不同信任环境下在线知识社区知识系统演化研究

信任作为在线知识社区中的社会影响因素,对社区中的成员进行沟通学习、知识共享有着重要的作用。不同的在线知识社区有着不同的信任环境,而信任环境的不同会影响社区中用户的学习模式和观点传播。基于此,本文提出了基于信任与Deffaunt的组合观点影响模型。信任模型主要将社区中的信任分为认知信任和情感信任,通过调节参数结构,对应不同信任环境中信任的动态演化过程。Deffaunt模型作为基本观点影响模型,模拟了不同信任环境下的在线知识社区的知识观点的演化过程。实验结果发现,信任环境的高低决定了社区中的观点是否收敛,并且社区中的群体理性人占比和信任程度都能影响观点的收敛速度。     

决策偏好不确定条件下群体决策收敛一致性研究

决策个体之间交互影响是达成群体一致意见的重要因素。本文引入决策个体决策偏好的不确定性以及个体间不对称的影响权重,以此为基础建立群体决策动态观点演化模型,并进行matlab仿真。文中取决策个体的偏好不确定性为常数,作为决策个体对自身观点确定性的大致考量,并把群体中所有决策者的决策偏好不确定性分为相同和不相同两种情况来讨论。仿真实例证明:决策个体观点的不确定性是最终影响群体决策一致性的重要因素,当决策个体偏好不确定性较大时,决策群体更容易得到一致意见;当偏好不确定性较小时,决策群体难以达成一致意见,最终形成几个观点簇;在初始观点分布均匀的情况下,群体观点最终会演化出怎样的观点形态,是由群体中观点不确定性较小的群体所持有的观点决定的。研究内容有助于理解群体决策中一致观点的演化规律,能够为群体决策的引导策略提供参考和借鉴。     

考虑平台影响因素的虚拟社区知识共享演化博弈研究

知识共享是虚拟社区用户学习和传播知识的重要途径。本文考虑到虚拟社区平台对参与用户知识共享行为的激励作用,引入平台的奖励和不分享知识所产生的机会成本,以及知识共享收益因素,构建考虑虚拟社区平台影响因素的虚拟社区知识共享演化博弈支付矩阵,并求得演化均衡解。通过小米社区的案例引入,仿真模拟平台对用户知识共享的奖励、机会成本、收益分配系数以及参与用户共享程度等因素对参与用户知识共享行为意愿的影响。研究发现:虚拟社区参与用户的知识共享意愿对因为知识共享所产生新知识的收益系数变化较为敏感;平台的奖励以及不分享知识所产生的机会成本对用户知识共享意愿影响巨大;同时,不同参与用户因为知识水平层次不同,参与知识共享的目的也不相同,这会影响到他们的知识共享策略选择。     

压力、补偿机制与知识分享:基于演化博弈的分析

以往对于知识分享影响因素的研究并不少见,但从员工所面临的来自组织环境的多重压力出发,采用演化博弈理论进行分析的研究仍是空白。本文引入时间压力、同辈压力和补偿机制,构建组织内员工知识分享的演化博弈动态模型,通过求解复制动态方程得到演化博弈的稳定均衡解,再进行数值模拟。研究结果表明,时间压力和同辈压力是知识分享的重要影响因素。其中,时间压力通过影响分享者的时间成本而对知识分享产生负向影响,同辈压力通过让分享者获得同辈群体的认可而对知识分享产生正向影响;补偿机制能够有效地促进知识分享行为的发生。此外,系统的初始状态对系统的演化结果也具有显著影响。最后,针对组织实际提出管理建议。     

基于传播模式演化的组织内知识传播研究

组织内个体之间的知识传播存在不同的模式,传播模式会影响传播效果,反过来传播模式会随着传播的进行而发生演化,反作用于知识传播,进而影响组织绩效和创新。基于此,本文先归纳了六种知识传播模式之间的演化规则,然后对应不同类型的组织,利用复杂网络设置了多组仿真实验,模拟了传播模式不断演化时组织知识传播的动态过程。对比分析实验结果发现:专家对组织知识传播至关重要,专家能够大大提高组织知识储量;顺畅的沟通机制不仅能显著提高组织知识储量,还能让知识传播更兼顾公平;组织内个体的知识分享习惯是其获取知识的关键,越懂得分享的人越能获取更多的知识。     

开源软件社区中人类知识创造行为的个体差异性研究

近年来,利用复杂性科学解释人类行为成为一个新兴的研究热点.与以群体或群体中部分个体为研究对象不同,以codeplex开源软件社区所有个体知识创造行为为研究对象,利用人类行为动力学方法从活跃度、阵发性、时间间隔分布、长程相关性四个侧面对社区中所有个体知识创造过程中的生产行为、沟通行为、bug讨论行为进行分析.发现开源软件社区中生产者个体的这三种行为在活跃度、分布形式及分布参数、阵发性指标、长程相关性都呈现出显著的差异性.最后,对造成这种差异性的原因进行了讨论.     

社会资本视角下的咨询网络演化研究

针对当前社会网络理论中静态结构主义的研究局限,本文基于过程的观点,将知识转移分为转移对象的搜索与对转移条件的判断两个过程,并从社会资本的三个分析维度建立了个体在知识转移过程中的行为模型,并基于此行为模型设计仿真实验,考察了涌现出来的咨询网络的结构特征,以及群体绩效的变化.实验结果表明,群体绩效与关系衰减系数以及成员的认知阈值正相关,咨询网络的集聚系数与衰减函数负相关,与成员的认知阈值正相关.研究结论将为交互式记忆系统及组织中know-who知识对群体绩效的影响提供有益的参考.     

基于参与主体异质性条件下囚徒困境合作演化博弈模型研究

利用演化博弈理论,对参与主体异质性条件下的囚徒困境模型进行了探讨,求出了满足不同条件下的演化稳定策略,并对种群中个体异质性对演化稳定策略的影响进行了分析,得出种群中选择相同策略的个体异质性差异越大,参与个体选择合作行为作为演化稳定策略的可能性就越大.极端地,当个体的异质性趋向于无穷大时,合作成为唯一的演化稳定占优策略,为现实大多数合作系统中能保持长期的一种合作稳定状态提供了合理地解释.     

实践社群知识扩散模型及仿真研究

实践社群知识扩散的效率和效果影响实践社群的创新能力。通过考虑实践社群成员知识异质性对知识扩散的影响,引入知识生态位的观点,比较实践社群成员知识生态位的重叠程度,进而构建实践社群知识扩散模型,考虑实践社群成员间的相互作用,对知识扩散模型进行求解,得到相应的稳定均衡点并利用相图分析法进行验证,最后利用MATLAB进行数值仿真研究。结果表明:实践社群成员知识生态位的重叠程度对知识扩散效果有重要影响,实践社群成员间影响程度的大小对实践社群知识扩散模型均衡点有显著影响,实践社群成员知识表现为知识互补时,实践社群知识扩散的效率与效果最优。研究结果为促进实践社群知识扩散提供理论依据。     

基于个体策略选择的知识传播研究

本文采用小世界网络模型模拟现实组织结构,构建了组织内部基于个体策略选择的知识传播模型,探讨了个体策略选择和网络结构对组织内部知识传播效果的影响。研究发现组织中无条件传播知识的个体的数量与平均知识水平成正比,对知识传播起促进作用;在网络结构方面,小世界网络对知识传播更加有利;组织规模较小时,更换策略的时间间隔对知识传播有较大影响。研究结果为组织有效地促进知识传播提供了支持。     

基于网络混合学习策略的股市投资者行为演化模型及仿真

针对投资者有限理性决策行为的模仿学习等特质,运用复杂网络、贝叶斯学习和社会学习理论,分别对个人投资者和机构投资者的投资行为决策机制进行系统性分析,由此构建基于网络混合学习策略的投资者行为演化机理和数理模型,并进行数理解析和模拟仿真。研究发现,投资者金融关联网络的拓扑结构对个人投资者行为动态演变具有较强影响,但对机构投资者的影响较弱;个人投资者的学习策略偏好影响其行为动态演变均衡状态的稳定性较为显著;机构投资者获取的私有信息精确度与其行为动态演变的均衡状态之间存在显著的非线性关联;投资者的初始行为状态对其投资行为动态演变均衡状态的影响效应均较弱。     

Knowledge sharing in communities of practice: A game theoretic analysis

This research applies game theory to analyze the incentives of knowledge-sharing activities in various types of communities of practice (COPs), characterized by individual profiles and decision structures. Indeed, individual decision making results in the under-provision of knowledge; however, the benefit of knowledge sharing may be raised by IT investment and suitable incentive mechanisms we study here. In general conditions, improving communication and collaboration technologies should be prior to developing data mining technologies. However, when the number of community members is sufficiently small and the heterogeneity of the expected value of knowledge among community members is sufficiently large, developing data mining technologies should be considered more important than the other if most community members are low-type ones. On the other hand, based on a screening technique, we find that the benefit of knowledge sharing in the incomplete information setting can be the same as that in the complete information setting if the cost of more efficient community member is smaller than that of less efficient one.     

The Social Embeddedness of Academic Online Groups in Offline Networks as a Norm Generating Structure: An Empirical Test of the Coleman Model on Norm Emergence

This paper analyzes two questions. First, under which conditions does a norm emerge in academic online groups that prescribes members to help others during group discussions? Second, what effects does such a norm, and other social conditions, have on the contributing behavior of researchers during online discussions? It is argued that the Coleman model (1990) on the emergence of norms points to an important condition that facilitates the realization of such a norm. According to the Coleman model (1990) a dense network among members of a group tends to strengthen a group norm. The paper makes a distinction between different kinds of academic online groups. The criterion of the distinction is the extent to which within the membership a highly integrated research community exists. An online group with a highly integrated research community is called to have a high degree of social embeddedness of its online communication in offline networks. It is hypothesized that a high degree of embeddedness has a number of effects. A higher degree of embeddedness leads to a stronger help-prescribing norm. The stronger the norm the more researchers send online answers to questions of their co-members during public online discussions. Furthermore, a high degree of embeddedness increases the answering behavior of researchers directly because it provides opportunities to gain reputation within the academic community through contributing to the discussion. The study makes use of data that consist of a combination of survey data and observed data of the communication behavior of researchers in about 50 international academic emailing lists. The results provide evidence for the expected effect of embeddedness on the strength of the norm and for the effect of embeddedness on the answering behavior of researchers. The strength of the help-prescribing norm indirectly influences the answering behavior.     

The impact of socio-technical communication styles on the diversity and innovation potential of global science collaboratories

Emerging cyber-infrastructure tools are enabling scientists to transparently co-develop, share, and communicate about real-time diverse forms of knowledge artifacts. In these environments, communication preferences of scientists are posited as an important factor affecting innovation capacity and robustness of social and knowledge network structures. Scientific knowledge creation in such communities is called global participatory science (GPS). Recently, using agent-based modeling and collective action theory as a basis, a complex adaptive social communication network model (CollectiveInnoSim) is implemented. This work leverages CollectiveInnoSim implementing communication preferences of scientists. Social network metrics and knowledge production patterns are used as proxy metrics to infer innovation potential of emergent knowledge and collaboration networks. The objective is to present the underlying communication dynamics of GPS in a form of computational model and delineate the impacts of various communication preferences of scientists on innovation potential of the collaboration network. Gained insight can ultimately help policy-makers to design GPS environments and promote innovation.     

United States teachers’ views of effective mathematics teaching and learning

This study investigates US teachers’ cultural beliefs concerning effective mathematics teaching using semi-structured interviews with 11 experienced teachers. For US teachers, effective teaching is student-centered. Cognitively appropriate mathematical content should be understood through many hands-on activities that allow students to explore by themselves the relationship between mathematical knowledge and their life experiences. Correspondingly, the US teachers view an effective teacher as a facilitator who is sensitive to student social and cognitive needs and is skillful at organizing collaborative learning. The result of this study helps researchers and educators understand the student-centered learning model in US classrooms.     

Network formation and knowledge gains

Incentive-based models for network formation link micro actions to changes in network structure. Sociologists have extended these models on a number of fronts, but there remains a tendency to treat actors as homogenous agents and to disregard social theory. Drawing upon literature on the strategic use of networks for knowledge gains, we specify models exploring the co-evolution of networks and knowledge gains. Our findings suggest that pursuing transitive ties is the most successful strategy, as more reciprocity and cycling result from this pursuit, thus encouraging learning across the network. We also discuss the role of network size, global network structure, and parameter strength in actors’ attainment of knowledge resources.     

Finding the needles in the haystack: efficient intelligence processing

As a result of communication technologies, the main intelligence challenge has shifted from collecting data to efficiently processing it so that relevant, and only relevant, information is passed on to intelligence analysts. We consider intelligence data intercepted on a social communication network. The social network includes both adversaries (eg terrorists) and benign participants. We propose a methodology for efficiently searching for relevant messages among the intercepted communications. Besides addressing a real and urgent problem that has attracted little attention in the open literature thus far, the main contributions of this paper are two-fold. First, we develop a novel knowledge accumulation model for intelligence processors, which addresses both the nodes of the social network (the participants) and its edges (the communications). Second, we propose efficient prioritization algorithms that utilize the processor’s accumulated knowledge. Our approach is based on methods from graphical models, social networks, random fields, Bayesian learning, and exploration/exploitation algorithms.     

考虑内外因素的电子商务产业与大数据产业协同演化研究

在新经济体系建设中,电子商务产业与大数据产业协同与融合成为必然趋势。在讨论二者协同关系和交叉业务内容的基础上,从系统的角度出发,基于经典哈肯模型构建了电子商务-大数据系统(EBDS)协同演化模型,并讨论了绝热消去方法的应用条件。接着,运用熵权法确定两子系统序参量的权重,进而量化了二者的有序度及协同度。通过数值拟合,验证了所构建演化模型的可信度,并发现了EBDS存在两个稳定点,但EBDS仍处于协同程度较低的互助协同阶段,对此从协同应用、产业链、风险、外部环境和生态圈五个方面给出了建议。     

A cognitively based simulation of academic science

The models used in social simulation to date have mostly been very simplistic cognitively, with little attention paid to the details of individual cognition. This work proposes a more cognitively realistic approach to social simulation. It begins with a model created by Gilbert (1997) for capturing the growth of academic science. Gilbert’s model, which was equation-based, is replaced here by an agent-based model, with the cognitive architecture CLARION providing greater cognitive realism. Using this cognitive agent model, results comparable to previous simulations and to human data are obtained. It is found that while different cognitive settings may affect the aggregate number of scientific articles produced, they do not generally lead to different distributions of number of articles per author. The paper concludes with a discussion of the correspondence between the model and the constructivist view of academic science. It is argued that using more cognitively realistic models in simulations may lead to novel insights. Isaac Naveh obtained a master’s degree in computer science at the University of Missouri. His research interests include hybrid cognitive models and multi-agent learning. Ron Sun is Professor of Cognitive Science at Rensselaer Polytechnic Institute, and formerly the James C. Dowell Professor of Engineering and Professor of Computer Science at University of Missouri-Columbia. He received his Ph.D in 1992 from Brandeis University. His research interest centers around studies of cognition, especially in the areas of cognitive architectures, human reasoning and learning, cognitive social simulation, and hybrid connectionist models. For his paper on integrating rule-based and connectionist models for accounting for human everyday reasoning, he received the 1991 David Marr Award from Cognitive Science Society. He is the founding co-editor-in-chief of the journal Cognitive Systems Research, and also serves on the editorial boards of many other journals. He is the general chair and program chair for CogSci 2006, and a member of the Governing Board of International Neural Networks Society. His URL is: http://www.cogsci.rpi.edu/~rsun