Using activity focus networks to pressure terrorist organizations

The predominant idea for using network concepts to fight terrorists centers on disabling key parts of their communication networks. Although this counternetwork strategy is clearly a sound approach, it is vulnerable to missing, incomplete, or erroneous information about the network. This paper describes a different and complementary application of network concepts to terrorist organizations. It is based on activity focus networks (AFNs), which represent the complex activity system of an organization. An activity focus is a conceptual or physical entity around which joint activity is organized. Any organization has a number of these, which are in some cases compatible and in some cases incompatible. The set of foci and their relations of compatibility and incompatibility define the AFN. A hypothetical AFN for a terrorist organization is specified and tested in a simulation called AQAS. It shows that certain activity foci, and in particular one combination, have high potential as pressure points for the activity system. The AFN approach complements the counternetwork approach by reducing the downside risk of incomplete information about the communication network, and enhancing the effectiveness of counternetwork approaches over time. Steven R. Corman is Professor in the Hugh Downs School of Human Communication at Arizona State University and Chair of the Organizational Communication Division of the International Communication Association. His research interests include communication networks and activity systems, high-resolution text and discourse analysis, and modeling and simulation of human communication systems.     

Complexity of networks II: The set complexity of edge‐colored graphs

We previously introduced the concept of “set‐complexity,” based on a context‐dependent measure of information, and used this concept to describe the complexity of gene interaction networks. In a previous paper of this series we analyzed the set‐complexity of binary graphs. Here, we extend this analysis to graphs with multicolored edges that more closely match biological structures like the gene interaction networks. All highly complex graphs by this measure exhibit a modular structure. A principal result of this work is that for the most complex graphs of a given size the number of edge colors is equal to the number of “modules” of the graph. Complete multipartite graphs (CMGs) are defined and analyzed. The relation between complexity and structure of these graphs is examined in detail. We establish that the mutual information between any two nodes in a CMG can be fully expressed in terms of entropy, and present an explicit expression for the set complexity of CMGs (Theorem 3). An algorithm for generating highly complex graphs from CMGs is described. We establish several theorems relating these concepts and connecting complex graphs with a variety of practical network properties. In exploring the relation between symmetry and complexity we use the idea of a similarity matrix and its spectrum for highly complex graphs. © 2012 Wiley Periodicals, Inc. Complexity, 2012     

On a Mathematical Comparison between Hierarchy and Network with a Classification of Coordination Structures

This paper proposes a mathematical model to compare a network organization with a hierarchical organization. In order to formulate the model, we define a three-dimensional framework of the coordination structure of a network and of other typical coordination structures. In the framework, we can define a network structure by contrasting it with a hierarchy, in terms of the distribution of decision making, which is one of the main features of information processing. Based on this definition, we have developed a mathematical model for evaluating coordination structures. Using this model, we can derive two boundary conditions among the coordination structures with respect to the optimal coordination structure. The boundary conditions help us to understand why an organization changes its coordination structure from a hierarchy to a network and what factors cause this change. They enable us, for example, to find points of structural change where the optimal coordination structure shifts from a hierarchy to a hierarchy with delegation or from a hierarchy with delegation to a network, when the nature of the task changes from routine to non-routine. In conclusion, our framework and model may provide a basis for discussing the processes that occur when coordination structures change between a hierarchy and a network.     

An analysis of the structural complexity of supply chain networks

Each enterprise in a supply chain network needs quantitative indicators to analyze and manage its interactions with different business partners in the network. Supply chains exhibit the characteristics of complex systems. In a supply chain network, a large number of firms cooperate simultaneously with many suppliers and customers, and interact through a variety of information and material flows to achieve a balance between supply and demand. However, the complexity of a supply chain is not a simple linear structure where a small change often results in a chain reaction. When supply chain complexity increases, monitoring and managing the interaction between different elements of the chain becomes more difficult. An entropy model based on information theory provides an appropriate means of quantifying the complexity of a supply chain system by delivering information required to describe the state of the system. The entropy measure links uncertainty and complexity so that, as a system grows in uncertainty, it becomes more complex and more information is required to describe and monitor it. In this paper, we propose an entropy-based measure for analyzing the structural complexity in relation to the structure and system uncertainty. The method provides guidelines for estimating the complexity throughout the supply chain structure.     

On the measurement of complexity in activity networks

The measurement of the ‘complexity’ of activity networks seems to be needed in order to estimate the computing requirements and/or to validly compare alternative heuristic procedures. This paper critically evaluates past contributions to the problem, and explores the underlying concepts of measurement. It suggests that the objective of analysis of the network is a determining factor in the process of measurement, and discusses three different objectives; they are: to determine the critical path assuming deterministic time estimates; to determine the probability distribution function of project completion assuming random durations of the activities; and to determine the optimal schedule under limited availability of a single resource. It proposes the form of the measure of network complexity for each objective, and explicitly exhibits the form of the measure relative to the first objective based on a sample of 104 networks.     

Cooperative game theoretic centrality analysis of terrorist networks: The cases of Jemaah Islamiyah and Al Qaeda

The identification of key players in a terrorist organization aids in preventing attacks, the efficient allocation of surveillance measures, and the destabilization of the corresponding network. In this paper, we introduce a game theoretic approach to identify key players in terrorist networks. In particular we use the Shapley value as a measure of importance in cooperative games that are specifically designed to reflect the context of the terrorist organization at hand. The advantage of this approach is that both the structure of the terrorist network, which usually reflects a communication and interaction structure, as well as non-network features, i.e., individual based parameters such as financial means or bomb building skills, can be taken into account. The application of our methodology to the analysis results in rankings of the terrorists in the network. We illustrate our methodology through two case studies: Jemaah Islamiyah’s Bali bombing and Al Qaedas 9/11 attack, which lead to new insights in the operational networks responsible for these attacks.     

模糊多属性格序决策的冗余指标的消除策略

在模糊多属性决策中,属性权重的确定对于整个评价工作有十分重要的意义.如果评价属性数量过多,指标间的相关性将影响评价的科学性和公平性.本文建立了评价值为梯形模糊数的"相似"概念和模糊相似评价模型,并基于格序决策的理论,得到了一种新的模糊格序决策方法.结合传统的TOPSIS方法,通过计算将各方案的属性值的中心进行加权后与正负理想中心的贴近度的大小,实现备选方案的格序化排序.实例分析的结果表明:方法合理、易行.     

基于社会网络结构的创新扩散动力机制及其仿真研究

随着社会资本的大量涌入,创新扩散逐渐受到社会网络关系的影响。在分析了创新扩散机理的基础上,构建了基于不同拓扑结构的创新扩散演化动力模型。将信息获取、领导者创新能力及机会利益作为创新扩散的动力因子。通过利用复杂网络的演化博弈仿真分析,揭示了小世界、无标度等不同网络拓扑结构下,创新技术的扩散情况。仿真结果表明:在网络结构相同的情况下,信息获取对创新扩散的影响较大;在动力因子设定相同的情况下,网络主体连接越规则,创新扩散越充分。     

企业研发组织沟通网络实证及优化策略研究

鉴于企业研发组织内部沟通网络对研发组织中高效、低成本地实现技术信息共享的重要性,本文首先介绍了结构洞的测度方法,并对中间中心性方法进行改进;其次分析了某企业研发组织沟通网络的结构洞情况,指出非正式沟通网络和专业沟通网络的相关性;然后通过比较改进的中心度算法、传统的中心度算法与限制度指标的相似性,验证了该算法的有效性;最后提出一种削弱沟通网络中“核心人物”垄断地位的“搭桥”策略。结果发现:改进的中心度算法适用于带权值沟通网络结构洞的测定,验证了该算法加权方法的有效性,并给出通过非正式组织促进技术信息共享的对策     

Supply chain coordination with downstream operating costs: Coordination and investment to improve downstream operating efficiency

There are many conceptualizations and formalizations of decision making. In this paper we compare classical decision theory with qualitative decision theory, knowledge-based systems and belief–desire–intention models developed in artificial intelligence and agent theory. They all contain representations of information and motivation. Examples of informational attitudes are probability distributions, qualitative abstractions of probabilities, knowledge, and beliefs. Examples of motivational attitudes are utility functions, qualitative abstractions of utilities, goals, and desires. Each of them encodes a set of alternatives to be chosen from. This ranges from a small predetermined set, a set of decision variables, through logical formulas, to branches of a tree representing events through time. Moreover, they have a way of formulating how a decision is made. Classical and qualitative decision theory focus on the optimal decisions represented by a decision rule. Knowledge-based systems and belief–desire–intention models focus on an alternative conceptualization to formalize decision making, inspired by cognitive notions like belief, desire, goal and intention. Relations among these concepts express an agent type, which constrains the deliberation process. We also consider the relation between decision processes and intentions, and the relation between game theory and norms and commitments.     

Classification of Structural Complexity for Mine Ventilation Networks

The mine ventilation system is most important and technical measure for ensuring safety production in mines. The structural complexity of a mine ventilation network can directly affect the safety and reliability of the underground mining system. Quantitatively justifying the degree of complexity can contribute to providing a deeper understanding of the essential characteristics of a network. However, so far, there is no such a model which is able to simply, practically, reasonably, and quantitatively determine or compare the structural complexity of different ventilation networks. In this article, by analyzing some typical parameters of a mine ventilation network, we conclude that there is a linear functional relationship among five key parameters (number of ventilation network branches, number of nodes, number of independent circuits, number of independent paths, and number of diagonal branches). Correlation analyses for the main parameters of ventilation networks are conducted based on SPSS. Based on these findings, a new evaluation model for the structural complexity of ventilation network (which is represented by C) has been proposed. By combining SPSS classification analyses results with the characteristics of mine ventilation networks, standards for the complexity classification of mine ventilation systems are put forward. Using the developed model, we carried out analyses and comparisons for the structural complexity of ventilation networks for typical mines. Case demonstrations show that the classification results correspond to the actual situations. © 2014 Wiley Periodicals, Inc. Complexity 21: 21–34, 2015     

Stable network topologies using the notion of covering

An alternative perspective to evaluate networks and network evolution is introduced, based on the notion of covering. For a particular node in a network covering captures the idea of being outperformed by another node in terms of, for example, visibility and possibility of information gathering. In this paper, we focus on networks where these subdued network positions do not exist. We call these networks stable. Within this set we identify the minimal stable networks, which frequently have a ‘bubble-like’ structure. Severing a link in such a network results in at least one of the nodes being covered. In a minimal stable network therefore all nodes cooperate to avoid that one of the nodes ends up in a subdued position. Our results can be applied to, for example, the design of (covert) communication networks and the dynamics of social and information networks.     

项目网络中考虑支配与扩散关系的组合决策

单纯侧重项目自身属性而不考虑项目关联性以及由项目衍生而来的技术、经验/信息扩散对项目组合决策时的影响,易导致决策偏差,低估具有潜在技术先导性项目的价值。对此,引用复杂网络理论,以项目关联性的视角,将项目间支配和扩散关系分别抽象为有向加权网络,运用K-shell分解方法构建项目组合网络中基于支配关系的项目影响力模型以及技术、经验/信息在项目间扩散传播的模型。然后,基于PageRank算法,综合考虑项目间支配与扩散关系,建立了项目优先级排序决策模型。最后,通过算例分析说明了该模型与算法的可行性与有效性,为企业项目组合决策提供了有益的参考。     

On effectiveness of wiretap programs in mapping social networks

Snowball sampling methods are known to be a biased toward highly connected actors and consequently produce core-periphery networks when these may not necessarily be present. This leads to a biased perception of the underlying network which can have negative policy consequences, as in the identification of terrorist networks. When snowball sampling is used, the potential overload of the information collection system is a distinct problem due to the exponential growth of the number of suspects to be monitored. In this paper, we focus on evaluating the effectiveness of a wiretapping program in terms of its ability to map the rapidly evolving networks within a covert organization. By running a series of simulation-based experiments, we are able to evaluate a broad spectrum of information gathering regimes based on a consistent set of criteria. We conclude by proposing a set of information gathering programs that achieve higher effectiveness then snowball sampling, and at a lower cost. Maksim Tsvetovat is an Assistant Professor at the Center for Social Complexity and department of Public and International Affairs at George Mason University, Fairfax, VA. He received his Ph.D. from the Computation, Organizations and Society program in the School of Computer Science, Carnegie Mellon University. His dissertation was centered on use of artificial intelligence techniques such as planning and semantic reasoning as a means of studying behavior and evolution of complex social networks, such as these of terrorist organizations. He received a Master of Science degree from University of Minnesota with a specialization in Artificial Intelligence and design of Multi-Agent Systems, and has also extensively studied organization theory and social science research methods. His research is centered on building high-fidelity simulations of social and organizational systems using concepts from distributed artificial intelligence and multi-agent systems. Other projects focus on social network analysis for mapping of internal corporate networks or study of covert and terrorist orgnaizations. Maksim’s vita and publications can be found on Kathleen M. Carley is a professor in the School of Computer Science at Carnegie Mellon University and the director of the center for Compuational Analysis of Social and Organizational Systems (CASOS) which has over 25 members, both students and research staff. Her research combines cognitive science, social networks and computer science to address complex social and organizational problems. Her specific research areas are dynamic network analysis, computational social and organization theory, adaptation and evolution, text mining, and the impact of telecommunication technologies and policy on communication, information diffusion, disease contagion and response within and among groups particularly in disaster or crisis situations. She and her lab have developed infrastructure tools for analyzing large scale dynamic networks and various multi-agent simulation systems. The infrastructure tools include ORA, a statistical toolkit for analyzing and visualizing multi-dimensional networks. ORA results are organized into reports that meet various needs such as the management report, the mental model report, and the intelligence report. Another tool is AutoMap, a text-mining systems for extracting semantic networks from texts and then cross-classifying them using an organizational ontology into the underlying social, knowledge, resource and task networks. Her simulation models meld multi-agent technology with network dynamics and empirical data. Three of the large-scale multi-agent network models she and the CASOS group have developed in the counter-terrorism area are: BioWar a city-scale dynamic-network agent-based model for understanding the spread of disease and illness due to natural epidemics, chemical spills, and weaponized biological attacks; DyNet a model of the change in covert networks, naturally and in response to attacks, under varying levels of information uncertainty; and RTE a model for examining state failure and the escalation of conflict at the city, state, nation, and international as changes occur within and among red, blue, and green forces. She is the founding co-editor with Al. Wallace of the journal Computational Organization Theory and has co-edited several books and written over 100 articles in the computational organizations and dynamic network area. Her publications can be found at: http://www.casos.cs.cmu.edu/bios/carley/publications.php     

Informational measure of certainty and its use in the identification of plants

This paper introduces a measure of certainty, the characteristic of the similarity of the mathematical model to the actual plant, based on the basic concepts of information theory. The properties of the newly introduced quantity are investigated, and so is its relation to the dispersional measure of certainty. An example is considered where the economic efficiency of the control system is computed by its informational characteristics.     

Collaborative distributed decision making for large scale disaster relief operations: Drawing analogies from robust natural systems

One of the most ignored, but urgent and vital challenges confronting society today is the vulnerability of urban areas to extreme events. Current organization of response systems, predominantly based on a command and control model, limits their effectiveness and efficiency. Particularly, in decision‐making processes where a large number of actors may be involved. In this article, a new distributed collaborative decision‐making model is proposed to overcome command and control limitations encountered in stressful, hostile, chaotic, and large‐scale settings. This model was derived by borrowing concepts from the collective decision making of honeybees foraging, a successful process in solving complex tasks within complex settings. The model introduced in this article was evaluated through differential equations, i.e., continuous analysis, and difference equations, i.e., discrete analysis. The most important result found is that the best available option in any large‐scale decision‐making problem can be configured as an attractor, in a distributed and timely manner. We suggest that the proposed model has the potential to facilitate decision‐making processes in large‐scale settings. © 2005 Wiley Periodicals, Inc. Complexity 11:28–38, 2005     

Integrated decision support for aviation safety inspectors

This paper presents a systems viewpoint for developing an advanced decision support system for aircraft safety inspectors. Research results from a Federal Aviation Administration (FAA) sponsored project to use neural network and expert systems technology to analyze aircraft maintenance databases are summarized. One of the main objectives of this research is to define more refined “alert” indicators for national comparison purposes that can signal potential problem areas by aircraft type for safety inspector consideration.

Integration aspects are addressed on two levels: (1) integration of the various technical components of the decision support system, and (2) integration of the decision support system with individual behavior, management systems and organizational structure, as well as corporate culture across both formal and informal dimensions. The paper summarizes the creation of strategic “inspection profiles” for aging aircraft and reliability curve fitting for structural components both based upon using neural network technology. Also, the potential use of a model-based expert system to facilitate field inspection diagnostics is presented. Finally, a framework for developing an intelligent decision system to support aircraft safety inspections is proposed that links expert systems, neural networks, as well as a paradigm of the decision making process typically used in unstructured situations.     

Structure and logic in knowledge-based representations of systems simulation models

The paper focuses on the similarity between modelling and knowledge representation, trying to bring together the OR/Systems Science and the Artificial Intelligence views when referring to a computer system simulation, especially of the discrete-event or the network types. The models we consider are generalized activity networks with resources, including either models with a finite lifetime, such as project scheduling networks, or steady state models, such as queueing networks. By enhancing the structure of entities and states and the logic of transitions within a model specification, modularity is improved and one may adopt a more declarative approach. The relational and rule-based representation formalisms are a convenient choice for that purpose. Then, the use of knowledge bases both for the static (i.e. consultative) and the dynamic (i.e. experimental) study of the model turns up to be more natural. Moreover, the task of building an expert system for decision support on system analysis or synthesis becomes easier. The paper reports some original work in the above directions, using a logic programming approach and an associated specification methodology based on general systems concepts.     

Intra-organizational Networks and Performance: A Review

Intra-organizational network research had its first heyday during the empirical revolution in social sciences before World War II when it discovered the informal group within the formal organization. These studies comment on the classic sociological idea of bureaucracy being the optimal organization. Later relational interest within organizational studies gave way to comparative studies on the quantifiable formal features of organizations. There has been a resurgence in intra-organizational networks studies recently as the conviction grows that they are critical to organizational and individual performance. Along with methodological improvements, the theoretical emphasis has shifted from networks as a constraining force to a conceptualization that sees them as providing opportunities and finally, as social capital. Because of this shift it has become necessary not only to explain the differences between networks but also their outcomes, that is, their performance. It also implies that internal and external networks should no longer be treated separately.Research on differences between intra-organizational networks centers on the influence of the formal organization, organizational demography, technology and environment. Studies on outcomes deal with diffusion and adaptation of innovation; the utilization of human capital; recruitment, absenteeism and turnover; work stress and job satisfaction; equity; power; information efficiency; collective decision making; mobilization for and outcomes of conflicts; social control; profit and survival of firms and individual performance.Of all the difficulties that are associated with intra-organizational network research, problems of access to organizations and incomparability of research findings seem to be the most serious. Nevertheless, future research should concentrate on mechanisms that make networks productive, while taking into account the difficulties of measuring performance within organizations, such as the performance paradox and the halo-effect.     

Minimum vertex cover problem for coupled interdependent networks with cascading failures

This paper defines and analyzes a generalization of the classical minimum vertex cover problem to the case of two-layer interdependent networks with cascading node failures that can be caused by two common types of interdependence. Previous studies on interdependent networks mainly addressed the issues of cascading failures from a numerical simulations perspective, whereas this paper proposes an exact optimization-based approach for identifying a minimum-cardinality set of nodes, whose deletion would effectively disable both network layers through cascading failure mechanisms. We analyze the computational complexity and linear 0–1 formulations of the defined problems, as well as prove an LP approximation ratio result that generalizes the well-known 2-approximation for the classical minimum vertex cover problem. In addition, we introduce the concept of a “depth of cascade” (i.e., the maximum possible length of a sequence of cascading failures for a given interdependent network) and show that for any problem instance this parameter can be explicitly derived via a polynomial-time procedure.