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.     

Predictive analysis of concealed social network activities based on communication technology choices: early-warning detection of attack signals from terrorist organizations

Terrorist threat prevention and counteraction require timely detection of hostile plans. However, adversary efforts at concealment and other challenges involved in monitoring terrorist organizations may impede timely intelligence acquisition or interpretation. This study develops an approach to analyzing technological means rather than content of communications produced within the social networks comprising covert organizations, and shows how it can be applied towards detecting terrorist attack precursors. We find that differential usage patterns of hi-tech versus low-tech communication solutions could reveal significant information about organizational activities, which may be further used to detect signals of impending terrorist attacks. (Such potential practical utility of our method is supported by the detailed empirical analysis of available al Qaeda communications.) The described approach thus provides a common framework for utilizing diverse activity records from heterogeneous sources as well as contributes new tools for their rapid analysis aimed at better informing operational and policy decision-making.     

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     

A Bayesian record linkage model incorporating relational data

In this article, we introduce a novel Bayesian approach for linking multiple social networks in order to discover the same real world person having different accounts across networks. In particular, we develop a latent model that allows us to jointly characterize the network and linkage structures relying on both relational and profile data. In contrast to other existing approaches in the machine learning literature, our Bayesian implementation naturally provides uncertainty quantification via posterior probabilities for the linkage structure itself or any function of it. Our findings clearly suggest that our methodology can produce accurate point estimates of the linkage structure even in the absence of profile information, and also, in an identity resolution setting, our results confirm that including relational data into the matching process improves the linkage accuracy. We illustrate our methodology using real data from popular social networks such as Twitter , Facebook , and YouTube .     

Modelling ‘contracts’ between a terrorist group and a government in a sequential game

In this paper, we apply a sequential game to study the possibility of ‘contracts’ (or at least mutually beneficial arrangements) between a government and a terrorist group. We find equilibrium solutions for complete and incomplete information models, where the government defends and/or provides positive rent, and the terrorist group attacks. We also study the sensitivities of equilibria as a function of both players’ target valuations and preferences for rent. The contract option, if successful, may achieve (partial) attack deterrence, and significantly increase the payoffs not only for the government, but also for some types of terrorist groups. Our work thus provides some novel insights in combating terrorism.     

A methodology for analyzing decision networks, based on information theory

This paper assumes the organization as a distributed decision network. It proposes an approach based on application and extension of information theory concepts, in order to analyze informational complexity in a decision network, due to interdependence between decision centers.Based on this approach, new quantitative concepts and definitions are proposed in order to measure the information in a decision center, based on Shannon entropy and its complement in possibility theory, U uncertainty. This approach also measures the quantity of interdependence between decision centers and informational complexity of decision networks.The paper presents an agent-based model of organization as a graph composed of decision centers. The application of the proposed approach is in analyzing and assessing a measure to the organization structure efficiency, based on informational communication view. The structure improvement, analysis of information flow in organization and grouping algorithms are investigated in this paper. The results obtained from this model in different systems as distributed decision networks, clarifies the importance of structure and information distribution sources effect’s on network efficiency.     

考虑防御拓扑特征的暴恐事件演化博弈模型和仿真分析

暴恐分子有意避开政府机关、机场等关键设施的严密防御范围,选择早市、火车站卖票口等尚未得到有效防御的人群密集场所发动袭击。本文考虑政府反恐力量防御拓扑特征,即政府反恐力量防控范围与恐怖分子发动攻击范围之间相离、相切、相交和相含等拓扑关系,构建了暴恐事件的演化博弈模型,分析多种情景下均衡稳定性,在Netlogo平台下对多种情景的理论结果进行社会模拟分析。结果表明:政府和恐怖分子行为演化均衡策略与政府防控范围、恐怖分子发动攻击范围、政府防控成本、政府防控收益等多种因素有关,随着政府进行有效防控的范围不断增加,恐怖分子选择袭击的可能性将不断减小,直到采取不攻击策略。     

Location of terror response facilities: A game between state and terrorist

We study a leader follower game with two players: a terrorist and a state where the later one installs facilities that provide support in case of a terrorist attack. While the Terrorist attacks one of the metropolitan areas to maximize his utility, the State, which acts as a leader, installs the facilities such that the metropolitan area attacked is the one that minimizes her disutility (i.e., minimizes ‘loss’). We solve the problem efficiently for one facility and we formulate it as a mathematical programming problem for a general number of facilities. We demonstrate the problem via a case study of the 20 largest metropolitan areas in the United States.     

Incorporating the threat of terrorist attacks in the design of public service facility networks

In this paper, we consider the design problem of a public service facility network with existing facilities when there is a threat of possible terrorist attacks. The aim of the system planner, who is responsible for the operation of the network, is to open new facilities, relocate existing ones if necessary, and protect some of the facilities to ensure a maximum coverage of the demand that is assumed to be aggregated at customer zones. By doing so, the system planner anticipates that a number of unprotected facilities will be rendered out-of-service by terrorist attacks. It is assumed that the sum of the fixed cost of opening new facilities, the relocation costs, and the protection costs cannot exceed a predetermined budget level. Adopting the approach of gradual (or partial) coverage, we formulate a bilevel programming model where the system planner is the leader and the attacker is the follower. The objective of the former is the maximization of the total service coverage, whereas the latter wants to minimize it. We propose a heuristic solution procedure based on tabu search where the search space consists of the decisions of the system planner, and the corresponding objective value is computed by optimally solving the attacker??s problem using CPLEX. To assess the quality of the solutions produced by the tabu search (TS) heuristic, we also develop an exhaustive enumeration method, which explores all the possible combinations of opening new facilities, relocating existing ones, and protecting them. Since its time complexity is exponential, it can only be used for relatively small instances. Therefore, to be used as a benchmark method, we also implement a hill climbing procedure employed with the same type of moves as the TS heuristic. Besides, we carry out a sensitivity analysis on some of the problem parameters to investigate their effect on the solution characteristics.     

Identifying sets of key players in a social network

A procedure is described for finding sets of key players in a social network. A key assumption is that the optimal selection of key players depends on what they are needed for. Accordingly, two generic goals are articulated, called KPP-POS and KPP-NEG. KPP-POS is defined as the identification of key players for the purpose of optimally diffusing something through the network by using the key players as seeds. KPP-NEG is defined as the identification of key players for the purpose of disrupting or fragmenting the network by removing the key nodes. It is found that off-the-shelf centrality measures are not optimal for solving either generic problem, and therefore new measures are presented. Stephen P. Borgatti is Professor of Organization Studies at the Carroll School of Management, Boston College. His research is focused on social networks, social cognition and knowledge management. He is also interested in the application of social network analysis to the solution of managerial problems.     

One-way flow network formation under constraints

We study the effects of institutional constraints on stability and efficiency in the “one-way flow” model of network formation. In this model the information that flows through a link between two players runs only towards the player that initiates and supports the link, so in order for it to flow in both directions, both players must pay whatever the unit cost of a directional link is. We assume that an exogenous “societal cover” consisting of a collection of possibly overlapping subsets covering the set of players specifies the social organization in different groups or “societies,” so that a player may initiate links only with players that belong to at least one society that he/she also belongs to, thus restricting the feasible strategies and networks. In this setting, we examine the impact of such societal constraints on stable/efficient architectures and on dynamics.     

Network formation when players seek confirmation of information

We study network formation in a situation where the network allows players to obtain information (signals) about other players. This information is important for making a payoff relevant decision. However, not all information is reliable and so players may have an incentive to check it. By obtaining multiple messages about the same player through the network, a player learns whether his information is reliable for making the payoff relevant decision. We study the existence and architecture of strict Nash networks. We find that players who are involved in at least three links sponsor all links they are involved in. These players are similar to the central players in center sponsored stars. We show that strict Nash networks can be over-connected as well as under-connected as compared to efficient networks. Finally, we extend the basic model to study heterogeneous populations. In the first scenario, we allow for the co-existence of players who only value checked information and players who also value information with unknown reliability. In the second scenario, players who do not care about checking their information co-exist with players who do. Our results are robust to both types of heterogeneity, with one exception: the presence of a single player who cares only about checked information is enough to ensure that center sponsored stars are no longer stable.     

Key potential-oriented criticality analysis for complex military organization based on FINC-E model

The complex social organizations, which can self-organize into the region “at the edge of chaos”, neither too ordered nor too random, now have become an interdisciplinary research topic. As a kind of special social organization, the complex military organization usually has its key entities and relations, which should be well protected in case of attacks. In order to do the criticality analysis for the military organization, finding the key entities or relations which can disrupt the functions of the organization, two problems should be seriously considered. First, the military organization should be well modeled, which can work well in the specialized military context; secondly it is critical to define and identify the key entities or relations, which should incorporate the topological centrality and weighted nodes or edges. Different from the traditional military organizations which are usually task-oriented, this paper proposes the Force, Intelligence, Networking, and C2 Extended (FINC-E) Model for complex military organization, with which a more detailed and quantitative analysis for the military organization is available. This model provides the formal representation for the nodes and edges in the military organization, which provides a highly efficient and concise network topology. In order to identify the critical nodes and edges, a method based on key potential is proposed, which acts as the measurement of criticality for the heterogeneous nodes and edges in the complex military organization. The key potential is well defined on the basis of topology structure and of the node’s or edge’s capability, which helps to transform the organization from the heterogeneity to the homogeneity. In the end, the criticality analysis case study is made for both small-world networked military organization and scale-free networked military organization, showing that the measure of key potential has the advantage over other classical measures in locating the key entities or relations for complex military organization.     

Subsidizing to disrupt a terrorism supply chain—a four-player game

Terrorism with weapons of mass destruction (WMDs) is an urgent threat to homeland security. The process of counter-WMD terrorism often involves multiple government and terrorist group players, which is under-studied in the literature. In this paper, first we consider two subgames: a proliferation game between two terrorist groups or cells (where one handling the black market for profits proliferates to the other one to attack, and this is modelled as a terrorism supply chain) and a subsidization game between two governments (where one potential WMD victim government subsidizes the other host government, who can interfere with terrorist activities). Then we integrate these two subgames to study how the victim government can use the strategy of subsidization to induce the host government to disrupt the terrorism supply chain. To our knowledge, this is the first game-theoretic study for modelling and optimally disrupting a terrorism supply chain in a complex four-player scenario. We find that in the integrated game, when proliferation payment is high or low, the practical terrorist group will proliferate and not proliferate, respectively, regardless of government decisions. In contrast, in the subsidization subgame between the two governments, the decision of subsidization depends on its cost. When proliferation payment is medium, the decision of subsidization depends on not only its cost but also the preparation cost and the attacking cost. Findings from our results would assist in government policymaking.     

考虑合作网络稳健性的三参照点决策方法

在合作网络的决策中,由于分散的成员之间缺乏直接交流及表达主观偏好的机会,关键成员会因为满意度下降而导致合作网络的退化甚至解体。为了维护合作网络的稳健性,本文基于特殊心理参照点对网络决策成员的影响,提出了一种合作网络中考虑关键节点的三参照点决策方法。首先,依据三参照点理论,考虑“底线”、“目标”、“现状”等参照点,构建符合决策者主观感受的满意度函数,进而计算网络中参与决策的成员对各方案的综合满意度;然后,依据网络节点(成员)在网络结构上的重要性,计算成员的决策权重;进一步地,计算全体成员的整体满意度,据此进行方案排序;最后,通过一个协同创新中心的决策案例说明本方法的可行性。     

On the topologic structure of economic complex networks: Empirical evidence from large scale payment network of Estonia

This paper presents the first topological analysis of the economic structure of an entire country based on payments data obtained from Swedbank. This data set is exclusive in its kind because around 80% of Estonia's bank transactions are done through Swedbank; hence, the economic structure of the country can be reconstructed. Scale-free networks are commonly observed in a wide array of different contexts such as nature and society. In this paper, the nodes are comprised by customers of the bank (legal entities) and the links are established by payments between these nodes. We study the scaling-free and structural properties of this network. We also describe its topology, components and behaviors. We show that this network shares typical structural characteristics known in other complex networks: degree distributions follow a power law, low clustering coefficient and low average shortest path length. We identify the key nodes of the network and perform simulations of resiliency against random and targeted attacks of the nodes with two different approaches. With this, we find that by identifying and studying the links between the nodes is possible to perform vulnerability analysis of the Estonian economy with respect to economic shocks.     

Cooperative Cross-Entropy method for generating entangled networks

Survivability is rapidly becoming an important criterion in network design and planning. This is due to our increased dependence on ever more complex communication networks. Another important criterion which plays a central role in design and planning decisions is cost. As a result, network planners tend to design sparse networks to minimise cost. There is a class of networks known as entangled networks which seems to satisfy both criteria of survivability and sparseness. In this paper, we demonstrate how the Cross-Entropy method may be utilised to generate entangled networks. We also propose a cooperative optimisation approach to further improve the generation of an optimal entangled network.     

Stochastic optimization on social networks with application to service pricing

In this paper we develop a combined simulation and optimization approach for solving difficult decision problems on complex dynamic networks. For a specific reference problem we consider a telecommunication service provider who offers a telecommunication service to a market with network effects. More particularly, the service consumption of an individual user depends on both idiosyncratic characteristics and the popularity of this service among the customer’s immediate neighborhood. Both the social network and the individual user preferences are largely heterogeneous and changing over time. In addition the service provider’s decisions are made in absence of perfect knowledge about user preferences. The service provider pursues the strategy of stimulating the demand by offering differentiated prices to the customers. For finding the optimal pricing we apply a stochastic quasi-gradient algorithm that is integrated with a simulation model that drives the evolution of the network and user preferences over time. We show that exploiting the social network structure and implementing differentiated pricing can substantially increase the revenues of a service provider operating on a social network. More generally, we show that stochastic gradient methods represent a powerful methodology for the optimization of decisions in social networks.     

A Network Analysis Model for Disambiguation of Names in Lists

In research and application, social networks are increasingly extracted from relationships inferred by name collocations in text-based documents. Despite the fact that names represent real entities, names are not unique identifiers and it is often unclear when two name observations correspond to the same underlying entity. One confounder stems from ambiguity, in which the same name correctly references multiple entities. Prior name disambiguation methods measured similarity between two names as a function of their respective documents. In this paper, we propose an alternative similarity metric based on the probability of walking from one ambiguous name to another in a random walk of the social network constructed from all documents. We experimentally validate our model on actor-actor relationships derived from the Internet Movie Database. Using a global similarity threshold, we demonstrate random walks achieve a significant increase in disambiguation capability in comparison to prior models. Bradley A. Malin is a Ph.D. candidate in the School of Computer Science at Carnegie Mellon University. He is an NSF IGERT fellow in the Center for Computational Analysis of Social and Organizational Systems (CASOS) and a researcher at the Laboratory for International Data Privacy. His research is interdisciplinary and combines aspects of bioinformatics, data forensics, data privacy and security, entity resolution, and public policy. He has developed learning algorithms for surveillance in distributed systems and designed formal models for the evaluation and the improvement of privacy enhancing technologies in real world environments, including healthcare and the Internet. His research on privacy in genomic databases has received several awards from the American Medical Informatics Association and has been cited in congressional briefings on health data privacy. He currently serves as managing editor of the Journal of Privacy Technology. Edoardo M. Airoldi is a Ph.D. student in the School of Computer Science at Carnegie Mellon University. Currently, he is a researcher in the CASOS group and at the Center for Automated Learning and Discovery. His methodology is based on probability theory, approximation theorems, discrete mathematics and their geometries. His research interests include data mining and machine learning techniques for temporal and relational data, data linkage and data privacy, with important applications to dynamic networks, biological sequences and large collections of texts. His research on dynamic network tomography is the state-of-the-art for recovering information about who is communicating to whom in a network, and was awarded honors from the ACM SIG-KDD community. Several companies focusing on information extraction have adopted his methodology for text analysis. He is currently investigating practical and theoretical aspects of hierarchical mixture models for temporal and relational data, and an abstract theory of data linkage. Kathleen M. Carley is a Professor of Computer Science in ISRI, School of Computer Science at Carnegie Mellon University. She received her Ph.D. from Harvard in Sociology. Her research combines cognitive science, social and dynamic networks, and computer science (particularly artificial intelligence and machine learning techniques) to address complex social and organizational problems. Her specific research areas are computational social and organization science, social adaptation and evolution, social and dynamic network analysis, and computational text analysis. Her models meld multi-agent technology with network dynamics and empirical data. Three of the large-scale tools she and the CASOS group have developed are: BioWar a city, scale model of weaponized biological attacks and response; Construct a models of the co-evolution of social and knowledge networks; and ORA a statistical toolkit for dynamic social Network data.     

Private Games are too Dangerous

Given the difficulty of observing interpersonal relations as they develop within an organization, I use iterated prisoner&2018;s dilemma games to simulate their development. The goal is to understand how trust could develop as a function of private games, that is, as a function of interaction sequences between two people independent of their relationships with other people. My baseline is Axelrod&2018;s results with TIT for TAT showing that cooperation can emerge as the dominant form of interaction even in a society of selfish individuals without central authority. I replicate Axelrod&2018;s results, then show that the results only occur in a rare social context&2014;maximum density networks. Where people form less dense networks by withdrawing from unproductive relationships, as is typical in organizations, the competitive advantage shifts from TIT for TAT to abusive strategies. A devious PUSHY strategy wins in moderate to high density networks. A blatantly HOSTILE strategy wins in less dense networks. Abusive players do well in sparse networks because their abuse is lucrative in the initial exchanges of a relationship&2014;before the other person knows to withdraw. Wise players avoiding the abusive players leaves the abusive players free to concentrate on naive players (con men thrive in big cities). The implication is that what keeps abusive players at bay are friends and acquaintances warning managers away from people known to exploit their colleagues. I reinforce the point with illustrative survey data to conclude that private games are not only too dangerous, but also too rare and too slow to be the foundation for trust within organizations. The results are an evidential call for the sociological intuition that trust and distrust cannot be understood independent of the network context in which they are produced.