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Cumulative disruptions: interdependency and commitment escalation as mechanisms of illicit network failure

Michelle D. Fabiani & Brandon Behlendorf

To cite this article: Michelle D. Fabiani & Brandon Behlendorf (2020): Cumulative disruptions: interdependency and commitment escalation as mechanisms of illicit network failure, Global Crime, DOI: 10.1080/17440572.2020.1806825 To link to this article: https://doi.org/10.1080/17440572.2020.1806825

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ARTICLE Cumulative disruptions: interdependency and commitment escalation as mechanisms of illicit network failure Michelle D. Fabiani a and Brandon Behlendorfb aDepartment of Social Sciences, Homeland Security Program,DeSales University, Center Valley, PA, USA; bCollege of Emergency Preparedness, Homeland Security, and Cybersecurity, University at Albany (State University of ), Albany, USA

ABSTRACT ARTICLE HISTORY Disruptions can take many forms resulting from both internal and Received Rxx xxxx xxxx external tensions. How illicit networks fail to adapt to a wide range Accepted Axx xxxx xxxx of disruptions is an important but understudied area of network KEYWORDS analysis. Moreover, disruptions can be cumulative, constraining the Criminal networks; network possible set of subsequent adaptations for a network given pre­ failure; escalation of vious investments. Drawing from a multi-national/multi-year inves­ commitment; tigation of a prominent Chinese human smuggling network interdependency; human operated by Cheng Chui Ping (‘’), we find that the net­ smuggling; cumulative work’s failure was a product of two interrelated factors. First, efforts disruption to scale the network to meet increased demand made the network more interdependent, adding new members and increasing vulner­ abilities to internal disruptions. Second, internal and external dis­ ruptions during a shipment cumulatively constrained the network’s ability to adapt, forcing the network to escalate their commitment rather than abandon the transit. The results suggest network dis­ ruptions should be examined holistically to improve our under­ standing of network failure.

Introduction Network disruption is a central problem in the study of smuggling and trafficking net­ works. The removal of key nodes and subsequent network adaptation form the primary disruptive interaction between the state and illicit actors1. When pressure is applied, the removal of these nodes can substantially alter the organisation’s capabilities, depending on how the network adapts. For many, the fluid nature of the network itself (i.e. the ability to incorporate new actors into the network) allows them to rapidly adjust and continue operations2. In focusing on adaptation, however, illicit network resilience tends to be over­ emphasised, producing paradoxical conclusions. Adaptation in an illicit network is explained as both a response to successful interdictions (e.g. forcing new pathways, nodes, modes of transportation, etc.) and as a reason behind the interdiction’s failure3. For example, Bright & Delaney conclude that a network’s resilience resulted

CONTACT Michelle D. Fabiani [email protected] Brisson Hall 209, 2755 Station Ave, DeSales University, Center Valley, PA 18034 Supplemental data for this article can be accessed here. © 2020 Informa UK Limited, trading as Taylor & Francis Group 2 M. FABIANI AND B. BEHLENDORF from their adaptation to what would otherwise be termed ‘successful’ law enforce­ ment interdictions (i.e. arrest, prosecution, and sentencing of a central node).4 Thus, successful interdictions can produce successful adaptations, casting the network as resilient and flexible. In contrast, little is known about how illicit networks fail to successfully adapt, whether to external disruptions from law enforcement or internal interpersonal dynamics5. Qualitative approaches to criminal organisations, including Chinese humans smuggling groups6, focus on dynamics of illicit organisations (e.g. classifying types of groups based on their activities, goals, etc.), how different social-behavioural characteristics influence the organisation’s success (e.g. social embeddedness, trust, etc.). Quantitative approaches evaluate key structural changes, such as network fragmentation and resilience7. Yet, leaders overreach, distributors act irrationally, and transporters fail to fix broken taillights. Characteristics such as flexibility, resilience, and adaptability could be strengths or weak­ nesses, depending on the potential vulnerabilities introduced8. Examining network failure requires looking at both structural adaptations to disruption and the dynamic responses that led to those adaptations. The emphasis on understanding structural changes in the illicit network literature provides a complement to the focus on behavioural decision- making that dominates the qualitative literature. Moreover, most analyses (qualitative or network-based) ignore the potentially cumula­ tive aspect of disruptions. Studies on adaptation regularly rely on single points in time9, while ethnographic approaches address emergent behaviours across time. Yet, interven­ tions by law enforcement could lead to structural changes in the network that create opportunities for future interdictions. Leadership changes in response to interdiction can generate internal conflict, leading to operational errors that make the network more visible and vulnerable to law enforcement intervention. Thus, what is missing is an examination of suboptimal adaptations from both a historical and structural perspective. Including the potential for suboptimal adaptations allows for a more complete examination a network’s evolution in response to multiple interdictions and external disruptions, which may or may not become more resilient10. Similarly, combining historical and network approaches offers an opportunity to examine the process of network failure in more detail. To expand our understanding of how illicit networks11 can fail to adapt, we focus on a single case study of a Chinese human smuggling network operated by Cheng Chui Ping (‘Sister Ping’). We trace how the cumulative responses to internal and external disruptions not only increased the network’s vulnerability to interdiction, but ultimately led to its failure. We present one possible path to network failure, demonstrated via our mixed- method case study combining social network and historical analyses. Below is a brief review of the literature on network failure and criminal organisational decision-making, followed by overviews of the proposed path to network failure and the Sister Ping network. Data and methods are discussed in the next section followed by a detailed presentation of the findings. Lastly, we consider the implications and limitations of the results with respect to network failure.

Network failure and organisational decision-making Although fluid in nature, illicit network structures are shaped by both internal and external forces12. Covert networks need to balance security from law enforcement and GLOBAL CRIME 3 external disruptions with efficiency of internal operations. This requires a structure that is easily concealable, allows for rapid diffusion of information, and is robust to disrup­ tion. For example, Xu and Chen found that many covert or illicit networks exhibit structures with small-world properties, where members (‘nodes’) are highly clustered and connected to all others through a short average number of connections (‘edges’ – typically less than six)13. Clusters ease rapid information diffusion, making the network more difficult to disrupt as a single person removed from the cluster can be easily replaced14. Decision-making also plays an important role in the success of an illicit organisation. Though it is not possible to observe these processes directly, scholars have used both empirical and qualitative approaches to examine the inner workings of these groups. The illicit network literature has identified several characteristics of successful networks that work to their advantage in decision-making, including flexibility or fluidity adaptability and resiliency15. Flexibility refers to the ease with which membership, structure, and operations can change within a network. Relatedly, adaptability can be described as the ability for a network to change in response to pressure16. Resilience refers to ‘the ability of market participants to preserve the existing levels of exchanges between buyers and sellers despite external pressure aimed at disrupting the trade.’17 These concepts are interconnected – for a network to be adaptable, it must be flexible and networks that are resilient are often also adaptable and flexible18. Of these, flexibility – or the ease with which membership, structure, and operations can change within a network – is the most commonly discussed in the literature and often facilitates other characteristics19. For example, Williams identified that individuals will move in and out of an illicit network as they see fit, depending on the opportunities and needs for their services20. When new skills are required, networks can expand quickly and at low cost by leveraging existing members for new connections through what Kleemans calls the ‘social snowball effect.’21 Such flexible boundaries makes illicit networks able to navigate disruptive environmental changes, market conditions, or law enforcement interdictions22. Whether these characteristics provide an advantage depends on the type of commod­ ity or operation of the network. For example, many studies looking at these issues either focus on illegal drug markets or terrorist networks23. The decision-making processes in drug markets differ from a terrorist organisation, which in turn will differ from a human smuggling network. While a drug network can easily change where its operations take place24, a human smuggling network may be less moveable. There are a fixed number of ways a person can cross a border and if those options are compromised the network will be hard pressed to adapt successfully. Or internal decision-making may be more relevant for success than a network’s struc­ ture. Eilstrop-Sangiovanni and Jones note that characteristics such as flexibility, resilience, and adaptability could be strengths or weaknesses, depending on what vulnerabilities are introduced by these adaptations25. When faced with a disruption, adaptations such as leaders alienating others, distributors acting irrationally, or members going rogue are not ‘positive’ reactions. Little is known about how illicit networks fail to adapt to a disruption nor what such unsuccessful decision-making processes look like. Other studies of disrup­ tion highlight structural adaptations networks take in response to interdiction attempts. Morselli and Petit find that the seize-but-do-not-arrest strategy of investigation forced the Caviar drug network to decentralise and re-order, allowing law enforcement to see more 4 M. FABIANI AND B. BEHLENDORF of the players in the network and ultimately lead to the arrest of many participants26. While both Morselli and Petit’s work on the Caviar drug network and Malaviya and colleague’s analysis of multi-period network interdictions do focus on longer time peri­ ods, most of the disruption literature focuses on short time periods or isolated disruptions. The challenges of examining decision-making with network analysis are less prevalent in the literature on criminal organisations. Ethnographies and field interviews in this area provide insight into how these group’s see themselves and make decisions27. Adler’s study of drug dealers challenged popular stereotypes and explored their motivations28. By focusing on actions of groups rather than individual behaviours, these studies are able to draw parallels across organisational types, from mafia-like structures to transnational criminal organisations, that inform our understanding of how criminal groups operate29. For example, multiple studies have found by comparing across activities that illicit operations whose activities are largely transactional (e.g. weapons, human smuggling, drugs) may be more vulnerable to structural disruptions30. Indeed field interviews and archival analysis of Chinese human smuggling operations highlight the contractual nature of human smuggling, where snakeheads often try make deals one-on-one according to the opportunities present, and the resilience of these operations in the face of external disruption31. Drawing from interviews with 129 human smugglers in , Los Angeles, and Fouzhou (), Zhang found that not only did they see their operations as largely opportunistic, few considered themselves to be ‘bosses.’ Instead, they were individuals making business agreements with their friends32. Despite the focus on the dynamics of decision-making in smuggling and criminal operations, few studies in the qualitative literature examine ‘failure.’ Instances of ‘failure’ are easy to find33, and yet with few exceptions discussions of the causes of failure are rare34. This is in part because in focusing on group dynamics, these studies can examine decision- making outside of the context of whether the group ‘succeeded’ or ‘failed.’ Similarly, these studies often take an atemporal perspective to identify patterns in dynamics within and across groups. While important, doing so ignores the evolution of decision-making in response to specific disruptions and so limits what this literature can say about structural changes or the cumulative effect of disruptions on the dynamics they examine. To our knowledge, across both the illicit network or criminal organisational decision- making literatures there have been few published studies looking directly at unsuccessful decision-making and none that address network failure. Even the most current research looking at disruptions focuses on aspects other than network decision-making35. Similarly, despite a growing call in the literature to look at network and organisational decision-making evolution over time, no studies have yet sought to examine the cumu­ lative effect of disruptions over time. The current study seeks to address these gaps by examining network failure and cumulative disruption in a human smuggling network through a combined network- and historical-analysis of disruption and failure. To do so, we propose one possible path to failure an illicit network might take. We then demon­ strate this path through a case study of a human smuggling network.

A proposed path to network failure Illicit networks are adaptive, complex organisations and their demise is likely to result from multiple factors36. We propose one path to network failure through the cumulative GLOBAL CRIME 5 influence of disruptions to the network (see Figure 1). When targeted at a network, the effects of disruption are not independent. Rather, they may trigger increasingly poor adaptations by the network through two mechanisms: interdependency and escalation of commitment. Disruptions that require structural additions increase network interdependency, intro­

Figure 1. A proposed path to network failure. ducing 1) new pathways for suboptimal decisions to affect network decision-making and 2) greater uncertainty from new ‘unvetted’ nodes. Indeed, as Erickson suggests, the risk introduced by disruption can compel recruitment through pre-existing relationships and networks37. Subcontracting with another group in response to a disruption adds a ‘vetted’ key actor to decision-making processes through which many new ‘unvetted’ actors join the criminal enterprise. This creates a ‘dyadic cartwheel’ structure, where agreements are made between two individuals who may each have their own ego- network (see Figure 2)38. Such a structure maximises efficiency by avoiding redundancies and increasing individual accountability, making the ‘cartwheels’ both interdependent for survival and potentially vulnerable to external disruptions and structural changes. This combination can both increase the network’s overall risk tolerance and limit viable options for future choices in their enterprise.

Figure 2. Example of ‘Dyadic Cartwheel’ Network Structure. Example based on Zhang (2014)’s original description of this network structure. 6 M. FABIANI AND B. BEHLENDORF

A second mechanism that may interact with or operate alongside growing interde­ pendency is an escalation of commitment39. Faced with the perception of fewer viable options in response to pressure, network leadership may become less willing to consider alternatives to their current course of action40. This encourages them to continue their current path and double down on poor decisions. Together, these mechanisms accelerate and create a cumulative disruption/poor adaptation process, which in turn can culminate in network failure. Understanding both mechanisms and how they interact to facilitate cumulative disruption contributes to a theoretical understanding of why some illicit networks are maladaptive to disruptions. As we discuss next, these mechanisms ulti­ mately led to the downfall of the Sister Ping network.

Overview of the sister ping network Cheng Chui Ping (‘Sister Ping’) operated a transnational human smuggling network from 1982–2000, despite near constant visibility to law enforcement41. Over 18 years, Sister Ping was estimated to have smuggled more than 3,000 Fujianese immigrants into the , grossing nearly 40 USD million42. Headquartered in New York City and her small village of Shengmei, Sister Ping established herself as a reliable and successful businesswoman to the communities in both China and . To establish her network, Sister Ping relied on personal and family connections on both continents, allowing her to use a combination of international and domestic routes to evade law enforcement43. Drawing primarily from the Fujian province in the People’s Republic of China (‘China’), those she smuggled were then required to work off their debt in her family’s convenience store in New York City44. Figure 3 provides an overview of the key events of Sister Ping’s network from 1990–1993, including five key disruptions that cumulatively led to the failure of Sister Ping’s network. By early 1992, Sister Ping’s network had reached its height of activity, including: associates and subcontractors in more than nine countries, a money laundering operation, a restaurant, a large convenience store, a travel agency, and a real estate company45. These assets were owned by Sister Ping’s family, operated by close friends, and staffed by recent arrivals46. They also provided a legitimate front through which she could launder proceeds from human smuggling and coordinate an increasing number of subcontractors47. In February 1992, the interdiction of corrupt officials at a key airport transit route from to the United States required Sister Ping and other snakeheads (human smug­ glers) to seek alternative routes out of Fujian province. As a result, Sister Ping financedand coordinated the smuggling of nearly 300 undocumented immigrants from China to New York via the transport (see Figure 4). The route for the transport called for a four-month journey to circumvent security checkpoints; however, the final journey required two ships (the Najd II and later the Golden Venture) and lasted over a year48. Originally, the snakeheads planned to rent a fishing trawler that would transport the immigrants to the East Coast of the United States, where the Fuk Ching gang would offload them. Setting out from Thailand in February 1992 in a rented ship named the Najd II, the ill-maintained vessel broke down in Mombasa, eight months later49. A smaller vessel (the Tong Sern) was purchased in China in January 1993 and sent to rescue those GLOBAL CRIME 7

Figure 3. Key events in sister ping’s network 1990–1993.

Figure 4. The golden venture transport. 8 M. FABIANI AND B. BEHLENDORF stranded in Mombasa. While en route, the network added more customers in Pattaya, Thailand and renamed the vessel the Golden Venture50. The network transferred the customers to the new vessel in early May 1993 and departed Mombasa for the East Coast of the United States51. While en route to the off-shore rendezvous, a rivalry between the leader of the Fuk Ching gang (Guo Liang Qi) and his second in command (Dan Xin Lin) in New York City led to an attempted assassination and a subsequent shootout that left all members dead, arrested, or in hiding52. Thus, the Golden Venture could not be offloaded by the Fuk Ching Gang as planned at any of the rendezvous locations (North Carolina; Nantucket, RI; Far Rockaway, NY). Crewmembers decided to beach the ship in Far Rockaway and abandon it; however, the ship could not see the light signal on the beach and ran aground on a sandbar, killing 10 migrants53. Coincidentally, the Coast Guard were present when the ship ran aground, leading to the arrest and detention of everyone onboard (or who survived the swim to shore). Figure 5 summarises the important actions within the transport journey.

Figure 5. Key decision points in Najd II/golden venture transport.

While all those who survived were arrested and detained, Sister Ping fled to Shengmei, China. Remaining associates were arrested between 1993 and 2000; Sister Ping was active until her 2000 arrest in International airport attempting to visit her son. She fought extradition from 2000–2003, underwent trial from 2003–2005, and in 2006 was convicted on conspiracy to engage in smuggling, trafficking in ransom proceeds, and money laundering54. In total, Sister Ping was sentenced to 35 years and died in prison on 27 April 2014. GLOBAL CRIME 9

Data Data on Sister Ping’s network were coded from open sources of information, including over 3,000 pages of court records (wiretap transcripts, statements by network members, and testimony by Sister Ping herself), journalistic investigations of the network’s activities (including a book on her network by Patrick Keefe)55, and news reports discussing the network. Combined, these sources were used to code structured data on the network and its operations, including identifying individuals, illicit and licit relationships, and events/ transactions conducted by the network (smuggling journeys, financial agreements, etc.). Information was cross-referenced to verify the validity and accuracy of the data. Relational and nodal attribute data were coded from analysis of the court records and Keefe’s book and then triangulated with other open source information to the extent possible. Open sources and news stories provided additional contextual information on the network’s activities. Where information conflicted, court records and then the book- length manuscript were prioritised in that order. Initial coding identified 65 individuals that could be traced to Sister Ping’s operation from 1982 to 2000. Individuals who were identifiedas being involved in one of Sister Ping’s legal or illegal business operations from 1990 to 1993 were coded for directed relationships. Attribute data for network members were also collected for individual functions based on their primary activity in the network across all time periods. Sister Ping is designated as the ‘manager’ of the network as she oversaw daily operations of the network and recruitment. The ‘partner or subcontractor’ function was assigned to key members of smaller smuggling operations and other criminal groups (e.g. the Fuk Ching Gang) who frequently worked with Sister Ping. ‘Brokers and coordinator’ functions were assigned to nodes that facilitated business deals between management and different groups. ‘Peripheral’ actors included document forgers, customs officers, and any other actors who participated in a limited capacity in the network (usually on a local rather than international level). Nodes in the ‘enforcer’ category maintained the payment plans of the immigrants travelling through the network. Finally, ‘Transportation’ nodes included nodes that that were involved in physically transporting immigrants along the network, includ­ ing offloading people from boats to transport to the shore. The final network analysed here includes only those who were involved in Ping’s legal or illegal business operations from 1990 to 1993, including: facilitating the transportation of illegal immigrants, enforcing payment of debt by immigrants, moving money between differentdeposits, operating the business ‘fronts,’ and coordinating the activities of others involved in the above activities. Of the 65 individuals originally identified, only 46 operated in these capacities during the three-year period56. From 1990 to 1993, we identified five disruptions to the network (see Figure 3) and using them as a guide, created seven time periods: T1 (1990-1991 – before first known disruption), T2 (January – February 1992), T3 (March – December 1992), T4 (January – February 1993), T5 (March – April 1993), T6 (May 1993), and T7 (June 1993 – after last known disruption). Directed relationships57 were coded for all seven time periods, where a relationship between actors is defined as there being evidence of a transaction (e.g. financial, coordination of action of another, etc.) or of an acknowledged personal tie (friendship, familial, rivalry)58 between two actors in the service of the illegal or legal business operations of the network. Relationships were coded with beginning and end 10 M. FABIANI AND B. BEHLENDORF dates with the first mention of the relationship marking the start59. Each relationship was coded as a separate edge with the earliest date available (N ¼ 197edges), meaning two individuals could have multiple edges connecting them at different times. For example, if A paid B to move people between locations from 1990 to February of 1992, then a directed relationship would be coded from A to B for both T1 and T2. As the network evolved, its membership fluctuated from 11 actors in the beginning to the peak of 37 actors and then decreasing to 23 towards the end (see Table 2 below). To ensure that the network’s evolution could be traced, each time period represents a snapshot of the entire network at that time. Since illicit networks actively seek to conceal their activities and those involved, the potential for missing data exists. Further, court transcripts and media accounts empha­ sised Sister Pings familial connections and the smuggling operation itself. Indeed, as demonstrated in Table 1, transportation is the largest role in the network (34.78%), followed by peripheral actors and brokers/coordinators (19.57% each). However, given the volume of information available on the network, it is less likely that any key or influential actors have been excluded.

Table 1. Distribution of functions in sister ping network 1990–1993. Function in the Network Count Management 1 Partner/Subcontractor 3 Broker/Coordinator 9 Transportation 16 Enforcers 7 Peripheral Actor 9

Methods We use both network analytic techniques and historical analysis to better understand the decision-making dynamics over the time period and in response to disruptions. Combining these two approaches allowed for a more nuanced analysis of an illicit network that can speak to both patterns in decision-making and structural changes over time60. We first examined historical documents to identify possible patterns in behaviour that might explain the network’s collapse during this time. This included plotting a detailed timeline of events, including law enforcement actions against the network, exogenous processes (e.g. Tiananmen Square), and the actions of key actors in the network. Based on this analysis, we identified the five disruptions in Figure 3 and the resultant organisational responses. These patterns revealed a potential path to network failure through the interaction of two mechanisms discussed above – interdependency and escalation of commitment – and how cumulative disruptions can interact with both to lead to failure. Second, we used network analytic techniques to evaluate changing power dynamics at the individual level and structural changes at the network level that illustrate interde­ pendency and escalation of commitment. Third, we combined the two approaches to GLOBAL CRIME 11 illustrate the network’s responses to the structural changes and how interdependency and escalation of commitment interact in the face of cumulative disruptions to create a path to network failure. Using this mixed methods approach was essential to under­ standing how these mechanisms presented and interacted in the Sister Ping network to produce cumulative effects over time. Looking solely at network analysis would provide a limited view of the dynamics within the network and how the network responded to adaptations over time. Similarly relying only on qualitative analysis would ignore structure changes in the network and among key actors. As such, evidence for interdependency is derived from both network- and individual- level measures, contextualised by relevant historical examples. At the network level, interdependency may be reflected in an increased number of ties between nodes or changes in the network structure as a whole (e.g. fragmentation). Fragmentation mea­ sures the proportion of nodes that cannot reach each other at any given time61, indicating the network’s cohesion, or lack thereof. At the individual level, interdependency may be reflected in changes in power and influence in the network. Such changes can be demonstrated through two measures of nodal centrality – degree and betweenness. Degree centrality measures which nodes have the most relationships to other actors in the network and so are the most ‘active.’62 For a directed network, centrality is calculated both for who receives the most relationships (in-degree) and who initiates the most relationships (out-degree). Betweenness centrality identifies central actors ‘on whom others are locally dependent.’63 Evidence for escalation of commitment derives from a combination of network- level measures like average distance and historical analysis of the details of the network’s decision-making. The average distance between two actors reflects the ease with which information can travel through the network64. Faster flows of information (i.e. shorter distances) could support greater resilience by allowing changes in course of action to be communicated quickly. By examining the flow of information, patterns of decisions, and adaptations by the network in response to disruption we see both opportunities for alternative paths and the points at which commitment to the failing course escalates. The evidence for the cumulative process leading to failure comes from the qualitative comparison of network responses to adaptation and how interdependency and escalation of commitment interact in this network over time.

Restructuring, decentralising, and increasing interdependency We first consider the interplay between network disruptions and the resulting interdepen­ dency. The five disruptions experienced by the network from 1990 to 1993 did not indivi­ dually cause it to fail65. Rather, each disruption cumulatively altered available opportunities, prompting network adaptation. In each case, Sister Ping’s network responded in two ways: (1) restructuring the network and (2) decentralising decision-making among key actors among key actors (e.g. Sister Ping and Guo Liang Qi). These structural changes resulted in increased interdependency and affected the efficiency of communication and decision- making. We explore each of these dynamics in detail below and highlight them in Figure 6. 12 M. FABIANI AND B. BEHLENDORF

Figure 6. Network responses to disruptions.

Restructuring through expansion From 1990 to 1992, Sister Ping faced two key exogenous influences which resulted in restructuring her network (see Figure 7). Changes in demand (1990–1991) and law enforcement interdiction (1992 – Disruption #1) both encouraged expansion and the network grew 345%, from 11 to 37 actors and from 32 ties between actors to 158, by the end of 1992 (see Table 2). Combined, these periods of expansion slowly increased the interdependency in her network, initially by scaling through subcontracting and then by expanding the number of connections among lesser actors.

Table 2. Overview of changes in network size (nodes and directed edges). Network Size Time Period Nodes Ties T0 (<1990) 11 32 T1 (1990–1991) 35 142 T2 (Jan – Feb 1992) 37 156 Disruption #1 ( Airport Crackdown) T3 (Mar-Dec 1992) 37 158 T4 (Jan – Feb 1993) 37 143 Disruption #2 (Hit ordered on Dan Xin Lin) Disruption #3 (Pattaya Interdiction) T5 (Mar – Apr 1993) 36 138 T6 (May 1993) 36 138 Disruption #4 (Teaneck Massacre) T7 (June 1993) 23 65 Disruption #5 (Golden Venture Interdiction) End 11 29 GLOBAL CRIME 13

(a) Sister Ping’s original network (11 nodes, 32 ties)

(b) Network Expansion #1: 1990 – 1991 (35 nodes, 142 ties)

(c) Network Expansion #2: Jan – December 1992 (38 nodes, 163 ties).

Figure 7. (a-c) Network expansions. Node shape indicates its role (triangles – partner/subcontractor, diamonds – coordinators/brokers). Nodes and relations added during these expansions are indicated in grey. Note since the expansion took place over two analytic time periods (T2 and T3), the third panel (c) shows the collective number of nodes and ties. 14 M. FABIANI AND B. BEHLENDORF

The first period of expansion occurred in response to the 1989 Tiananmen Square protests and subsequent government repression, encouraging many people to flee China by 1990. Finding a cooperative legal process for asylum in the United States, the push towards migration created both a large demand for Sister Ping’s services and an unpar­ alleled business opportunity. To remain competitive, Sister Ping’s network needed to scale quickly. In 1990, Sister Ping responded by contracted Guo Liang Qi, the leader of the Fuk Ching gang in New York City, for the gang members to enforce timely repayment for transit post-arrival66. Then, in 1991, the gang expanded their role in the network by offloading migrants from ships in international waters and transporting them to safe houses in New York City. The dyadic structure created by subcontracting with the Fuk Ching Gang enabled Sister Ping to scale quickly, with minimal redundancies, and created a structural interdependency between both groups. The second period of expansion occurred in response to an international effort between Thai law enforcement and the United States to reduce human smuggling through the Bangkok International Airport in Thailand67. In February 1992, the joint effort randomised screeners at the Bangkok airport and conducted random sweeps of the city to combat corruption and flush out smugglers (Disruption #1 – see Figure 3)68. In response, Sister Ping and other smugglers stopped using the Bangkok airport completely, creating a bottleneck of people waiting to be moved and increasing the vulnerability of their networks to disruption. Bypassing the airport required adding new routes and modality, as well as new personnel with experience in the new methods. Ping and Guo Liang Qi were approached by an aspiring about a plan to use a cargo ship to relieve the bottleneck. Based on her experience using small boats to transport people across the Niagara River69, she agreed to partner on the venture along with Qi and a few other snakeheads. Yet again, Sister Ping’s network expanded, increasing her interdependency with partners and subcontractors outside her initial operations. Though the two expansions facilitated a successful adaptation by Ping’s network in the short term, they ultimately made the network more vulnerable to subsequent disruption in the long-term. The dyadic structure created by scaling through subcontracting resulted in an immediate decrease in the degree of fragmentation in the network (see Figure 8 – from 18.2% of nodes unable to reach each other to 8.6% of nodes) at the same time as it created a fourfold increase in the number of edges between nodes.

Figure 8. Degree of fragmentation over time. Vertical bars indicate when disruptions occurred relative to changing levels of fragmentation. GLOBAL CRIME 15

However, the added connections also altered the power dynamics at the individual level (see Figure 9), decentralising the decision-making in the network and increasing the interdependency among actors. Beginning in 1993, the risk of fragmentation increases steadily, suggesting that while scaling the network was effective in the short-term, it increased the network’s vulnerability to disruption in the long-term. In particular, as each new decision-maker brought their own network, Ping became more dependent on their judgement. Qi also had considerable autonomy over the implementation of the Fuk Ching Gang’s assigned tasks, and thus made Sister Ping’s original network more reliant on members they could not control. The combination of group decision-making and subcon­ tractor autonomy increased the network’s vulnerability to internal conflict and splintering70.

Decentralising decision-making Scaling the network impacted both its overall structure and Sister Ping’s centrality in the network. Though the network did not decentralise (see Table 2), the number of key actors increased and impacted Sister Ping’s central position (see Figure 9). It is not surprising to see a key actor’s individual centrality decline as the size of their network grows. However, the trend of Ping’s in-degree and out-degree measures over time suggest that even while her relative importance declines (in-degree), her reliance on others increases (out-degree). Similarly, Figure 10 shows that Sister Ping’s centrality relative to both her subcontractors and brokers declines as her network increases. Combined, these measures indicate a dispersal of centrality among subcontractors and a slow increase in interdependence, firstbetween Ping and other partners (like Guo Liang Qi) and then with a broader group of contractors more generally. This is reflected their increased involvement in day-to-day operations and the increased decision-making authority Sister Ping gave them. By the time of the Golden Venture transport, approximately one third of the network (34%) had a voice (with varying degrees of weight) in the decision-making process.

Figure 9. Normalised degree centrality measures 1990–1993 for sister ping.

This can be seen in two key examples (see Figure 6) – in February 1992 (Disruption #1) and in May 1993 (Disruption #4). In response to the joint Thai and U.S. crackdown of the Bangkok Airport in February 1992, Sister Ping’s network added new partners, decreasing her control in several ways. First, Sister Ping agreed to the proposed Golden Venture plan brought to her by an aspiring snakehead with his own connections outside her network. 16 M. FABIANI AND B. BEHLENDORF

Figure 10. Betweenness centrality measures 1990–1993 for key actors by function.

Second, to ensure the success of this plan, Sister Ping made her former subcontractor Guo Liang Qi a partner in the venture and her network. These two decisions diffused the network’s decision-making from primarily Sister Ping to a group of partners, each with their own interests and agendas. Indeed, though involved in initial planning of the Golden Venture, neither Ping nor Qi took lead on the plan. The second example of decentralisation occurred towards the end of the Golden Venture transport in May 1993, when all remaining Fuk Ching gang members were arrested in the aftermath of a shootout. With the gang eliminated, there was no one to offload the Golden Venture at its designated rendezvous. Until this point, all decisions about the transport were made by the snakeheads in New York and one key partner on the transport providing information to the group. With no ability to offload the ship, the onshore snakeheads told it to turn around and go back to Mombasa. The key partner on the Golden Venture instead led a mutiny, took control of the ship, and decided to beach it, forcing Sister Ping and her snakehead partner to attempt to help the ship successfully beach itself somewhere remote. The elimination of a group of essential nodes in the network forced a confrontation of network members and resulted in the decision-making autonomy shifting even further from Sister Ping. GLOBAL CRIME 17

The effects of increased interdependency in the network The success of Sister Ping’s operation became deeply dependent on the capabilities (and failures) of those with whom she partnered. Sister Ping’s attempt to restructure her network made her immediately more efficient but ultimately less secure and the net­ work’s ability to function was severely crippled after an internal conflict fractured the Fuk Ching gang (Disruptions #2 and #4). At the end of 1992, Dan Xin Lin (second in command within the Fuk Ching gang) defected with several gang members to Philadelphia in response to unequal pay distribution in the gang, creating a rivalry between the two groups. In January of 1993, Qi ordered that Lin be assassinated to eliminate the threat he posed to their operations (Disruption #2 – see Figure 3). This resulted in a shootout in New York City that left several gang members injured, but Lin unharmed. Concerned about retribution, Qi fled to China, leaving his family to maintain the gang’s activities and presence. Four months later (May 1993), when Lin learned the whereabouts for Qi’s family safe houses (in Teaneck, New Jersey) he and his associates massacred the rest of the remaining Fuk Ching gang members in retribution for the attempted hit (Disruption #4 – see Figure 3). Almost immediately, Lin and all but one of the defectors were arrested. Both the attempted assassination of Lin and the subsequent massacre in Teaneck removed the entire Fuk Ching gang from Sister Ping’s network by May 1993 (see Figure 11). In the aftermath of this conflict, the network’s size shrank to 24 actors, and 34.8% of nodes could not reach each other. Many of the disconnected nodes performed essential transportation and liaison functions in the network. Overall, the increased interdependency among key decision-makers substantially increased the network’s vulnerability to subsequent disruption and contributed to its failure.

Figure 11. Teaneck Massacre. Clear nodes indicate gang members that were arrested or killed. The dotted line represents closed lines of communication after Guo Liang Qi went into hiding. After May of 1993, the network contained 23 nodes, 68 ties, and a 36.8% fragmentation level.

Escalation of commitment as a response to cumulative disruptions In addition to restructuring and decentralising decision-making, the increasing frequency of disruptions cumulatively restricted the network’s flexibility and ability to adapt, 18 M. FABIANI AND B. BEHLENDORF escalating the network’s commitment to the status quo (see Figure 6). The first two disruptions are spread out – the first occurring two years into the time period (in February 1992) and the second a year after that (in January 1993). The next three occur in much closer proximity, with less than four months between each disruption (see Figure 3). While the five major disruptions may not have individually led to network failure, their sequential and rapid nature made communication less efficient (see Table 3), particularly during the Golden Venture shipment.

Table 3. Changes in flow of information in the network. Network Size Time Period Nodes Ties Average Distance T0 (<1990) 11 32 1.800 T1 (1990–1991) 35 142 3.012 T2 (Jan – Feb 1992) 37 156 2.384 Disruption #1 (Bangkok Airport Crackdown) T3 (Mar-Dec 1992) 37 158 2.369 T4 (Jan – Feb 1993) 37 143 3.116 Disruption #2 (Hit ordered on Dan Xin Lin) Disruption #3 (Pattaya Interdiction) T5 (Mar – Apr 1993) 36 138 3.140 T6 (May 1993) 36 138 3.140 Disruption #4 (Teaneck Massacre) T7 (June 1993) 23 65 2.527 Disruption #5 (Golden Venture Interdiction) End 11 29 1.370

The average distance between network members initially decreases from 3.012 in the first time period (1990–1991) to 2.369 in the third time period (March – December 1992). This reflects the increased communication and interdependency in the network after it scaled. However, the distance increases over the course of the next three time periods (January 1993 – May 1993), peaking in May 1993 at 3.140. This is a substantial change over a relatively short period of time, suggesting that communication quickly became more difficult in the network. Indeed, by the beginning of 1993, the network had already faced challenges in communicating with the crew and passengers on the Tong Sern, which was stranded in Mombasa. These challenges in communication coupled with the increasing frequency of disrup­ tions also shortened the timeline for the network to respond, providing fewer possible options for adaptation. Initially, the network responded with proactive changes designed to shield the network from future disruption – restructuring, changing modes of trans­ portation, and decentralising the decision-making. The time between the first two dis­ ruptions allowed for these kinds of changes to take place. However, as disruptions increased in frequency, the network opted for more reactive changes to salvage its operations. This can be seen in the increasingly risky decisions made to try and save the Golden Venture transport. The network escalated its commitment to the transport at any cost. Figure 5 identifies key decision points for Sister Ping’s network in the Golden Venture transport. At each point, the network could have abandoned their current course of action. Instead, the cumulative nature of disruptions constrained the network’s decision- GLOBAL CRIME 19 making, increased their tolerance for risk, and escalated their commitment to continuing the journey71. First, the initial vessel (the Najd II) broke down in Mombasa, Kenya, stranding over 200 passengers. The network’s significant investment in this mode of transportation had momentarily failed while they continued to face a bottleneck of passengers in Thailand. Further, the arrival of several hundred Chinese immigrants stranded in Mombasa on route to the United States directed the attention of law enforcement to the transport’s route72. The pressure of clearing the bottleneck, the desire to protect their investment, and the threat of visibility to law enforcement all constrained the available viable options. Purchasing the Tong Sern in Thailand and continuing the transport quickly after a stop in Pattaya to pick up additional passengers reflects an adaptation to attempt to re-secure the network’s operations while recouping some losses with extra customers. Second, Thai authorities interdicted the network’s attempt to pick up the additional passengers in Pattaya, arresting all but the 70 that made it to the ship (Disruption #3). The authorities had learned of the potential smuggling effort and tried to prevent as many people from boarding the Tong Sern as possible. Though law enforcement had contacted individuals within the network before, it was for isolated incidents that could not be tied to the larger network and took place across multiple jurisdictions (including both U.S. and Thai officials across local, state, federal, and international jurisdictions) who did not communicate with each other73. In this case, the arrests exposed more of the network directly to law enforcement, leading to the first successful interdiction of the network74. Without the pressing need to rescue the passengers stranded in Mombasa and to recoup money on both groups of passengers, the network could have suspended operations for a short time to avoid additional law enforcement attention. Instead, with so much already invested and a heightened vulnerability to interdiction, Sister Ping and her partners opted to continue the transport. To preserve the security of the network, the Panama-registered Tong Sern became the Honduras-registered Golden Venture en route to Kenya75. Unfortunately for the network, media outlets and U.S. law enforcement had already connected the Pattaya interdiction with the passengers stranded in Mombasa76. Both events escalated commit­ ment to the current course of action, resulting from constraints on decision-making stemming from the Najd II failure. It was not until the Fuk Ching gang rivalry and the Teaneck Massacre (Disruption #4), however, that the decentralised decision-making within the network rapidly increased their tolerance for risk, escalating even further their commitment to an (ultimately) failed course of action. The consequences of the rivalry affected the entire network, crippling its ability to complete a transport by ship and significantly reducing the network’s resilience to further disruption. As indicated in Figure 5, the Golden Venture arrived at the first rendezvous location only to be informed that the gang was in turmoil and unable to assist. This triggered a complete reorganisation of decision-making within the network. Network members sent to act as both enforcers and crew members took control of the decision-making process from Sister Ping and her partners (see Figure 10), signalling Ping’s final loss of control of the operation77. Continuing to the second rendezvous increased the risk of further discovery by law enforcement and the decentralisation of decision-making increased the likelihood of additional risky behaviour. By deciding to continue, those on the ship escalated their 20 M. FABIANI AND B. BEHLENDORF commitment to an already suboptimal course of action and set a new threshold for what was an acceptable level of risk. The Teaneck massacre escalated the commitment to finishing the transport by further constraining the number of viable decisions for those onboard. Prior to losing their ability to offload, they could have turned the ship around or altered their plans. However, the increasingly constrained, risky, and decentralised deci­ sion-making at this point committed the network to what was by then a failed course of action. By the time the ship beached, Sister Ping and Guo Liang Qi’s centrality in the network had declined severely, while that of those onboard the Golden Venture increased (see Figure 8 above), reflecting the change in power in the network and its ultimate commitment to a suboptimal course of action. At each decision point in Figure 6, the network’s reactionary decisions increased its tolerance for risk. In turn, this increased the network’s vulnerability to further disruption and escalated commitment to the Golden Venture transport. The interdependency in the network created through responses to earlier disruptions decentralised the decision-making, limiting the ability of any one actor to veto a decision. The increased frequency of disruptions made it difficult to implement any responses designed to be proactive nor communicate efficiently. As such, the cumulative nature of the disruptions constrained the perceived available options, facilitating the escalation of commitment and ultimately, the network’s failure.

A proposed path for network failure through cumulative disruptions The case study of Sister Ping’s network exemplifies one possible model for the effect of cumulative disruptions on network failure. Importantly, when looking at the cumulative effect of disruptions on a network, a single disruption may be both the result of prior events and a contributing factor or trigger for future events. Both interdependency and an escalation of commitment were the result of prior network responses to disruption. Separately, they may lead to successful outcomes, including a more resilient criminal network. In the case of Sister Ping, however, each disruption led to greater interdepen­ dency and/or a further escalation of commitment, constraining decision-making. This process can be clearly seen in the decisions made by the network in response to each disruption. Figure 12 outlines how each disruption builds off previous disruptions and the mechanisms of interdependency and/or an escalation of commitment. Once Sister Ping became interdependent with subcontractors (especially the Fuk Ching gang), the network’s decision-making processes became increasingly decentralised. Sister Ping’s decrease in autonomy over time (as measured by betweenness centrality) is matched by the increasing importance of her subcontractors and partners, especially Guo Liang Qi. Each new disruption further escalated commitment, decentralised decision- making, making the network more vulnerable to disruption. By the interdiction of the Golden Venture (Disruption #5), the tolerance for risk was so high that beaching the ship was the most viable option. The presence of the U.S. Coast Guard where the crew attempted to beach the ship was coincidental. Yet, if it had not been for the series of disruptions that increased the escalation of commitment and its interaction with interdependency, the network may not have been vulnerable to law enforcement interdiction. Instead, the interaction of these two mechanisms through the series of disruptions cumulatively led to the failure of Sister Ping’s network. GLOBAL CRIME 21

Figure 12. Cumulative effect of disruptions to sister ping network.

Discussion The above analyses speak to and contextualise our understanding of Chinese human smuggling organisational structures. Sister Ping’s network exhibited a partial dyadic cart­ wheel structure – Ping’s and Qi’s ego networks or ‘cartwheels’ were connected by multiple relationships (see Figure 7). Even with these extra connections, the network’s minimal redundancies and insular structure should have provided it with maximum security and insulation from removal of key nodes78. Yet, the loss of the Fuk Ching Gang put the network at great risk and contributed to its failure. It may be that in the absence of other disruptions and constraints on the network’s decision-making, this loss would have been inconvenient but not insurmountable. Or it may be that the contractual nature of human smuggling, which promotes a cartwheel-like structure with minimal redundancies79, also puts these networks at greater risk of structural disruptions. Such a structure may be protected from the removal of a single node, but not the elimination of an entire cartwheel. 22 M. FABIANI AND B. BEHLENDORF

The findingsof this analysis also speak to the wider literature on illicit network failure in three areas: the complexity of network disruptions, the evolution of responses to disrup­ tion, and implications for law enforcement efforts. The current literature often frames criminal networks as stable in the absence of law enforcement and disruptions to the network as the removal of key nodes. This case study demonstrates that the process of disruption may be more complex than previously thought. Instead of key node removal, the network was disrupted by a combination of exogenous and endogenous pressures that had a cumulative effect on the evolution of the network. The cumulative nature of the disruptions shaped the decision-making process of the network as well as the choices made by law enforcement in response to network attempts at adaptation. Second, both the structure and decision-making processes of networks evolve in response to disruptions (singular and cumulative). For example, initial disruptions in Sister Ping’s network resulted in adaptation through adding connections to new actors and networks. This change to the network’s structure resulted in an interdependency between actors and a new process for making decisions. The network’s vulnerabilities to future disruption also changed as a result of this increased interdependency. This is consistent with findings from other recent work on network evolutions. For example, Bright and colleagues found that their network’s operations changed and evolved in response to pressure from law enforcement80. Though they focused on the activity and structure of the network, these changes reflect evolution in the priorities of the network’s decision-making process. Finally, this case study demonstrates that it matters both who is targeted for interdiction in a network and how long they are targeted for. The extant literature has largely focused on identifying the optimal actor to target for interdiction or removal, reflecting the shift towards more efficiently allocating limited resources81. Though the results here do demon­ strate that removing key actors (in this case Guo Liang Qi) can contribute significantly to network failure, they also suggest that there may be a benefit to considering longer periods of targeting. Interdiction efforts of criminal networks would ideally be efficient process requiring a single attempt, the adaptability and resilience of many networks makes such an approach difficult. The case of Sister Ping demonstrates that exerting pressure on the network over a longer period of time with disruptions from multiple angles can lead to cumulative effects that more permanently damage the operations of the network.

Conclusion This study looks at the effect cumulative disruptions have on an illicit network’s vulner­ ability to failure. A network’s characteristics can be either advantageous or disadvanta­ geous, depending on the context in which they are utilised and the dynamics of the organisational decision-making. Because networks are an evolving system of constraints, dependencies, and commitments, a series of disruptions (internal or external) can have a larger collective impact than each would individually. We argue that cumulative disruptions can significantly impede the ability of an illicit network to operate, and potentially lead to failure, through two inter-related mechanisms: interdependency and commitment escalation. After a disruption, as networks expand or reconfigure, they increase their interdependency, decentralise decision-making and increase the network’s overall tolerance for risk. These in turn encourage an escalation of commitment to a chosen course of action. An escalation of commitment may also arise from constraints GLOBAL CRIME 23 placed on the network by responses to previous disruptions. Combined, increases in interdependency and an escalation of commitment increase the vulnerability of the network to further disruptions. Each subsequent disruption builds on the effects of previous ones, significantly impairing the network’s ability to function. Looking at the evolution of Sister Ping’s network, cumulative disruptions led to net­ work failure through the combination of interdependency and an escalation of commit­ ment to a failed course of action. The events of Tiananmen Square provided a context that shaped the decisions made within Sister Ping’s network over the next three years. The increase in supply of people seeking to leave the Republic of China forced Sister Ping to rapidly expand all aspects of her operations. Such rapid scaling forced her to extend her network by adding a subcontractor who had his own network in the Fuk Ching gang. She then had to scale her network again as well as optimise to adapt to the interdictions at the Bangkok airport by adding partners (other snakeheads) to her network based on recom­ mendations instead of personal contacts. In both cases, scaling the network decentralised Sister Ping’s control ultimately leading to the fragmentation in the network. As with any study, several limitations should be considered. We propose one way of looking at the effect of cumulative disruptions and network failure that is based on a network’s decision to expand via subcontracting. Other factors could also influence the development of interdependency and escalation of commitment. For example, trust plays an important role in any illicit network. There is a growing body of literature looking at the extent to which trust between network actors strengthens or weakens a network’s characteristics like resilience and adaptability82. There is also evidence that trust is a multi- dimensional concept in illicit networks. The key actor’s ego-network is more likely to be made of family and friends, characterised by individualised trust83. Scaling by subcon­ tracting, a common method of scaling in human smuggling84, might instead build relationships on mutual understanding of the end goal rather than trust developed from individual relationships. This in turn could create vulnerabilities in the network should goals change or misunderstandings arise85. It is possible that trust plays an important role in the model proposed here and should be further investigated. The results of this study also suggest several directions for future research. First, greater attention should be placed on collecting and coding data on network decision-making processes. Second, scholars should consider multi-modal network analysis for examining the evolution of networks. Though it is not possible to truly capture dynamic relationships in network analysis, looking at multiplex relationships over time provides one way of getting at a dynamic decision-making process without requiring simulations. Finally, the analysis presented here is based on a single case study. As such, future research should look to see if the mechanisms proposed here apply to other types of criminal networks in other settings.

Notes

1. Bright, “Disrupting and Dismantling Dark Networks,” 39–51; Bright and Delaney, “Evolution of a Drug Trafficking Network,” 238–60; Lozano and Arenas, “A Model to Test how Diversity AffectsResilience,” 1–8; Malaviya et al., “Multi-Period Network Interdiction Problems,” 368–80; Malm and Bichler, “Networks of Collaborating Criminals,” 275; Morselli and Petit, “Law- Enforcement Disruption of a Drug Importation Network,” 109–130; Washburn, “Continuous 24 M. FABIANI AND B. BEHLENDORF

Network Interdiction,” 1–22. Whether or not an interdiction is “successful” depends on the goal it is trying to achieve (see Williams, “Transnational Criminal Networks,” 61–97.). 2. Eilstrop-Sangiovanni and Jones, “Assessing the Dangers of Illicit Networks,” 7–44. 3. Bright and Delaney, “Evolution of a Drug Trafficking Network,” 238–60; Lozano and Arenas, ‘A Model to Test how Diversity Affects Resilience,” 1–8; Malm and Bichler, “Networks of Collaborating Criminals,” 275; and Morselli and Petit, “Law-Enforcement Disruption of a Drug Importation Network,” 109–130. 4. Bright and Delaney, “Evolution of a Drug Trafficking Network,” 238–60. 5. Helfstein and Wright, “Covert or Convenient?” 785–813; and Podolny and Page, “Network Forms of Organization,” 57–76. 6. Adler, “Wheeling and Dealing”; Erickson, “Secret Societies and Social Structure,” 188–210; Chin, “Smuggled Chinese”; Chin and Kelly, “Human Snakes”; Kleemans, “Organized Crime, Transit Crime, and Racketeering,” 163–215; Stephenson, “Gangs of Russia”; Venkatesh, “Off the Books”; Williams, “Transnational Criminal Networks,” 61–97; Zhang, “Smuggling and Trafficking in Human Beings”; Zhang, “Chinese Human Smuggling Organizations”; Zhang, “Snakeheads and the Cartwheel Network”; and Zhang and Chin, “Enter the Dragon,” 737–768. 7. Eilstrop-Sangiovanni and Jones, “Assessing the Dangers of Illicit Networks,” 7–44; Morselli, “Hells Angels in Springtime,” 145–158; and Williams, “Transnational Criminal Networks,” 61–97. 8. Ibid., 97. 9. Bright and Delaney, “Evolution of a Drug Trafficking Network,” 238–60; Bright et al., “Criminal Network Vulnerabilities and Adaptations,” 424–441; Bright, Koskinen, and Malm, “Illicit Network Dynamics,” 1–22. One exception to this is Duxbury and Haynie, “Criminal Network Security,” 314–342. 10. Bright and Delaney, “Evolution of a Drug Trafficking Network,” 238–60; Malaviya et al., “Multi- Period Network Interdiction Problems,” 368–80; and Washburn, “Continuous Network Interdiction,” 1–22. 11. Podolny and Page, “Network Forms of Organization,” 57–76. Podolny and Page define a network as “any connection of actors (N > 2) that pursue repeated, enduring exchange relations with one another, and at the same time lack a legitimate organizational authority to arbitrate and solve disputes that may arise during the exchange.” This definition broadly encompasses any group or organisation, including illicit groups, organisations with hierarch­ ical structures, and loosely grouped individuals (see Bright & Delaney, “Evolution of a Drug Trafficking Network,” 238–260). 12. Bright and Delaney, “Evolution of a Drug TraffickingNetwork,” 238–260; Bright et al., “Criminal Network Vulnerabilities and Adaptations,” 424–441; Bright, Koskinen, and Malm, “Illicit Network Dynamics,” 1–22. 13. Xu and Chen, “The Topology of Dark Networks,” 65. See also Watts and Stogatz, “Collective Dynamics of “Small-world” Networks” 440–442. 14. Malm and Bichler, “Networks of Collaborating Criminals,” 275; Xu and Chen, “The Topology of Dark Networks,” 65. 15. Eilstrup-Sangiovanni and Jones, “Assessing the Dangers of Illicit Networks,” 7–44. 16. Ibid. 17. Bouchard, “On the Resilience of Illegal Drug Markets,” 325–344. 18. Bakker, Raab, and Milward, “A Preliminary Theory of Dark Network Resilience,” 33–62; Eilstrop- Sangiovanni and Jones, “Assessing the Dangers of Illicit Networks,” 7–44. 19. Williams, “Transnational Criminal Networks,” 61–97. 20. Ibid. 21. Kleemans, “Organized Crime, Transit Crime”; and Williams, “Transnational Criminal Networks,” 61–97. 22. Eilstrup-Sangiovanni and Jones, “Assessing the Dangers of Illicit Networks,” 7–44; Malm and Bichler, “Networks of Collaborating Criminals,” 275; Morselli and Petit, “Law-Enforcement Disruption of a Drug Importation Network,” 109–130; and Williams, “Transnational Criminal Networks,” 61–97. GLOBAL CRIME 25

23. Bakker, Raab, and Milward, “A Preliminary Theory of Dark Network Resilience,” 33–62; Bright and Delaney, “Evolution of a Drug Trafficking Network,” 238–60; Bouchard, “On the Resilience of Illegal Drug Markets,” 325–344; Eilstrop-Sangiovanni and Jones “Assessing the Dangers of Illicit Networks,” 7–44; Malm and Bichler, “Networks of Collaborating Criminals,” 271–297; Morselli and Petit, “Law-Enforcement Disruption of a Drug Importation Network,” 109–130. 24. See note 21 above. 25. See note 15 above. 26. Morselli and Petit, “Law-Enforcement Disruption of a Drug Importation Network,” 109–30. 27. Adler, “Wheeling and Dealing”; Erickson, “Secret Societies and Social Structure,” 188–210; Chin, “Smuggled Chinese”; Chin & Kelly, “Human Snakes”; Kleemans, “Organized Crime, Transit Crime, and Racketeering,” 163–215; Venkatesh, “Off the Books”; Zhang, “Snakeheads and the Cartwheel Network,” 111–125; and Zhang and Chin, “Enter the Dragon.” 28. Adler, “Wheeling and Dealing,” 29. Bouchard and Malm, “Opportunistic Structures of Organized Crime,” 288–302; Erickson, “Secret Societies and Social Structure,” 188–210; Kleemans, “Organized Crime, Transit Crime, and Racketeering,” 163–215. 30. Ibid. 31. Chin & Kelly, “Human Snakes”; and Zhang, “Snakeheads and the Cartwheel Network,” 111–25. 32. Zhang, “Snakeheads and the Cartwheel Network,” 111–25. 33. Ibid. 34. One exception is Zhang, “Snakeheads and the Cartwheel Network,” 111–125. 35. Bright, Koskinen, and Malm, “Illicit Network Dynamics,” 1–22; Bright et al., “Criminal Network Vulnerabilities and Adaptations,” 424–41. 36. See note 26 above. 37. Erickson, “Secret Societies and Social Structure,” 188–210. 38. Zhang, “Snakeheads and the Cartwheel Network,” 116–130. 39. Brockner, “The Escalation of Commitment to a Failing Course of Action,” 39–61. 40. Zyglidopoulos et al., “Rationalization, Overcompensation and the Escalation of Corruption in Organizations,” 65–73; Southerland, Mittie, and Potter, “Applying Organizational Theory to Organized Crime,” 251–267; Vaughn, “The Dark Side of Organizations,” 271–305. 41. Kilgannon and Singer, “A Smuggler of Immigrants Dies in Prison.” 42. Keefe, The Snakehead: An Epic Tale; and USA v. Ping, [1994]. 43. Barnes, “Two-Faced Woman.” 44. Lusher, “At Least 10,000 People Died.” 45. Ibid. 46. Ibid. 47. Keefe, The Snakehead: An Epic Tale. 48. Ibid. 49. Ibid. 50. Ibid. 51. Guo Liang Qi went into hiding shortly before the shootout and was one of two gang members that were not arrested or killed. Qi was arrested in August of 1993 in Hong Kong. The other gang member and one of the instigators of the shootout, Shing Chung, was never caught. 52. Keefe, “The Snakehead.” 53. Keefe, The Snakehead: An Epic Tale; USA v. Ping, [1994]. 54. U.S. Attorney”s Office, “Sister Ping Sentenced to 35 Years in Prison.” 55. Keefe”s book was selected as a primary source because it includes detailed source informa­ tion that could be used to verify and triangulate relationships and network activities. 56. Each node in the final network represents an individual actor, apart from the passengers of the Golden Venture. These have been grouped together as a single node for ease of visualisation. 26 M. FABIANI AND B. BEHLENDORF

57. A directed relationship is a one-direction relationship that does not assume two people mutually know each other. For example, if person A paid person B, then it would be assumed that person A had a “relationship” with person B, but it is not necessarily true that person B would have a “relationship” with person A. 58. Personal ties and transactions were not assumed to be mutual or bi-directional and so were coded separately as directional ties. 59. For example, if two people, A and B worked together to move a group of immigrants for 3-months, then they would be coded as “related” for the 3-months of the job and the start and end dates would be recorded. 60. See Baker and Faulkner “The Social Organization of Conspiracy,” 837–60. 61. Wasserman and Faust, Social Network Analysis: Methods and Applications. 62. Ibid., 174. 63. Ibid., 188–191. 64. See note 61 above. 65.. Though the network operationally failed in June 1993, Sister Ping continued to evade law enforcement until 2003. From 1993 to 2003, she attempted to operate a smaller network from the Fujian province with little success. 66. USA v. Ping, [1994]. 67. See note 52 above. 68. Ibid. 69. Ibid. 70. Sageman, Understanding Terror Networks. 71. Constraints can take differentforms – the number of directions forward can be reduced or the relative weight of priorities for the network can change in response to the disruption. In either case, the network”s decision-making will be constrained by the available options or perceived options. 72. Woolrich, Bohoko, and Dobson, “Immigrants Escape in High Seas Drama” 1–3. 73. See note 47 above. 74. Ibid. 75. Bernstein, “The Empire of Sister Ping.” Changing the name and registration information of a ship is a way of avoiding detection by international authorities. 76. See note 72 above. 77. See note 47 above. 78. Zhang, “Snakeheads and the Cartwheel Network,” 121. 79. See note 19 above. 80. Bright, Koskinen, and Malm, “Illicit Network Dynamics,” 1–22. 81. Bright, “Disrupting and Dismantling Dark Networks,” 39–51; Kleemans, “Organized Crime, Transit Crime, and Racketeering,” 163–215; Zhang, “Snakeheads and the Cartwheel Network,” 111–25. 82. See Molm et al., “Fragile and Resilient Trust,” 1–32; and Von Lampe and Johanssen, “Organized Crime and Trust,” 159–84. 83. Von Lampe and Johanssen, “Organized Crime and Trust,” 159–84. 84. Zhang and Chin, “Enter the Dragon,” 737–68. 85. Dujin et al., “The Relative Ineffectiveness of Criminal Network Disruption,” 1–15.

Disclosure statement

No potential conflict of interest was reported by the authors. GLOBAL CRIME 27

Funding

This work was supported by the U.S. Department of Homeland Security [HSHQDC-13-J00368] and the National Science Foundation [#1348416].

Notes on contributors

Michelle D. Fabiani is an Assistant Professor in the Department of Social Sciences, Homeland Security Program at DeSales University. Her research focuses on developing new theories and methods to better understand international and transnational crimes. Her work focuses on several themes: behavioral dynamics in transnational crime; developing methods to disrupt and prevent transnational crimes; structural and developmental patterns in illicit economies. Recent studies have focused on identifying spatio-temporal patterns in archaeological looting through the framework of routine activity theory and examining the relationship between antiquities looting and armed conflict temporally. She received her PhD in Criminology and Criminal Justice at the University of Maryland, College Park. Brandon Behlehndorf is an Assistant Professor in the College of Emergency Preparedness, Homeland Security, and Cybersecurity at the University at Albany (State University of New York). Dr. Behlendorf's research utilizes interdisciplinary approaches to address policy-relevant problems within homeland and national security, drawing on theories and methods from social and compu­ tational sciences. Funded by a number of federal agencies (Department of State, Justice, and Homeland Security; National Science Foundation) his research focuses on several themes, including: geospatial modeling of criminal and terrorist activity: network vulnerabilities of illicit trafficking networks; game theoretic approaches to border security; public perceptions of security-related authorities; and criminal decision-making processes of violent non-state actors. His work has been published in Policing, Journal of Quantitative Criminology, and Studies in Conflict and Terrorism. He received his PhD in Criminology and Criminal Justice at the University of Maryland, College Park.

ORCID

Michelle D. Fabiani http://orcid.org/0000-0003-4277-9115

Bibliography

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