Cooperation in an evolutionary prisoner’s dilemma game with probabilistic strategies

In this work, we investigate an evolutionary prisoner’s dilemma game in structured populations with probabilistic strategies instead of the pure strategies of cooperation and defection. We explore the model in details by considering different strategy update rules and different population structures. We find that the distribution of probabilistic strategies patterns is dependent on both the interaction structures and the updating rules. We also find that, when an individual updates her strategy by increasing or decreasing her probabilistic strategy a certain amount towards that of her opponent, there exists an optimal increment of the probabilistic strategy at which the cooperator frequency reaches its maximum.     

Evolutionary games with facilitators: When does selection favor cooperation?

We study the combined influence of selection and random fluctuations on the evolutionary dynamics of two-strategy (“cooperation” and “defection”) games in populations comprising cooperation facilitators. The latter are individuals that support cooperation by enhancing the reproductive potential of cooperators relative to the fitness of defectors. By computing the fixation probability of a single cooperator in finite and well-mixed populations that include a fixed number of facilitators, and by using mean field analysis, we determine when selection promotes cooperation in the important classes of prisoner’s dilemma, snowdrift and stag-hunt games. In particular, we identify the circumstances under which selection favors the replacement and invasion of defection by cooperation. Our findings, corroborated by stochastic simulations, show that the spread of cooperation can be promoted through various scenarios when the density of facilitators exceeds a critical value whose dependence on the population size and selection strength is analyzed. We also determine under which conditions cooperation is more likely to replace defection than vice versa.     

An adaptive algorithm for discrete-time differential games

This paper presents an algorithm for a player to improve his performance by adapting optimally over his non-optimally playing opponent in discrete-time differential games. The algorithm first estimates the opponent's actual strategies and then constructs an adaptive strategy for the player. The adaptive strategy is periodically updated according to the opponent's behavior using the neighboring optimal closed-loop solution technique. An example is given which demonstrates the superiority of this algorithm over the conventional one which assumes that the opponent plays optimally.     

Prisoner's dilemma game on adaptive networks under limited foresight

We study the emergence of cooperation in an environment where players in prisoner's dilemma game (PDG) not only update their strategies but also change their interaction relations. Different from previous studies in which players update their strategies according to the imitation rule, in this article, the strategies are updated with limited foresight. We find that two absorbing states—full cooperation and full defection—can be reached, assuming that players can delete interaction relations unilaterally, but new relations can only be created with the mutual consent of both partners. Simulation experiments show that high levels of cooperation in large populations can be achieved when the temptation to defect in PDG is low. Moreover, we explore the factors which influence the level of cooperation. These results provide new insights into the cooperation in social dilemma and into corresponding control strategies. © 2012Wiley Periodicals, Inc. Complexity, 2012     

A receding horizon control approach for integrated vector management of Aedes aegypti using chemical and biological control: A mono and a multiobjective approach

This paper addresses an integrated vector management (IVM) approach for combating Aedes aegypti, the transmission vector of dengue, zika, and chikungunya diseases, some of the most important viral epidemics worldwide. In order to tackle this problem, a receding horizon control (RHC) strategy is adopted, considering a mono-objective and a multiobjective version of the optimal control model of combating the mosquito using chemical and biological control. RHC is essentially a suboptimal scheme of classical optimal control strategies considering discrete-time approximations. The integrated vector control actions used in this work consist in applying insecticides and inserting sterile males produced by irradiation in the population of mosquitoes. The cost function is defined in terms of social and economic costs, in order to quantify the effectiveness of the proposed epidemiological control throughout a time window of 4 months. Numerical simulations show that the obtained results are better than those from the optimal control strategies found in literature. Furthermore, through the application of the multiobjetive approach, varying the scenarios in the mono-objective formulation is no longer necessary and a set of optimal strategies can be obtained at once. Finally, in order to help health authorities in the choice of the best solution of the Pareto-optimal set to be implemented in practice, a cost-effectiveness analysis is performed and a strategy representing the most cost-effective control policy is obtained.     

Mutual punishment promotes cooperation in the spatial public goods game

Punishment has been proved to be an effective mechanism to sustain cooperation among selfish individuals. In previous studies, punishment is unidirectional: an individual i can punish j but j cannot punish i. In this paper, we propose a mechanism of mutual punishment, in which the two individuals will punish each other if their strategies are different. Because of the symmetry in imposing the punishment, one might expect intuitively the strategy to have little effect on cooperation. Surprisingly, we find that the mutual punishment can promote cooperation in the spatial public goods game. Other pertinent quantities such as the time evolution of cooperator density and the spatial distribution of cooperators and defectors are also investigated.     

The Optimal Control of a Train

We consider the problem of determining an optimal driving strategy in a train control problem with a generalised equation of motion. We assume that the journey must be completed within a given time and seek a strategy that minimises fuel consumption. On the one hand we consider the case where continuous control can be used and on the other hand we consider the case where only discrete control is available. We pay particular attention to a unified development of the two cases. For the continuous control problem we use the Pontryagin principle to find necessary conditions on an optimal strategy and show that these conditions yield key equations that determine the optimal switching points. In the discrete control problem, which is the typical situation with diesel-electric locomotives, we show that for each fixed control sequence the cost of fuel can be minimised by finding the optimal switching times. The corresponding strategies are called strategies of optimal type and in this case we use the Kuhn–Tucker equations to find key equations that determine the optimal switching times. We note that the strategies of optimal type can be used to approximate as closely as we please the optimal strategy obtained using continuous control and we present two new derivations of the key equations. We illustrate our general remarks by reference to a typical train control problem.     

竞争环境下供应链的BOPS合作策略及其对交通拥堵改善的影响

针对由一个拥有线上直销渠道的制造商以及一个线下零售商组成的供应链,考虑消费者对不同渠道的偏好构建了供应链博弈模型,研究了竞争环境下制造商与零售商的最优BOPS合作策略,并分析了BOPS合作在改善交通拥堵方面的作用。研究表明,BOPS合作在缓解供应链双重边际效应的同时会加剧渠道之间的价格竞争,所以并不总是对供应链有利;仅当消费者线上渠道偏好程度比较低且线下麻烦成本比较大时,制造商和零售商才会都从BOPS合作中受益从而自愿达成合作,其他情形下制造商可根据供应链利润变化设计转移支付契约促成与线下零售商的BOPS合作并实现双赢;此外,一定条件下BOPS合作能够通过调整渠道需求结构,在保证供应链成员利润的同时对交通拥堵改善也带来正向作用。     

基于供需网企业合作博弈模型的演化路径分析

传统企业间合作博弈问题的研究大都是基于合作策略和不合作策略两种情形展开,而与传统研究不同,本文在此基础上将企业间不合作策略细分为竞争策略和中立策略。以多功能开放型供需网企业为研究对象,运用演化博弈理论分析方法,通过构建供需网企业合作演化博弈模型,进而分析供需网企业合作过程中策略的选择以及博弈演化路径问题。研究结果表明:在长期的演化博弈过程中,企业策略的选择情形出现不稳定现象,即出现完全合作、完全不合作或者一方合作而另一方选择不合作策略的多种状态,其演化路径最终稳定于何种情形与模型的支付矩阵和初始参数设置有关。     

A systematic analysis of the N‐person chicken game

We report computer simulation experiments based on our agent‐based simulation tool to model the multiperson Chicken dilemma game for the case when the agents are greedy simpletons who imitate the action of that of their neighbors who received the highest payoff for its previous action. The individual agents may cooperate with each other for the collective interest or may defect, i.e., pursue their selfish interests only. After a certain number of iterations the proportion of cooperators stabilizes to either a constant value or oscillates around such a value. The payoff (reward/penalty) functions are given as two straight lines: one for the cooperators and another for the defectors. The payoff curves are functions of the ratio of cooperators to the total number of agents. Even for linear payoff functions, we have four free parameters that determine the payoff functions that have the following properties: (1) Both payoff functions increase with the increasing number of cooperators. (2) In the region of low cooperation the cooperators have a higher reward than the defectors. (3) When the cooperation rate is high, there is a higher payoff for defecting behavior than for cooperating behavior. (4) As a consequence, the slope of the D function is greater than that of the C function and the two payoff functions intersect. (5) All agents receive a lower payoff if all defect than if all cooperate. We have investigated the behavior of the agents systematically. The results show that the solutions have predictable tendencies but they are nontrivial and quite irregular. The solutions show drastic changes in the parameter ranges 0.6 ≤ R ≤ 0.65 for all values of S and 0 ≤ S ≤ 0.2 when R < 0.6 (R is the reward for mutual cooperation and S is the sucker's payoff to a lonely cooperator). © 2010 Wiley Periodicals, Inc. Complexity, 2010     

非零和动态对策的一类闭环合作策略

本文了研究了非零和动态对策的最优性策略问题，分析了动态对策闭环策略的最优性条件，提出了一类闭环合作策略的概念和设计方法，证明了该策略的最优性，并对线性二次型动态对策问题的闭环合作策略进行了设计。     

污染产业转移下府际间合作治理大气污染的演化博弈分析

大气污染具有跨区域性,府际间合作治理是大气污染防治的必由之路,合作治理中存在“搭治污便车”的困境。由于府际间大气污染治理博弈的具有长期性和动态性,由此建立了多人两策略的演化博弈模型。分别研究,无约束机制和有约束机制两种情况下地方政府演化稳定策略及其影响因素。考虑到政府间污染产业转移,建立了污染产业转移政府对承接方政府治污补偿机制下的演化博弈模型,分别研究无约束和有约束两种情形下(补偿,治理)策略成为演化稳定均衡状态的条件。研究表明:府际间的合作治理策略选择不会受到外部效应的影响,大气污染治理成本、治理时污染物减排量、不治理时污染物排放增加量、中央政府对治理的奖励和对不治理的惩罚力度是影响地方政府决策的重要因素。将大气质量作为政绩考核的重要指标,提高其权重系数,可有效促使地方政府策略向参与合作治理演化。无约束下自主选择时,最优策略(补偿,治理)不会成为稳定均衡策略,引入中央政府约束后,当经济惩罚在一定范围内,(补偿,治理)成为稳定均衡策略。     

“互联网+回收”情境下基于演化博弈的电子废弃物回收合作机理研究

基于“互联网+回收”情境,研究网络回收商和流动回收小贩在电子废弃物回收中的竞合关系,构建演化博弈模型,刻画演化博弈过程,分析相关因素对演化稳定策略走向的影响。研究结果表明,网络回收商和流动回收商小贩选择合作策略的概率与违约惩罚力度、合作后的超额收益正相关,与合作后的共同成本、单独选择合作策略付出的成本以及因搭便车行为增加的收益负相关。由于分别存在最优的超额收益分配率和共同成本分摊率,网络回收商和流动回收小贩选择合作策略的概率最大。     

演化博弈与自组织合作

主要研究复杂网络上的演化博弈.首先研究具有社团结构的无标度网络上的演化囚徒困境博弈及Newman-Watts小世界网络中异质性对合作演化的影响.然后考察了在不同合作者和作弊者初始分布配置情况下,不同的初始比例条件对合作水平的影响,且在社会网络上研究了雪堆博弈中的合作演化.进一步地,讨论了网络拓扑和博弈动力学的共同演化问题和网络上演化囚徒困境中的强化学习问题.最后给出了复杂网络上演化博弈论的未来发展方向与应用前景.     

Mathematical modeling and control of population systems: Applications in biological pest control

The aim of this paper is to apply methods from optimal control theory, and from the theory of dynamic systems to the mathematical modeling of biological pest control. The linear feedback control problem for nonlinear systems has been formulated in order to obtain the optimal pest control strategy only through the introduction of natural enemies. Asymptotic stability of the closed-loop nonlinear Kolmogorov system is guaranteed by means of a Lyapunov function which can clearly be seen to be the solution of the Hamilton–Jacobi–Bellman equation, thus guaranteeing both stability and optimality. Numerical simulations for three possible scenarios of biological pest control based on the Lotka–Volterra models are provided to show the effectiveness of this method.     

Norms of Cooperation in a Game of Partnership

We study a model of partnership with costly commitment in an evolutionary game-theoretic framework. We introduce various types of cooperative players with different degrees of strategic sophistication. We find that a necessary condition for cooperation at the social equilibrium is that the probability of a high opportunity value falls below a certain critical threshold. Such condition, however, is not sufficient. If players are too naively cooperative, the permanence of cooperation at the equilibrium is put at risk because naive cooperation entails playing inefficient responses to other equilibrium strategies. As a consequence, naive cooperation will be present at the equilibrium selected by the social dynamics only under somewhat special conditions. If cooperators are more flexible, in that they remain in the partnership only if mated to a first stage cooperator and only in lack of favorable outside opportunities, cooperation is almost granted provided that it is Pareto efficient (perverse lock in cases under unfavorable initial conditions are possible). Finally, the selection of Pareto superior cooperation with certainty may be granted by a certain norm-oriented type of strategically sophisticated behavior, that we call Rawlsian altruism.     

Fuzzy control of thyristor-controlled series compensator in power system transients

Power system transient stability is one of the most challenging technical areas in electric power industry. Thyristor-controlled series compensation (TCSC) is expected to improve transient stability and damp power oscillations. TCSC control in power system transients is a nonlinear control problem. This paper presents a T–S-model-based fuzzy control scheme and a systematic design method for the TCSC fuzzy controller. The nonlinear power system containing TCSC is modelled as a fuzzy “blending” of a set of locally linearized models. A linear optimal control is designed for each local linear model. Different control requirements at different stages during power system transients can be considered in deriving the linear control rules. The resulting fuzzy controller is then a fuzzy “blending” of these linear controllers. Quadratic stability of the overall nonlinear controlled system can be checked and ensured using H^∞ control theory. Digital simulation with NETOMAC software has verified that the fuzzy control scheme can improve power system transient stability and damp power swings very quickly.     

基于进化博弈视角的水源地与下游生态补偿合作演化分析

水源地与下游地方政府之间的协议合作可以协调流域上下游之间的矛盾和利益,是一种可实现流域生态保护良性循环的生态补偿尝试。本文应用进化博弈的双种群博弈理论,研究了水源地和下游地方政府之间博弈演化过程及影响因素。研究表明,流域上下游地方政府合作的演化方向主要受水源地不保护及下游不补偿受到的惩罚、下游对水源地的补偿额度、水源地和下游合作的初始成本、水源地生态保护的成本及收益等八个因素的影响。降低水源地的保护成本及水源地和下游合作的初始成本,提高水源地因保护而获得的综合效益、提高水源地不保护及下游不补偿受到的惩罚额度、合理确定下游对水源地的补偿额度将有利于水源地生态补偿机制向合作方向演进。     

基于关系契约视角的农户与农产品电子商务企业合作机制演化博弈分析

本文基于关系契约的视角,运用演化博弈理论,构建有限理性特性下的农户与农产品电子商务企业合作演化博弈模型,分析双方合作策略的演进过程、稳定策略及其影响因素,并通过Matlab仿真模拟分析影响双方合作策略选择的因素,验证博弈结果正确性。研究结果表明:农户和农产品电子商务企业选择合作策略与合作投入成本、实现的超额收益密切相关,博弈初始状态、再交易成本、单方不合作获得的收益及违约罚金等因素影响系统的演化稳定结果。因此完善农户与农产品电子商务企业的合作机制,需要构建合理的利益分配机制、良好的关系治理机制、有效的监督和惩罚机制和信息共享、沟通与协调机制。     

常态及故障条件下的多传感器任务管控调度研究

针对多传感器控制中的常态和故障情况问题,建立了描述常态和故障条件下的多传感器控制的多目标规划模型,通过偏离度指数,应用遗传算法求得常态控制问题最优解.其次将传感器故障转化成伪执行器故障运用改进的遗传算法,实现了多传感器故障情形下最优控制求解.仿真结果表明了最优控制方案的有效性.