartite game could be affected by the initial state strategy choices of the other two players, but more influential were the cost and external incentive levels of the game players. The results indicate that the government and informal institutions should regulate enterprises with a rational external incentive mechanism that boosts the enterprises’ incentive to cooperate honestly. Thus, an effective external incentive mechanism can significantly improve the probability of enterprises behaving honestly in cooperation-sharing and promote the development of the creative industry.     

一种缩短下载时间优先的自适应BitTorrent激励协议

BitTorrent激励机制的目标是保证节点上传和下载之间的公平性,但相比公平性而言,实际应用中的节点更优先考虑的是文件下载时间,据此文中提出了一种缩短文件下载时间优先的自适应BitTorrent激励协议AIPS.文中首先基于Markov模型对BitTorrent现有激励机制的效果给出了定量分析,分析了激励机制下的文件传输结构,并用概率分析方法给出了该传输结构下最小化文件下载时间的条件.应用分析结果文中定义了一个以缩短文件下载时间为效用的博弈,在该博弈达到Nash平衡时各节点采用的策略就是激励协议AIPS.模拟实验表明文中提出的AIPS较现有的BitTorrent激励协议能明显提高文件共享系统性能,提高文件下载效率.     

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.     

DART: A DISTRIBUTED ANALYSIS OF REPUTATION AND TRUST FRAMEWORK

Artificial societies—distributed systems of autonomous agents—are becoming increasingly important in open distributed environments, especially in e‐commerce. Agents require trust and reputation concepts to identify communities of agents with which to interact reliably. We have noted in real environments that adversaries tend to focus on exploitation of the trust and reputation model. These vulnerabilities reinforce the need for new evaluation criteria for trust and reputation models called exploitation resistance which reflects the ability of a trust model to be unaffected by agents who try to manipulate the trust model. To examine whether a given trust and reputation model is exploitation‐resistant, the researchers require a flexible, easy‐to‐use, and general framework. This framework should provide the facility to specify heterogeneous agents with different trust models and behaviors. This paper introduces a Distributed Analysis of Reputation and Trust (DART) framework. The environment of DART is decentralized and game‐theoretic. Not only is the proposed environment model compatible with the characteristics of open distributed systems, but it also allows agents to have different types of interactions in this environment model. Besides direct, witness, and introduction interactions, agents in our environment model can have a type of interaction called a reporting interaction, which represents a decentralized reporting mechanism in distributed environments. The proposed environment model provides various metrics at both micro and macro levels for analyzing the implemented trust and reputation models. Using DART, researchers have empirically demonstrated the vulnerability of well‐known trust models against both individual and group attacks.     

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。     

Solution of multiobjective optimization problems: coevolutionary algorithm based on evolutionary game theory

When attempting to solve multiobjective optimization problems (MOPs) using evolutionary algorithms, the Pareto genetic algorithm (GA) has now become a standard of sorts. After its introduction, this approach was further developed and led to many applications. All of these approaches are based on Pareto ranking and use the fitness sharing function to keep diversity. On the other hand, the scheme for solving MOPs presented by Nash introduced the notion of Nash equilibrium and aimed at solving MOPs that originated from evolutionary game theory and economics. Since the concept of Nash Equilibrium was introduced, game theorists have attempted to formalize aspects of the evolutionary equilibrium. Nash genetic algorithm (Nash GA) is the idea to bring together genetic algorithms and Nash strategy. The aim of this algorithm is to find the Nash equilibrium through the genetic process. Another central achievement of evolutionary game theory is the introduction of a method by which agents can play optimal strategies in the absence of rationality. Through the process of Darwinian selection, a population of agents can evolve to an evolutionary stable strategy (ESS). In this article, we find the ESS as a solution of MOPs using a coevolutionary algorithm based on evolutionary game theory. By applying newly designed coevolutionary algorithms to several MOPs, we can confirm that evolutionary game theory can be embodied by the coevolutionary algorithm and this coevolutionary algorithm can find optimal equilibrium points as solutions for an MOP. We also show the optimization performance of the co-evolutionary algorithm based on evolutionary game theory by applying this model to several MOPs and comparing the solutions with those of previous evolutionary optimization models. This work was presented, in part, at the 8th International Symposium on Artificial Life and Robotics, Oita, Japan, January 24#x2013;26, 2003.     

Evolutionary Dynamics in Public Good Games

This paper explores the question whether boundedly rational agents learn to behave optimally when asked to voluntarily contribute to a public good. The dynamic game is described by an Evolutionary Algorithm, which is shown to extend the applicability of ordinary replicator dynamics of evolutionary game theory to problem sets characterized by finite populations and continuous strategy spaces. We analyze the learning process of purely and impurely altruistic agents and find in both cases the contribution level to converge towards the Nash equilibrium. The group size, the degree of initial heterogeneity and the propensity to experiment are key factors of the learning process. JEL Classifications: C6, C73, D83, H41     

A Comparative Study of Game Theoretic and Evolutionary Models of Bargaining for Software Agents

Most of the existing work in the study of bargaining behavior uses techniques from game theory. Game theoretic models for bargaining assume that players are perfectly rational and that this rationality is common knowledge. However, the perfect rationality assumption does not hold for real-life bargaining scenarios with humans as players, since results from experimental economics show that humans find their way to the best strategy through trial and error, and not typically by means of rational deliberation. Such players are said to be boundedly rational. In playing a game against an opponent with bounded rationality, the most effective strategy of a player is not the equilibrium strategy but the one that is the best reply to the opponents strategy. The evolutionary model provides a means for studying the bargaining behaviour of boundedly rational players. This paper provides a comprehensive comparison of the game theoretic and evolutionary approaches to bargaining by examining their assumptions, goals, and limitations. We then study the implications of these differences from the perspective of the software agent developer.