利益驱动对创新网络合作行为演化的影响机理及仿真——基于复杂网络拓扑结构视角

随着合作创新的网络化发展,创新网络中各主体的利益诉求对网络演化的影响不断加深。基于不同网络拓扑结构视角下不同利益对创新网络主体合作行为的影响,建立了创新网络演化博弈模型。在无标度与小世界网络拓扑结构下,结合声誉机制、学习行为、网络环境等因素,对创新网络中合作行为的演化进行了仿真研究。结果表明:创新网络的合作行为演化受到网络结构及规模的双重影响,在无标度网络下,合作率更加稳定;合作利益的公平分配促进创新网络合作行为的演化;机会利益的诱惑导致创新网络合作行为的显著波动。     

基于复杂网络演化博弈的企业低碳创新合作行为网络演化机理研究

针对企业低碳创新合作所面临的复杂问题,基于现实复杂网络结构特征,运用演化博弈理论研究有限理性下企业低碳创新合作行为网络演化机理,利用Matlab仿真技术探究无标度网络载体上微观因素对低碳创新合作行为的影响。研究结果表明:低碳创新利益分配、协同效益和违约惩罚对低碳创新合作行为网络演化结果的影响最为显著,网络规模越大网络演化速度越慢,网络规模越小对协同系数和利益分配系数的敏感性越强,网络规模越大对技术溢出系数和违约惩罚的敏感性越强。研究结论可以为企业低碳创新合作策略制定提供解决依据。     

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。     

多主体参与下我国光伏设备合作研制演化博弈模型

针对我国光伏设备合作研制的问题,建立了以光伏企业、设备制造商和地方政府为博弈主体的三方演化博弈模型。通过演化稳定策略分析可知,实现更多的创新价值增值是各方主体趋于合作的基础,确定合理的利益分配比例是光伏设备合作研制成为可能的关键;与此同时,应构建惩罚机制以抑制各方主体的背叛行为,并通过政府的有效激励以降低合作研制成本。     

基于强互惠理论的企业合作创新行为演化研究

基于强互惠理论构建了企业合作创新行为演化模型,将企业分成投机、纯合作与强互惠三种类型,对基本的演化博弈模型进行了分析,利用复制动态方程求出基本模型的两个演化稳定均衡解,并通过仿真方法对放松约束后的演化模型做进一步分析,发现了新的系统均衡状态并给出解释。研究结果表明:强互惠行为可以通过演化生成,并抑制企业合作创新中的投机行为,与其他两种类型企业共同发展,并形成企业合作创新系统的演化稳定均衡。研究结果为企业合作创新组织的管理提供了决策依据,同时有助于推动企业合作创新理论的深入发展。     

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。     

一种基于tit-for-tat策略的伪度优先算法

复杂网络中的两个节点,随着时间的推移,由于利益冲突,可能会采取一些行动,合作或者背叛,对于背叛过多的节点,需要断开重连,这也恰好反映了现实情况.根据重复博弈中的tit-for-tat策略,提出了一种伪度优先算法,研究了在不改变节点个数情况下,复杂网络的统计特性.仿真结果表明,该算法并不改变网络的无标度特征,但是改变了最大度的节点分布,并且大大提高了网络的聚集系数.另外,还研究了对网络社团结构的影响,结果表明可以优化网络的社团结构.     

“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。     

基于合作博弈纳什议价的几种网络定价结构模型比较

本文基于目前的网络资费模式,提炼出三种网络定价结构模型,研究本地网络服务提供商(ISP)及用户之间的利润分配.首先,利用一个简化的网络业务质量(Qos)保证模型构造目标函数,说明合作博弈得到的解比非合作博弈情形下更优,合作博弈时得到纳什议价解与双方的相对议价权力有关.然后,从社会结构理论角度,说明议价权力取决于网络结构类型以及局中人在结构中所处的位置.相对议价权力大,获益较多;相对议价权力小,获益较少.最后,通过实例分析得到三种网络定价结构模型下的纳什议价解.结果表明,本地网络服务提供商的合并、收购、互联有利于提高其相对议价权力,从而获益增加.     

供应链网络道德风险演化与仿真研究

针对目前供应链网络存在的越来越严重的道德风险问题,利用演化博弈和系统动力学理论,从供应链网络结构入手,对其演化过程进行了研究。首先基于供应商之间合作过程中的诚信和败德行为,建立供应链网络同级企业间道德风险的演化博弈模型,并分析了其演化路径。其次通过对制造商和供应商之间博弈的动态性分析,建立供应链网络上下游企业间道德风险演化的SD模型,并对其演化过程进行了仿真分析。研究结果表明:供应链网络道德风险的演化结果取决于网络内败德企业获得的超额收益、收益调整程度、惩罚力度和惩罚机制。另外,罚款力度大小与供应链网络道德风险演化的动态性密切相关;动态惩罚机制在其演化的波动性控制方面,具有明显效果。     

Effect of local information within network layers on the evolution of cooperation in duplex public goods games

Traditional works of public goods game (PGG) are often studied in simplex networks where agents play games through the same type of social interactions. In order to promote cooperation against the defection in PGGs in simplex network environment, many mechanisms have been proposed from different perspectives, such as the volunteering mechanisms, and the punishment and reward approaches. However, due to diverse types of interactions between agents in reality, the study of PGG should also consider the characteristic of multiplexity of networks. Hence, we firstly model the public goods game in the duplex network (for simplification of analysis, the duplex network is considered), in which agents have two types of social interactions, and thus the network is modeled as two network layers. This type of PGG is naturally named as duplex public goods game (D-PGG), in which agents can select one of the network layers to allocate their limited resources. Then for the new game environment (D-PGG), we propose a novel perspective to promote cooperation: degrading the information integrity, i.e., agents get information just from one network layer (local information) rather than from the whole duplex network (global information) in the evolution process. Finally, through theoretical analyses and simulations, we find that if agents imitate based on the local information of the payoff in the evolution, cooperation can be generally promoted; and the extent of promotion depends on both the network structure and the similarity of the network layers.     

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.     

An improved public goods game model with reputation effect on the spatial lattices

How to model the evolution of cooperation within the population is an important and interdisciplinary issue across the academia. In this paper, we propose an improved public goods game model with reputation effect on spatial lattices to investigate the evolution of cooperation regarding the allocation of public resources. In our model, we modify the individual utility or fitness as a product of the present payoff and reputation-related power function, and strategy update adopts a Fermi-like probability function during the game evolution. Meanwhile, for an interaction between a pair of partners, the reputation of a cooperative agent will be accrued beyond two units, but the defective player will decrease his reputation by one unit. Extensive Monte Carlo numerical simulations indicate the introduction of reputation will foster the formation of cooperative clusters, and greatly enhance the level of public cooperation on the spatial lattices. The larger reputation factor leads to the higher cooperation level since the reputation effect will be enormously embedded into the utility evaluation under this scenario. The current results are vastly beneficial to understand the persistence and emergence of cooperation among many natural, social and synthetic systems, and also provide some useful suggestions to devise the feasible social governance measures and modes for the public resources or affairs.     

企业创新网络中跨组织知识共享行为的演化博弈研究

新时期企业创新网络中的跨组织知识共享,成为组织间知识资源有效利用的重要方式。通过构建有限理性、知识位势条件下主客体组织的跨组织知识共享演化博弈模型,研究分析了创新网络中主客体组织跨组织知识共享的策略选择。结果表明企业跨组织知识共享中的合作意愿、合作利益分配、合作双方的知识存量及知识结构差异、知识保护力度、社会联系等均是影响双方合作效率的重要因素。其中,最优的利益分配范围、主客体组织的知识存量及知识结构、知识保护力度等对企业创新网络中跨组织知识共享合作的成功起到关键性作用。     

Role of update dynamics in the collective cooperation on the spatial snowdrift games: Beyond unconditional imitation and replicator dynamics

In this paper, we investigate the role of update or imitation rules in the spatial snowdrift game on regular lattices. Three different update rules, including unconditional imitation (UI), replicator dynamics (RD) and the Moran process, are utilized to update the strategies of focal players during the game process in the spatial snowdrift on the lattice. We observe that the aggregate cooperation level between players is largely elevated by using the Moran process in the spatial snowdrift game, when compared to the UI or replicator dynamics. Meanwhile, we carefully explore the dynamical evolution of frequency of cooperators and the cluster formation pattern for these three update rules. Moreover, it is also shown that the evolutionary behavior under the Moran update is independent of and insensitive to the randomly initial configurations of cooperators and defectors. The current results clearly indicate that the introduction of moderate randomness in the strategy update will highly promote the maintenance and persistence of cooperation among selfish individuals, which will be greatly instrumental to deeply understand the evolution of cooperation within many natural, biological and social systems.     

High school students' emotional experiences in mathematics classes

The aim of this qualitative research is to identify Mexican high school students' emotional experiences in mathematics classes. In order to obtain the data, focus group interviews were carried out with 22 students. The data analysis is based on the theory of the cognitive structure of emotions, which specifies the eliciting conditions for each emotion and the variables that affect the intensity of each emotion. The participant students' emotional experiences in mathematics classes are composed of: (1) satisfaction and disappointment while solving a problem; (2) joy or distress when taking a test; (3) fear and relief during classes; (4) pride and self-reproach during classes; and (5) boredom and interest during classes. Finally, we discuss how the theory of the cognitive structure of emotions and our analysis contribute to emotion research in mathematics education.