Modeling the Combined 

Terrorist-Narcotics Trafficker Threat 

to National Security 

Alexander Woodcock and Samuel Musa 

Center for Technology and National Security Policy 

National Defense University 

May 2012 
The views expressed in this article are those of the authors and do not reflect the official policy 

or position of the National Defense University, Department of Defense, or U.S. Government. All 

information and sources for this paper were drawn from unclassified materials. 

Alexander Woodcock is currently Principal Operations Research Analyst at MITRE 

Corporation, a consultant to the National Defense University, and an Affiliate Professor at the 

School of Public Policy, George Mason University. He is a Fellow of the Royal Society of 

Medicine, a foreign member of the Royal Swedish Academy of War Sciences, and a full member 

of Sigma Xi. He was a consultant to the Institute for Defense Analyses, the Naval Research 

Laboratory, and the intelligence community. He was also a Senior Research Professor and 

Director of the Societal Dynamics Research Center at the School of Public Policy, George 

Mason University. Dr. Woodcock was Chief Scientist, Vice President, and Director of the 

Advanced Mathematics Program, BAE SYSTEMS-Portal Solutions (formerly Synectics 

Corporation), a Guest Professor at the Swedish National Defence College, and a Visiting 

Professor at The Royal Military College of Science, England. He is the author of Assessing

Iraq’s Future, published by the Royal Swedish Academy of War Sciences and co-editor with 

John Dockery of The Military Landscape: Mathematical Models of Combat. Dr. Woodcock has a 

PhD in biology and an MSc in biophysics from the University of East Anglia, England, and a 

BSc with Honours in physics from Exeter University, England. He was a Fulbright Fellow and 

Research Associate at Yale University, an IBM (World Trade) Postdoctoral Fellow at IBM 

Research, an IBM (UK) Postdoctoral Fellow at the University of Warwick Mathematics Institute, 

and a Visiting Scholar in Biology at Stanford University. 

Samuel Musa is a Senior Research Fellow at the Center for Technology and National Security 

Policy (CTNSP) of the National Defense University. He has held the Homeland Security Science 

and Technology Chair at CTNSP. He was previously Associate Vice President for Strategic 

Initiatives and Professor of electrical and computer engineering at Northwestern University. He 

has served in various positions in academia, government, and industry, including University of 

Michigan, University of Pennsylvania, Institute for Defense Analysis, and Office of the 

Secretary of Defense. Dr. Musa served on the Defense Intelligence Advisory Board, Army 

Science Board, and Air Force Scientific Advisory Board. He was Executive Secretary of Defense 

Science Board Summer Studies and Task Forces, and a member of the Scientific and Technical 

Intelligence Committee of the Director of Central Intelligence. Dr. Musa received his BA and BS 

degrees in Electrical Engineering from Rutgers University, and MS and PhD degrees in Applied 

Physics from Harvard University. 

Acknowledgments 

SM acknowledges the funding support from the Office of University Programs, Science and 

Technology Directorate, Department of Homeland Security, and Office of Deputy Assistant 

Secretary for Defense (DASD) Counternarcotics/Global Threat (CN>). 

ii 
CONTENTS 

Modeling the Combined Terrorist-Narcotics Trafficker Threat to National Security .......... 1

Introduction................................................................................................................................... 2

Model-Based Analyses of Counter-Narcotics Activities............................................................ 5

Building and Using a Prototype Narcotics, Counter-Narcotics, and Trafficker Double Agent 

Model.......................................................................................................................................... 7 

A Narcotics, Counter-Narcotics, and Trafficker Double Agent Model...................................... 7 

Modeling Actual Narcotics Capture Activities......................................................................... 10 

Policy Cycle Models Can Manage Trafficker Double Agent Conversion Policies.................. 11 

Building and Using Combined Terrorist-Narcotics Trafficker Models.................................... 14 

A Societal Deprivation, Affection, Disaffection, Advanced Terrorist Recruitment, Training, 

and Narco-Terrorist Support Model.......................................................................................... 15 

Entity Security and Terrorist Activity Models.......................................................................... 18 

A Violence Generation Model.................................................................................................. 20 

Policy Cycle Models Can Represent the Management of Societal Violence Levels and Entity 

Security Policies .......................................................................................................................... 21

A Social Policy Cycle Model for the Management of Violence .............................................. 21 

A Security Policy Cycle Model for Entity Security Management............................................ 25 

Summary, Discussion, and A Way Forward ............................................................................ 28

Appendicies 1 To 6: Implementation of the Models in Systems Dynamics Software........... 30

Appendix 1: A Narcotics, Counter-Narcotics, and Trafficker Double Agent Model ........... 30

Preliminary Model-Building Considerations............................................................................ 30 

Implementation of the Narcotics, Counter-narcotics, and Trafficker Double Agent Model.... 32 

Appendix 2: Policy Cycle Models Can Manage Trafficker Double Agent Conversion 

Policies.......................................................................................................................................... 34

Appendix 3: Prototype Societal Deprivation, Affection, Disaffection, Advanced Terrorist 

recruitment, Training, and Narco-Terrorist Support Model................................................. 36

Appendix 4: Entity Security and Terrorist Activity Models .................................................. 40

Appendix 5: A Violence Generation Model.............................................................................. 42

Appendix 6: Policy Cycle Models can Represent the Management of Social Violence and 

Entity Security Policies............................................................................................................... 43

A Social Policy Cycle Model.................................................................................................... 43 

A Security Policy Cycle Model ................................................................................................ 44 

References.................................................................................................................................... 47

iii 
LIST OF FIGURES 

Figure 1. Model-based analyses of counter-narcotics activities involved studies of the impact of 

creating double agents and of the possible synergy of terrorist and trafficker activities................ 6 

Figure 2. A prototype model illustrates the processes involved in the conversion of traffickers to 

double agents and use of the information they provide to identify and capture or disrupt illicit 

narcotics products. .......................................................................................................................... 7 

Figure 3. Impact of narcotics trafficker conversion efforts on the creation of trafficker double 

agents, represented by (Conv Effrt) and (Doubl Agnts), the disruption of narcotics shipments 

(Narcot Evnts), and the capture of illicit narcotic substances during a notional period of 360 

days. Conversion of traffickers into double agents was assumed to reduce or depress the 

threshold for detection (Thresh Dep) and increase the likelihood of detecting or disrupting 

shipments of narcotics substances. ................................................................................................. 8 

Figure 4. Software slider entities permit user selection of model parameters and their use in 

studies and experiments. The model control panel shows that an initial threshold for detecting 

narcotics products of 95 percent and 0 percent trafficker conversion effort leads to 68 counternarcotics events and the disturbance of 3,376 tons of narcotic shipments (Tot Evnt Size)............. 9 

Figure 5. Number and size of narcotics disruptions without counter-narcotics intelligence efforts. 

Some 3,376 tons are seized in 68 events during a notional 360-day time period......................... 10 

Figure 6. Estimated cocaine shipments and actual cocaine seizures or disruptions (Narco Seized) 

can be used to set parameters for the random process model. ...................................................... 11 

Figure 7. Policy cycle actions identify a problem for a government; set an agenda; and formulate, 

implement, evaluate, change and/or terminate a policy to address identified problems (Modified 

after Lester and Stewart, 2000)..................................................................................................... 12 

Figure 8. Increased efforts (Conv Effrt) targeted at the creation of double agents (Doubl Agnts) 

with a counter-narcotics effort (CN Efft) of 0.1 increases the amount of disturbed notional 

narcotics substances due to a reduction of the threshold level for triggering disturbances (Thresh 

Dep)............................................................................................................................................... 13 

Figure 9. Increased rates of policy cycle parameter (PCM Param) information transfer from 0 to 

0.1 (0 to 10 percent per time step) increase the amount of disturbed narcotics shipments and 

number of converted double agents (Doubl Agnts) caused by depression of the threshold for 

detection (Thresh Dep). ................................................................................................................ 14 

Figure 10. Increased levels of corruption (Corrupt Lvl) can offset the impact of increased policy 

information transfer rates, reducing the amount of disturbed narcotics shipments and the number 

of double agents (Doubl Agnts). ................................................................................................... 14 

Figure 11. Insights gained from modeling the combined terrorist-narcotics trafficker threat to 

national security can provide guidance to policymakers and decisionmakers. ............................ 15 

Figure 12. Key activities of the Societal Deprivation, Affection, Disaffection, Advanced 

Terrorist Recruitment, Training, and Narco-Terrorist Support Model generate notional trained 

terrorists who are available to undertake terrorist activities. ........................................................ 17 

Figure 13. Model-generated numbers of notional basic and advanced terrorists. In this particular 

case some 574 basic terrorists and 37 advanced terrorists were produced................................... 17 

Figure 14. (a) Trafficker-supplied (Traff Staff) personnel can train to become terrorists with basic 

(Basic Terr) and advanced (Adv Terr) training and (b) Trafficker supplied personnel can be 

iv 
combined with disaffected individuals recruited from the general population (Recrt Rte) to 

provide terrorists with basic and advanced training. .................................................................... 18 

Figure 15. Entity Security and Terrorist Activity models calculate the level of security based on 

security investment and losses. Security investment levels can be increased as a result of 

government policy actions. Terrorist events can take place when adequate advanced trained 

terrorists are available and the security level is sufficiently low.................................................. 19 

Figure 16. Increasing the terrorist team size (Team Size) reduces the total number of actual 

terrorist events (Terr Evnts) because of staffing considerations. Smaller teams can become 

involved in a larger number of events and may generate more total casualties (Tot Casults) and 

fewer terrorist losses (Terrst Loss). Selected output from the Entity Security and Terrorist 

Activity Models with a security investment of 0 percent per time step, an initial security level of 

100, and security loss rate of 0.0004 (0.04 percent) are shown.................................................... 19 

Figure 17. The Violence Generation Model calculates the level of violence based on notional 

levels of disaffection and affection caused by deprivation and satisfaction, respectively, and 

social violence sensitivity, reflected in the violence warning threshold parameter. Feelings of 

insecurity and threat as a result of violence and terrorist-related casualties, for example, can 

cause enhanced deprivation. ......................................................................................................... 20 

Figure 18. Model-generated output shows the production of violent activity (Violence) and (AD 

Concern) output when the ratio of affected to disaffected individuals rises above the (AD POL 

Warn) threshold. The (AD Concern) output triggers policy cycle-based activities that generate 

instructions for creating a new policy (Soc New Pol)................................................................... 21 

Figure 19. Key features of the Social Policy Cycle Model with policy-related corruption.......... 22 

Figure 20. Social Policy Cycle parameters set at 0.8 and corruption 0.0 generates a single burst 

of violent activity (Violence) when (AF DF Ratio) exceeds the (AD POL Warn) threshold. ...... 22 

Figure 21. Setting the Social Policy Cycle parameters at 0.8 and corruption parameters at 0.04 

generated bursts of violent activity followed by continuous violence because of an inability 

caused by corruption of a government entity to reduce disaffection to sufficiently low levels 

where violence does not occur...................................................................................................... 23 

Figure 22. Social Policy Cycle parameters set at 0.1 and corruption levels of 0.0 generate a 

longer period of violence compared with the situation involving parameter values of 0.8 (Figure 

20) when the (AF DF Ratio) exceeds the (AD POL Warn) threshold. ......................................... 24 

Figure 23. Social Policy Cycle parameter values of 0.1 and corruption levels of 0.04 generated a 

period of violent activity followed by continuous violence. Compare Figure 23 with Figure 21, 

which reflects the impact of Social Policy Cycle parameters set at 0.8. ...................................... 24 

Figure 24. Key features of the Security Policy Cycle Model with policy-related corruption. ..... 25 

Figure 25. Increased rate of policy-related information transfer can reduce total terroristgenerated casualties. Impact of policy cycle parameter values (PCM parms) on security level 

(Fin Sec Lvl), potential (Tot Events) and actual (Act Evnts) terrorist events, and total casualties 

(Tot Casult) with security political sensitivity (Sec Pol Sens) set at 0.2; security investment (Sec 

Invest) was set at 0.01; and corruption was set at zero. ................................................................ 26 

Figure 26. Increased levels of corruption can lead to increased numbers of terrorist-generated 

casualties. Impact of policy cycle corruption (Corruption) on security level (Fin Sec Lvl), 

potential (Tot Events) and actual (Act Evnts) events, and total casualties (Tot Casult) with 

security political sensitivity (Sec Pol Sens) set at 0.2, the security investment (Sec Invest) set at 

0.01, and the policy cycle parameters set at 0.1............................................................................ 27 

v 
Figure A1.1. The Sample Model structure involves stocks, flows, converters, and action 

converters provided by the STELLATM system............................................................................ 30 

Figure A1.2. Sample Model source code is generated automatically by STELLATM.................. 30 

Figure A1.3. Sample Model control panel showing sliders and numerical data displays. ........... 31 

Figure A1.4. Sample Model graphical output showing calculated Process and Product values for 

a notional 100 days. ...................................................................................................................... 31 

Figure A1.5. Counter-narcotics model that represents the impact of creating trafficker double 

agents on the number and size of narcotics seizure activities. The model also provides output to 

(PolCycle In) and input from a policy cycle-based management model (DAgt New Pol) described 

in the text....................................................................................................................................... 32 

Figure A1.6. Control panel for the Narcotics, Counter-narcotics, and Trafficker Double Agent 

Model permits the display of selected model outputs................................................................... 33 

Figure A2.1. Policy Cycle Model describing the creation of double agent generating policies. 

Policies supporting the creation of double agents can result in an increase the amount of 

narcotics substances disturbed by counter-narcotics actions; corruption of the policymaking 

process can reduce that amount. Double agent new policy (DAgt New Pol) provides input to the 

Narcotics, Counter-narcotics, and Trafficker Double Agent Model............................................. 34 

Figure A2.2. Control panel for the Double Agent Policy Cycle Model shows slider value inputs 

and numerical output values of selected parameters..................................................................... 35 

Figure A3.1. Advanced Terrorist Recruitment, Training, and Narco-Terrorist Support Model 

represents the processes of affection, disaffection, recruitment, and production of basically 

trained and advanced terrorists. Those activities can be supported by the use of trafficker 

personnel and training facilities that create additional terrorists. ................................................. 37 

Figure A3.2. The Advanced Terrorist Recruitment, Training, and Narco-Terrorist Support Model 

control panel provides capabilities for selecting model parameters and undertaking parameter 

impact analysis and assessment studies. ....................................................................................... 38 

Figure A3.3. Numerical cross-check to verify involvement of all notional participants in Figure 

A3.1............................................................................................................................................... 39 

Figure A4.1. Implementation of the Entity Security and Terrorist Activity Models provides a 

basis for undertaking studies of the impact of the number of trained terrorists and the 

vulnerability of targets on casualty levels. The key feature of the model is the level of security 

(SecLevel) that can be increased by investment in security and decreased by lack of maintenance 

and/or hostile actions. ................................................................................................................... 40 

Figure A4.2. The Control Panel for the Entity Security and Terrorist Activity Models shows 

selected parameter inputs and model-generated output. Settings indicate a maximum initial 

security level of 100, a security investment rate (SecInvsRte) of 0.0 and loss rate (SecLosRte) of 

0.0004 (0 percent and 0.04 percent per time step, respectively), a maximum of 100 casualties 

(MaxCas) per incident and terrorist team size (AdvTerTeamSize) of 1. This creates 1,396 notional 

casualties (Tot Casults) in 26 incidents (TotActEvnts) that cause the loss of 26 terrorists (Losses).

....................................................................................................................................................... 41 

Figure A5.1. Implementation of the Violence Generation Model. The model calculates the level 

of violence assumed to correspond to the ratio of (Disaffected/(Affected + Disaffected)) of 

disaffected and total affected and disaffected individuals and violence sensitivity as described in 

the text. The model also generates enhanced deprivation (Dep Enh) based on the level of 

violence and total casualties caused by terrorist events................................................................ 42 

vi 
vii 

Figure A5.2. The Violence Generation Model control panel permits user selection of the violence 

sensitivity (Viol Sens; set at 0.1), violence multiplier (Viol Mult; 0.0008), and the (AD POL Warn 

Thresh; 0.2) parameters. ............................................................................................................... 42 

Figure A6.1. Implementation of the Social Policy Cycle Model provides an environment for 

assessing the impact of Policy-Related Corruption on the production of new policies................ 43 

Figure A6.2. Control panel for the Social Policy Cycle Model With Policy-Related Corruption.

....................................................................................................................................................... 44 

Figure A6.3. Implementation of the Security Policy Cycle Model provides an environment for 

assessing the impact of the speed of policy cycle actions and the effect of policy-related 

corruption on the production of new policies and their effect on changing security levels and on 

the number of terrorist-related events and casualties.................................................................... 45 

Figure A6.4. Security Policy Cycle Model with policy-related corruption control panel. Settings 

indicate a sensitivity of 0.2 and policy cycle and corruption parameters of 0.1 (10 percent per 

time step)....................................................................................................................................... 46 
MODELING THE COMBINED TERRORIST-NARCOTICS TRAFFICKER 

THREAT TO NATIONAL SECURITY 

The relationship between terrorism, drug trafficking, and policymaking is examined through the 

development, implementation, and use of a series of systems dynamics-based models. These 

activities are intended to provide the basis for future development of a decision aid to support 

policymakers in combating the narco-terror threat. The models developed for this purpose are: a 

narcotics, counter-narcotics, and trafficker double agent model; a policy cycle model to manage 

the trafficker double agent conversion policies; a prototype societal deprivation, affection, 

disaffection, and advanced terrorist recruitment, training, and narco-terrorist support model; 

entity security and terrorist activity models; a violence generation model; and policy cycle 

models to represent the management of social violence and entity security policies. These models 

illustrate the relationships between deprivation of key resources to individuals and disaffection 

and ultimate terrorist activity; attack of notional targets by teams of terrorists; deprivation of 

individuals leading to violence, which can lead to an increase in the level of perceived 

deprivation; dynamics of policymaking in response to perceived needs; and the impact of 

corruption on policymaking. 

The U.S. Federal Government has well documented the strong ties between terrorist 

organizations and drug-trafficking organizations. A number of indictments are further proof of 

this relationship. In some cases, both organizations need the same facilitators: improve financial 

gains, expand geographical domains, provide common personnel protection, and utilize common 

logistical support. The U.S. administration has released two important national strategies to 

counter each of these threats separately, although their linkage is recognized. Some policy 

considerations must be taken into account when both elements operate in the same theater, 

including the amount of funds dedicated to combat these operations, the personnel resources 

required, and the level of intelligence gathering and dissemination necessary to produce 

meaningful results. Strategic and tactical considerations also must be acknowledged. On the 

strategic side, allocating resources to interrogation and “flipping” of traffickers after their arrest 

can be productive in learning more about the entire network. On the tactical side, it may be more 

productive to devote resources to monitoring and detection. In the case of terrorism, more 

resources directed toward radicalization, disaffection, and deprivation of individuals may be just 

as productive as intelligence gathering and kinetic means of action. 

This modeling study demonstrated that model-generated data closely resembled actual reports 

about tons of disrupted narcotics substances in three consecutive years. The policy cycle model 

showed that increased rates of policy cycle activity increased the amount of narcotics disruption, 

while increased levels of corruption reduced those levels. Model-generated results show that 

policymaking can lead to a reduction in the level of deprivation, disaffection, and violence and 

that policy-related effects can be inhibited by corruption. The study also showed that availability 

of trained terrorists can be a rate-limiting factor and that targets of opportunity may not be 

attacked because trained terrorists are lacking. Many more results can be generated depending on 

the assumptions used in the parameters of the models. These assumptions have to be validated by 

actual field data. These models can be enhanced to provide guidance to the policymaker and 

decisionmaker in selecting options for the allocation of limited resources to support future 

counter-terror and counter-narcotics actions. 

1 
INTRODUCTION 

Interest in examining the relationship between drug cartels and terrorist organizations is 

increasing, as has been reflected in the recent congressional testimonies by leading authorities on 

the subject on July 7, 2011.1 In particular, the Hezbollah group has been alleged to be working 

with the Mexican drug cartels to use existing drug pipelines to penetrate the U.S. homeland. In 

fact, research conducted by the American Enterprise Institute for Public Policy Research has 

concluded that at least two parallel terrorist networks are growing in Latin America. One is 

operated by Hezbollah, and another is managed by Qods operatives. These networks cooperate to 

carry out various criminal activities, including narcotics smuggling. The study concluded there 

are more than 80 operatives in at least 12 countries throughout the region, and the regions of 

Brazil, Venezuela, and the Southern Cone are of the greatest concern.2 Several reports address 

Hezbollah’s financial ties to the contraband center of the Tri-Border region of Paraguay, 

Argentina, and Brazil, and the contributions of the Lebanese diaspora on Isle Margarita and other 

locations.3 More recently, a new case, called Operation Red Coalition, began in May 2011 when 

an Iranian-American from Corpus Christi, Texas, approached a U.S. Drug Enforcement 

Administration (DEA) informant. He was seeking the help of a Mexican drug cartel to 

assassinate the Saudi ambassador, according to counter-terrorism officials. The IranianAmerican thought he was dealing with a member of the feared Zetas Mexican drug organization, 

according to agents; instead, the suspect was arrested on October 11, 2011. 

Terrorist organizations are working with narcotics traffickers to increase the magnitude and 

extent of terrorist actions. Drug-trafficking organizations (DTO) in Colombia and Venezuela and 

the terrorist organization FARC, of Colombia, were indicted for moving cocaine through Liberia 

to Europe.4

 Considerable evidence indicates that FARC, operating on the Ecuadorian border, has 

developed ties to the Sinaloa cartel, which operates inside the Ecuadorian border.5

 Also well 

known is that West African criminal syndicates cooperate in illicit smuggling operations with Al 

Qaeda operatives in Islamic Maghreb (AQIM). DEA has confirmed that 19 foreign terrorist 

organizations have ties with DTOs. Furthermore, Taliban ties to Latin American DTOs and 

Hezbollah have been well established. These are a few of the drug terrorism alliances that have 

been reported in the literature. 

The case of Hezbollah’s ties to the drug cartels in Mexico is somewhat controversial even with 

the mounting evidence from leading officials. Some experts think it is pure speculation that 

Hezbollah intends to launch terrorist operations against U.S. interests in the western hemisphere. 

One expert believes that Hezbollah is involved in mainstream Lebanese politics and has become 

more pragmatic and is less likely to confront the United States.6 A closer examination of the 

Mexican DTOs suggests they are loosely organized with drug production based on demand and 

distribution through independent providers after point of entry to the United States. DTOs are 

 

1

 US Congressional Subcommittee on Counterterrorism and Intelligence, House Committee on Homeland 

Security, United States Congress, Hezbollah in Latin America: Implications for U.S. Security, July 7, 2011. 2

 Testimony by Ambassador Roger Noriega before the Subcommittee. 

3

 Testimony of Douglas Farah before the House Committee on Foreign Relations, Subcommittee on Oversight and 

Investigations, October 12, 2011. 4

 Douglas Farah, Terrorist-Criminal Pipelines and Criminalized States: Emerging Alliances for the 21st Century, 

Prism, Center for Complex Operations, Vol. 2, No. 3, June 2011. 5

 Ibid reference 3. 

6

 Melani Cammett, Testimony before the Subcommittee on Counterterrorism and Intelligence, July 7, 2011. 

2 
family-based with ties to the land and community. Their strong religious affiliations and lack of 

interest in promoting terrorism differentiates their culture from that of Hezbollah. They certainly 

do not want to add visibility to their operations by being involved in terrorist acts. The main 

priority for such groups is making money. It is well known that the Lebanese communities in 

Mexico are wealthy but do not have any known ties to DTOs. The above evidence of 

partnerships between the cartels and terrorist organizations makes it clear that the goal is to 

generate profits for later use in recruiting, training, purchasing weapons, and carrying out 

terrorist attacks in selected regions. The groups use the same pipelines, exploit the same 

structures, and capitalize on the vulnerabilities of the population and legal system for mutual 

benefit. 

Because of the growing importance of this threat, the U.S. administration has released two 

significant documents: the National Strategy for Counterterrorism, published in June 2011, and 

the Strategy to Combat Transnational Organized Crime (TCO), published in July 2011. Both of 

these publications address the terrorist and TCO threats separately, although linkage between the 

two is recognized. The latter document focuses on breaking the economic power of TCOs and 

protecting strategic markets and the U.S. financial system. It also seeks to build international 

consensus, multilateral cooperation, and public-private partnerships to defeat TCOs. The new 

capabilities created for this effort include a new executive order that establishes a sanctions 

program to block property and prohibit transactions. Another is a Presidential proclamation that 

denies transnational criminal aliens entry into the United States. A rewards program was also 

established to obtain TCO information, and the Interagency Threat Mitigation Working Group 

was initiated to identify threat and coordinate the means to combat TCOs. 

This paper examines how terrorist activities might be supported by resources and facilities 

provided by narcotics traffickers. Of particular interest is determining how terrorist activities 

supported by narcotics traffickers might influence government policies and actions aimed at 

reducing threats and providing protection to the general population. 

Trafficker-supported terrorist activities can corrupt the actions of the government by delaying the 

development and implementation of counter-terrorist policies and reducing their effect. This 

work builds on earlier research that described processes in which deprivation creates disaffection 

and creates the potential for violence. Disaffected individuals can be recruited into terrorist 

networks and trained to carry out terrorist actions within the wider society (Davis et al. 2003, 

Woodcock, 2003; also Woodcock and Cobb, 1994, and Woodcock and Dockery, 1989). Terrorist 

organizations can facilitate those activities and increase the scope and magnitude of their actions 

by working with narcotics trafficker entities. One of the main concerns is that terrorist 

organizations such as Hezbollah are providing technology for the highly sophisticated narcotunnels being discovered along the U.S.-Mexican border. These tunnels resemble the ones along 

the Lebanese border and the Ghaza strip that Hezbollah used. The tunnels start inside the homes 

of recruited and disaffected families and end inside the homes of other disaffected families 

across the border. Detecting such tunnels by conventional means is very difficult. Extensive 

modeling efforts are needed to assess the impact of narcotic trafficker support to terrorist 

operations. 

3 
The estimated value of drug-trafficking operations is on the rise. The value of cocaine from 

South America to North America was estimated to be $38 billion in 20087

 and from South 

America to Europe, it was estimated to be $34 billion. Heroin traffic from West Asia to Europe 

was valued at $20 billion and from West Asia to Russia at $13 billion.8

 FARC in Colombia is the 

world’s main cocaine producing organization. A highly decentralized narco-terrorist organization 

with extensive linkages to Hezbollah, FARC has more than 7,000 armed combatants operating 

out of the jungles on the borders with Ecuador and Venezuela. FARC has a strong relationship 

with high-ranking Venezuelan officials, including the president. The ties between Venezuela and 

Iran are becoming stronger and are designed to facilitate the funding of terrorist organizations. 

As for Mexico, the narcotics industry provides from $25 billion to $40 billion in profits from 

illicit drug sales. According to the U.S. Department of State, cartels and gangs employed 

450,000 people in the cultivation, processing, and sale of illegal drugs, with one-third involved in 

processing and selling.9

It appears that both terrorist and drug-trafficking organizations need the same facilitators and 

leverage the relationship to mutual benefit. Other benefits of these partnerships include financial 

gains, geographic growth, personnel protection, and logistical support, but partnership-produced 

vulnerabilities include increased attention from law enforcement, potential compromise of 

internal security, risk of infiltration, and capture of leadership. A recent study 10 provided 10 

case studies on the variations in the crime-terrorism nexus. These ranged from the full 

convergence/fusion of crime and terrorist organizations in the Dawood Ibrahim’s 500-member 

D-Company operating in Pakistan, India, and the United Arab Emirates, to the terrorist 

organizations with in-house criminal structures such as FARC-based groups in Colombia, to the 

terrorist organization with criminal sympathizers such as Hezbollah. Other examples include the 

2004 Madrid bombers, with decentralized terrorist cells having in-house criminal capabilities, 

and the Taliban with coalitions between terrorist groups and criminal organizations. The judicial 

system is almost nonexistent in West Africa, and the drug trade enjoys open sponsorship in 

Venezuela. As a result, the alliances between terrorist groups and criminal organizations will 

continue to grow.11

Some policy considerations may be relevant to the terrorism-crime/drug trafficking nexus. For 

example, how to prioritize the counter policies when both elements are present, as in the case in 

Afghanistan; the sharing of foreign aid funds to combat terrorism and crime; the role of the U.S. 

Department of Defense (DOD) in what has been considered a criminal justice and law 

enforcement responsibility; and following the crime-terrorism money trail.12

The above discussion makes it clear that narcotics traffickers and terrorists have distinct domains 

in which they operate. The narcotics domain consists of production and general assembly, 

primary and secondary transit systems, staging areas, disassembly, wholesale functions, and 

distribution for consumption. In each of these elements, the value of the drugs changes 

 

7

 Robert Killebrew, Criminal Insurgencies in the Americas and Beyond, Prism, Center for Complex Operations, 

Vol. 2, No. 3, June 2011. 8

 Ibid, Reference 7. 

9

 Ibid, Reference 7. 

10 John Rollins and Liana Sun Wyler, International Terrorism and Transnational Crime: Security Threats, U.S. 

Policy, and Considerations for Congress, Congressional Research Service, March 2010. 11 Ibid, Reference 4. 

12 Ibid, Reference 7. 

4 
depending on the size, shipment, and location of the distribution centers. The value is highest 

during the transit stage because of the size of the shipment. From a detection and monitoring 

perspective, this stage becomes the most significant. The terrorist domain is somewhat different. 

Certain precipitants can start the process, and these include dissatisfaction, discrimination, 

ideological beliefs, and so forth. This leads to internalizing values, possible indoctrination, 

following a charismatic leader, contact with a group, possible ideological seeking, and then 

committing an act of terror. This process of radicalization was discussed in a previous paper.13 A 

definite profile for an individual ready to commit terrorist acts no longer exists. Furthermore, the 

availability of the Internet is changing the process of radicalization. The potential interaction of 

the narcotics trafficker and the terrorist results from the need for the same facilitators, financial 

gains, logistical support, and personnel protection as mentioned previously. Models that 

characterize the nature and degree of interaction in the different domains is a useful 

consideration. 

The role of law enforcement and policymakers in countering the drug traffickers and terrorists 

requires a more in-depth analysis. In some cases, allocating resources to address the issues that 

lead to radicalization and terrorist acts can be productive. In others, providing resources to 

intelligence gathering and dissemination to the appropriate authorities can be more productive. 

The same can be said for the role of the policymaker in the counter-drug-trafficking area. More 

attention to dealing with the strategic aspects of allocating resources to interrogation and the 

flipping of traffickers after their arrest can be quite productive in learning more about the entire 

network. In other cases, it can be productive to focus on detection and monitoring for tactical 

reasons. It is then useful to consider policy cycle models to characterize the role of the 

policymakers in countering these threats, taking into account the number of factors that influence 

this process, including corruption of policymaking and policy-implementation activities. 

MODEL-BASED ANALYSES OF 

COUNTER-NARCOTICS ACTIVITIES 

In order to address a number of the issues raised in the introduction, the narcotics, counternarcotics process through the production and use of several models is examined in more detail. 

These models will provide insight on the operations of the drug traffickers and means to counter 

their actions. Figure 1 outlines the models that were developed; implemented in STELLATM (a 

commercially available systems dynamics-based software system) by Woodcock;14 and used to 

study the counter-narcotics and terrorist problem. Two main activities were undertaken: (1) a 

study of counter-narcotics activities and the impact of converting traffickers into double agents 

to disrupt narcotics shipments, and (2) an examination of the possible synergy of terrorist and 

trafficker activities, the impact of public policy aimed at reducing violence and increasing 

counter-terrorist security levels, as well as the corruption of those processes by traffickers and 

other actions. Descriptions of the construction and use of the models are presented below. 

Implementation of the models is discussed in more detail in the appendices. 

 

13 Samuel Musa and Samuel Bendett, Islamic Radicalization in the United States: New Trends and a Proposed 

Methodology for Disruption, Defense and Technology Paper 77, CTNSP, September 2010. 14 The models developed for the study described in this paper were developed by Woodcock using STELLATM, a 

commercially available software system (http://www.iseesystems.com). Details of the implemented software are 

presented in the appendices. Interested parties are invited to communicate with the authors for further 

information. 

5 
Double Agent

Conversion Model

Double Agent

Policy Cycle Model

Violence Generation

Model 

Societal Deprivation, Disaffection,

Recruitment, Advanced Terrorist Training,

and Narco-Terrorist Support Model

Entity Security and

Terrorist Activity Models

Security Policy Cycle

Model 

Social Policy Cycle

Model 

Counter-Narcotics

Disruption Model

Figure 1. Model-based analyses of counter-narcotics activities involved studies of the 

impact of creating double agents and of the possible synergy of terrorist and 

trafficker activities. 

• Counter-narcotics actions and the creation of double agents: This paper describes 

results of an investigation of counter-narcotics activities and the impact of efforts 

aimed at converting traffickers into double agents. Model-generated data closely 

resembled actual reports of tons of disrupted narcotics substances detected in 2008, 

2009, and 2010. Success in counter-narcotics and trafficker conversion activities can 

have important public policy implications. To examine those possibilities, a policy 

cycle model was developed and used to study the potential impact of increased levels 

of policymaking support as well as corruption of the policy process on the ability to 

form double agents and to disrupt trafficker activities. Increased rates of policy cycle 

activity increased the amount of narcotics disruption while increased levels of 

corruption reduced such disruption. 

• Examination of the possible synergy between narco-traffickers and terrorists and the 

impact of policy-related actions. In this paper, model-generated data show that 

policymaking can lead to a reduction in the level of deprivation, disaffection, and 

violence and that policy-related effects can be inhibited by corruption, which can 

change the overall political dynamics of societies of interest. Modeling the combined 

terrorist-narcotics trafficker threat to national security has involved the production of 

several component models, including the following: (1) a Societal Deprivation, 

Disaffection, Recruitment, Advanced Terrorist Training, and Narco-Terrorist Support 

Model that illustrates how notional individuals deprived of key resources can become 

disaffected and recruited and trained to become advanced terrorists, (2) an Entity 

Security and Terrorist Activity Model that illustrates teams of trained terrorists 

attacking notional targets, (3) Entity Security and Violence Generation Models that 

describe how deprived and disaffected individuals may become violent and how such 

violence can increase the level of perceived deprivation and disaffection, and (4) 

Social and Security Policy Cycle Models that describe the processes of identifying a 

6 
problem and formulating, implementing, evaluating, changing, and/or terminating a 

policy in response to perceived need, as well as the impact of corruption in preventing 

successful policy-related outcomes. 

Building and Using a Prototype Narcotics, Counter-Narcotics, and Trafficker 

Double Agent Model15

A significant counter-narcotics strategy involves capturing narcotics trafficker operatives and 

forcing them to “flip” or to give up information regarding their organization that could be used to 

reduce future trafficker activities. Such information could be used to create double agents whose 

activities might reduce the trafficking of narcotics and reduce the ability of the traffickers to 

provide supplies to their clients. A prototype model has been developed and implemented by 

Woodcock in STELLATM, a commercially available systems dynamics software system, to study 

the impact of those actions on trafficker capabilities. Model-based studies show that increasing 

the effort to create double agents can lead to a corresponding increase in the amount of seized 

narcotics substances. The model could serve as a basis for a more advanced model that captures 

additional components of the actions of counter-narcotics forces. 

A Narcotics, Counter-Narcotics, and Trafficker Double Agent Model16

The major components of the prototype Narcotics, Counter-narcotics, and Trafficker Double 

Agent Model are shown in Figure 2. Introductory systems dynamics-based modeling activities 

are presented in Appendix 1. Selected details of the implementation of the model in systems 

dynamics software are presented in Appendix 1 to increase the initial accessibility of the 

materials for the reader. In the model, a notional effort is assumed to take place that results in the 

transformation of narcotics traffickers into individuals that provide information on trafficker 

activities. Traffickers that provide such information can support counter-narcotics efforts to 

detect, capture, remove from the marketplace, or otherwise disrupt the supply of narcotics 

substances. 

Double Agent

Conversion

Double Agent

Conversion Effort

Narcotics

Detection Effort

Narcotics

Detection

Narcotics Capture

Figure 2. A prototype model illustrates the processes involved in the conversion of 

traffickers to double agents and use of the information they provide to identify and 

capture or disrupt illicit narcotics products. 

The model was used to examine the effect of converting narcotics traffickers into double agents 

on the disruption of narcotics shipments. Results of those activities are presented below. In each 

case, the initial threshold for detecting narcotics substances (represented by the parameter: 

NarcoThresh in the model [Appendix 1]) was assumed to be 95 percent. This represented an 

 

15 The models developed for the study described in this paper were developed by Woodcock using STELLATM, a 

commercially available software system (http://www.iseesystems.com). Details of the implemented software are 

presented in the Appendix. 16 Details of the implementation of this model are presented in Appendix 1. 

7 
assumed likelihood of detecting 5 percent of the actual supply of narcotics substances. The 

model included a random number generator and a threshold device. Random numbers greater 

than the threshold were considered to represent events in which narcotics substances were 

captured or disrupted by counter-narcotics activities. Thus, a threshold of 95 percent would lead 

to the capture or disruption of 5 percent of the total supply of narcotics. Each detected or 

disturbed narcotics event served as input to a second random number process that determined the 

size of individual shipments. A notional maximum seizure size, represented by (MaxEvntSize), 

of 100 tons was assumed. In this case, disturbed or captured shipments were assumed to range 

from 0 to 100 tons of narcotics. A level of counter-narcotics effectiveness of using traffickerprovided information to increase the likelihood of detecting or disrupting narcotics shipments, 

represented by (CN Efft) of 0.1 (or a 10-percent effectiveness of using trafficker-provided 

information) was also assumed. 

Conv Effrt Total (tons) Narcot Evnts Doubl Agnts Thresh Dep

0 3376 68 0 0

5 6448 129 70 7

10 8881 173 142 14.2

15 10936 214 205 20.5

20 13263 260 275 27.5

25 16332 319 357 35.7

30 18990 373 427 42.7

Figure 3. Impact of narcotics trafficker conversion efforts on the creation of 

trafficker double agents, represented by (Conv Effrt) and (Doubl Agnts), the 

disruption of narcotics shipments (Narcot Evnts), and the capture of illicit narcotic 

substances during a notional period of 360 days. Conversion of traffickers into 

double agents was assumed to reduce or depress the threshold for detection (Thresh 

Dep) and increase the likelihood of detecting or disrupting shipments of narcotics 

substances. 

The results of a series of preliminary studies of the impact of different levels of effort aimed at 

converting traffickers into double agents, with trafficker conversion effort-related results ranging 

from 0 percent to 30 percent, are shown in Figure 3. Typical model-generated outputs are shown 

in Figure 4, which shows the control panel created for the model, and Figure 5, which shows the 

number and size of narcotics seizures in the absence of counter-narcotics intelligence efforts. 

Figures 4 and 5 and similar figures are screenshots taken directly from the STELLATM output 

from the model. 

8 
95.000

0.000 100.000

U

NarcoThresh

TotNarcoEvnt 68

0

0 100

U

ConvEffort

0.1000

0.0000 1.0000

U

CN Efft

TotDoubAgt 0

DecrThresh 0.0

100.000

0.000 100.000

U

MaxEvntSize

Conv Thresh 100

Tot Evnt Size 3,376

Mod NarThresh 95

Narcotics, Counternarcotics, and Trafficker Double Agent Model

Figure 4. Software slider entities permit user selection of model parameters and 

their use in studies and experiments. The model control panel shows that an initial 

threshold for detecting narcotics products of 95 percent and 0 percent trafficker 

conversion effort leads to 68 counter-narcotics events and the disturbance of 3,376 

tons of narcotic shipments (Tot Evnt Size). 

In this case, the conversion of traffickers into double agents is assumed to increase the likelihood 

of detecting and disrupting narcotics shipments. This increased likelihood of detection is 

reflected in a reduction or depression of the detection threshold in the model (Thresh Dep). Such 

a reduction would lead to the increased detection or disruption of narcotics substances. As an 

example, some 3,376 notional tons are seized in 68 counter-narcotics events without the 

conversion of double agents, while 6,448 tons might be seized or disrupted with a 5-percent 

conversion effort involving 70 double agents and a maximum shipment size of 100 tons. A 

conversion effort of 10 percent generated 142 double agents and 173 narcotics events that 

resulted in the disruption of 8,881 tons of narcotics. A double agent conversion effort of 30 

percent generated 427 notional double agents and led to the disruption of 18,990 tons of 

narcotics in 373 events (Figure 3). 

9 
4:51 PM Mon, Dec 12, 2011

Total Narcotics Events

Page 3

0.00 90.00 180.00 270.00 360.00

Time

1:

1:

1:

2:

2:

2:

0

10000

20000

2: Tot Evnt Size

0

100

200

1: EvntSize

1

1 1 1 2

2 2

2

Figure 5. Number and size of narcotics disruptions without counter-narcotics 

intelligence efforts. Some 3,376 tons are seized in 68 events during a notional 360-

day time period. 

To summarize: Model-based studies show that increasing the effort to create double agents can 

lead to a corresponding increase in the amount of seized narcotics substances. The model 

represents the likelihood that a trafficker would be converted into a double agent and that 

narcotics substances might be detected, seized, or otherwise disrupted by a random process and 

associated threshold. Details of the implementation of the model are presented in Appendix 1. 

Events are assumed to take place when a generated random number exceeds the threshold. The 

model could serve as the basis for a more advanced version that would capture additional 

components of the actions of counter-narcotics forces, including details of the process of 

obtaining and using trafficker-related information to increase the likelihood of detecting and 

seizing or otherwise disrupting the supply of narcotics substances. 

Developing a more advanced model could involve validating the overall model-based processes 

and modifications that generate output data equivalent to actual field-generated data. A 

preliminary comparison of model-generated and actual data of the disruption of counternarcotics supplies is presented below. 

Modeling Actual Narcotics Capture Activities 

An estimated 1,200 to 1,500 tons of cocaine are shipped from South America to Central and 

North America on an annual basis.17 Of that amount, it is reported that the movement of some 

264 tons were disrupted in 2008, 274 tons were disrupted in 2009, and 194 tons were disrupted 

in 2010 (Figure 6). Those data can be used to determine model thresholds and to modify the 

model described in Figure 3 and Appendix 1 to calculate actual amounts of narcotics seizure or 

disruption. 

 

17 Private communication with Allen McKee, Joint Interagency Task Force-South (JIATF-S). 

10 
Year Narco Seized % Threshold Total (2.0) Total (1.9) Total (1.8)

2008 264 tons 19.6 80.4 277 tons 263 tons 249 tons

2009 274 tons 20.3 79.7 291 tons 276 tons 262 tons

2010 194 tons 14.4 85.6 206 tons 196 tons 185 tons

Figure 6. Estimated cocaine shipments and actual cocaine seizures or disruptions 

(Narco Seized) can be used to set parameters for the random process model. 

With an estimated 1,200 to 1,500 tons of cocaine shipments from South America, it is assumed 

that the mean annual shipment of cocaine was 1,350 tons. Of this total amount, 264 tons 

represent 19.6 percent, 274 tons represent 20.3 percent, and 194 tons represent 14.4 percent. 

These percentages can be translated into thresholds for the random process model. Thus it is 

assumed that model thresholds should be set at 80.4 percent for 2008, 79.7 percent for 2009, and 

85.6 percent for 2010. Such thresholds reflect the effectiveness of counter-narcotics operational 

efforts. 

The actual amount of seizures or disruptions generated by the random process model depends on 

the assumed maximum size of the individual shipments. Reportedly, shipments by airplane may 

range from 1 to 10 tons; shipments by “gofast” boat may be a maximum of 1 ton; shipments by 

fishing boat may be some 1.5 tons; and shipments by submersible may be up to 3 to 4 tons.18

These values can be used with the model described in Appendix 1 to estimate the maximum size 

of narcotics shipments disrupted or seized by counter-narcotics forces. 

Calculation of the amount of disrupted narcotics shipments is essentially a two-stage process. 

The first stage calculates whether or not a disruption has taken place. The second stage calculates 

the size of the individual disruptions. Addition of the individual disrupted shipments generates 

the total amount of the disruptions. Threshold values were set at 80.4 percent for 2008, 79.7 

percent for 2009, and 85.6 percent for 2010. At the outset, the model-based maximum shipment 

size was set at 2.0 tons and without double agent-related activities. Model-generated data with 

that setting were 277, 291, and 206 tons for 2008, 2009, and 2010, respectively. With the 

maximum amount set at 1.9 tons, some 263, 276, and 196 tons were generated by the model for 

2008, 2009, and 2010, respectively. Lower total amounts were generated when the maximum 

amount was set at 1.8 (Figure 6). It is evident that the random process model with maximum 

shipment size of 1.9 tons can generate model seizure or disruption amounts that are almost equal 

to the actual amounts of those substances that are disrupted. This success in replicating the 

amounts of seized or disrupted narcotics provides at least a measure of verification for the model. 

Additional research could be aimed at providing additional model verification. 

Policy Cycle Models Can Manage Trafficker Double Agent Conversion Policies19

The role of U.S. counter-narcotics policy on the “flipping” procedure varies from increased 

allocation of agents involved in the interrogation process, to advanced training, to increased 

funds to pay the informants, and more. Modeling of some of these activities can be useful in 

identifying the major impact on the policymaking and the policy cycle. Processes involved in the 

development, implementation, use, and modification of policies associated with a wide range of 

activities can be represented by an entity called the policy cycle (Lester and Stewart, 2000; 

Woodcock and Falconer, 2012; and Woodcock, Christensson, and Dockery; 2006, for example). 

 

18 Ibid, reference 13. 

19 Details of the implementation of this model are presented in Appendix 2. 

11 
Major features of the policy cycle are shown in Figure 7. Policy cycle actions can begin when 

problems arise in society and impact existing government policies and activities. Thus, terrorist 

and drug-trafficking actions can create demands for more protective measures for the general 

population as these actions are reported in the media and discussed within the society. 

A Problem for Government Stage I: Agenda Setting

Stage II: Policy Formulation

Stage III: Policy Implementation

Stage IV: Policy Evaluation

Stage V: Policy Change

Stage VI: Policy Termination

Figure 7. Policy cycle actions identify a problem for a government; set an agenda; 

and formulate, implement, evaluate, change and/or terminate a policy to address 

identified problems (Modified after Lester and Stewart, 2000). 

Stage I of the policy cycle sets an agenda to address problems and could involve discussions of 

the causes and effects of the problems created by terrorist attacks and narcotics trafficking and 

related actions. In Stage II, policies are formulated and legislation aimed at addressing identified 

terrorist and narcotics trafficker-related problems is developed and passed. Such legislation 

might increase penalties for trafficker-and terrorist-related activities. 

In Stage III, policy implementation occurs and can involve the definition and promulgation of 

new anti-terrorist or counter-narcotics laws and regulations. In Stage IV, policy is evaluated and 

can involve assessing the effectiveness of anti-terrorist or counter-narcotics legislation in 

reducing access to drugs. The results of such evaluations can lead to policy changes in Stage V 

or to termination of parts or all existing policies in Stage VI. These activities can create new 

problems that could lead to setting new agendas and undertaking further actions to manage 

terrorist or drug-trafficking problems. 

A policy cycle model of policies aimed at increasing the effort to convert traffickers into double 

agents has been developed and implemented in systems dynamics software (Appendix 2). A key 

feature of this model is the inclusion of properties that reflect the impact of corruption on 

undermining the counter-narcotics policy-related actions of a government. Corruption can inhibit 

the double agent recruitment process and reduce the amount of narcotics substances disturbed by 

counter-narcotics actions, for example.

Selected results obtained from the use of the double agent-related policy cycle model are shown 

in Figures 8, 9, and 10 and described below. Those studies used different settings of the policy 

cycle parameters: Input to double agent policy evaluation (DAgt Eval Inpt), policy change (DAgt 

Chnge Inpt), and policy termination (DAgt Term Inpt); new policy formation (DAgt New Inpt); 

and the corruption of notional new policy (DAgt CorrRte NewPol) and formulation and 

implementation activities (DAgt F&I Corrupt). Those settings can reflect different rates of 

policymaking and the effect of different levels of corruption on the development of new counter12 
narcotics policies involving the creation of double agents. Additional information is provided in 

Appendix 2. 

• Establishing a performance baseline: Increased conversion efforts increase the 

amount of disturbed substances (Figure 8): A first study established a performance 

baseline for the combined Narcotics, Counter-narcotics, Trafficker Double Agent 

(Appendix 1, Figure A1.5) and policy cycle models (Appendix 2, Figure A2.1) 

without policy cycle input. The (DAgt PolSens) parameter was set at zero to prevent 

policy cycle input. The maximum size of the narcotics shipments was set at 1.9 tons 

and the (CN Efft) parameter was set arbitrarily at 0.1. Model-generated results show 

that 263 tons were disturbed in the absence of actions that created double agents 

(Figure 10). Increasing the level of effort of trafficker conversion to 5 percent, 10 

percent, and 15 percent creates 70, 142, and 205 double agents and increases the 

amount of disturbed substances from 263 to 320, 371, and 410 tons, respectively. 

Levels of effort of 20 percent, 25 percent, and 30 percent result in the creation of 275, 

357, and 427 double agents, and the disturbance of 445, 498, and 550 tons, 

respectively. 

Conv Effrt Total (tons) Doubl Agnts CN Efft Thresh Dep

0 263 0 0.1 0

5 320 70 0.1 7

10 371 142 0.1 14.2

15 410 205 0.1 20.5

20 445 275 0.1 27.5

25 498 357 0.1 35.7

30 550 427 0.1 42.7

Figure 8. Increased efforts (Conv Effrt) targeted at the creation of double agents 

(Doubl Agnts) with a counter-narcotics effort (CN Efft) of 0.1 increases the amount 

of disturbed notional narcotics substances due to a reduction of the threshold level 

for triggering disturbances (Thresh Dep). 

• Increased policy cycle-related information transfer rates without corruption can 

increase the amount of disturbed substances (Figure 9): An established performance 

baseline (Figure 8) provides a basis for a study of the impact of different rates of 

transfer of notional information within the policy cycle model in the absence of 

notional corruption (Appendix 2, Figure A2.1). The influence of corruption was 

prevented by setting the corruption of new policy (DAgt CorrRte NewPol) and the 

corruption of formulation and implementation process (DAgt F&I Corrupt)

parameters equal to zero. Increasing the policy cycle parameters of policy evaluation 

(DAgt Eval Inpt), policy change (DAgt Chnge Inpt), policy termination (DAgt Term 

Inpt), and new policy (DAgt New Inpt) from 0 to 0.1 (10 percent per time step) 

increased the amount of disturbed narcotics substances from 320 tons to 369 tons; the 

number of double agent conversions increased from 70 to 180. 

13 
PCM Param Total (Tons) Doubl Agnts Thresh Dep

0 320 70 7

0.02 341 133 13.3

0.04 354 160 16

0.06 358 169 16.9

0.08 368 177 17.7

0.1 369 180 18

Figure 9. Increased rates of policy cycle parameter (PCM Param) information 

transfer from 0 to 0.1 (0 to 10 percent per time step) increase the amount of 

disturbed narcotics shipments and number of converted double agents (Doubl

Agnts) caused by depression of the threshold for detection (Thresh Dep). 

• Policy cycle-related corruption decreases the amount of disturbed substances (Figure 

10): A third study demonstrated that increased levels of corruption can offset the 

impact of increased policy cycle information transfer rates, reducing the amount of 

disturbed narcotics shipments and the number of double agents. Thus some 369 tons 

of narcotics substances can be disturbed and 180 double agents can be created 

without corruption. By contrast, 327 tons can be disturbed and 78 agents created with 

a corruption level of 0.05 (5 percent per time step). 

Corrupt Lvl Total (Tons) Doubl Agnts Thresh Dep

0 369 180 18

0.01 339 111 11.1

0.02 331 91 9.1

0.03 330 84 8.4

0.04 327 78 7.8

0.05 327 78 7.8

Figure 10. Increased levels of corruption (Corrupt Lvl) can offset the impact of 

increased policy information transfer rates, reducing the amount of disturbed 

narcotics shipments and the number of double agents (Doubl Agnts). 

The studies reported above have shown that notional increased efforts involving creation of 

double agents can increase the amount of disturbed notional narcotics substances. Additional 

studies have illustrated the notional impact of policymaking on counter-narcotics efforts 

involving the creation of double agents. Increased rates of policy information transfer increase 

the amount of disturbed narcotics shipments and the number of converted double agents. A third 

study demonstrated that increased levels of corruption can offset the impact of increased policy 

information transfer rates and reduce the amount of disturbed narcotics shipments and the 

number of converted double agents. These results suggest that further investigation of the 

process of double agent formation and the role of policymaking might be pursued with benefit. 

The studies were continued to investigate the possible impact of a synergy between narcotics 

traffickers on the one hand and terrorist and insurgent entities on the other. 

Building and Using Combined Terrorist-Narcotics Trafficker Models 

Modeling the combined terrorist-narcotics trafficker threat to national security has involved the 

development and implementation in systems dynamics software of several component models 

(Figure 11). Production and use of those models in a series of studies are described. The overall 

model consists of the following major functional components. 

14 
Violence Generation

Model 

Societal Deprivation, Disaffection,

Recruitment, Advanced Terrorist Training,

and Narco-Terrorist Support Model

Entity Security and

Terrorist Activity Models

Security Policy Cycle

Model 

Social Policy Cycle

Model 

Figure 11. Insights gained from modeling the combined terrorist-narcotics 

trafficker threat to national security can provide guidance to policymakers and 

decisionmakers. 

• A Societal Deprivation, Disaffection, Recruitment, Advanced Terrorist Training, and 

Narco-Terrorist Support Model illustrates how notional individuals deprived of key 

resources can become disaffected, recruited, and trained to become advanced 

terrorists. Terrorist activities can be supported by narcotics trafficker personnel and 

resources and can lead to increased violence and casualties within an overall society. 

• An Entity Security and Terrorist Activity Model illustrates teams of trained terrorists 

attacking notional targets. Attacks take place when targets are sufficiently vulnerable 

and sufficient trained terrorists are available. Model-generated data provide estimates 

of the number of casualties caused by terrorist attacks as well as terrorist losses from 

those activities. 

• A Violence Generation Model describes how deprived and disaffected individuals 

may become violent and how such violence can increase the level of perceived 

deprivation and disaffection. Policy-related actions by notional government entities 

can act to reduce the level of disaffection and the corresponding level of violence. 

• Security and Social Policy Cycle Models describe the processes of identifying a 

problem and formulating, implementing, evaluating, changing, and/or terminating a 

policy in response to perceived need. Policy Cycle models have been developed to 

permit changes in policies that increase security and reduce deprivation by increasing 

satisfaction and reducing violence levels, respectively. A novel feature added to the 

Policy Cycle models is the impact of corruption on the policymaking processes that 

can reduce overall policymaking effectiveness and prevent lowering of violence 

levels, for example. 

A Societal Deprivation, Affection, Disaffection, Advanced Terrorist Recruitment, 

Training, and Narco-Terrorist Support Model20

A prototype Societal Deprivation, Affection, Disaffection, Advanced Terrorist Recruitment 

Training, and Narco-Terrorist Support Model has been developed and implemented in systems 

dynamics software (Figure 12 and Appendix 3). The model represents the action of two basic 

processes assumed to be involved in the production and fielding of terrorist assets. One process 

 

20 Details of the implementation of this model are presented in Appendix 3. 

15 
involves the recruitment and training of disaffected individuals from the wider society to provide 

terrorist operatives. The second process involves the recruitment and advanced training of 

individuals provided by narcotics trafficker organizations. Those trafficker organizations are also 

assumed to provide resources that could support training and other operationally related actions. 

They are also assumed to provide resources to corrupt and reduce the effectiveness of 

government policymaking and other activities aimed at reducing disaffection and violence levels 

and stabilizing an overall society. 

Major features of the Affection, Disaffection Terrorist Recruitment, Training, and NarcoTerrorist Support Model are shown in Figure 12. The implemented model is shown in Appendix 

3, Figure A3.1, and the model control panel is shown in Appendix 3, Figure A3.2. The violence, 

casualties, and other deprivations caused by terrorist actions are assumed to disaffect individuals 

who were previously affected toward a government entity. Government actions aimed at 

satisfying deprivations can transform disaffected into affected individuals. Disaffected 

individuals can be recruited by terrorist organizations and may undergo some form of basic 

terrorist-related training. Basic training could be followed by more advanced training aimed at 

providing operationally competent terrorists. The model has facilities representing the 

recruitment of narcotics trafficker personnel into the advanced training activities. Following 

advanced training, the individuals would be available to undertake terrorist operations. Such 

operations have been modeled as described in the Entity Security and Terrorist Activity Models 

section of this paper. 

Selected output levels of basically trained and advanced terrorists are shown in Figure 13 and 

Appendix 3, Figure A3.2. An initial population of 100,000 individuals and 200 personnel 

provided by narcotics traffickers was assumed. Levels of deprivation and satisfaction for a 

particular setting of model parameters created 84,798 affected and 13,208 disaffected 

individuals. After the passage of a notional 300-day period, some 574 basic terrorists and 37 

advanced terrorists were generated by the modeled training and terrorist event processes (Figure 

13). The “stair-step” nature of the output of trained terrorist personnel reflects the assumed size 

of the trainee terrorist cohorts and training duration in the model. Training throughput can be 

increased by increasing capacity and reducing the time needed for training. It was envisioned 

that trafficker resources might support increased advanced terrorist training capacity, and this 

might be examined in future model-based studies. 

16 
Affected

Disaffected

Casualties

Casualties Policy Cycle Impact

Violence & Casualty

Impact

Recruitment of

Disaffected Individuals

Basic Terrorist

Training

Advanced Terrorist

Training

Narco-Terrorist

Personnel

Available Trained

Advanced Terrorists

Narco-Terrorist

Training Support

Narco-Terrorist

Corruption Impact

Figure 12. Key activities of the Societal Deprivation, Affection, Disaffection, 

Advanced Terrorist Recruitment, Training, and Narco-Terrorist Support Model 

generate notional trained terrorists who are available to undertake terrorist 

activities. 

12:38 PM Tue, Nov 1, 2011

Basic and Advanced Terrorist Levels

Page 11

0.00 75.00 150.00 225.00 300.00

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20

40

2: AvailAdvTerr

0

300

600

1: BasicTrainTerr

1

1

1

1

2

2

2

2

Figure 13. Model-generated numbers of notional basic and advanced terrorists. In 

this particular case some 574 basic terrorists and 37 advanced terrorists were 

produced. 

An additional study investigated the impact of different numbers of trafficker-provided basically 

trained terrorists on the availability for operations of terrorists with advanced training. Selected 

results are presented in Figure 14. With a recruitment rate for advanced terrorist training of 0.001 

(0.01 percent per time step) some 200 trafficker recruits can provide 181 basically trained 

terrorists and 19 terrorists with advanced training after a notional period of 300 time steps. A 

combination of trafficker-supplied personnel and disaffected individuals recruited at a rate of 

0.0001 (0.01 percent of available personnel per time step) can provide 326 basic and 28 

advanced terrorists after 300 notional days, for example. 

17 
Traff Staff Basic Terr Adv Terr

50 45 5

100 90 9

150 136 14

200 181 19

Recrt Rte Basic Terr Adv Terr

0 181 19

0.0001 326 28

0.0005 883 62

0.001 1579 77

Figure 14. (a) Trafficker-supplied (Traff Staff) personnel can train to become 

terrorists with basic (Basic Terr) and advanced (Adv Terr) training and (b) 

Trafficker supplied personnel can be combined with disaffected individuals 

recruited from the general population (Recrt Rte) to provide terrorists with basic 

and advanced training. 

Entity Security and Terrorist Activity Models21

Prototype Entity Security and Terrorist Activity Models have been produced to represent the 

impact of model-generated numbers of basic and advanced terrorists and the level of security of 

notional target entities on terrorist activity outcomes. Major functional features of the models are 

shown in Figure 15. Implementation of the models is illustrated in Appendix 4, Figure A4.1. 

Reduced security and increased recruitment rates can increase the number of terrorist events. 

The Entity Security Model component represents the level of security associated with notional 

target entities. Security investments can increase the level of security and security losses can 

reduce the level of security from user-selected initial conditions. Security levels can also be 

increased as a result of the implementation of new security policy actions triggered by losses 

caused by policy cycle-related terrorist actions, as described below. The level of new policy 

activity is calculated by the security policy cycle model components. 

The Terrorist Activity Model component generates notional terrorist events. Those events, 

referred to as potential terrorist events, are possible when the notional security level falls below a 

number that has been randomly generated by the model. However, such events may not take 

place if sufficient terrorists with advanced training are not available. Actual terrorist events take 

place when sufficient terrorists with advanced training are available and the level of security falls 

below the generated random number. Security assessments are generated by the model and 

represent the likelihood that notional entities might be subject to a terrorist attack. 

 

21 Details of the implementation of these models are presented in Appendix 4. 

18 
Security Level

Damage and 

Casualties

Potential Terrorist

Events

Advanced Terrorist

Team Size

Risk and Security

Assessments

Available Trained

Advanced Terrorists

Security Investment Security Losses

Actual Terrorist

Events

Terrorist Losses

Security Policy Actions

Figure 15. Entity Security and Terrorist Activity models calculate the level of 

security based on security investment and losses. Security investment levels can be 

increased as a result of government policy actions. Terrorist events can take place 

when adequate advanced trained terrorists are available and the security level is 

sufficiently low. 

Team Size Terr Evnts Tot Casults Terrst Loss

1 26 1396 26

2 13 727 26

3 9 418 27

4 7 342 28

5 6 290 30

6 5 281 30

Figure 16. Increasing the terrorist team size (Team Size) reduces the total number of 

actual terrorist events (Terr Evnts) because of staffing considerations. Smaller teams 

can become involved in a larger number of events and may generate more total 

casualties (Tot Casults) and fewer terrorist losses (Terrst Loss). Selected output from 

the Entity Security and Terrorist Activity Models with a security investment of 0 

percent per time step, an initial security level of 100, and security loss rate of 0.0004 

(0.04 percent) are shown. 

The Control Panel for the Entity Security and Terrorist Activity Models is presented in Appendix 

4, Figure A4.2. Selected model output data involving teams formed from one to six members are 

presented in Figure 16. Inspection of the model-generated output suggests that terrorist actions 

may not take place under some circumstances because suitably trained individuals to fulfill team 

size requirements may not be available, for example. As a result, care should be exercised when 

attempting to identify cause-and-effect relationships. Model-generated data for particular settings 

show that terrorist teams consisting of a single individual create conditions in which more 

terrorist events can be undertaken compared with situations involving larger teams because 

personnel are available. In a particular use of the model, the initial security level was set at 100 

units and declined to 88.7 after 300 time units due to losses of security, perhaps because of 

terrorist actions and lack of concern of security personnel. Model-generated data show that 

terrorist teams consisting of one member might undertake 26 terrorist events and create 1,396 

19 
notional casualties with an assumed loss of 26 terrorists. Teams of six terrorists could be 

involved in five events and create some 281 casualties with the assumed loss in action of 30 

terrorists, for example (Figure 16). 

A Violence Generation Model22

The prototype Violence Generation Model calculates the notional level of violence generated in 

response to societal deprivation, disaffection, and other factors. The major features of the 

Violence Generation Model are illustrated in Figure 17. Implementation of the model in systems 

dynamics software is shown in Appendix 5, Figure A5.1. The model control panel is presented in 

Appendix 5, Figure A5.2. Deprivation is assumed to transform affected individuals into 

disaffected individuals. Satisfaction of needs, perhaps due to government and other entity 

actions, is assumed to transform disaffected into affected individuals. Deprivation and 

satisfaction are assumed to take place at user-selected rates. Relatively high rates of deprivation 

can create increased levels of disaffection. 

Affected

Disaffected

Violence Sensitivity

Violent Social Activity

Violence Warning

Threshold

Affected/Disaffected

Ratio

Enhanced Deprivation

Violence Multiplier

Deprivation Satisfaction

Terrorist Casualty

Events

Affection/Disaffection

Ratio Policy Concern

Figure 17. The Violence Generation Model calculates the level of violence based on 

notional levels of disaffection and affection caused by deprivation and satisfaction, 

respectively, and social violence sensitivity, reflected in the violence warning 

threshold parameter. Feelings of insecurity and threat as a result of violence and 

terrorist-related casualties, for example, can cause enhanced deprivation. 

Sample model-generated output is presented in Figure 18. In this case, randomly generated 

violent activity (Violence) and AD Concern outputs are produced when the ratio 

(Disaffected/(Affected + Disaffected)) rises above the AD POL Warn threshold. That threshold 

was assumed to represent the historical experience of a country of interest. Violence in countries 

with a history of violence is more likely to occur at relatively lower disaffection levels, for 

example. The AD Concern model-generated output triggers activities that generate instructions 

for creating a new policy (Soc New Pol), as described below and in Appendix 6. 

 

22 Details of the implementation of this model are presented in Appendix 5. 

20 
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Figure 18. Model-generated output shows the production of violent activity 

(Violence) and (AD Concern) output when the ratio of affected to disaffected 

individuals rises above the (AD POL Warn) threshold. The (AD Concern) output 

triggers policy cycle-based activities that generate instructions for creating a new 

policy (Soc New Pol). 

POLICY CYCLE MODELS CAN REPRESENT THE MANAGEMENT OF 

SOCIETAL VIOLENCE LEVELS AND ENTITY SECURITY POLICIES23

Policy cycle models for the Management of Societal Violence Levels and Entity Security have 

been developed and implemented in systems dynamics software (Appendix 6). A key new 

feature of these and the other policy cycle models in this paper is the inclusion of properties that 

are assumed to reflect the impact of corruption in undermining the policy-related actions of a 

government entity. In the following studies, policy cycle-related corruption reduced the impact 

of policies aimed at reducing violence and increasing security. Corruption increased violence 

levels and prevented increased security-related processes from being implemented. 

A Social Policy Cycle Model for the Management of Violence 

A Social Policy Cycle Model for the Management of Violence has been developed. Increasing 

rates of policy-related information transfer in the absence of corruption can reduce the level of 

societal violence caused by disaffection. Figure 19 presents the key features of the model. Details 

of model implementation are described in Appendix 6, Figure A6.1. Input to the model 

represents policy concerns related to the levels of disaffection and violence. Those concerns 

trigger actions assumed to represent the formulation and implementation of social policy aimed 

at reducing disaffection and violence. Those actions are followed by actions assumed to 

represent the evaluation and change of an existing policy involving either new policy creation 

and/or termination. Corruption is assumed to act by reducing the input to policy formulation and 

implementation and new policy generation actions. New policy actions would be aimed at 

reducing violence levels by increasing satisfaction levels within a notional population. 

 

23 Details of the implementation of this model are presented in Appendix 6. 

21 
Formulate and

Implement

Social Policy

Affection/DisaffectionRelated Policy Concern

Corruption

Losses

Evaluate

Social Policy

Social Policy

Termination

New Social

Policy

Change

Social Policy

Figure 19. Key features of the Social Policy Cycle Model with policy-related 

corruption. 

Selected results obtained from the use of the Social Policy Cycle model with different settings of 

the policy cycle parameters are shown in Figures 20, 21, 22, 23, and 24 (also Appendix 6). The 

(Soc Pol Sensitiv) parameter was set at 0.2 and the (Sec PolSens) parameter was set at zero, 

thereby preventing contributions from Security Policy Cycle activities. Studies were undertaken 

in which the Policy Cycle parameters were set at 0.8 (an 80 percent transfer of information per 

time step) or 0.1 (10 percent per time step) and either with or without the influence of corruption. 

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Figure 20. Social Policy Cycle parameters set at 0.8 and corruption 0.0 generates a 

single burst of violent activity (Violence) when (AF DF Ratio) exceeds the (AD POL 

Warn) threshold. 

• Social Policy Cycle parameter values of 0.8 and corruption levels of 0.0 reduce 

violence levels (Figure 20): Cycle parameters social policy evaluation (Soc Eval 

Inpt), social policy change (Soc Chnge Inpt), social policy termination (Soc Term 

Inpt), and new social policy (Soc New Inpt) set at 0.8 represent an 80-percent transfer 

rate per time step between entities. Without corruption, the corruption of new policy 

(Soc CorrRte NewPol) and formulate and implementation activities (Soc F&I 

Corrupt) were set at 0. These settings generate a single short period of violent 

activity. That period stops when policy-based actions reduce the level of disaffection 

below the warning threshold. In this case, relatively rapid migration takes place 

through these entities: social policy formulate and implement (Soc F&I Tot), social 

policy evaluation (Soc Eval Tot), social policy change (Soc Chnge Tot I), social 

policy termination (Soc Term), and new social policy (Soc New Pol). New policy 

actions increase the level of satisfaction and reduce the level of disaffection in a 

relatively rapid manner in the absence of corruption. Those inputs create a single 

22 
period of violence (Violence), which is terminated as the new policy leads to an 

increase in satisfaction and a corresponding reduction in disaffection (Figure 20). 

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Figure 21. Setting the Social Policy Cycle parameters at 0.8 and corruption 

parameters at 0.04 generated bursts of violent activity followed by continuous 

violence because of an inability caused by corruption of a government entity to 

reduce disaffection to sufficiently low levels where violence does not occur. 

• Social Policy Cycle parameter values of 0.8 and corruption levels of 0.04 can increase 

the duration of violence (Figure 21): Those inputs create conditions in which multiple 

bursts of violent activity are followed by a period of continued violence as 

government policies impacted by corruption are unable to reduce disaffection to 

sufficiently low levels in order to prevent violence. Corruption could be caused by 

use of trafficker-generated resources that interfere with those government-related 

actions aimed at increasing the level of satisfaction and decreasing the level of 

societal violence. Multiple bursts of violence reflect an inability to create a permanent 

reduction in the level of disaffection. Corruption could be funded by narcotics 

trafficker resources and lead to increased levels of violence. Violent events could 

destabilize government entities and lead to an increase in trafficker and terrorist 

influence within the society, for example. 

• Social Policy Cycle parameter values of 0.1 and corruption levels of 0.0 can increase 

the duration of violence (Figure 22) and create conditions that produce a single period 

of violent activity of longer duration than that generated with policy cycle parameter 

values set at 0.8 (Figure 20). Violence occurs when the level of disaffection exceeds 

threshold levels. Slower policy cycle-related actions reduce the rate of policy-related 

actions aimed at reducing disaffection. Policy cycle parameter values of 0.1 represent 

a transmission of 10 percent of notional information per time step, significantly less 

than the 80-percent rate mentioned above (Figures 20 and 21). The slower rate might 

represent normal practice and the higher rate an emergency fast-track response to 

increased levels of societal violence, for example. 

23 
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Figure 22. Social Policy Cycle parameters set at 0.1 and corruption levels of 0.0 

generate a longer period of violence compared with the situation involving 

parameter values of 0.8 (Figure 20) when the (AF DF Ratio) exceeds the (AD POL 

Warn) threshold. 

• Social Policy Cycle parameter values of 0.1 and corruption levels of 0.04 can increase 

the duration of violence (Figure 23). Slower notional information transmission rates 

coupled with corruption create conditions that produce a single period of violence 

followed by continuous violent activity. Activities in the (Soc F&I Tot), (Soc Eval 

Tot), (Soc Chnge Tot), (Soc Term), and (Soc New Pol) entities are slower than with 

the Policy Cycle parameters set at 0.8. This represents the inability of the government 

entity to generate policies that are adequate to reduce disaffection-related violence. 

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Figure 23. Social Policy Cycle parameter values of 0.1 and corruption levels of 0.04 

generated a period of violent activity followed by continuous violence. Compare 

Figure 23 with Figure 21, which reflects the impact of Social Policy Cycle 

parameters set at 0.8. 

These preliminary experiments have demonstrated that modeled policy cycle-related intervention 

can act to reduce the level of disaffection in a notional population by increasing the level of 

satisfaction. Reduced levels of disaffection can reduce the level and duration of violence within a 

society. Corruption of the policy cycle process can reduce or even stop the disaffection-reducing 

24 
actions, including implementation of new government policy cycle-generated policies. The level 

of societal violence is assumed to be determined by the ratio of disaffected to the total number of 

affected and disaffected individuals. Violence could occur when that ratio exceeds a value that 

reflects the historical behavior of a society of interest. Higher levels of violence can be created 

by increasing the overall level of disaffection; lower violence levels can occur with relatively 

lower levels of disaffection caused by increased policy-based societal satisfaction. 

Policy cycle-generated events that reduce the level of disaffection can act to reduce overall 

violence levels. Even relatively modest levels of corruption can cause a reduction in policy 

effectiveness. It is assumed that corruption could be mediated by the infusion of traffickergenerated resources into the policymaking processes of a national government or other 

policymaking entities. Higher levels of violence can act to reduce the legitimacy of a government 

so that trafficker-generated corruption can provide a basis for facilitating terrorist actions. 

Increased terrorist effectiveness could also facilitate trafficker actions by reducing the number 

and extent of government-led counter-narcotics actions. 

A Security Policy Cycle Model for Entity Security Management 

A Security Policy Cycle Model for the management of entity security has been developed and 

implemented in systems dynamics software. Figure 24 presents the key features of the model. 

Details of implementation of the model are shown in Appendix 6, Figure A6.3. Input to the 

policy cycle model represents policy concerns related to the impact of casualty levels generated 

by notional terrorist activities. Terrorist actions can occur when targets are vulnerable and trained 

terrorists are available. In the model, terrorist actions trigger policy cycle-based actions assumed 

to represent the formulation and implementation of a security policy. That policy is responsive to 

existing conditions and involves evaluation and change of an existing policy, creation of a new 

policy, and/or termination of the existing policy. Corruption is assumed to act by reducing the 

input to the policy formulation- and implementation-related activities. Corruption could also 

reduce the input responsible for generating a new security-related policy, thereby increasing the 

likelihood that terrorist events might occur because of an inability to increase security policy. 

Formulate and

Implement

Security Policy

SecurityRelated Policy Concern

Corruption

Losses

Evaluate

Security Policy

Security Policy

Termination

New Security

Policy

Change

Security Policy

Figure 24. Key features of the Security Policy Cycle Model with policy-related 

corruption. 

Activities within the implemented model are triggered when the number of actual terrorist events 

(TotATActEvents) is greater than zero (Appendix 6, Figure A6.3). The magnitude of policyrelated responses is also determined by the level of security-policy-related sensitivity (Sec Pol 

Sensitiv). Policy-related changes are intended to raise the threshold above which terrorist actions 

would be triggered when the randomly generated number (which can vary between 0 and 100) 

exceeds the threshold. Under some circumstances, excessive policy cycle output levels cause the 

threshold to increase above 100, representing some form of excess security level for triggering 

25 
terrorist actions. Corruption-related activities, by contrast, can reduce the threshold and increase 

the likelihood that notional terrorist actions would be triggered when the generated random 

number exceeds the threshold value. 

Two studies were undertaken to examine the impact of (1) increasing the speed of notional 

policy-related information transfer within the security-related policy cycle model by increasing 

the value of the policy cycle parameters in the absence of corruption, and (2) increasing the level 

of corruption within the policy cycle model at fixed information transfer rates. Those studies 

demonstrate that higher rates of notional information transfer within the cycle can generate 

instructions that cause a relatively rapid increase in the security threshold, thereby reducing the 

likelihood of terrorist actions from taking place. Higher corruption levels reduce the level of 

policy-related changes in security levels and can lead to increased numbers of terrorist events 

and casualties. Results are presented in Figures 25 and 26. 

PCM parms Fin Sec Lvl Tot Evnts Act Evnts Sec New Pol Tot Casult

0 88.7 70 27 0 1496

0.02 95.8 57 25 10.5 1358

0.04 102 39 21 16 1114

0.06 105 32 16 18 846

0.08 106.8 29 15 19 840

0.1 108 26 13 20 727

Figure 25. Increased rate of policy-related information transfer can reduce total 

terrorist-generated casualties. Impact of policy cycle parameter values (PCM parms) 

on security level (Fin Sec Lvl), potential (Tot Events) and actual (Act Evnts) terrorist 

events, and total casualties (Tot Casult) with security political sensitivity (Sec Pol 

Sens) set at 0.2; security investment (Sec Invest) was set at 0.01; and corruption was 

set at zero. 

• The impact of policy cycle information transfer rate on security levels and terroristrelated casualties: Increased rate of policy-related information transfer can reduce 

total terrorist-generated casualties (Figure 25). The first study investigated the impact 

of increasing the speed of notional information transfer within the security-related 

policy cycle model from 0 to 0.1 (10-percent transfer per time step) on the potential 

and actual number of terrorist events and the production of notional casualties. 

Increasing the rate of policy cycle-related information transfer reduces the number of 

events and the number of casualties. (1) Baseline conditions were established by 

setting the policy cycle parameters at zero and preventing policy cycle involvement 

and policy cycle-mediated security investments. Notional security losses perhaps 

because of increased vulnerability, of 0.0004 (0.04 percent) per time step caused the 

security threshold to decline from 100 to a final security level (Fin Sec Lvl) of 88.7 

during a notional period of 300 days. The reduced security threshold permitted 

generation of 70 possible terrorist events (Tot Evnts) of which only 27 (Act Evnts) 

actually took place because of the lack of trained terrorist personnel. Those events 

created some 1,496 notional casualties (Tot Casult)). (2) Policy cycle parameters set 

at 0.1 provide an increased investment in security that can offset the impact of 

security losses. Under these conditions, the security threshold initially decreased and 

then increased to 108, which is above the initial value of 100, and represents 

conditions in which entities are not vulnerable to terrorist attack. Model-generated 

26 
data suggest that under policy-related security conditions of transient vulnerability, 13 

of 26 actual terrorist events with 727 casualties occurred (Figure 25). 

• The impact of corruption on security levels and total terrorist-related casualties: 

Increased levels of corruption can lead to increased numbers of terrorist-generated 

casualties. (Figure 26). The second security study investigated the impact of 

corruption on security level, number of terrorist events, and the level of casualties. 

The policy cycle parameters were held at 0.1 and the corruption parameters varied 

from 0 to 0.1. Studies show that with zero corruption, the policy cycle process results 

in a final security level of 108, some 13 actual terrorist events that create 727 total 

casualties take place. Increasing the level of corruption to 0.1 prevents policy cyclerelated increases in security levels from taking place. This creates a final security 

level of 89.6, which results in 27 actual terrorist events with 1,496 total casualties 

taking place. 

Corrruption Fin Sec Lvl Tot Evnts Act Evnts Sec New Pol Tot Casult

0 108 26 13 20 727

0.02 94.8 50 26 4 1396

0.04 91.7 64 27 1.8 1496

0.06 90.6 65 27 1 1496

0.08 90 66 27 0.7 1496

0.1 89.6 66 27 0.5 1496

Figure 26. Increased levels of corruption can lead to increased numbers of terroristgenerated casualties. Impact of policy cycle corruption (Corruption) on security level 

(Fin Sec Lvl), potential (Tot Events) and actual (Act Evnts) events, and total 

casualties (Tot Casult) with security political sensitivity (Sec Pol Sens) set at 0.2, the 

security investment (Sec Invest) set at 0.01, and the policy cycle parameters set at 

0.1. 

It could be assumed that synergetic interactions involving narcotics traffickers and terrorists 

could involve use of trafficker resources to corrupt the counter-narcotics policymaking 

processes. Such actions could block attempts to increase security and lead to an increased 

number and scope of terrorist events with higher casualty levels. This could create an increased 

perception of a government that was unable to protect the citizenry and lead to increased levels 

of disaffection and create further violence. Those changes could act to undermine a government 

and facilitate future trafficker and terrorist actions, to the benefit of both traffickers and terrorists. 

Higher security thresholds can have the effect of preventing terrorist events from taking place as 

the result of a notional increase in security or reduction in entity vulnerability. Such changes 

could reflect an increase in the alert status and responsiveness of entities involved in setting and 

implementing security policy. Increased responsiveness can reduce vulnerabilities and the 

number of terrorist events and associated casualties that could occur. Studies have examined the 

impact of corruption on the ability of policy-related changes in alert status and responsiveness 

and the production of notional casualties. Corruption can prevent policy-related security 

increases from taking place, thereby increasing vulnerability and fostering the perception of an 

inability of a government to protect its people. 

27 
SUMMARY, DISCUSSION, AND A WAY FORWARD 

This paper has provided a review of the drug trafficking and terrorist domains as well as means 

to counter their activities. This was accomplished through the development of models for drug 

trafficking, terrorist activities, and the role of the policymaker in countering their activities. More 

specifically, the conclusions of the overall study are as follows: 

• The study included an investigation of the counter-narcotics activities and the role of 

efforts aimed at converting traffickers into double agents. Model-generated data 

closely resembled actual reports of tons of disrupted narcotics substances detected in 

2008, 2009, and 2010. A policy cycle model was developed and used to study the 

potential impact of increased levels of policymaking support as well as corruption of 

the policy process on the ability to form double agents and to disrupt trafficker 

activities. Increased rates of policy cycle activity increased the amount of narcotics 

disruption. However, increased levels of corruption reduced those levels.

• Model-generated results show that policymaking can lead to a reduction in the level 

of deprivation, disaffection, and violence and that policy-related effects can be 

inhibited by corruption, which can change the overall political dynamics of societies 

of interest. The policies may range from providing social services, employment, 

medical and housing assistance, and so forth. This is similar to what Hezbollah 

provides to poor communities in Lebanon and other Diaspora locations. The goal is to 

win the hearts and minds of the people. The prevention of actions taken by the 

government tend to increase the duration of disaffection-related violence, and they 

also increase the number of disaffected individuals available for recruitment into 

terrorist training programs. In order to accurately assess this impact, it is necessary to 

have data that provide the range of possible actions taken by terrorist organizations 

and their impact on the population. A leading counter-terrorism expert conducted an 

extensive interview process of prisoners in Israel that reveals the rationale for 

undertaking terrorist acts such as planting improvised explosive devices (IED) in 

public places. The level of disaffection ranges from ideological concerns to personal 

grievances and family tragedies as a result of the occupation. 

• Modeling the combined terrorist-narcotics trafficker threat to national security has 

involved production of several component models, including the following: 

- A Societal Deprivation, Disaffection, Recruitment, Advanced Terrorist 

Training, and Narco-Terrorist Support Model that illustrates how notional 

individuals deprived of key resources can become disaffected and 

recruited and trained to become advanced terrorists. 

- An Entity Security and Terrorist Activity Model that illustrates the attack 

of notional targets by teams of trained terrorists. 

- Entity Security and Violence Generation Models describe how deprived 

and disaffected individuals may become violent and how such violence 

can increase the level of perceived deprivation and disaffection. 

- Social and Security Policy Cycle Models that represent the processes of 

identifying a problem and formulating, implementing, evaluating, 

changing, and/or terminating a policy in response to perceived need, as 

28 
well as the impact of corruption in preventing successful policy-related 

outcomes. 

• Model-based studies also show that the availability of trained terrorists needed for 

operations is a rate-limiting factor and that vulnerable targets of opportunity may not 

be attacked because trained terrorist operatives are lacking. This suggests that the 

narcotics trafficker networks could perform important intelligence functions by 

identifying vulnerable targets and alerting terrorist organizations to pre-position 

operatives in order to take advantage of opportunities. Trafficker networks could also 

increase target vulnerability by creating climates of fear and reduced social activity in 

target areas. These potential actions by the cartel networks could be of great value to 

the terrorists and may be worth the financial investment they would make in such 

cartel organizations. In order to assess the value, a detailed examination of specific 

indictments and the contribution of intelligence are required. However, most of the 

unsealed cases focus on the tactics used by the cartels in moving shipments of 

narcotics and the actions of facilitators in this process. A distinct connectivity 

between criminal and terrorist groups is well known. Their interaction is based on the 

ability of each group to provide a critical service to another and profiting from the 

transaction. The accomplishment here is beyond the academic exercise of model 

development but is focused on the interaction and the benefits/gains that are achieved. 

Looking forward, additional model-based studies are needed to assess the effect of narcotics 

trafficker and terrorist involvement in at least the following activities: 

• Support to terrorist training capacity and the increased rate of recruitment of 

disaffected individuals as terrorists 

• The supply of trained terrorist personnel, resources, and facilities to support advanced 

terrorist training and operational activities 

• The effect of narcotics trafficker and terrorist involvement in and the impact of 

corruption on security-related protective policy-based measures 

• The effect of corruption on implementation of government policies aimed at 

preventing social deprivation, disaffection, and violence 

• The effect of terrorist and narcotics trafficker personnel deployment on entity 

vulnerability and intelligence collection actions. 

The authors believe that modeling these activities can provide a deeper understanding of the 

relationship between drug trafficking and terrorist activities as well as the role of the 

policymaker in countering these threats. Additional insight can be generated by studies that 

identify values for the assumed model parameters, potentially leading to the development of 

enhanced and more realistic models. Those models could support production of new facilities for 

counter-terrorist and counter-narcotics policymaking and decisionmaking. 

29 
APPENDICIES 1 TO 6: IMPLEMENTATION OF THE 

MODELS IN SYSTEMS DYNAMICS SOFTWARE 

Details of the models developed and used in the studies reported in this paper are presented 

below. 

APPENDIX 1: A NARCOTICS, COUNTER-NARCOTICS, AND 

TRAFFICKER DOUBLE AGENT MODEL 

Preliminary Model-Building Considerations 

The models described in this paper were developed and implemented in STELLATM, a 

commercially available systems dynamics-based software system (http://www.iseesystems. com) 

by Woodcock. Figure A1.1 and other figures are screenshots taken from the models developed 

and used as described in this paper. Programming in STELLATM involves dragging icons 

representing different properties and capabilities onto a workspace provided by the system and 

forming functional links between appropriate entities. Key components include, but are not 

limited to, stocks, flows, converters, and action converters. Figure A1.1 represents a sample 

model of the Flow Input to a Process entity (represented by the rectangular stock construct) at a 

rate determined by the Input Rate parameter. Output from the Process entity to a Product entity 

takes place at a rate determined by multiplication of the value of the Process and Output Rate

parameters. Once the model is assembled, the system asks the user for information and data. 

Process

Input Output

Input Rate Output Rate

Product

Sample Model

Figure A1.1. The Sample Model structure involves stocks, flows, converters, and 

action converters provided by the STELLATM system. 

Process(t) = Process(t - dt) + (Input - Output) * dt 

INIT Process = 0 

INFLOWS: 

Input = Input_Rate 

OUTFLOWS: 

Output = Output_Rate*Process 

Product(t) = Product(t - dt) + (Output) * dt 

INIT Product = 0 

INFLOWS: 

Output = Output_Rate*Process 

Input_Rate = 0.1 

Output_Rate = 0.05 

Figure A1.2. Sample Model source code is generated automatically by STELLATM. 

30 
User development of the model structure, model relationships, and parameter values (Figure 

A1.1) sets the scene for automatic source code generation for the model (Figure A1.2). The 

rectangular icons inside the Input and Output Rate converter icons (Figure A1.1) represent the 

fact that sliders have been implemented to permit user selection of specific Process and Output 

Rate parameter values. Those sliders are shown in the model control panel in Figure A1.3. 

Sliders permit rapid user selection of parameter values during experimental studies with the 

model. The control panel can be constructed to provide numerical data displays and other 

information. In this case Input and Output Rates set at 0.1 generate Process and Product values 

of 1 and 9, respectively. STELLATM also provides a graphical display facility that has been used 

to display the increase with time of the Process and Product parameters (Figure A1.4). The 

system permits user selection of Run Specifications that define the duration and other properties 

of the runtime of the model. In this case the runtime has been set at a notional 100 days. 

0.10

0.00 1.00

Input Rate

0.10

0.00 1.00

U

Output Rate

Process 1.0

Product 9.0 Graph 1

Sample Model

Figure A1.3. Sample Model control panel showing sliders and numerical data 

displays. 

3:43 PM Tue, Jan 31, 2012

Product

Page 1

0.00 25.00 50.00 75.00 100.00

Time

1:

1:

1:

1: Process 2: Product

2:

2:

2:

10

0

5

1 1 1

2

2

2

2

1

Figure A1.4. Sample Model graphical output showing calculated Process and 

Product values for a notional 100 days. 

31 
It is hoped that this brief introduction to the use of STELLATM has provided an adequate 

foundation to permit the reader to access the following descriptions of the development, 

implementation, and use of the models described below. Additional information can be obtained 

from the http://www.iseesystems.com website. 

Implementation of the Narcotics, Counter-narcotics, and Trafficker Double Agent 

Model 

Implementation of the Narcotics, Counter-narcotics, and Trafficker Double Agent model in 

STELLATM (http://www.iseesysystems.com) is shown in Figure A1.5. The model provides a 

notional representation of the process of converting traffickers into double agents and using the 

information they might provide to disrupt additional shipments of narcotics. Details of these 

activities are presented below. 

RandGen1

NarcoThresh

NarcoEvnt

MaxEvntSize

Add NarcoEvnt

TotNarcoEvnt

ConvEffort

RandGen2 DoubleAgent

AddDoubAgt

DecrThresh TotDoubAgt

CN Efft

Mod NarThresh

EvntSize

Add EvntSize

Tot Evnt Size

Conv Thresh

Mod NarThresh

PolCycle In

DAgt New Pol

Narcotics, Counternarcotics, and Trafficker Double Agent Model

Figure A1.5. Counter-narcotics model that represents the impact of creating 

trafficker double agents on the number and size of narcotics seizure activities. The 

model also provides output to (PolCycle In) and input from a policy cycle-based 

management model (DAgt New Pol) described in the text. 

Narcotics substance seizures or disruptions are assumed to take place when a randomly 

generated number (RandGen1) is greater than a user-selected threshold value (NarcoThresh) 

(Figure A1.5). The threshold for detection or disruption of narcotics substances can be modified 

by the effect of the generation of double agents, (Mod Nar Thresh) as described below. 

Disruption events are represented by the (NarcoEvnt) parameter which has value 1 for a modelgenerated event and 0 otherwise. Non-zero (NarcoEvnt) data trigger another random number 

generator (EvntSize), the output of which ranges from 0 to the user-selected (MaxEvntSize) 

parameter representing the maximum size of disrupted narcotics shipments. The total event size 

(Tot Evnt Size) is formed from the addition of all events; the total number of counter-narcotics 

32 
events is represented in the model as the (TotNarcoEvnt) parameter. Model facilities were also 

created that provide input to a policy cycle management model (PolCycle In), and output from 

the policy cycle model can change the policy on double agent recruitment (DAgt NewPol). The 

policy cycle model is described below (Appendix 2). Use of that model enabled consideration of 

the impact of the rate at which policymaking could take place and how policymaking might be 

influenced by corruption. Data on the total number of narcotics seizures and the total size of 

those seizures was collected and displayed both numerically and graphically. 

The conversion of narcotics traffickers into double agents is represented by a process in which 

conversion is assumed to take place when a random number generated by a random number 

generator (RandGen2) is greater than a user-selected threshold determined by the value of the 

user-selected ConvEffort and Conv Thresh parameters (Figure A1.5). Higher thresholds represent 

conditions where such conversion is relatively unlikely to take place. Increased conversion 

efforts are assumed to reflect a lowering of the threshold (Mod Nar Thresh) representing 

conditions where a greater likelihood exists for the generated random number (RandGen1) to 

exceed the threshold and represent disruption of narcotics shipments. 

The trafficker double agents (TotDoubAgt) generated by the conversion process are assumed to 

provide information that could be used to decrease the threshold for detection of narcotics 

trafficker activities (DecrThresh). Actual narcotics detection was assumed to depend on the level 

of effort involved in converting trafficker-provided intelligence into operational counternarcotics activities represented by the (CN Efft) parameter. Higher levels of such efficiency are 

assumed to generate a greater likelihood for narcotics detection, seizure, or other types of 

disruption. 

80.400

0.000 100.000

U

NarcoThresh

TotNarcoEvnt 273

0

0 100

U

ConvEffort

0.1000

0.0000 1.0000

U

CN Efft

TotDoubAgt 0

DecrThresh 0.0

1.900

0.000 100.000

U

MaxEvntSize

Conv Thresh 100

Tot Evnt Size 263

Mod NarThresh 80

Narcotics, Counternarcotics, and Trafficker Double Agent Model

Figure A1.6. Control panel for the Narcotics, Counter-narcotics, and Trafficker 

Double Agent Model permits the display of selected model outputs. 

The control panel for the Narcotics, Counter-narcotics, and Trafficker Double Agent Model is 

shown in Figure A1.6. The panel permits user selection of the narcotics detection threshold 

(NarcoThresh; set at 80.4), counter-narcotics effectiveness (CN Efft; 0.1), double agent 

conversion effort (ConvEffort; 0), and maximum size of disturbed shipments (MaxEvntSize; 1.9) 

and their use in model-based studies, for example. 

33 
APPENDIX 2: POLICY CYCLE MODELS CAN MANAGE TRAFFICKER 

DOUBLE AGENT CONVERSION POLICIES 

A policy cycle model of policy aimed at increasing the level of effort to convert traffickers into 

double agents has been developed and implemented in systems dynamics software (Figure A2.1)

The model has facilities that detect actions aimed at producing double agents. Notional policies 

are developed to enhance the rate of creation of those agents. A key feature of this model is the 

inclusion of properties that reflect the notional impact of corruption in undermining the counternarcotics policy-related actions of a government. Corruption inhibits the double agent 

recruitment process and reduces the amount of narcotics substances disrupted or disturbed by 

counter-narcotics actions. 

DAgt F&I Tot DAgt Eval Tot DAgt Chnge Tot

DAgt Term

DAgt New Pol DAgt In F&I

DAgt Pol Sensitiv

DAgt Enab DAgt Pol Eval

DAgt Eval Inpt DAgt Chnge Inpt

DAgt Pol Term

DAgt Pol New

DAgt Term Inpt

DAgt New Inpt DAgt NewPolLoss

DAgt CorrRte NewPol

DAgt CorrLossNewPol

DAgt F&I Corr

DAgt Lost F&I

DAgt F&I Corrupt

PolCycle In

Narcotics/Counternarcotics Policy Cycle Model With Policy-Related Corruption

Figure A2.1. Policy Cycle Model describing the creation of double agent generating 

policies. Policies supporting the creation of double agents can result in an increase 

the amount of narcotics substances disturbed by counter-narcotics actions; 

corruption of the policymaking process can reduce that amount. Double agent new 

policy (DAgt New Pol) provides input to the Narcotics, Counter-narcotics, and 

Trafficker Double Agent Model. 

Activities within the implemented policy cycle model are triggered when the TotNarcoEvnt

parameter is equal to 1 and the (PolCycle In) parameter is then set to 1 (Figures A1.5 and A2.1). 

Policy-related responses are also determined by the level of sensitivity to double agent-related 

actions (DAgt Pol Sensitiv). Double agent policy cycle-related activities involve double agent 

policy formulation and implementation (DAgt F&I Tot), double agent policy evaluation (DAgt 

Eval Tot), double agent policy change (DAgt Change Tot), new double agent policy creation 

(DAgt New Pol), and double agent policy termination (DAgt Pol Term). These activities take 

place at user-selected rates: policy evaluation (DAgt Eval Inpt), policy change (DAgt Chnge 

Inpt), new policy (DAgt New Inpt), and policy termination (DAgt Term Inpt), respectively. 

Corruption of the double agent policy formulation and implementation processes takes place at a 

rate determined by the (DAgt F&I Corrupt) parameter and corruption of the new double agent 

policy takes place at a rate determined by the (DAgt CorrRte NewPol)parameter. Output from the 

counter-narcotics model (Figure A1.5) (PolCycle In) triggers events in the policy cycle aimed at 

increasing the number of converted trafficker double agents. The ability to increase the number 

of conversions can be affected by corruption of the policymaking processes since that reduces 

34 
the value of the (DAgt New Pol)parameter as well as the efforts involved in agent recruitment 

(Figure A1.5). 

The control panel for the Double Agent Policy Cycle Model is shown in Figure A2.2. The 

control panel permits user selection of the following parameters: double agent policy sensitivity 

(DAgt Pol Sensitiv; set at 0.0), input rate to double agent policy evaluation (DAgt Eval Input; 

0.1), input rate to double agent policy change (DAgt Chnge Input; 0.1), input rate to double agent 

policy termination (DAgt Term Input; 0.1), input rate to new double agent policy (DAgt New 

Input; 0.1), new double agent policy corruption rate (DAgt CorrRte NewPol; 0), and corruption 

of double agent policy formulation and implementation (DAgt F&I Corrupt; 0). 

0.10

0.00 1.00

U

DAgt Chnge Inpt

0.00

0.00 1.00

U

DAgt CorrRte NewPol

0.10

0.00 1.00

U

DAgt New Inpt

0.10

0.00 1.00

U

DAgt Eval Inpt

0.00

0.00 1.00

U

DAgt F&I Corrupt

0.10

0.00 1.00

U

DAgt Term Inpt

0.0000

0.0000 1.0000

U

DAgt Pol Sensitiv

DAgt New Pol 0.00

DAgt CorrLossNewPol 0.00

DAgt Lost F&I 0.00

Conv Thresh 100.00 Tot Evnt Size 263

Double Agent Policy Cycle Model

Figure A2.2. Control panel for the Double Agent Policy Cycle Model shows slider 

value inputs and numerical output values of selected parameters. 

35 
APPENDIX 3: PROTOTYPE SOCIETAL DEPRIVATION, AFFECTION, 

DISAFFECTION, ADVANCED TERRORIST RECRUITMENT, TRAINING, 

AND NARCO-TERRORIST SUPPORT MODEL 

Implementation of the Societal Deprivation, Affection, Disaffection, Advanced Terrorist 

Recruitment, Training, and Narco-Terrorist Support Model is shown in Figure A3.1. In the 

model, affected individuals (Affect) are transformed into disaffected (Disaffected) individuals at 

rates determined by the level of deprivation (DepLvl and Dep Enh). Disaffected individuals can 

become affected by actions aimed at increasing the level of satisfaction either by existing 

government policies (SatLvl) or by new government policies (determined by the values of the 

(Soc New Pol) and policy multiplier (PolMult) entities). Both affected and disaffected 

individuals may be lost as casualties as a result of terrorist actions (represented by the modelgenerated Damage parameter) and reduce the overall size of the populations of interest. 

36 
Affected

Disaffected

DisProc

SatProc

DepLvl

Recruit

Recruited

RecruitRte

SatLvl

Soc New Pol

PolMult

Sat Enhnce

Dep Enh

TerrTrain

TrainRecruit

BasicTrainTerr

BasicTrained

AdvTerror

RecAdvTr

AvailAdvTerr

AdvTrRctRte

AdvTerr

TrainProg

AddTmm StopTmm

CountNarco

NarcoTerror

Add NarTerr

AffLosTot

Damage

Disaffected 13,208

Tot Casults 1,565

Affected 84,798

DisLosTot 783

DisLosTot

Total 100,200

DissLoss

AffLoss

Damage

AffLosTot 783

Advanced Terrorist Training and Narco-Terrorist Support 

Model: Affection, Disaffection, Recruitment, and Training

Figure A3.1. Advanced Terrorist Recruitment, Training, and Narco-Terrorist 

Support Model represents the processes of affection, disaffection, recruitment, and 

production of basically trained and advanced terrorists. Those activities can be 

supported by the use of trafficker personnel and training facilities that create 

additional terrorists. 

Terrorists can be recruited from among disaffected individuals at a rate determined by the 

RecruitRte parameter. Recruited terrorists can undergo notional basic training (TerrTrain) and be 

recruited into advanced training (AdvTerror) at a rate determined by the (AdvTrRctRte) 

parameter generating available advanced terrorists (AvailAdvTerr). Training can be stopped by 

37 
setting the (TrainProg) parameter to zero. Additional individuals can be supplied by drug 

trafficker organizations (NarcoTerror) between times (AddTmm) and (StopTmm). The level of 

notional government actions against that process is represented by the (CountNarco) process. In 

addition, narcotics trafficker resources could be used to increase the overall model-based 

capacities and throughput rates of the notional basic and advanced terrorist training facilities. 

0

50000

100000

100000

U

Affected

0.010

0.000 0.010

U

DepLvl

0.010

0.000 0.010

U

SatLvl

Affected 84,798

Disaffected 13,208

0.00010

0.00000 0.00100

U

RecruitRte

0.10

0.00 0.50

U

PolMult

Recruited 2

Total 100,200

1.0

0.0 1.0

U

TrainProg

BasicTrainTerr 574

0

0 300

U

AddTmm

0.001

0.000 0.010

AdvTrRctRte

AvailAdvTerr 37

300

0 300

U

StopTmm

0

100

200

200

U

NarcoTerror

0.0000

0.0000 1.0000

U

CountNarco

Advanced Terrorist Training and Narco-Terrorist Support Model: 

Affection, Disaffection, Recruitment, and Training

Figure A3.2. The Advanced Terrorist Recruitment, Training, and Narco-Terrorist 

Support Model control panel provides capabilities for selecting model parameters 

and undertaking parameter impact analysis and assessment studies. 

The control panel for the Advanced Terrorist Training and Narco-Terrorist Support Model is 

presented in Figure A3.2. User selection of model parameter values with the use of software 

sliders can provide an environment for undertaking studies of the impact of model parameter 

values on model-generated outcomes. User-selected parameter values can include the following: 

initial affected population (Affected; set at 100,000); terrorists supplied by narcotics traffickers 

(Narcoterror; 200); deprivation rate (DepLvl; 0.01); satisfaction rate (SatLvl; 0.01); recruitment 

rate for disaffected individuals (RecruitRte; 0.0001); advanced terrorist recruiting rate 

(AdvTrRctRte; 0.001); training program in place (TrainProg; 1); start time for adding trafficker 

38 
personnel (AddTmm; 0); end time (StopTmm; 300); counter-narcotics effort (CountNarco; 0); and 

political multiplier (PolMult; 0.1). 

Figure A3.3 provides a numerical cross-check to determine the involvement of all participants. 

Thus the total number of notional participants (Total) = 100,200 = (Affected) + (Disaffected) + 

(Recruited) + (AffLosTot) + (DisLosTot) + (TerrTrain) + (AvailAdvTerr) + (BasicTrainTerr) + 

(AdvTerror) + (NarcoTerror). 

Affected

Disaffected

Recruited

Total

TerrTrain BasicTrainTerr

AdvTerror

AvailAdvTerr

Total 100,200

NarcoTerror

AffLosTot

DisLosTot

Total Participants

Figure A3.3. Numerical cross-check to verify involvement of all notional 

participants in Figure A3.1. 

39 
APPENDIX 4: ENTITY SECURITY AND TERRORIST ACTIVITY MODELS 

Implementation of the Entity Security and Terrorist Activity Models in systems dynamics 

software is shown in Figure A4.1. The key feature of the model is the representation of the level 

of entity security. The initial level of security (SecLevel), which can range from 100 (most 

secure) to 0 (least secure), can be selected by the user at the outset with the aid of the control 

panel (Figure A4.2). The security level (SecLevel) is increased by investment (SecInvestRte) and 

policy cycle-generated actions (Sec New Pol). The level of security can be reduced by losses 

caused by lack of maintenance and attention and by hostile terrorist actions (SecLosRte), for 

example. 

Actual terrorist events (ActAdvTerr) can take place when adequate numbers of advanced 

terrorists (determined by the terrorist team size [AdvTerTeamSize] and numbers of advanced 

terrorists [AvailAdvTerr]) are available and security levels are appropriately low. Terrorist 

numbers are reduced by operationally generated casualties (Losses). Terrorist losses are 

calculated based on the size of the team and number of terrorist events, assuming that total 

terrorist casualties occur during each event. 

SecInvest

SecInvstRte

SecLevel SecLoss

SecLosRte

ATRndEvents

TotATEvnts ATEvntAdd

ActAdvTerr

ATActEvntAdd

TotATActEvnts

RndmProcess

RndmProcess

AdvTerTeamSize

Sec Agn AdvTerr

AvailAdvTerr

ActAdvTerr AdvTerTeamSize

AddLoss Losses

LossAdd

Losses

RemATerr

Damage

Tot Casults

AddCas

MaxCas

Sec New Pol

Entity Security and Terrorist Activity Models

Figure A4.1. Implementation of the Entity Security and Terrorist Activity Models 

provides a basis for undertaking studies of the impact of the number of trained 

terrorists and the vulnerability of targets on casualty levels. The key feature of the 

model is the level of security (SecLevel) that can be increased by investment in 

security and decreased by lack of maintenance and/or hostile actions. 

40 
Potential events represent situations when the model-generated security level is below the value 

of the model-generated random number (RandomProcess) but insufficient trained terrorists are 

available to take action. The ratio (Sec Agn AdvTerr) of total actual (TotATActEvents) and total 

potential (TotATEvents) number of terrorist events provides an indication of the relative 

availability of trained terrorists for operational purposes. 

The Control Panel for the Entity Security and Terrorist Activity Models is presented in Figure 

A4.2. The Panel permits user selection of the investment rate in maintaining security 

(SecInvstRte; set at 0), the rate of loss of security (SecLosRte; 0.0004), the maximum number of 

casualties per incident (MaxCas; 100), the terrorist group size (AdvTerTeamSize; 1), and initial 

security level (SecLevl; 100) of the of the modeled security environment. 

TotATActEvnts 26

Sec Agn AdvTerr 63.38

0.0000

0.0000 1.0000

U

SecInvstRte

0.00040

0.00000 0.10000

U

SecLosRte

RemATerr 6

Losses 26

1

0 10

U

AdvTerTeamSize

TotATEvnts 70

Total 100,200

Tot Casults 1,396

100

0 100

U

MaxCas

0

50

100

100

U

SecLevel

SecLevel 88.69

AdvTerTeamSize 1 CasImpact 0.01

AvailAdvTerr 32

Entity Security and Terrorist Activity Models

Figure A4.2. The Control Panel for the Entity Security and Terrorist Activity 

Models shows selected parameter inputs and model-generated output. Settings 

indicate a maximum initial security level of 100, a security investment rate 

(SecInvsRte) of 0.0 and loss rate (SecLosRte) of 0.0004 (0 percent and 0.04 percent 

per time step, respectively), a maximum of 100 casualties (MaxCas) per incident and 

terrorist team size (AdvTerTeamSize) of 1. This creates 1,396 notional casualties (Tot 

Casults) in 26 incidents (TotActEvnts) that cause the loss of 26 terrorists (Losses). 

41 
APPENDIX 5: A VIOLENCE GENERATION MODEL 

Implementation of the Violence Generation Model in systems dynamics software is shown in 

Figure A5.1. The model calculates the level of violence (Violence) per incident and total violence 

(Total Viol) assumed to correspond to the ratio (Disaffected/(Affected + Disaffected)) of affected 

(Affected) and disaffected (Disaffected) individuals (AF DF Ratio), the affection-disaffection 

Political Warning threshold (AD POL Warn Thresh), and violence sensitivity (Viol Sens). The 

model also generates enhanced deprivation (Dep Enh) levels based on the values of a violence 

multiplier (Viol Mult), the level of violence (Tot Viol), and total casualties (Tot Casults) caused 

by terrorist events calculated by the Entity Security and Terrorist Activity Models (Appendix 4). 

The control panel for the Violence Generation Model is shown in Figure A5.2. The Control 

panel permits user selection of the violence sensitivity (Viol Sens; set at 0.1) and multiplier (Viol 

Mult; 0.0008) and the (AD POL Warn Thresh; 0.2) parameters. 

Affected

Disaffected

AF DF Ratio

AD Concern

AD POL Warn Thresh

Violence

Viol Sens

Total Viol AddViol

Dep Enh Viol Mult

Tot Casults

CasImpact

Affected Disaffected

Violence Generation Model

Figure A5.1. Implementation of the Violence Generation Model. The model 

calculates the level of violence assumed to correspond to the ratio of 

(Disaffected/(Affected + Disaffected)) of disaffected and total affected and disaffected 

individuals and violence sensitivity as described in the text. The model also 

generates enhanced deprivation (Dep Enh) based on the level of violence and total 

casualties caused by terrorist events. 

AF DF Ratio 0.28

0.20

0.00 1.00

U

AD POL Warn Thresh

0.10

0.00 1.00

U

Viol Sens

0.00080 Total Viol 39.55

0.00000 0.00100

U

Viol Mult

Violence Generation Model

Figure A5.2. The Violence Generation Model control panel permits user selection of 

the violence sensitivity (Viol Sens; set at 0.1), violence multiplier (Viol Mult; 0.0008), 

and the (AD POL Warn Thresh; 0.2) parameters. 

42 
APPENDIX 6: POLICY CYCLE MODELS CAN REPRESENT THE 

MANAGEMENT OF SOCIAL VIOLENCE AND ENTITY SECURITY 

POLICIES 

A Social Policy Cycle Model for the management of societal violence levels and a Security 

Policy Cycle Model for the management of entity security have been developed. Those models 

have been implemented in systems dynamics software and used in a series of preliminary 

studies. Details of the software implementation are presented below. Results of the studies are 

presented above. 

A Social Policy Cycle Model 

A Social Policy Cycle Model for the management of societal violence has been developed. Figure 

A6.1 presents implementation of the model in systems dynamics software. Input to the model 

represents policy concerns related to the impact of levels of affection and disaffection and related 

levels of violence. Those concerns trigger notional actions assumed to represent the formulation 

and implementation of social policy responsive to existing model-related conditions. These 

actions are followed by actions assumed to represent the evaluation and change of existing policy 

involving either the creation of a new policy and/or termination of an existing policy. Corruption 

is assumed to act by reducing the input to policy formulation and implementation-related 

activities and to reduce the input responsible for generating a new policy aimed at increasing the 

overall level of satisfaction within the notional population. 

Soc F&I Tot Soc Eval Tot Soc Chnge Tot

Soc Term

Soc New Pol Soc In F&I

Soc Pol Sensitiv

Soc Enab Soc Pol Eval

Soc Eval Inpt

Soc Chnge Inpt

Soc Pol Term

Soc Pol New

Soc Term Inpt

Soc New Inpt

AD Concern

Soc NewPolLoss

Soc CorrRte NewPol

Soc CorrLossNewPol

Soc F&I Corr

Soc Lost F&I

Soc F&I Corrupt

Social Policy Cycle Model With Policy-Related Corruption

Figure A6.1. Implementation of the Social Policy Cycle Model provides an 

environment for assessing the impact of Policy-Related Corruption on the 

production of new policies. 

Activities within the implemented model are triggered when the ratio of disaffected individuals 

compared with the total of affected and disaffected individuals (AD Concern) exceeds a userselected threshold (AD POL Warn) (Figure A6.1). Policy-related responses are also determined 

by the level of social-policy-related sensitivity (Soc Pol Sensitiv). Activities involve social policy 

formulation and implementation (Soc F&I Tot), evaluation (Soc Eval Tot), change (Soc Change 

Tot), new policy creation (Soc New Pol), and policy termination (Soc Term). These activities 

take place at user-selected rates: policy evaluation (Soc Eval Inpt), policy change (Soc Chnge 

Inpt), new policy (Soc New Inpt), and policy termination (Soc Term Inpt), respectively. 

43 
Corruption of the policy formulation and implementation processes takes place at a rate 

determined by the Soc F&I Corrupt parameter and corruption of the new policy takes place at a 

rate determined by the Soc CorrRte NewPol parameter. 

The control panel for the Social Policy Cycle Model, shown in Figure A6.2, permits the userselection of specific model parameter values, including social policy sensitivity (Soc Pol 

Sensitiv; set at 0.2), input rate to social policy evaluation (Soc Eval Input; 0.1), input rate to 

social policy change (Soc Chnge Input; 0.1), input rate to social policy termination (Soc Term 

Input; 0.1), input rate to new social policy (Soc New Input; 0.1), new social policy corruption rate 

(Soc CorrRte NewPol; 0.04), and corruption rate of social policy formulation and 

implementation (Soc F&I Corrupt; 0.04), for example. 

0.10

0.00 1.00

U

Soc Term Inpt

0.10

0.00 1.00

U

Soc Chnge Inpt

0.10

0.00 1.00

U

Soc Eval Inpt

0.10

0.00 1.00

U

Soc New Inpt

0.20

0.00 1.00

U

Soc Pol Sensitiv

0.04

0.00 1.00

U

Soc CorrRte NewPol

Soc CorrLossNewPol 9.74

Soc New Pol 1.70

0.04

0.00 1.00

U

Soc F&I Corrupt

Social Policy Cycle Controls

Figure A6.2. Control panel for the Social Policy Cycle Model With Policy-Related 

Corruption. 

A Security Policy Cycle Model 

A Security Policy Cycle Model for the management of entity security has been developed and 

implemented in systems dynamics software. Figure 24 presents the key features of the model and 

Figure A6.3 shows implementation of the model in systems dynamics software. Input to the 

policy cycle model represents policy concerns related to the effect of casualty levels generated 

by notional terrorist actions that are made possible by lowered security threshold conditions and 

availability of trained terrorists. In the model terrorist actions trigger policy cycle-based actions 

assumed to represent the formulation and implementation of security policy that are aimed at 

increasing the level of entity security. Increased security can prevent terrorist events from taking 

place. Corruption within the security policy cycle is assumed to reduce the ability of a 

government entity to increase security in response to increased amounts of terrorist activity. 

Security-related policy cycle model activities involve the formulation and implementation (Sec 

F&I Tot), evaluation (Sec Eval Tot), change (Sec Change Tot), new policy creation (Sec New 

Pol), and policy termination (Soc Term) (Figure A6.3). These activities take place at user44 
selected rates determined by user-selected security policy evaluation (Sec Eval Inpt), security 

policy change (Sec Chnge Inpt), new security policy (Sec New Inpt), and security policy 

termination (Sec Term Inpt) values, respectively. 

Corruption of the policy formulation and implementation processes takes place at a rate 

determined by the (Sec F&I Corrupt) parameter and corruption of the new policy takes place at a 

rate determined by the (Sec CorrRte NewPol) parameter. Typical settings of the user-selectable 

parameters for the Security Policy Cycle Model control panel are shown in Figure A6.4. 

Sec Concern

Sec F&I Tot Sec Eval Tot Sec Chnge Tot

SecTerm

Sec New Pol Sec In F&I

Sec PolSens

Sec Enab Sec Pol Eval

Sec Eval Inpt

Sec Chnge Inpt Sec Pol Term

Sec Pol New

Sec Term Inpt

Sec New Inpt Sec NewPol Loss

Sec CorrLoss NewPol

Sec F&I Corr

Sec Lost F&I

Sec F&I Corrupt

Sec CorrRte NewPol

TotATActEvnts

Security Policy Cycle Model With Policy-Related Corruption

Figure A6.3. Implementation of the Security Policy Cycle Model provides an 

environment for assessing the impact of the speed of policy cycle actions and the 

effect of policy-related corruption on the production of new policies and their effect 

on changing security levels and on the number of terrorist-related events and 

casualties. 

The control panel permits the user selection of specific model parameter values, including 

security policy sensitivity (Sec Pol Sensitiv; set at 0.2), input rate to security policy evaluation 

(Sec Eval Input; 0.1), input rate to security policy change (Sec Chnge Input; 0.1), input rate to 

security policy termination (Sec Term Input; 0.1), input rate to new security policy (Soc New 

Input; 0.1), new security policy corruption rate (Sec CorrRte NewPol; 0.1), and corruption rate of 

security policy formulation and implementation (Sec F&I Corrupt; 0.1). 

45 
Sec CorrLoss NewPol 9.68

0.10

0.00 1.00

U

Sec Chnge Inpt

0.10

0.00 1.00

U

Sec CorrRte NewPol

0.10

0.00 1.00

U

Sec Eval Inpt

0.10

0.00 1.00

U

Sec F&I Corrupt

0.10

0.00 1.00

U

Sec New Inpt

0.2000

0.0000 1.0000

U

Sec PolSens

0.10

0.00 1.00

U

Sec Term Inpt

SecLevel 89.62

Sec New Pol 0.50

TotATActEvnts 27

TotATEvnts 66

Sec Concern 1.00

Security-Related Policy Cycle Controls

Figure A6.4. Security Policy Cycle Model with policy-related corruption control 

panel. Settings indicate a sensitivity of 0.2 and policy cycle and corruption 

parameters of 0.1 (10 percent per time step). 

46 
47 

REFERENCES 

Davis David, Allison Frendak-Blume, Tia Wheeler, Clarence Worrell, and Alexander E. R. 

Woodcock, 2003. Implementation of the Conceptual Model of Counter-Terrorist Operations 

(CMCTO) as a Systems Dynamics Model: Technical Report 1: The Conceptual Model of 

Counter-Terrorist Operations. Fairfax and Arlington, Virginia: George Mason University. 

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