Modelling the spatial and social dynamics of insurgency
© Giabbanelli; licensee Springer. 2014
Received: 4 November 2013
Accepted: 6 February 2014
Published: 2 May 2014
Insurgency emerges from many interactions between numerous social, economical, and geographical factors. Adequately accounting for the large number of potentially relevant interactions, and the complex ways in which they operate, is key to creating valuable models of insurgency. However, this has long been a challenging endeavour, as insurgency imposes specific limitations on the data that could speak to these interactions: quantitative data is limited by the difficulties of systematic collection in war, while qualitative data may include vague or conflicting insights from direct observers. In this paper, we designed a computational framework based on Fuzzy Cognitive Maps and Complex Networks to face these limitations. A software solution fully implements this framework and allows analysts to conduct simulations, in order to better understand the current dynamics of insurgency or test ‘what-if’ scenarios. Two approaches are presented to guide analysts in developing models based on our framework, either through a nuanced reading of the literature, or by aggregating the knowledge of domain experts.
Initially of interest primarily to soldier-scholars seeking to systematise their experiences as counter-insurgents in wars of decolonisation [1, 2], insurgency (and its cognates) increasingly became the subject of several landmark studies by political scientists [3–5], historians , and economists . Together with this considerable renaissance in the study of insurgency, computational research into the dynamics of terrorism and rebellion has grown in prominence . One of the drivers of this growth is the ability of computational approaches to account for the many complex interactions between the factors that shape a conflict, which makes such approaches a valuable complement to the associational analyses stemming from economics and seeking to identify ‘root causes’. Computational models have indeed been able to systematise and articulate many of the key processes which define insurgency as a particular type of conflict. For example, a recent model used 19 factors and over 80 parameters, accounting for processes such as the consequences of intelligence gathering on the insurgent’s organization or the impact of outside support on the insurgents’ actions . The task of populating the model’s parameters with real-world data is often left to the analysts  but this task can be particularly challenging as models require specific numerical values despite sources mostly providing qualitative data from empirical evidence. While the challenge of creating quantitative models of insurgency when given qualitative data has already been thoroughly addressed in the political methodology from a statistical perspective , this challenge remains relatively unexplored from a computational perspective.
In this paper, we develop a novel computational method that can be used to model how an array of interacting factors come together to determine loyalty or rebelliousness. This method involves two computational techniques whose synergies are essential to address the specific needs of modelling in insurgency. The artificial intelligence technique of Fuzzy Cognitive Maps (FCMs) has a proven track record in allowing for the development of models when supporting data is vague or conflicting. Using this technique will allow us to address one of the main shortcomings of current models as aforementioned. While FCMs have been used to model complex political phenomena, such as crises in the Republic of Macedonia  or Cyprus , this technique is not designed to capture the spatial dynamics that play a very important role in conflicts (c.f.,  for the role that urban geography plays in terrorism). For example, not adequately capturing the diffusion of civil wars over space  or the localized nature of attacks  would significantly lower the relevance of models to analysts. We previously demonstrated that FCMs could be used to model social processes but that they would have to be combined with another technique for local dynamics . Our early work combined FCMs with Cellular Automaton, which have a long history of being used to model spatial dynamics  but suffer from having to rigidly divide the space into equal square cells. Therefore, we propose to represent the space as a Complex Network (CN), which offers several advantages over the early version of this framework: the space can be arbitrarily divided, standard generators can be used to test how a strategy would unfold in different types of space, and the analyses tools developed for complex networks can be used to explore the relationship between the structure of that space and the dynamics of insurgency.
Contribution of the paper
The principal contributions of the present work can be summarized as follows:
We propose a computational framework to support researchers in mathematically expressing the important but often hard-to-formalise relationships spanning social and physical geography.
We present a novel software solution that guides modellers and analysts in designing a model of insurgency in an interactive manner and then simulate it.
We show two different approaches for the practical development of a model of insurgency based on our framework. One approach relies on a nuanced reading of a large corpus by a domain expert, while the other focuses on synthesizing the knowledge of an international group of experts.
Organization of the paper
We start by providing the technical background to our computational framework, and we formally specify it. Then, we detail the first and key step in building a model based on this framework: how to design a conceptual map articulating the interactions between several factors contributing to insurgency. We further highlight the functioning of the framework through our software implementation, with a focus on how it enables experts to interactively and efficiently set up computer models of insurgency tailored for the context they are interested in. Finally, we discuss the strengths and limitations of this framework.
Insurgents can be mobile and difficult to identify, which limits the potential of terrain-centric and enemy-centric approaches for counterinsurgency (COIN) operations. In contrast, the population is easy to identify and often less mobile. Unlike conventional warfare, opposing forces in counterinsurgency warfare thus do not only seek to reduce each other’s military capability but are also competing for the support of the population . In societies with tribal power structures such as Iraq, winning the ‘hearts and minds’ of local opinion leaders is critical to secure support. Once opinion leaders are clearly identified (e.g., using the Tactical Conflict Assessment Framework in Iraq) the question becomes: who among them should be targeted ? This question can also be approached from a geographical standpoint. Leaders must not only be persuaded that their interests are in line with counterinsurgents, but also that their interests will be protected. Given that forces cannot be deployed everywhere, the question becomes: where can protection be guaranteed in return for political support? Whether it is approached from a social-network perspective (who) or a geographical-network perspective (where), this is often studied as a variation of an influence maximization problem. The influence maximization problem posed by Domingos and Richardson  and modelled by Kempe and colleagues  asks, for a parameter k, to find the set of k nodes that provide the maximum influence. This needs to be extended in the case of insurgencies, since both insurgents and counterinsurgents want to spread their influence while blocking that of their opponent. This leads to an influence blocking maximization problem, which is different from the competitive influence maximization problem where forces are solely interested in maximimizing their influence instead of limiting others’. In the context of insurgency, these problems have often been modelled using complex networks (which account for the great variation in ties between people or places) and agent-based systems (which highlight the reasoning behind a switch in allegiance).
In [19, 22], each node of the network represents a person. The attitude of a person with respect to the competing forces is represented by a value ranging from -0.5 (favourable to Taliban) to +0.5 (favourable to the US). Individuals are also categorized by their level of influence, which includes head of household or village leader. Individuals change attitude solely based on pairwise interactions between them. The outcome of interactions depend on set probabilities, and can lead to both individuals changing to their average attitude, retaining the original attitudes, or having one change to become more like the other based on a parameter. The model in  also propagates opinions through social links using probabilistic rules. One noteworthy simplification in these models was the absence of the context, that is, the set of political, economical, and social aspects that shape leaders’ attitudes. For example, a village leader’s loyalty to the government may wither as a larger number of young men in the village become unemployed. These aspects also mitigate the influence exerted during COIN operations or by the insurgent. While tribal relationships have been taken into account in some models , aspects such as the trustworthiness of institutions or the discrimination of counter-insurgent violence would impact the outcome of interactions with forces supportive of the government.
Taking into account the broader context in which individuals make decisions can be very challenging due to the complexity of this context and the difficulty of collecting data about it. Focusing on the counterinsurgency environment, Upshur and colleagues wrote that "researchers are not impartial but rather armed actors in a conflict; thus there is a ‘combatant observer effect’. The interviewer’s obvious association with a combatant organization affects the openness and honesty of respondents, as does the power disparity between a member of an occupying military force and an unarmed local population" . Consequently, data corruption results in uncertainty and bias. Furthermore, disagreements on the mechanisms are not only found between the reports of direct observers but also in the analyses of scholars. For example, Fearon and Laitin reported that "the effect of primary commodity exports is considerable: [...] a country with no natural resource export only has a probability of warstart of 1%" compared to 22% when exporting" , but Ross considered that "the claim that primary commodity exports are linked to civil war appears fragile and should be treated with caution" . Therefore, there is a need for computational models that can use the qualitative data provided by process-tracking and ethnographical studies, and have specific mathematical ways to address the uncertainty and conflicts found in the data.
Many frameworks have been proposed to model diffusion in networks , and they account for uncertainty in different ways. In the Weighted Generalized Annotated Program (wGAP) framework, changes are expressed by rules whose probability reflects the certainty . For example, the rule supportInsurgents(A)supportInsurgents(B) ∧ leader(B, A) states that if the village leader B for inhabitant A supports insurgents, then A will support the insurgents with 75% certainty. Similarly, the Linear Threshold Model (LTM) and the Independent Cascade Model (ICM) have rules for changes in the nodes’ attitudes, and random thresholds are associated with these rules to model uncertainty . The Multi-Attribute Networks and Cascades (MANCaLog) framework provides more flexibility, the properties of nodes and edges have a weight whose uncertainty is represented by an interval that can be open or closed . However, a key distinction is that these frameworks are designed to operate once the values for the uncertainty have been specified: they are not made to take in (possibly contradictory) qualitative assessments and turn them into values based on the uncertainty.
Fuzzy Cognitive Maps
Fuzzy Logic Theory is a valuable mathematical technique to deal with the conflicting and uncertain evidence found in the wealth of testimonies, media reports, and other forms of ‘thick description’. As described by Li, Fuzzy Logic Theory 
resembles human reasoning under approximate information and inaccurate data to generate decisions under uncertain environments. It is designed to mathematically represent uncertainty and vagueness, and to provide formalized tools for dealing with imprecision in real-world problems.
Once the evidence is summarized via a linguistic term, a set of IF-THEN rules is produced. For example, we asked experts to evaluate the extent to which an inhospitable terrain would contribute to the government’s institutional weakness. Two experts judged the effect to be ‘medium’ while two saw it as ‘high’ and one called it ‘very high’. The IF-THEN rules associate a crisp antecedent (e.g., whether the terrain is inhospitable) to a fuzzy consequent (e.g., a ‘medium’ or ‘high’ impact on the government’s institutional weakness) and a confidence factor (e.g., number of experts who produced that rule). In this example, we obtain:
R1: IF (Inhospitable terrain is PRESENT) THEN the impact on the (Government’s institutional weakness) is medium (2/5)
R2: IF (Inhospitable terrain is PRESENT) THEN the impact on the (Government’s institutional weakness) is high (2/5)
R3: IF (Inhospitable terrain is PRESENT) THEN the impact on the (Government’s institutional weakness) is very high (1/5)
These rules are combined to formulate a Fuzzy Inference Systems that yields one quantitative value. Fuzzy Set Theory is repeatedly applied to obtain one crisp value from the evidence supporting each relationship in the model. These relationships are connected: for example, an inhospitable terrain can impact the ability of insurgents to control the population which in turns affects the socio-economic advantage to insurgency. Therefore, these connections can be viewed as a network named a Fuzzy Cognitive Map (FCM). The nodes of the network represent fuzzy domain concepts (e.g., trustworthiness of institutions, baseline tension). The edges stand for causal connections, and their values are obtained via Fuzzy Set Theory. Edges are either positive or negative to indicate that the target concept respectively increases or decreases with the source concept. Since the introduction of FCM by Kosko in 1986  and its initial application as an artificial intelligence tool to public policy , FCMs have been used successfully in critical situations where accuracy has to be obtained despite vagueness , such as evaluating the vulnerability of facilities to terrorism .
where f is a threshold function (also known as transfer function) that bounds the output in the interval [0, 1]. It is common practice to use such function in order to keep concepts within a specific range . This function can be, for instance, a sigmoid function such as the hyperbolic tangent .
As our framework aims to support analysts, the space must be represented in a way that is not only efficient for calculations: analysts should also be able to navigate this space in an intuitive way. The primary reason to use cellular automata in our previous work was that their straightforward mapping of space onto a grid could easily be navigated . However, this came at the expense of the aforementioned issues in efficiency. Using networks addresses the issue of efficiency, but questions are then raised regarding the ease of use by analysts. While navigating a general network can be a challenge, this task is simple in our case since a geographical division of space into neighbourhoods or regions produces planar networks. Indeed, the sub-spaces represented as vertices are linked by edges only when they are adjacent, and thus edges do not need to cross in order to display the network. A wealth of research has been conducted to display planar graphs, initially motivated by the need to display electrical schemes of large circuits for engineering purposes. Therefore, a number of algorithms such as surveyed in  offer convenient displays that can support analysts in exploring the geographical dynamics of insurgency. Furthermore, additional support is provided through ongoing research in natural interaction techniques for networks (c.f., the work of Nathalie Henry Riche).
Formally, we extend the notation of FCMs to express the value of a concept i at time t in a location by Ci, t, v. The extent to which a concept a of the FCM is influenced by a (not necessarily distinct) concept b is given by Wa, b. The influence function f takes in two concepts, where the first is under the influence of the second, and computes the the impact of the influence. One time step of the simulation is given in Algorithm 1. The Algorithm has two parts. First, it applies the influence of neighbouring locations: for each location and each neighbour , all concepts of i will be influenced by the concepts of j. While we previously used dedicated functions to specify which concepts were either influenced or influencing , this new formalism simply encodes such roles in Wa, b since Wa, b>0 states that b is an influencing concept and a the concept under influence. The second part uses the local context to mediate the influences that were received, by independently updating each FCM using the standard procedure in the literature .
Creating conceptual maps
The first step in designing models based on our framework is to create a conceptual map that uses expert knowledge to assess which factors are relevant to insurgency in a specific context and how these factors can be articulated. The product of this step is a network consisting of representative factors (i.e., vertices) whose interactions (i.e., edges) are either positive (i.e., the presence of a factor increases another) or negative and are given a strength. Two broad approaches support the elicitation of knowledge from an expert in order to construct maps : direct approaches assist the experts in constructing maps (e.g., Novak’s concept mapping ) for example by asking them which concepts are important and how they are related, while indirect approaches infer the maps from written documents or interviews (e.g,, Trochim’s concept mapping ). Both approaches can be used to create the conceptual map used by our framework, and the choice depends on the availability of experts on the different aspects of the insurgency. A direct approach may be taken when modellers and experts can meet several times to iteratively construct maps, while an indirect approach may be necessary when experts on the field communicate by sending intelligence reports. This section illustrates both approaches for a typical example of a "revolutionary war"  or of a terrorist campaign , in which insurgents are engaged in a population-centric war by shattering the confidence of the local communities in their government.
The rebelliousness of a community indicates the level of its participation in insurgent activities. It is thus the most closely monitored factor for this scenario. It is determined by two factors: motive and opportunity. The motive is determined by the socio-economic advantage to insurgency, which collectively incorporates the social, political, and economic reasons for which an individual or community would want to rebel. Opportunities consist of the mechanisms and material conditions that make rebellious acts possible on an incidental basis. This model also accounts for the self-reinforcing effect of rebellion, in which existing rebelliousness facilitates further rebelliousness though mechanisms such as insurgent recruitment networks and the solidification of ascribed political loyalties .
The socio-economic advantage to insurgency depends on several factors. The ability of insurgents to control the population and to use discriminating violence determines the extent to which they can offer enticements and coercion to the local community, as does the power of the government to employ discriminate violence. Indeed, "control - regardless of the ‘true’ preferences of the population - precludes options other than collaboration by creating credible benefits for collaborators and, more importantly, sanctions for defectors" . Community economic factors also play a powerful role. The rate or level of economic development determines the opportunity costs of participation, where economic recession makes participation in rebellion less risky or costly in comparison to times of economic boom . Natural resources vulnerable to looting or military capture present a tantalising incentive to join armed groups . For example, in the developing world, "conflict diamonds" or the drug trade influenced the development and proliferation of militias  described as a class of ‘feral’ insurgent , since their activism is a means of survival instead of an institutional mechanism to secure popular support. High unemployment among young men produces a likely pool for insurgent recruitment, as it provides them with opportunities to advance economically and socially . On a more micro level, a household so poor that its members have little means to sustain themselves is particularly likely to join whichever group happens to offer the best immediate chance of survival . For example, while Pakistani rebels often had better education than their Afghani counterparts, both came from large, impoverished households .
The opportunity to rebel is strongly linked to the strength of the insurgent organisation, which affects whether recruitment mechanisms, arms , information , and logistical capacity make it possible to rebel in an organised or meaningful way. The strength of an insurgent organisation increases in the presence of weak government institutions, limited in their ability to identify and neutralise insurgent agents [2, 3, 49, 53]. Demographic precursors for insurgency establish conditions of instability that can be exploited by insurgent groups to provide political justifications and normative space for rebellion. An existing territorial dispute can evolve into an enduring ethnic or sectarian rivalry, providing the fault-lines for civil conflict and increased fractionalisation . The presence of foreign military forces staging an intervention can inhibit the power of any party to achieve significant political progress through force of arms, paradoxically often making conflict longer-lasting and more intractable . A recent previous civil war can ensure that the population has both lurking hostilities and access to weapons. The Balkan wars are one example of the facilitating effect that weapons-saturation has upon making participation in civil wars feasible . A supportive foreign diaspora can make funding insurgent activities easier, while the social exclusion of certain groups makes conflict increasingly easier to justify and prosecute as the excluded group grows in size.
The process was carried out for each edge, using the Mamdani algorithm, the sum method of aggregation, and the centroid method for defuzzification. These technical choices are common practice, and we refer the interested reader to  for the technical aspects.
In a direct approach, a purposeful sample of experts is assembled and guided through a three step process. First, experts are given a question that will prompt them to iteratively identify concepts (e.g., writing them on sticky notes), arrange them (e.g., creating clusters or hierarchies by moving the notes), link them and re-arrange them to facilitate the display of links . This step results in the map’s structure. While that step can be carried on straightforwardly for simple problems when experts are all available in one place, it can be challenging for complex problems where a panel of international experts is needed in order to account for each part of the problem . Thus, the map’s structure may be set based on a sub-committee of experts. The second step is to ask all experts about the strength of each relationship, and the final step combines their knowledge using Fuzzy Logic Theory as in the previous section.
Three steps process
The final step is to generate the spatial network. During the early phases of model development, an exact mapping might not be available to modellers. Consequently, the model might have to operate on assumptions regarding the broad characteristics of the space, and only if the model proves useful then partnerships can support the acquisition of accurate data. Our software provides extensive support for the early phase by allowing modellers to choose network generators with a desired set of properties (Figure 8b-1), such as creating planar power-law networks (Figure 8b) which represent a densely connected urban centre linked to increasingly isolated settlements. Since such generators require a fine tuning, analysis tools are provided both for visual inspection (Figure 8b-2) as well as for the quantification of key metrics (e.g., clustering coefficient in Figure 8b-3 or degree distribution in Figure 8b-4). Once the right network has been generated, the simulation can be performed and analyzed to see how factors of the FCM changed over time (Figure 8b-5).
The simple model used in this section does not aim to make accurate recommendations regarding counterinsurgency strategies. Rather, the experiments focus on demonstrating the ability of our framework to easily represent complex dynamics and handle ‘what-if’ scenarios. We use the model represented in Figure 7, which articulates how geographical (e.g., inhospitable terrain), economical (e.g., economic development and unemployment of young men), and political factors come together in shaping rebelliousness. The values were obtained by aggregating expert knowledge using Fuzzy Logic, as in Figure 5. In this sample scenario, an insurgency has begun in a resource-based economy. Consequently, the following assumptions were made on the initial values of concepts:
the presence of resources is drawn from a normal distribution with mean 0.7 and standard deviation 0.4. That is, regions are resource-rich on average but significant inequalities are present. Given that the scenario abstracts a resource-based economy, the level of economic development is set to match the presence of resources, whereas the level of unemployment is inversely proportional to the presence of resources.
since recently started, the level of rebelliousness is still very low (set to 0.1). It is fueled partly by a slight socio-economic advantage to insurgency (set to 0.1) and the presence of some opportunities to rebel (set to 0.2). As the insurgency is only nascent, insurgents have a limited ability to control the population (set to 0.2).
clear ethnic differences are present and occasional skirmishes are followed by a large discrimination of counter-insurgent violence (set to 0.7).
the country has a wide variety of terrains with a minority deemed inhospitable. Consequently, the extent to which is terrain is inhospitable is drawn from a normal distribution with mean 0.3 and standard deviation 0.5.
Two factors are involved in influences across regions: the economic development, and the ability of insurgents to control the population (black in Figure 7). The Coupling Editor is used to formaly specify these influences (Figure 8a). A region’s level of economic development is impacted by the level of economical development of neighbouring regions, as they are potential trade partners. The relationship is modelled using the ‘threshold and impact’ template (Figure 8a), such that a (positive or negative) difference of at least 5% between the level of economical development of a region and its trading partners will lead to a difference of 5% in that region (c.f.,  for the equations). The control exerted by insurgents can hinder trades, which is accounted for by decreasing the level of economical development based on the insurgents’ control at a rate of 0.1. Finally, the geography was abstracted using a planar small-world graph generated using the method proposed by Zhang et al. ; parameter values and properties of the networks are displayed in Figure 8b.
Improvements (%) via different interventions
Ability of insurgents to control the population
In order to develop accurate models of conflicts that can support military analysis, computational techniques need to utilize vasts amounts of data to the best of its potential. However, uncertainty and conflicts abound in data collected by observers or synthesized by experts, making it challenging to effectively incorporate it into quantitative models. Furthermore, adequately capturing the spatial and social dynamics of insurgency tends to require different computational techniques. Our previous work proposed a novel approach to create models of insurgency from imperfect data while accounting for both spatial and social dynamics . While this early framework addressed some of the needs for modelling insurgency, it also came with three limitations. First, the space had to be divided into a set of square cells, which could lead to either an over-simplification of key spatial features (e.g., when cells are too large and cover very distinct neighbourhoods) or a computational burden (e.g., when small cells unnecessarily partition a homogeneous space). Second, the process of model building was centred on the nuanced reading of scholarly articles, which could not be straightforwardly applied to gathering first-hand observations. Finally, the initial development of software highlighted the need for a more intuitive approach to model design, such that modellers could focus on key aspects while the modelling process would be transparent for stakeholders. This paper extends our previous work and addresses all three shortcomings aforementioned. The space is now represented using complex networks, and generators are also provided to create space when detailed maps are not available. We detail how models can be built directly from participants’ experience, and provide a proof-of-concept that synthesizes the expertise of five scholars in insurgency. Finally, our focus on usability during software development has resulted in a set of tools that can effectively guide modellers and stakeholders through the process of building a computational model of insurgency.
Our framework supports the integration of data from different sources so that analysts can understand conflicts and run ‘what-if’ scenarios for counterinsurgency scenarios. It also provides methodological support for scholars of insurgency in two ways. First, it allows for military theories, individually or synthetically, to be tested for consistency by exploring the implications of their suppositions. Second, it allows for intriguing empirical phenomena to be encountered and explored as the disjuncture between the actual world and the world contained within the model. We expect that the use of our framework for these different endeavours will further drive its evolution, both through changes in software and refinement of its mathematical structure.
Our framework currently represents the space using an unweighted planar graph, such that influences either totally flow between two adjacent locations or do not. In practice, adjacency is dynamic: for example, a wall built for security purposes around the district of Adamiyah would virtually cut it off from neighbouring districts (Figure 2) once completed. Adjacency is also a social construct, as members of one ethnic group may rarely move to places populated by another ethnic group during a conflict. Our framework could be augmented to take these aspects into consideration, by having a dynamic network and weighting its edges depending on social factors. Furthermore, the assumption of a planar graph can be challenged due to the spread of violence in non-contiguous areas via tribal ties. This effect is particularly salient in Iraq, which has an estimated 150 tribes and 2,000 clans. While the Ba’athist ideology under Saddam Hussein emphasized the state over ethnic/sectarian divisions, tribal loyalties were nonetheless essential to maintain military support and continue to play a key role in Iraq . Consequently, models often focus on tribal relationships and road network accessibility to link locations . Capturing such relationships can be achieved in two stages. First, the requirement for planarity could be waived, as generating non-planar graphs is straightforward due to the availability of numerous graph generators . Second, the requirement for a single edge between two nodes can also be waived. Frameworks such as wGAP already use multiple labelled edges between nodes , which would allow to connect places based on multiple criteria such as geographical and ethnic proximity. Such additions are virtually endless in a modelling endeavour, which highlights the need for a trade-off between the accuracy of the models and the additional complexity brought into the modelling process. Therefore, applications of our framework will prove instrumental in gradually establishing the guidelines for computational methods of insurgency and continuing to meet them via innovative frameworks.
This work benefited from earlier collaborations with Piper Jackson (Simon Fraser University) on cellular automata, Vijay K. Mago (Troy University) on Fuzzy Cognitive Maps, and Simon Frankel Pratt (University of Toronto) on insurgency. The author is indebted to the scholars’ whose expertise informed the design of the expert system under a direct approach: Bruce Bueno de Mesquita (New York University), Isabelle Duyvesteyn (Universiteit Utrecht), Samuel S. Stanton, Jr (Groove City College), Ethan Bueno de Mesquita (University of Chicago), and James Morrow (University of Michigan).
- Galula D: Counterinsurgency Warfare: Theory and Practice. Westerport, Connecticut - London: Praeger Security International; 1964.Google Scholar
- Kitson F: Low Intensity Operations. Faber and Faber; 1971.Google Scholar
- Kalyvas S: Warfare in Civil Wars. In Rethinking the Nature of War. Edited by: Duyvesteyn I, Angstrom J. London: Frank Cass; 2005.Google Scholar
- Weinstein JM: Inside Rebellion: The Politics of Insurgent Violence. Cambridge University Press; 2007.Google Scholar
- Tarrow SG: The new transnational activism. Cambridge University Press; 2005.View ArticleGoogle Scholar
- Nagl JA: Learning to Eat Soup with a Knife: Counterinsurgency Lessons from Malaya and Vietnam. Westerport, Connecticut - London: Praeger Publishers; 2002.Google Scholar
- Fearon JD, Laitin DD: American political science review. Ethn. Insurgency Civil War 2003, 97: 75–90.Google Scholar
- Blouin S: Is your world complex? An overview of complexity science and its potential for military applications. Can. Mil. J 2013, 13(2):26–36.Google Scholar
- Arney DC, Arney K: Modeling insurgency, counter-insurgency, and coalition strategies and operations. J. Defense Modeling Simul. Appl. Methodol. Technol 2013, 10: 57–73.Google Scholar
- Fearon JD, Laitin DD: Integrating qualitative and quantitative methods. In Janet Box-Steffensmeier. Edited by: Brady H, Collier D. New York: Oxford University Press; 2008. pp. 756–76 pp. 756–76Google Scholar
- Tsadiras AK, Kouskouvelis I, Margaritis KG: Using Fuzzy Cognitive Maps as a decision support system for political decisions. Lect. Notes Comput. Sci 2003, 2563: 291–301.Google Scholar
- Neocleous CC, Schizas CN: Application of Fuzzy Cognitive Maps to the Political-Economic Problem of Cyprus. In Proceedings of the International Conference on Fuzzy Sets and Soft Computing in Economics and Finance 2004. St Petersburg; June 17–20, 2004:340–349.Google Scholar
- Graham S (Ed): Cities, War, and Terrorism: Towards an Urban Geopolitics. John Wiley & Sons; 2008.Google Scholar
- Schutte S, Weidmann NB: Diffusion patterns of violence in civil wars. Pol. Geogr 2011, 30(3):143–152. 10.1016/j.polgeo.2011.03.005View ArticleGoogle Scholar
- Townsley M, Johnson SD, Ratcliffe JH: Space time dynamics of insurgent activity in Iraq. Secur. J 2008, 21: 139–146. 10.1057/palgrave.sj.8350090View ArticleGoogle Scholar
- Giabbanelli PJ: A novel framework for complex networks and chronic diseases. Stud. Comput. Intell 2012, 424: 207–215.View ArticleGoogle Scholar
- Pratt SF, Giabbanelli PJ, Jackson P, Mago VK: Rebel with many causes: A computational model of insurgency. In Proceedings of the 2012 IEEE International Conference on Intelligence and Security Informatics (ISI). IEEE; 2012:90–95.View ArticleGoogle Scholar
- Batty M: Cities and Complexity: Understanding Cities with Cellular Automata, Agent-Based Models, and Fractals. MIT Press; 2007.Google Scholar
- Howard NJ: Finding optimal strategies for influencing social networks in two player games. Masters Thesis, Massachusetts Institute of Technology, 2011 Masters Thesis, Massachusetts Institute of Technology, 2011Google Scholar
- Domingos P, Richardson M: Mining the Network Value of Customers. In Proceedings of the Seventh International Conference on Knowledge Discovery and Data Mining. ACM Press; 2002:57–66.Google Scholar
- Kempe D, Kleinberg J, Tardos E: Maximizing the spread of influence through a social network. In Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining. NY, USA: ACM New York; 2003:137–146.View ArticleGoogle Scholar
- Hung BW, Kolitz SE, Ozdaglar A: Optimization-based influencing of village social networks in a counterinsurgency. Lect. Notes Comput. Sci 2011, 6589: 10–17. 10.1007/978-3-642-19656-0_3View ArticleGoogle Scholar
- Tsai J, Nguyen TN, Tambe M: Security games for controlling contagion. Proc. Assoc. Adv. Artif. Intell. (AAAI) 2012.Google Scholar
- Eyre S, Rotte R, Taggart T, Chewar C, Johnson A, Roos P, Shakarian P: Using RASCAL to find key villages in Afghanistan. Small Wars J 2012., 8(7):Google Scholar
- Upshur WP, Roginski JW, Kilcullen DJ: Recognizing systems in Afghanistan. Prism 2012, 3(3):87–104.Google Scholar
- Ross M: What do we know about natural resources and civil war? J. Peace Res 2004, 41(3):337–356. 10.1177/0022343304043773View ArticleGoogle Scholar
- Shakarian P, Simari GI, Callahan D: Reasoning about complex networks: a logic programming approach. Theory Pract. Logic Programming 2013., 13(4–5):Google Scholar
- Broecheler M, Shakarian P, Subrahmanian V: A scalable framework for modeling competitive diffusion in social networks. In Proceedings of the IEEE International Conference on Social Computing/Privacy, Security, Risk and Trust. IEEE; 2010:295–301.Google Scholar
- Li Z: Fuzzy Chaotic Systems. Springer; 2006. pp. 1–11 pp. 1–11MATHGoogle Scholar
- Mago VK, Morden HK, Friz C, Wu T, Namazi S, Geranmayeh P, Chattopadhyay R, Dabbaghian V: Analyzing the impact of social factors on homelessness: a Fuzzy Cognitive Map approach. BMC Med. Inform. Decis. Making 2013., 13(94):Google Scholar
- Giabbanelli PJ, Torsney-Weir T, Mago VK: A fuzzy cognitive map of the psychosocial determinants of obesity. Appl. Soft Syst 2012, 12(12):3711–3724.View ArticleGoogle Scholar
- Mago VK, Mago A, Sharma P, Mago J: Fuzzy logic based expert system for the treatment of mobile tooth. Adv. Exp. Med. Biol 2011, 696: 607–614. 10.1007/978-1-4419-7046-6_62View ArticleGoogle Scholar
- Kosko B: Fuzzy Cognitive Maps. Int. J. Man Mach. Stud 1986, 24: 65–75. 10.1016/S0020-7373(86)80040-2View ArticleMATHGoogle Scholar
- Kosko B: Adaptive inference in fuzzy knowledge networks. In Proceedings of the first IEEE International Conference on Neural Networks (ICNN). IEEE; 1987:261–268.Google Scholar
- Stylios CD, Georgopoulos VC, Malandraki GA, Chouliara S: Fuzzy Cognitive Map architectures for medical decision support systems. Appl. Soft Comput 2008, 8: 1243–1251. 10.1016/j.asoc.2007.02.022View ArticleGoogle Scholar
- Akgun I, Kandakoglu A, Ozok AF: Fuzzy integrated vulnerability assessment model for critical facilities in combating the terrorism. Expert Syst. Appl 2010, 37(5):3561–3573. 10.1016/j.eswa.2009.10.035View ArticleGoogle Scholar
- Stach W, Kurgan L, Pedrycz W, Reformat M: Genetic learning of fuzzy cognitive maps. Fuzzy Sets Syst 2005, 153(3):371–401. 10.1016/j.fss.2005.01.009View ArticleMathSciNetMATHGoogle Scholar
- Barthelememy M: Spatial networks. Phys. Rep 2011, 499: 1–101. 10.1016/j.physrep.2010.11.002View ArticleMathSciNetGoogle Scholar
- Reggiani A, Nijkamp P: Complexity and Spatial Networks. Springer; 2009.View ArticleGoogle Scholar
- Caldwell WB: Situational update (August 28). Multinational Force-Iraq (2006) Multinational Force-Iraq (2006)Google Scholar
- Nishizeki T, Rahman MS: Planar Graph Drawing, Volume 12 of Lecture Notes Series on Computing. World Scientific; 2004.Google Scholar
- Jones N, Ross H, Lynam T, Perez P, Leitch A: Mental models: an interdisciplinary synthesis of theory and methods. Ecol. Soc 2011, 16: 46.Google Scholar
- Moon B, Hoffman R, Novak J, Canas A: Applied Concept Mapping: capturing, analyzing and organizing knowledge. Boca Raton, Florida: CRC Press; 2011.Google Scholar
- Jackson K, Trochim W: Concept mapping as an alternative approach for the analysis of open-ended survey responses. Organ. Res. Methods 2002, 5: 307–332. 10.1177/109442802237114View ArticleGoogle Scholar
- Neumann PR, Smith M: The Strategy of Terrorism: How it Works, and Why It Fails. London and New York: Routledge; 2008.Google Scholar
- Collier P, Hoeffler A: Greed and Grievance in civil war. World Bank Policy Res. Working Paper (No. 2355), (2000) World Bank Policy Res. Working Paper (No. 2355), (2000)Google Scholar
- Duyvesteyn I: The concept of conventional war and armed conflict. In Rethinking the Nature of War. Edited by: Duyvesteyn I, Angstrom J. Frank Cass; 2005.Google Scholar
- Kett M, Rowson M: Drivers of violent conflict. J. R. Soc. Med 2007, 100(9):403–406. 10.1258/jrsm.100.9.403View ArticleGoogle Scholar
- Mackinlay J: The Insurgent Archipelago: From Mao to bin Laden. London: Hurst; 2009.Google Scholar
- Justino P: Poverty and violent conflict: a micro-level perspective on the causes and duration of warfare. J. Peace Res 2009, 46(3):315–333. 10.1177/0022343309102655View ArticleGoogle Scholar
- Fair CC: Who are Pakistan’s militants and their families? Terrorism Pol. Violence 2008, 20: 49–65.View ArticleGoogle Scholar
- Byman D, Chalk P, Hoffman B, Rosenau W, Brannan D: Trends in Outside Support for Insurgent Movements. RAND; 2001.Google Scholar
- Fjelde H, de Soysa I: Coercion, co-optation, or cooperation? State capacity and the risk of civil war, 1961–2004. Confl. Manag. Peace Sci 2009, 26: 5–25. 10.1177/0738894208097664View ArticleGoogle Scholar
- Fuhrmann M, Tir J: Territorial dimensions of enduring internal rivalries. Confl. Manag. Peace Sci 2009, 26(4):307–329. 10.1177/0738894209106478View ArticleGoogle Scholar
- Regan PM: Third-party interventions and the duration of intrastate conflicts’ journal of conflict resolution. J. Confl. Resolution 2002, 46: 55–73. 10.1177/0022002702046001004View ArticleGoogle Scholar
- de Graaf B: The wars in former Yugoslavia in the 1990s: bringing the state back in’. In Rethinking the Nature of War. Edited by: Duyvesteyn I, Angstrom J. Frank Cass; 2005.Google Scholar
- Yen J, Langari R: Fuzzy logic: intelligence, control, and information. Prentice-Hall, Inc.; 1998.Google Scholar
- Giabbanelli PJ, Alimadad A, Dabbaghian V, Finegood DT: Modeling the influence of social networks and environment on energy balance and obesity. J. Comput. Sci 2012, 3: 17–27. 10.1016/j.jocs.2012.01.004View ArticleGoogle Scholar
- Giabbanelli PJ, Crutzen R: An agent-based social network model of binge drinking among Dutch adults. J. Artif. Soc. Soc. Simul 2013., 16(2):Google Scholar
- Zhang Z, Rong L, Comellas F: Evolving small-world networks with geographical attachment preference. J. Phys.ics A 2006, 39(13):3253–3261. 10.1088/0305-4470/39/13/005View ArticleMathSciNetMATHGoogle Scholar
- National Security Council: National strategy for victory in Iraq. 2005.Google Scholar
- Myers CN: Tribalism and democratic transition in Lybia: lessons from Iraq. Global Tides 2013, 7: 5.Google Scholar
- Giabbanelli P: The small-world property in networks growing by active edges. Adv. Complex Syst 2011, 14(6):853–869. 10.1142/S0219525911003207View ArticleMathSciNetGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.