A Model for Evaluating Counter-IED Strategies Introduction

Lora Weiss, Elizabeth Whitaker, Erica Briscoe, and Ethan Trewhitt, A Model for Evaluating counter-IED Strategies. [Under Review]

A Model for Evaluating Counter-IED Strategies Lora Weiss*, Elizabeth Whitaker, Erica Briscoe, and Ethan Trewhitt Georgia Institute of Technology Atlanta, GA 30332 *[email protected], 404-407-7611

ABSTRACT A strategic means of quelling IED attacks involves understanding the behavioral processes that lead up to an IED event and exploiting that understanding to disrupt the processes at early stages. This paper presents a computational approach to modeling aspects of IED perpetration and enables the exploration of intervention strategies by an analyst or planner. The modeling framework supports the identification of potential interdiction points in the events leading to an IED detonation, with a specific focus on recruitment and the motivation to construct, emplace, and detonate IEDs. Knowledge engineering is used to extract and capture domain knowledge which is then represented in a system dynamics model to support the exploration and identification of behaviors associated with adversarial activities. Interchangeable submodels are incorporated to capture subtleties or differing opinions and to allow for the analysis of alternative decisions or courses of action.

Introduction Improvised explosive devices (IEDs) are one of the largest threats facing coalition forces in current military conflicts. The United States and other nations are greatly invested in mitigating these deadly devices. Past results have shown that completely technological counter-IED (cIED) efforts will be insufficient, and therefore, attention is focusing on augmenting the technological methods with neutralizing factors that contribute to human involvement in the IED perpetration process1. To successfully do so requires an understanding of the behavioral aspects and influences of human involvement. This has led to an interest in socio-technical and systemsbased models of terrorist activity. By integrating behavioral aspects of adversarial activities with computational methods, a greater understanding of these activities can be attained; simultaneously, potentially effective intervention points can be ascertained. This is often accomplished by modeling individuals, organizations, and societies via the creation of micro-, meso-, and macro-scale models to analyze and experiment with the impact of potential influences on population behavior2. In addition to providing insight, model flexibility and model dynamics are required to assess multiple interpretations of situations as they play out over time. Static models often cannot achieve this since disparate motivations and ideological factors evolve as a function of time3. This paper focuses on modeling the IED perpetration process, where knowledge was provided by subject 1

Lora Weiss, Elizabeth Whitaker, Erica Briscoe, and Ethan Trewhitt, A Model for Evaluating counter-IED Strategies. [Under Review]

matter experts (SMEs) from the United States and the United Kingdom, to ascertain behavioral aspects of cIED efforts.

Modeling Approach The specific modeling approach discussed in this paper is the result of a coupling between computational and social science research that has led to an improved capability to predict largescale activities and behaviors of potential IED developers before they have successfully deployed IEDs. This approach creates a modeling framework for exploring cIED efficacy that revolves around addressing several major scientific issues at the intersection of behavioral sciences, information science, computer science, and systems engineering. In order to capture relevant domain information, knowledge engineering methods were used to extract and represent information from various information sources, primarily from domain experts in the US and UK with knowledge about the general and specific motivations of terrorists, bombings, and other IED-related activities. Specific technologies included (i) the use of mind maps for preliminary knowledge structuring, (ii) the development of influence diagrams to reflect aspects of culture and society that affect the IED process, and (iii) the creation of systems dynamics models4 to represent causal relationships of stocks and flows (items, materials, people, etc.) within the IED process. These model components were then used to create a modeling environment with specific model instantiations and which can be subjected to evaluation by developers and SMEs. A report by Weiss, et al.5 describes the modeling cycle, complete with analyses that can be performed using such a modeling construct. The resulting tools then support what-if analyses to determine the effects of recruitment deterrents on the overall rate of IED detonation and support decision making related to identifying potential intervention points within the IED process. Figure 1 presents a summary of the approach used in the model development. Open literature, including military doctrine and historical scenarios, were assessed across social-cultural and technical domains. Subject matter experts were interviewed using a questionnaire and served as sources of knowledge. This information was then incorporated into various models, including influence models and system dynamics models. These model components create a modeling environment with specific model instantiations, which are then subjected to evaluation by the developers and SMEs. These models can then be adapted and updated as new uses and information is obtained.

2

Lora Weiss, Elizabeth Whitaker, Erica Briscoe, and Ethan Trewhitt, A Model for Evaluating counter-IED Strategies. [Under Review]

SMEs

SMEs

Doctrine Literature

Knowledge Engineering

Scenarios

Evaluation

Influence Models System Dynamics Models Agent-based Models

Models

Modeling

Figure 1. Approach to model construction

System dynamics models were applied to evaluate IED processes as a comprehensive system with various interacting influences. A system dynamics model is an executable model used to represent and understand the dynamic behavior of a complex system over time6. It uses stocks and flows to represent system elements and their relative influences upon each other. Figure 2 shows the IED model that is detailed below. Stocks represent an inventory of some accumulated entities (e.g., IEDs) and are indicated in the model using rectangular boxes. Flows, indicated by a double-lined arrow, show how entities move between stocks or between a stock and a cloud. Clouds indicate the world outside the scope of the model and act as either sources or sinks for modeled quantities. An influence model is a graphical representation of a group of causal relationships and offers a method to couple the essential elements of a situation, including decisions, uncertainties, and objectives, by describing how they influence each other.

The Core IED Model Despite the diversity in types of IED attacks, it has been noted that regardless of the perpetrator, key functions are consistent3. This consistency allows us to model central features related to IED perpetration, with the resulting model referred to as the “Core IED Model.” This Core IED Model consists of three focus areas: Materials and Supplies, the IED Process (construction, storage, emplacement, and detonation), and Insurgent Personnel (recruitment and disengagement). Each of these areas is represented by one or more stocks that are connected by flows indicating transition of entities between those stocks (see Figure 2). These core areas encompass the significant aspects of IED perpetration. Using a system dynamics approach allows for the representation of behaviors, influences, and processes that enable analysis of the high-level behaviors of the socio-technical system. Many of the stocks used in the model were designed to correspond to those identified by Joint IED Defeat Organization (JIEDDO) as significant in measuring the effectiveness of cIED efforts and the severity of IED attacks and include factors such as the number of IED incidents, the local population’s support of IED 3

Lora Weiss, Elizabeth Whitaker, Erica Briscoe, and Ethan Trewhitt, A Model for Evaluating counter-IED Strategies. [Under Review]

networks, and the local populations support of using IEDs against Coalition Forces, and the number of times an IED network is disrupted7. Materials and Supplies Focus Area - The top portion of the core model in Figure 2 depicts a single stock representing the inventory of generalized materials and supplies available to insurgent groups in the area. This stock is affected by two flows: Gathering (Materials and Supplies Gathering Submodel), and Consumption. The input flow of materials, Gathering, represents actions that cause the accumulation of materials and supplies. The output flow, Consumption, represents the use of these materials and supplies in the construction of IEDs. These materials are expressed by the generalized unit “item” to represent hypothetical items such as pounds of fertilizer or gallons of fuel. They can also be expanded to include financial resources that allow for the purchase of base materials8 as well as the gathering of illicit items that cannot be readily purchased. While simplistic, this area of the model represents base material that is gathered and subject to subsequent use for IED creation.

Figure 2: Core IED Perpetration Model

4

Lora Weiss, Elizabeth Whitaker, Erica Briscoe, and Ethan Trewhitt, A Model for Evaluating counter-IED Strategies. [Under Review]

IED Process Focus Area- The process of IED perpetration is represented in the center of the core model. The process is decomposed into different stages, where five stocks represent the IEDs. In practice, the process is varied and IEDs may move through it in different ways, but this model represents a generalized form. A typical IED is constructed either for the purpose of a particular attack or to be stored for future use. Once it is constructed it is moved into inventory, which may be a traditional form of inventory (such as a warehouse), or it may be stored in a less conventional way (e.g., distributed throughout the community). IEDs may also be held by individuals who have little knowledge of the item’s true nature or purpose. Once insurgents have decided to emplace an IED, it is removed from inventory and emplaced in the field or acquired by a carrier. Finally, whenever a target is near, the IED is triggered manually or automatically. Each of these stages is represented in the model by a stock that aggregates the IEDs currently existing within that stage. At any point during the process, cIED methods may be used to destroy an IED before it is used against a target. This destruction detours the modeled IED and deposits it in the Disrupted IEDs stock. The Disrupted IEDs stock’s related inflow can be further decomposed into Early Disruption, Middle Disruption, and Late Disruption, which are generalizations of disruptions occurring at different times. The value of these three flows is determined by an IED Disruption submodel. During execution, the modeling software handles the accumulation of constructed IEDs and transitions them into other phases of the process. Insurgent Personnel Focus Area- Understanding the people involved in IED activities includes understanding when and where they may be susceptible to being recruited or radicalized. While the indoctrination and recruitment of insurgents is a nuanced and multi-faceted process9, the model initially simplifies this so that critical aspects can be identified, while maintaining the understanding that, in many cases, individuals become terrorists or supporters of terrorism through a slow and gradual process10. At a high-level, people are categorized as: the General Population, the Grey Population, and Active Insurgents, which are represented as stocks in Figure 2. The stock representing the General Population shows the transition of a person into a sympathizer (a member of the Grey Population susceptible to further radicalization), then into Active Insurgent via participation within a terrorist group. Radicalization represents the transition of a person within the General Population into the Grey Population. This means that a previously neutral person has taken a position of sympathy for insurgent beliefs. Whenever a person holds a positive view of the insurgents’ goals and tactics, that person is considered vulnerable for recruitment. As this person becomes an active participant in the IED process, this person is considered recruited, which is represented as a Recruitment flow. This may be an overt decision by the participant, or it may be a gradual process in which an insurgent group slowly eases a sympathizer into increasingly more severe tasks. The model considers the person to be recruited whenever he or she is actively involved in the process of constructing, storing, emplacing, or detonating IEDs. 5

Lora Weiss, Elizabeth Whitaker, Erica Briscoe, and Ethan Trewhitt, A Model for Evaluating counter-IED Strategies. [Under Review]

Deradicalization occurs when the attitudes of an individual are moderated from the radical views of the insurgency to the more mainstream views of the general population. Finally, Disengagement and Death indicate that an active insurgent has left the group of active insurgents, reducing the number of active insurgents.

Submodels Figure 2 presents the core model. Feeding into the core model is a collection of submodels, shown in bold font in Figure 2. These submodels support the core model and represent an extension of the influences for certain areas. The use of submodels allows details to be included, while also allowing different perspectives to be codified in the submodels and then swapped out for comparison against other submodels. A submodel based on a particular set of assumptions can be replaced by a different submodel for experimentation. The core model utilizes the final result of each submodel as a single value that influences the stocks and flows. The use of submodels then allows for the development, modification, and reuse of model components as modules within the model. For modeling IED perpetration, the submodels are indicated as octagons with bold names: Materials and Supplies Gathering IED Disruption IED Motivation Population Radicalization Population Deradicalization Insurgent Recruitment Insurgent Disengagement Insurgent Death While the visual separation of submodels within the core model implies independence, submodels are actually connected via variables within themselves. For example, the Insurgent Disengagement / Recruitment submodels, which model a person’s willingness to join or leave an insurgent group, contain many of the same influences. The Population Radicalization / Deradicalization submodels also have many common influences. These influences can exist across submodel lines even though the core model eschews the visual connections to maintain visual readability. The Insurgent Disengagement / Recruitment submodels and the Population Radicalization / Deradicalization submodels are briefly described. Population Radicalization and Deradicalization Submodels - This submodel describes the variables that affect population radicalization and deradicalization. See Figure 3. General factors that affect individuals’ behavior are grouped into four categories, building on the work of Bartolomei, et al.11 Camus variables include those related to the morality of a population (e.g., is the population particularly violent or does common religious doctrine condemn violence). Dewey variables consist of the factors that concern social aspects, such as freedom of speech. Smith 6

Lora Weiss, Elizabeth Whitaker, Erica Briscoe, and Ethan Trewhitt, A Model for Evaluating counter-IED Strategies. [Under Review]

variables represent the economic variables of the society (e.g., GDP). Finally, Maslow variables cover quality of life factors, such as the availability of water and medical care. This combining of factors was selected to capture the broad influence on radicalization, while also taking into consideration important environmental and demographic variables.

Figure 3: Radicalization and Deradicalization Submodels

Insurgent Recruitment and Disengagement Submodels - The Recruitment and Disengagement submodel is presented in Figure 4. Recruitment and disengagement represent the voluntary or coerced actions of persons joining or leaving the insurgency. The surrounding variables represent the influences that drive those decisions. For example, within the executable model, the value of Disengagement Effectiveness, representing the effectiveness of cIED and counterinsurgency efforts, can be adjusted using a slider (as a method of user input) to experiment with various effectiveness values.

7

Lora Weiss, Elizabeth Whitaker, Erica Briscoe, and Ethan Trewhitt, A Model for Evaluating counter-IED Strategies. [Under Review]

Figure 4: Recruitment and Disengagement Submodels

Using Models for What-If Analyses This modeling approach is useful for conducting analyses of trade-offs associated with different cIED intervention strategies. Developing counter-insurgency strategies involves not only an understanding of what will be successful, but also what efforts will be ineffective. While a whatif analysis will not precisely predict who will become an insurgent and cannot predict the exact number of IEDs that will be detonated within a given timeframe, it can shed light in two important ways: (1) It can expose factors and influences of the IED perpetration process that are more significant than others. For example, a common perception is that the best intervention point is to influence the general population before they are radicalized, but if a large part of the population is inherently radicalized based on the local culture, there may be little benefit in attempting to influence the radicalization process. A more effective approach may be to address the flow from the grey population to an active insurgent. (2) Previously unconsidered aspects of the problem become exposed so that issues that may otherwise have caught analysts by surprise become known.

8

Lora Weiss, Elizabeth Whitaker, Erica Briscoe, and Ethan Trewhitt, A Model for Evaluating counter-IED Strategies. [Under Review]

These models also allow analysts to experiment with different sets of assumptions, parameters, variables, conditions, and relationships to explore actions and responses that are within an analyst’s control. Such actions and events include military actions, policies, and diplomatic decisions. Actions and events that cannot be controlled include weather, crop production, or the behavior of other, remotely related groups. The modeling environment allows for the assessment of the impact of the controllable variables. For example, raising literacy rates to educate a population as a means of improving their economic state (represented in the Dewey and Smith variables) may be counterproductive if a rise in literacy translates into a greater exposure to insurgent propaganda12. Likewise, reducing poverty may result in a redistribution of wealth that leaves some seeking alternate financial opportunities (such as placing IEDs for money). These types of tradeoffs can be evaluated with what-if analyses. An example analysis is presented below.

Example What-If Analysis: Overall IED Detonation Rate A straight-forward example of a what-if analysis involves determining the effects of various deterrents on the overall rate of IED detonation. Figure 5 shows the model as it appears during execution. The core model is on the left, with three output graphs on the right and three slider control bars in the center. The slider bars allow the analyst to adjust various options. In this example, the slider bars allow for the adjustment of Disengagement Effectiveness (within the Insurgent Disengagement submodel), Disruption Effectiveness (within the IED Disruption submodel), and Supply-gathering Interference (within the Materials and Supplies Gathering submodel). The first output graph presents the number of IEDs that exist per month for each of the stock categories of: Construction, Storage, Emplacement, Detonation, and Early/Middle/Late Disruption. The second output graph shows the total number of IEDs detonated over the course of a 36-month simulation. In this sample analysis, any independent variables not shown in the sliders are kept constant. The starting values for the three manipulated variables are set to initial values chosen by an analyst. For the example in Figure 4, the values used are: Disengagement Effectiveness: 5 individual insurgents disengage each month Disruption Effectiveness: 20% of IEDs at each step of the IED process are disrupted each month Supply-gathering Interference: 50% of desired supplies are blocked from gathering. These values are not based on actual military scenarios, but, rather, were chosen to show model execution and how analysts may use the model to obtain an understanding of the effects of various intervention options.

9

Lora Weiss, Elizabeth Whitaker, Erica Briscoe, and Ethan Trewhitt, A Model for Evaluating counter-IED Strategies. [Under Review]

Figure 5. Sample model execution for what-if analysis

Since the ultimate goal is to reduce the total number of IEDs detonated, the final value of the Detonated IEDs stock serves as a top-level indicator of counter-IED success. At these initial levels, there were approximately 210 IED detonations after 36 months of simulated time. To show the effect of potential interventions, the following changes were assessed and have equal overall costs with respect to implementation. Disengagement Effectiveness: increase to 10 persons per month Disruption Effectiveness: increase to 40% of IEDs per step, per month Supply-gathering Interference: increase to 75% of supplies blocked Executing the model with these changes yields the results shown in Figures 6. In these figures the dashed line represents the baseline values, while the solid line represents the updated values.

10

Lora Weiss, Elizabeth Whitaker, Erica Briscoe, and Ethan Trewhitt, A Model for Evaluating counter-IED Strategies. [Under Review]

Figure 6: Left: Disengagement improvement (from 5 to 10 ppl/mo); Middle: Disruption improvement (from 20% to 40%); Right: Supply interference improvement (from 50% to 75%)

These graphs show that an increase in disengagement yields the largest long-term reduction in overall IED detonations, while an increase in supply interference yields the smallest reduction. For the example scenario, it would therefore be most cost-effective to invest in efforts to improve disengagement effectiveness.

Conclusion This paper presented a system-based model of IED deployment to evaluate counter-IED efforts, using IED and terrorism subject matter experts. The scientific concept coupled computational and social science research to develop a capability to predict large-scale activities and behaviors of potential IED developers before they have successfully deployed IEDs. The resulting tool allows for what-if analyses to determine the effects of counter-IED efforts on the overall rate of IED detonation. An example what-if analysis was presented to demonstrate how such tools can support decision making and provide the ability to identify potential intervention points within the IED process.

Acknow ledgments This research was funded under an award from the Office of Naval Research, Contract # N00014081-0481, and we would like to acknowledge them for their support. We would like to thank our collaborator at the University of Hull (Caroline Kennedy-Pipe) for providing us access to subject matter experts and for providing insight into some of the behaviors and processes associated with perpetration of IEDs.

11

Lora Weiss, Elizabeth Whitaker, Erica Briscoe, and Ethan Trewhitt, A Model for Evaluating counter-IED Strategies. [Under Review]

References 1. National Research Council, “Disrupting Improvised Explosive Device Terror Campaigns” Workshop Report, Washington, D.C.: National Academies Press, 2008. 2. Greg Zacharias, JeanMacMillan, and Susan Van Hemel, eds. Behavioral Modeling and Simulation: from Individuals to Societies. Washington, D.C.: National Research Council, 2008. 3. Jeffrey Bale, “Jihadist Cells and I.E.D. Capabilities in Europe: Assessing the Present and Future Threat to the West”, Unpublished Report for Monterey Institute of International Studies, 2009. 4. John D. Sterman, Business Dynamics: Systems Thinking and Modeling for a Complex World, Boston: Irwin McGraw-Hill, 2000, p.42. 5. Lora Weiss, Elizabeth Whitaker, Erica Briscoe, and Ethan Trewhitt, “Modeling Behavioral Activities Related to Deploying IEDs in Iraq,” Technical Report #ATASD5757-2009-01, August, 2009. 6. John D. Sterman, Business Dynamics: Systems Thinking and Modeling for a Complex World, Boston: Irwin McGraw-Hill, 2000, p.42. 7. JIEDDO, “The Joint Improvised Explosive Device Defeat Organization: DOD’s Fight Against IEDs Today and Tomorrow.” U.S. House of Representatives: Committee on Armed Services. Subcommittee on Oversight & Investigations. Committee Print 110-11, Chairman Vic Snyder, November, 2008. 8. National Research Council, “Disrupting Improvised Explosive Device Terror Campaigns”, pp. 24. 9. Scott Gerwehr and Sara Daly, “Al-Qaida: Terrorist Selection and Recruitment.” In The McGraw-Hill Homeland Security Handbook, edited by D. Kamien. New York: McGrawHill, 2006. 10. John Horgan, “From profiles to pathways: The road to recruitment. Countering the Terrorist Mentality.” E-Journal USA. Washington, DC: U.S. Department of State. http://usinfo.state.gov/journals/itps/0507/ijpe/ ijpe0507.pdf, accessed 11 October 2007. 11. Jason Bartolomei, William Casebeer, Troy Thomas, “Modeling Violent Non-State Actors: A Summary of Concepts and Methods.” United States Air Force Academy, Institute for Information Technology Applications, Colorado, 2004. 12. Scott Atran, “Genesis of Suicide Terrorism.” Science, New Series. 299(5612) p. 15341539.

12

Lora Weiss, Elizabeth Whitaker, Erica Briscoe, and Ethan Trewhitt, A Model for Evaluating counter-IED Strategies. [Under Review]

13