Understanding of social processes—insights about the functioning of individuals, groups, and societies—is essential to analysts’ ability to answer enduring intelligence questions. As discussed in Chapter 4, intelligence analysts’ primary function is sensemaking, drawing meaningful conclusions from the vast stream of information to which they have access, and they already make use of decades of social and behavioral sciences (SBS) research in doing this. Our charge required us to look across the current landscape of SBS research to highlight new and emerging insights with potential to improve sensemaking, including from areas that have not been consistently applied in a national security or intelligence context. Recognizing that it would not be possible to develop a comprehensive picture of all opportunities relevant to the Intelligence Community’s (IC’s) needs—or to be certain of the degree to which any particular opportunity is already being exploited within the IC—we focused instead on demonstrating how and why the insights from SBS research are essential to supporting the knowledge, skills, and methods analysts use to tackle core analytic challenges (see Chapter 4): the need to understand power and influence; threats, opportunities, and social and organizational dynamics; complexity; and deception and gaps in information.
Our review of a wide range of ideas and trends in SBS research revealed four areas with the potential to be particularly fruitful in supporting analysts’ sensemaking efforts—the study of narrative, the study of social networks, the study of complex systems, and the affective sciences. We see in these four areas a combination of emerging developments and relevance to core analytic challenges that highlights the power of SBS research to
strengthen intelligence analysis. There are undoubtedly many other developments that have not yet been fully exploited by the IC, and we hope future collaborations between the IC and the academic community will continue to identify new opportunities (see Chapter 10).
The opportunities described in this chapter can best be understood in the context of their place in the overall SBS research landscape, and the chapter therefore begins with an overview of the origins, methods, and primary contributions of research in these four areas. We then describe the specific ways in which research in each area can be applied to core analytic problems, drawing on both emerging research from contexts not typically associated with intelligence analysis and work on phenomena well recognized as key to analytic work. We suggest potential direct applications for the analyst, with the caveat that some are much readier for practical application than others.
While each of the four areas highlighted in this chapter offers ways of understanding human behavior and social processes, they have different histories and are characterized by different theoretical models and methods. We briefly outline the unique contribution of each and recent trends that are ripe for application to core analytic challenges.
Understanding narratives—from the meaning of cultural traditions, to political themes in press coverage, to trends in social media communications—is fundamental for the intelligence analyst, who must understand the content of communications and how and why they are conveyed. Narratives have existed since long before there was written language, and their impact and influence are evident. The study of narratives and stories has long been important in the humanities and in such social sciences as anthropology and psychology. Scholars of narrative have contributed to the broader study of culture and have developed ways of understanding the content and structure of narratives, as well as other features. This work has produced numerous definitions of what constitutes a narrative, but in essence it refers to some sort of story, conveyed verbally or nonverbally, in words, pictures, or even through gestures. New technologies have brought new kinds of narratives that analysts must understand, along with new methods for studying them.
In the past, narratives were slower (books, letters), and sources were more identifiable (authors, groups). The influence of narratives has been
closely linked to the technologies available for creating and spreading them, and powerful new technologies that serve this purpose have emerged over the past two decades. The Gutenberg printing press made written stories readily available to large populations. More recently, train systems, the telegraph, and radio accelerated the speed at which stories could spread, while photography, film, and other video technologies intensified their emotional impact (Kaufer and Carley, 1993). Today, the Internet and associated digital technologies are again revolutionizing the nature and impact of narratives. Social media and other technologies allow for narratives to be generated and shared publicly at a much faster rate; narratives can quickly inspire group actions specific to time and location; and they can be shorter (e.g., presidential tweets) and more conversational.
At the same time, developments in the study of narratives have been fueled by the exponential growth in data created by social media such as Facebook and Twitter and by the fact that vast amounts of content are now stored digitally, which has made the study of narratives at a large scale much more practical. These capabilities offer new frontiers for applying the study of narratives to intelligence analysis.
Early narrative research focused on such widely shared narratives as folk tales. This research suggested that narratives have underlying patterns and structures; as early as the 1960s, computer technology was allowing researchers to identify such patterns and structures by searching large volumes of text (Colby et al., 1966). Interest in narratives has since spread beyond the traditional humanities to social science departments, and today’s academic programs in this area are often multidisciplinary, drawing on psychology, sociology, anthropology, and other fields while also utilizing cutting-edge developments in the analysis of big data (see Chapter 2). New for-profit businesses have also contributed to the study and tracking of public sentiment and social media memes.
The expansion of narrative studies to additional social science domains has led to the emergence of more research questions with direct relevance to national security. Even such age-old fields of study as rhetoric have been brought to bear on security-related challenges, such as decoding of the master narratives of Islamist extremism (Halverson et al., 2011) or the deployment of traditional rhetorical charms (such as humor and ridicule) to counter extremist narratives (Goodall et al., 2012). Methods used in narrative study have obvious applications to the study of political narratives and how they both shape and are shaped by events, trends, leaders, and more. The nature of narratives is changing as social media tools proliferate. These new technologies make it possible to construct, assess, share, alter, and counter the spread of those narratives at ever faster rates. SBS research provides a basis for understanding, analyzing, and responding to these changes in narratives.
Developments in the quantitative and qualitative analysis of texts, narratives, imagery, and videos have contributed to advances in researchers’ ability to characterize and anticipate human action, the contexts for issues of importance, the flow of ideas in societies or groups, and the emotional states and motivations of relevant individual populations. Advances in the use of natural language processing, machine learning, and computational approaches in the social sciences (see Chapter 2) are contributing means of analyzing big data in pursuit of questions about how narratives function and exert influence on individuals, groups, and societies. Study of multimodal communications (examining all aspects of a communication and its context) and transmedia storytelling (simultaneous transmission of a narrative across many media platforms) is helping to identify how and why some stories spread while others fizzle.
Researchers have long used several basic features that can be measured in analyzing narratives: the topics being addressed and concepts expressed, the sentiments or stances with respect to the topic that are implied or articulated, and the overall gist of the messages being conveyed. Today, new technologies are providing more sophisticated ways of using these and other measures. Machine learning tools, for example, now available for many languages, can be used to identify and track concepts. Sentiment, which formerly was assessed by coding text as positive or negative can now be assessed using such additional factors as social context, evidence of the author’s ideological position, and data on the way people read (e.g., eye movements).
The use of machine learning approaches alone for the analysis of narrative has not, however, been entirely successful. Machines may identify characteristics of an author or situation inaccurately; misread socially nuanced language or cultural differences; or be tripped up by typographical errors, intentional spelling errors, emoticons, emojis, images, humor, or satire and sarcasm. Thus, effective use of machine learning, like other developing tools for narrative analysis, requires human judgment (Huang et al., 2011; Menabney, 2016).
Understanding the complex and dynamic relationships among and within social and institutional networks is an essential facet of intelligence analysis. One tool long used by the IC is social network analysis—a structural approach to understanding the world based on the interdependencies among actors and their influences on behavior; the flow of information, disease, resources, and other phenomena; and both the opportunities available to individuals and groups and the actions they take. Social network anal-
ysis has also played an important role in social science fields that include anthropology, communication, sociology, and political science (Borgatti et al., 2009; Johnson, 1994). The social network analysis entails representing a network in terms of nodes and relations that form an interdependent, holistic system and identifying key actors, their group identifications, and other network features.
Social network analysis predates the development of current social media, and even the field of computer science. The effort to understand society in terms of social relationships goes back as far as descriptions of lines of descent in the bible or clan histories found in other historical contexts (Freeman, 2004). However, the advent of modern social network analysis is generally attributed to Jacob L. Moreno, a sociologist who became interested in social psychology in the 1930s (Moreno, 1934). Moreno attempted to explain and understand social behavior using “socio-grams”—graphical representations of the links between an individual and others.
Computational and statistical advances in the study of social and other types of networks have increasingly involved computer scientists, physicists, statisticians, and mathematicians. For example, work by Harary (1969) provided a mathematical basis for reasoning about networks. Since the attacks of September 11, 2001, the field of social network analysis has exploded as researchers and national security experts have come to recognize its utility for understanding, identifying, and breaking terrorist groups.
Today, researchers use statistical methods, mathematical modeling, and simulation to understand social networks. They also use such tools as machine learning, big data analytics, and neural imaging to investigate network behaviors involving individuals, groups, organizations, and countries. The methods of social network analysis have been fruitfully applied as well to networks involving language, neural systems, animals, and food and supply chains; computer networks; and networks involving nonhuman agents, such as machines powered by artificial intelligence (AI) that have agency and can serve as nodes in a network alongside humans (Contractor et al., 2011; Lanham et al., 2014; Ofem et al., 2012). The analytic methods used have expanded to support the study of high-dimensional, dynamic, and spatial networks (Carley, 2003).
Cutting-edge methods for social network analysis are developing rapidly. They rest on technological advances1—particularly improved capacity for application at very large scales. Indeed, some of these advances have
1 For example, such methods as homophily and diversity analysis, diffusion and epidemiological analysis, path analysis, and network visual analytics are being used to develop detailed profiles of networks and their functioning (National Academies of Sciences, Engineering, and Medicine, 2018b).
resulted from collaboration between researchers and the IC,2 and all of these advances have utility for the IC in such applications as counterinsurgency analysis, tracking of terrorists, and exploitation of open-source data.
At the same time, however, the utility of this research for intelligence analysis rests on interdisciplinary work with other SBS disciplines. It has been demonstrated that social networks have profound influences on many human sentiments (an aspect of affective sciences, which are discussed below), behaviors, and actions that are of interest to the IC. These include, for example, the distribution and exchange of resources, the development of trust within a group or society, ideological contagion,3 diffusion of beliefs, attitude formation, the establishment of normative constraints, the development of group and individual social capital,4 group and organizational effectiveness, the evolution of organizational leadership, group and organizational resilience and robustness, and political stability, among many others. Researchers analyzing social networks examine such issues as how homogeneous the members of a network are (e.g., whether they share beliefs and how they came to do so); how ideas are transferred within the network; how some network members are uniquely positioned to influence other members; how members of a network adapt to its influences; and what ties link network members and how these ties function to create network identities (Grosser and Borgatti, 2013). Methods of social network analysis can also be applied to texts and used to understand features of narratives, how narratives have changed (Diesner and Carley, 2011; Sudhahar et al., 2015), and the rhetorical strategies (strategic framing) they use (Schultz et al., 2012). Recent work combines these approaches with the study of emotion to examine identity and influence (e.g., Joseph et al., 2016; see the discussion of affective sciences below).
Intelligence analysis is challenging in part because almost all the issues of interest are complex and interact in ways that are difficult to trace and monitor. Work in a number of fields, including both mathematics and philosophy, has contributed to the development of a scientific approach
2 Such advances include the development of scalable algorithms for many network measures and clustering algorithms and greater attention to cascades in networks. Currently, advances in this area involve a movement to more metrics for high-dimensional or metanetwork data, as well as new abilities to analyze changes in networks over time and assess the network dynamics, as well as to assess geospatially embedded networks.
3 The notion that when a relatively small percentage of a population holds a new belief, that belief spreads to a majority of the population.
4 Social relationships and networks that are the source of economic and other benefits.
to studying complex systems in which the phenomena being examined are viewed from a holistic, or integrated, perspective.
A complex system comprises many interacting parts, each acting on its own and with no singular central control. These interacting parts structure themselves into a system but behave in a nonlinear way, such that outcomes cannot be predicted based on their behavior (Bar-Yam, 2002). In addition, complex systems typically have adaptive components (including actors that learn); they operate on multiple levels, often involving dynamic social networks and processes; and they occupy “wicked” problem spaces.5 For all these reasons, it is difficult to understand complex systems and to predict events and developments that may occur within the system or their potential consequences. As a result, people attempting to study or monitor complex systems are often caught off guard by so-called “black swan” events—consequential developments that were not expected—or by the unintended consequences of an event.
Many of the issues for which intelligence analysts are responsible exhibit the features of complex systems.6 For example, China, whose role in the world is a key issue for the IC, is a massively complex system. Numerous factors—including economic performance, the degree of social cohesion, rural–urban migration, leadership dynamics in the Communist Party, and environmental degradation, to name but a few—are likely to interact and shape future developments in that country. Yet in the face of this complexity, intelligence analysts are expected to provide policy makers with reliable insights about how developments in China will evolve in the future. Analysis of nonstate actors in international affairs, such as terrorist organizations or subnational groups, can be even more difficult because of their informal and often hidden nature.
It is difficult not only to forecast the future state of a complex system but also to understand its current dynamics. Analysts cannot be certain that they adequately comprehend the causes of past events, and they lack proven methods for projecting how events may unfold in the future. As a result, they are often surprised by developments, even those that in hindsight appear to have been predictable. According to one veteran intelligence officer and scholar, “complexity is the phenomenon that is fueling and/or complicating the management of the government’s—and indeed the nation’s—most vexing strategic challenges” (Kerbel, 2015). Equally difficult
6 We note also that the IC itself is a complex sociotechnical system, and research from this field is relevant to the management of the multiple entities and large workforce involved, a point we touch on in Chapters 6, 7, and 8.
for analysts is to help policy makers evaluate how their policy choices may interact with the complex dynamics of the issues at hand.
Subject matter experts in the IC face constant pressure, often within extremely compressed timeframes, to develop simulations and models for scenarios that are constantly changing. The need to communicate the results of modeling and simulation to policy and decision makers in relevant and timely ways is another persistent challenge.
Researchers have developed an interdisciplinary approach to studying complexity, sometimes termed complexity science, strategy, or theory. Based in systems theory that emerged in the mid–20th century, as well as developments in the natural sciences, this approach is used to study phenomena that are unpredictable and nonlinear, providing ways to identify and mitigate unintended consequences, as well as methods useful for considering a wide range of alternatives and thus supporting strategic analysis. Scholars of complexity theory use computational and mathematical methods to assess such phenomena,7 and many SBS researchers rely on complexity theory in studying social, cultural, and technological systems. This applied approach, which relies heavily on modeling and simulation, has proven relevant for national security–related applications (Anderson, 1999; Brown and Eisenhardt, 1997); the possibilities are discussed in detail below. Researchers in the field of international relations have also used simulations and other methods of complexity theory to examine dynamics relevant to international security (see, e.g., Elder et al., 2015; Frankenstein et al., 2015).
Emotion and affect play a fundamental role in human and social behavior. Societies and cultures would not function effectively, and humans would not survive as a species, if emotions were not regulated in culturally defined ways for the common, social good. Emotions can have extraordinary power to influence behavior and can be manipulated as a way of controlling others’ attitudes and actions. Understanding how emotion and affect function and influence people’s thoughts, beliefs, and actions therefore has clear utility for intelligence analysis; drawing on foundational and emerging work in this area to provide direct applications for intelligence analysis is a key frontier for SBS researchers and the IC.
The past few decades have witnessed a blossoming of research on a variety of topics under the rubric of “affective sciences,” which address
7 These methods include general algorithmic assessments (Flum and Crohe, 2006), computational algorithms that promote scalability, fractals, and techniques for identifying tipping points (Flake, 1998).
emotions, feelings, affect, moods, sentiments, and affectively based personality traits and psychopathologies. The study of all these components of the affective sciences, including the verbal and nonverbal signals of affective states, can provide insights into the mindsets, personalities, motivations, and intentions of the actors intelligence analysts seek to understand; help explain people’s actions, judgments, and decisions; and support more nuanced and sophisticated understanding of communication.
Emotion and affect are complex phenomena, and many fields—particularly branches of psychology, psychiatry, neuroscience, and biology, but also others, including sociology and anthropology—have contributed to a growing understanding of these human phenomena. Indeed, many topics within the affective sciences are studied across disciplines. Scholars in these fields use diverse approaches—including laboratory and field-based studies and experimental and observational methods—to study how people experience and express emotion and affect, what physiological processes accompany them, and how they affect other people, among other questions. Foundational work has significantly expanded understanding of many psychological processes, such as personality, development, pathology, and social behavior, that are critical to an understanding of people’s actions and behavior (Davidson et al., 2002; Gross, 2007; Matsumoto et al., 2008, 2013).
Broadly speaking, emotion refers to transient, biopsychosocial reactions to events that have consequences for an individual’s welfare and potentially require immediate action. Emotion is a metaphor for a host of physiological and psychological state changes that are produced by a cognitive appraisal process, and it can have profound influences on what people do. The experience of emotion involves multiple components, including affect, physiological response, mental changes, and expressive behavior. Emotions serve intrapersonal, interpersonal, and sociocultural functions that all have implications for intelligence analysis (Hwang and Matsumoto, 2016; Keltner and Haidt, 1999; Levenson, 1999) (see Box 5-1).
Affective states—including not only emotion but also mood (state of mind) and sentiment (a view or attitude with respect to a circumstance or event)—are signaled both verbally and nonverbally. The study of nonverbal communication, defined as “the transfer and exchange of messages in any and all modalities that do not involve words,” offers significant potential benefits for the IC (Matsumoto et al., 2013, p. 4). People communicate nonverbally through both conscious and unconscious actions of the face, voice, and body, such as vocal cues (e.g., tones), gestures, body postures, interpersonal distance, touching, and gaze. These communication modes serve multiple functions: they may define a communication by providing a backdrop for it, regulate a verbal communication, or constitute the message itself (see Box 5-2). Such nonverbal behaviors are observable, and thus can
provide signals of potential utility to analysts. Research over the past few decades has provided strong evidence of the validity and reliability of nonverbal expressions of specific emotional states and evidence for nonverbal signals of many other cognitive and emotional states (see, e.g., Cartmill and Goldin-Meadow, 2016; Hwang and Matsumoto, 2016; Re and Rule, 2016; Scott and McGettigan, 2016).
Recent research in affective sciences has provided new insights into specific areas of interest to the IC. These include the content and power of narratives, processes of judgment and decision making, and the spread of attitudes and beliefs associated with terrorism and other security threats.
Research in these four areas can be applied to support the analyst in tackling core sensemaking challenges, though further research is needed to bring these ideas closer to practical application. Many of the methods and ideas discussed here can be applied to more than one set of analytic problems; accordingly, this discussion of opportunities and how they may be applied is organized, somewhat arbitrarily, around the core challenges
of understanding power and influence, understanding threats and opportunities, and understanding complexity (see Chapter 4). Note that, because many of the most current developments in understanding deception address cyber-based deception, those challenges are addressed in Chapter 6.
The nature and sources of the power wielded by individuals, groups, and states and how these entities exert influence are fundamental topics for analysts. Examples of the specific challenges analysts confront include making sense of the power structure in a group or region, assessing lines of influence, identifying influencers (individuals who exert influence on key or emergent leaders), and tracking changes that could signal a developing shift in power. This section reviews opportunities for enhancing understanding of status and power, judgment and decision making, influences on individuals’ attitudes and behaviors, the vulnerability and adaptability of social networks, and network influences on political and economic environments.
Status and Power
Research in several fields has contributed to understanding of the nature of status and power, and of how leaders, groups, and states wield power and are influenced by the status and power of other actors. The fields of political science and international relations have provided foundational understanding of the fundamental importance of status and power. Inter-
national stability depends on two factors: aspiring (or rising) powers must believe in the stability of the current status hierarchy (generally maintained through the strength of leading powers, such as the United States), but they must also perceive that this hierarchy is legitimate and that the boundaries of the elite group are permeable, making admittance into that group possible (Larson and Shevchenko, 2014a; Wohlforth, 2009).
Status refers to an actor’s position within a social hierarchy and is defined by acknowledgment from other actors; actors cannot achieve higher status unilaterally (NASEM, 2018a).8 Status is important to state actors; they value their relative status and may seek to elevate it—independent of other foreign policy goals—through both violent and peaceful means (Renshon, 2016, 2017; Ward, 2013, 2017). Rising powers, such as Turkey, India, and Brazil, are especially sensitive to status concerns (Mares, 2016; Paul and Shankar, 2014), but less is known about how those concerns affect small states and middle powers. Nonstate actors play a growing role in counterinsurgencies and complex humanitarian emergencies, and it is not uncommon for nongovernmental organizations (NGOs), terrorist or insurgent groups, and state actors to come into conflict with one another (Murdie and Peksen, 2014; Murdie and Stapley, 2014; Murdie and Urpelainen, 2015).
Status and power are also framed in terms of overlapping hierarchies. That is, a person might have different statuses at home, at work, and in other contexts. Similarly, a world leader has local and international status, and an individual terrorist has status within the terrorist group, while the group has status relative to other groups. Actors of concern to the IC may be navigating within and between related yet distinct networks of power and status (see, e.g., McIntosh, 2005).
Other insights about how status can influence the actions of state actors come from social identity theory, a basic principle of social psychology that explains people’s behavior through a focus on their social identity (that part of the self-construct derived from membership in a social group). Individuals tend to experience the collective triumphs and defeats of groups with which they identify as if they were their own, and they evaluate their own group’s status by comparing it with that of a reference group of a similar but slightly higher rank. Researchers applying these ideas to political actors have found that they display similar dynamics. For example, whereas Russia and China might compare themselves with the United States, India might compare itself with China, while France might compare itself with Germany (Larson and Shevchenko, 2010, 2014a; Wohlforth, 2009, 2014).
8 Reputation, another attribute conferred upon a state or nonstate actor by others, refers to an inference that others draw about that nonstate actor, including expectations about future behavior based on observations of past behavior (NASEM, 2018a).
Social groups (such as states) interested in increasing their relative status may use one of three identity-management strategies—social mobility, social competition, or social creativity—to challenge international status hierarchies and improve their ranking. To pursue any of these strategies, a state must possess a minimum level of “hard power,” or military capability; reliance on hard power can, of course, result in conflict. States may also use other means of exerting influence that fall into the category of “soft power” (described in Box 5-3, the first of a series of boxes in this chapter on potential applications of SBS research to the work of the intelligence analyst).
The social mobility strategy is to seek membership in an elite group by following recognized rules and improving one’s standing with respect to the group’s recognized attributes. In the decade after the Cold War ended, for example, former Warsaw Pact member states adopted democracy and capitalist economies and then sought admission to the North Atlantic Treaty Organization and the European Union, not simply to attain military security and economic prosperity but also to achieve higher status.
The use of social competition encompasses the efforts of a lower-ranked group to equal or exceed the dominant group. Lower-ranked groups tend to use this strategy when the boundaries of the dominant group appear impermeable and the existing status hierarchy is illegitimate or unstable—for example, when higher-ranking members are perceived as bullying others or holding double standards (see Box 5-4).
Lower-status states may also use social creativity as a strategy for achieving higher status. This strategy tends to be used when the dominant-status
group is impermeable, but the existing status hierarchy is believed to be both stable (e.g., a change in the hierarchy is unlikely) and legitimate (e.g., its rules and norms are perceived to be fair). In these circumstances, a state pursuing greater status may seek to change the international perception of a trait or attribute previously viewed as negative, and thereby to excel in a new dimension. If, however, the dominant state refuses to see value in this new dimension or see the aspiring state as superior in that dimension, a social creativity strategy will prove ineffective (Larson and Shevchenko, 2014a).
Other areas of SBS research have also contributed to understanding of status and power. For example, social network analysis has built on traditional understanding of international relations (Hafner-Burton et al., 2009). Theoretical work over the past several decades has developed a portrait of power dynamics and the effectiveness of charismatic authority or control of communications, for example (see, e.g., Bonacich, 1987; Brass, 1992). More recent work has used network analysis to assess these dynamics (e.g., Golbeck and Hendler, 2004; Lawler et al., 2011).
Complexity models are also used to understand and reason about power. By taking into account sociological and biological findings on trust and reciprocity, for example, researchers can use computational models to consistently calculate scores of agents’ trust and reputation (Mui et al., 2002). Other complexity models point to the role of credibility in building and maintaining reputation (Herbig et al., 1994), as well as to the roles of trust (Prietula and Carley, 2001), rumor (Prietula, 2001), and the volatility of the environment (Carley and Prietula, 1993). Such models yield understanding of the conditions under which status, power, and reputation can be disrupted.
Judgment and Decision Making
One problem set facing intelligence analysts involves understanding and trying to anticipate the judgments and decisions of political leaders and other actors who wield power. These human processes have been studied by researchers in many fields, including anthropology, psychology, economics, sociology, political science, communication, and organization science. This work was long dominated by cognitive theories based on the premise that human beings are rational agents who make decisions based primarily on logical reasoning about possible alternatives (Camerer, 2003). Subsequent work showed, however, that humans make decisions under constraints of bounded rationality and that rational choices must be understood within cognitive and situational constraints (e.g., Gigerenzer and Selten, 2002; Kahneman and Tversky, 1979; Thaler et al., 2013).
Research by Kahneman and Tversky (1979), for example, provided models of decision making incorporating a complex combination of cognitive processes that could be predicted. Since then, examinations of economic decision making (Naqvi et al., 2006) and decision making in stressful situations (Starcke and Brand, 2016) have contributed to the development
of new theoretical models, and a significant body of work has focused on the role of emotions in these processes. This work indicates that emotions and cognition interact and portrays the processes of judgment and decision making as being imbued with emotion at multiple stages (Lerner et al., 2015).
Other work has explored the role of context in decision making. A number of researchers, for example, have examined the effects of context on choice behavior in the face of multiple alternatives. This research has shown that context limits what alternatives are considered (see, e.g., Bartels and Johnson, 2015; Bechara et al., 2000; Schwartz, 2000; Trueblood et al., 2013; Tversky and Simonson, 1993).
Influences on Individuals’ Attitudes and Behaviors
Several fields shed light on how ideas, beliefs, and attitudes form, evolve, and spread through a given population.
Analysis of human networks. Some researchers have used network analysis to explore the behavior of humans and groups. For example, study of the diffusion of innovations initially focused on the characteristics of individual potential adopters (such as their education levels). Subsequent work, however, showed that the network in which potential adopters were embedded was significantly more powerful in both explaining and predicting the dif-
Research on social influence—the process through which individuals’ states (such emotions as happiness, or other states, such as health or obesity) evolve over time as a result of social influence—has shown that it is one of the key dynamics involved in the diffusion of ideas (Carley, 1986; Carley et al., 2009; Christakis and James, 2011; Delre et al., 2007). Models of social influence, validated in both laboratory experiments and real-world settings (Algesheimer et al., 2005; Friedkin, 2006; Marsden and Friedkin, 1993), have been used successfully to predict political voting and reasoning about such events as the fall of the Berlin Wall (de Mesquita, 1998; Stokman and Van Oosten, 1994).
Affective sciences. Influencers are influential not only because of the persuasiveness of their logic and arguments but also because of the way they craft their messages to express specific emotions associated with their logic and arguments. Such communication packages may include the strategic use of nonverbal communication and behavior to reinforce their messages. These nonverbal signals may include contextual cues in the background as well as nonverbal behaviors of the influencer—facial expressions, tone of voice, and gesture. Indeed, these nonverbal cues are often critical to the power
of an argument. The IC today depends largely on intelligence reporting to identify the key influencers in a particular group or society. Improved understanding of emotion and how verbal and nonverbal communications work, especially in contexts relevant to the IC, could yield additional methods for evaluating influencers and interpreting the behavior of actors of interest.
Network analysis of digital data. Other research has focused on digital data that can be collected from, for example, interactions on social media platforms; digitized texts; and online commercial transactions, including video and audio posted online. This work includes both analysis of digital trace data and interdisciplinary approaches to this rich source of information.
Analysis of digital trace data. Internet users create data, in most cases unknowingly, as they use websites and interact on social media platforms, because they leave traces (known as digital trace data) that can be analyzed. Events occurring online, such as posted messages or comments, can be represented as individual interactions (e.g., by coding the sender, receiver, and time, possibly with other attributes of the sender, receiver, or interaction) and collected as a record of all events from a particular online platform.
Before digital trace data were available, researchers studying social network dynamics would examine a network at a single point in time and compare the resulting data with data collected at other discrete times (e.g., Graham and Carley, 2006; Palinkas et al., 2000). The availability of digital trace data has now made it possible to study any digital interaction in the context of all other such interactions occurring on a single social media
platform.9 However, the nature of the data—both the volume and the rapidity of interactions—has created the need for new dynamic network methods for streaming the data and “chunking” it into time periods.
Using time-stamped data, network researchers have in the past decade been able to jump-start the development of new statistical techniques for studying network dynamics (Brandes et al., 2009; Butts, 2008; McCulloh et al., 2012), sometimes in conjunction with machine learning techniques (Huang and Carley, 2018). This approach has made it possible to conduct longitudinal research at a much higher level of temporal resolution, and has the potential to yield substantial advances in theoretical understanding of social and behavioral processes for which relational event data (datasets that capture interactions among multiple actors or actions) are available (Leenders et al., 2016; Magelinski and Carley, 2018). This work has been the basis for the development of methods for assessing the importance of network nodes, tracking the trajectory of ideas, and other advances (Merrill et al., 2015) (see Box 5-6).
Integration of social network analysis with narrative research. The integration of social network analysis with narrative research offers additional possibilities for the intelligence analyst. Most social network researchers examine social influence on individuals in terms of the network structures in which they are embedded. There is, however, a robust body of literature in communication and psychology that considers social influence on networks as explained by the content of persuasive messages (Cialdini, 1984; Cialdini and Goldstein, 2004; O’Keefe, 2016). Combining these two approaches can provide a richer picture of networks and how they exert influence.
9 Cross-platform analysis is possible in theory; however, identifying actors across platforms is challenging, and combining data across platforms can lead to privacy breaches.
For example, computational models have been used to merge these two approaches to support reasoning about the coevolution of groups and what they talk about (Carley et al., 2009). Such models can be useful for assessing how interventions can affect groups or which media are most effective in carrying messages. These developments could be used to identify indicators for monitoring the developing strength of a minority opposition group’s message (Maxwell and Carley, 2009) (see Box 5-7).
The Vulnerability and Adaptability of Social Networks
Since the attacks of September 11, 2001, the IC has had an intensified interest in research on social networks, particularly terrorist networks (Ressler, 2006), and researchers have responded.10 One area of interest to
10 See, for example, work on particular terrorist networks (Perliger and Pedahzur, 2011; Sageman, 2004), the network position of lone wolf terrorists (Weimann, 2012), criminal networks (Carrington, 2011), adaptation in terrorist groups (Horgan et al., 2014), destabilization of terrorist networks (Carley et al., 2001), and target prediction (Campedelli et al., 2018).
analysts that has been addressed by social network research is the functioning, viability, and vulnerability of groups and organizations. This research has helped answer questions about what makes some groups or organizations effective while others are dysfunctional or fail, and what makes some more resilient than others.
Terrorists’ power is heavily dependent on their connection to others who can provide material and other forms of support. However, covert networks must constantly weigh the benefits of activating a network tie against the potential of unintentionally revealing that tie to the IC. Researchers have explored ways to exploit this tension, with the aim of suggesting ways to break up terrorist networks or inhibit their ability to function effectively. Much of this research has focused on how to remove the actors or linkages whose loss will optimally disrupt the network, sometimes referred to as the “key player” problem (Borgatti, 2002).
The complexity of the dynamics of social networks complicates such efforts. Networks have properties that help them resist such disturbances as the removal of a specific entity or relationship and adapt to changing circumstances (National Research Council, 2003; Sheffi, 2001). In examining how to break up the structure of terrorist and other “dark” networks, such as criminal cartels, and inhibit their effectiveness, researchers have used mathematical algorithms (Farley, 2003), simulation (Carley et al., 2001), mixed network analysis and simulation (Tsvetovat and Carley, 2005), and optimization algorithms (Chan et al., 2014). However, dismantling terrorist groups is a complex problem. Breaking up a covert network, for example, may paradoxically make it more efficient (Levitsky, 2003), as illustrated by the fact that suspending potential terrorist groups from social media platforms can simply send them to other venues that are more difficult to monitor.
Network Influences on Political and Economic Environments
Social networks that operate at large scales, such as the relationships that exist within and among cities and countries, also have important security implications. Social network analysis can serve as a valuable supplement to the work of such fields as political science and sociology for studying political and economic developments and international power dynamics.
Current theory and methods in social network analysis have yielded important insights into organizational, urban, and global dynamics. Research on organizational networks, for example, has revealed the importance of investigating cross-level network phenomena to making sense of complex network dynamics (Brass et al., 2004), the need for multilevel models combining characteristics of individuals with structural network models (Borgatti and Foster, 2003), and the important role of social networks in rapid globalization (Zhou et al., 2007). Research on urban and regional networks has examined global and regional patterns (Knox and Pinch, 2010); the robustness of transportation systems (Nagurney and Qiang, 2007); the spatial diffusion of illnesses and ideas (Carrington et al., 2005); and the effects of community infrastructures on sustainability (Dempsey et al., 2011), community cohesion (Gilchrist, 2009; Moody and White, 2003), segregation (Laurence, 2009), and polarization (Lee et al., 2014). Similar work has examined global organizational networks—for example, to identify cities at particular risk during global ecological and economic crises (Taylor and Derudder, 2015). And a multimethods approach blending computer simulation and network analysis has been used to gain a better understanding of ways to limit the impact of biological threats (Carley et al., 2006; Eubank et al., 2004); of information diffusion (Rahmandad and Sterman, 2008); and of how positive and negative alliances influence the use of weapons of mass destruction (Frankenstein et al., 2015).
Analysts constantly monitor and assess developing situations to understand whether they are likely to bring security threats, or perhaps open up opportunities for the United States to pursue a security, policy, or diplomatic objective. They use the results of these analyses to advise policy makers as to the possible interventions that might prevent a problem from occurring, or identify when the conditions in a society have changed or new actors and influencers have emerged that make that society amenable to a peaceful solution to a long-standing problem. Recent developments in Colombia illustrate this more positive analytic work: assessment involving a multitude of factors, including notice of the disarmament of guerilla groups and an assessment that the time was ripe, guided U.S. policy makers who worked to support negotiations that led to a peace agreement between the government and the Revolutionary Armed Forces of Colombia (FARC) in 2016.
This is a very broad arena, so we highlight here just a few areas with potential to enhance the analyst’s capacity to recognize threats and opportunities and understand complex situations: radicalization and extremism, parsing of the narratives used by actors and groups, insider threat, and deception.
Radicalization and Extremism
Recently, a considerable body of research in affective studies has emerged on the processes of indoctrination and radicalization and the characteristics of extremism. This work has shed light on the motivations of lead bad actors for committing acts of violence (Gill, 2016; Gill et al., 2017; Gurski, 2015; Horgan, 2014; McCauley and Moskalenko, 2008; Meloy and Gill, 2016; Meloy and Yakeley, 2014; Meloy et al., 2015; Moskalenko and McCauley, 2009; Speckhard, 2012). Horgan (2008), for example, points to three motivational factors: perceived injustice, identity (pursuing an individual purpose), and the desire for a sense of belonging.
This body of work strongly suggests that no set of demographic characteristics reliably distinguishes terrorists from nonterrorists (Gill et al., 2017; Meloy and Gill, 2016). Becoming a terrorist is a complex process, and terrorism is not understood as a disorder in the individual. Rather, becoming a terrorist can be viewed as a tool that individuals adopt in pursuit of their aims (Kruglanski and Fishman, 2006). Moghaddam (2005), for example, identifies stages an individual may go through on a path to adopting terrorism. The process often begins with a perception of injustice, a search for a solution, and anger when no solution is available. An individual may go on to engage morally with a terrorist organization and develop an alternative persona. The next stage entails irrevocably joining a terrorist organization and escalating one’s commitment, ultimately developing a belief that because the terrorists are fundamentally different from their enemies, engaging in violence on the organization’s behalf is acceptable.
11 Some research has focused specifically on the moods or emotional dispositions of groups, examining such questions as how group moods develop and are influenced and how they affect individuals’ behavior.
Parsing of the Narratives Used by Actors and Groups
Analysts know that narrative has significant power to shape and influence attitudes, values, beliefs, and motivations. Large organizations, for example, including nation-states, exercise influence by controlling their stories and their brands, and powerful institutions have generally been dominant in the production and proliferation of public narratives. Today, the Internet and social media have reduced the costs of creating and transmitting narratives, allowing individuals, organizations, states, and loose alliances to develop storylines with the potential to influence and mobilize other populations. Disaffected individuals and groups can more easily challenge narratives with which they disagree, and narratives are a particularly important weapon for nonstate actors, who deploy them to gain supporters and allies.
It is vital for analysts to track these developments. Narrative and network analysis and the study of emotion offer pathways for supplementing existing approaches for understanding the meaning of narratives and their influence.
12 Red Hen Lab was discussed at a workshop held by the committee on January 24, 2018, the proceedings of which are summarized in NASEM (2018c). Also see http://www.redhenlab.org [September 2018] for more information.
13 See, e.g., work on autogenerating narratives (Young, 2007); theme, topic, and gist identification and modeling (Mohr and Bogdanov, 2013); entity extraction (Etzioni et al., 2005); and opinion, sentiment, and stance mining (Liu, 2012; Pang and Lee, 2008; Snajder and Boltuzic, 2014).
A perennial threat within the IC is the possibility that an individual may use access to classified protected information or other opportunities afforded by his position within the IC in ways that pose a threat to security. Corporations are also affected by insider threat or industrial espionage (Crane, 2005). Inadvertent leaks are another risk for the IC as well as business interests (Carley and Morgan, 2016; Johnson and Dynes, 2007). The IC regards this phenomenon as a serious risk and has a robust structure for addressing it.14 While the committee has no information about the IC’s strategies for deterring such acts and detecting them when they occur
14 Available: https://www.dni.gov/index.php/ncsc-what-we-do/ncsc-insider-threat [June 2018].
beyond what is publicly available, we offer insights from recent research that may be useful to the IC, and suggest research directions.
Research has provided a general portrait of the nature of insider threat (e.g., Cappelli et al., 2012; Irvin and Charney, 2014; Warkentin and Willison, 2009), demonstrating that no single underlying psychological, economic, or political cause is consistent across all such cases (Colwill, 2009). Many cases begin with an inadvertent leak of classified or proprietary information (Carley and Morgan, 2016). Research also has shown that individuals who become insider threats share certain characteristics with individuals who engage in other types of clandestine subversive behavior (Moore et al., 2015). For example, individuals who will later become insider threats often begin to reduce or limit contact with family and work colleagues; increase contacts with those to whom they will leak documents; and keep those individuals separate from colleagues, friends, and family members (Moore et al., 2015). Over time, they change their patterns of interaction, often dropping ties with some family members and work colleagues while establishing links with outsiders or malicious actors (Moore et al., 2015).
Researchers have brought additional perspectives to bear in elaborating this general portrait. The example of Chelsea Manning, a U.S. Army intelligence analyst who released more than 750,000 classified or sensitive U.S. military and diplomatic documents to WikiLeaks in 2013, offers a useful case for exploring the possibilities from various disciplinary perspectives.
Some of the insights gained from the Manning case are psychological. Manning has been characterized as a shy individual who is idealistic, psychologically troubled by struggles with gender identification issues, and socially inept. She has a troubled family history, and prior to the leaks had a limited personal network (see Nicks, 2012). While serving a tour of duty during the war in Iraq, she was also introverted and had a few negative and violent altercations (Brevini et al., 2013). She used the Internet to establish relationships, including with individuals who sought her confidence but later turned her in. She felt understood by the individuals who sought the leaks and noted during her trial that she had believed she would be helping people by sharing the leaked information (Brevini et al., 2013).
Manning exhibited many of the characteristics of a lone wolf (Marlatt, 2016; Ranieri and Barrs, 2011). Such an individual (who may become either a terrorist or an insider threat [e.g., Spaaij, 2010]) often, though not always, suffers from social ineptitude and psychological disturbances, and acts in illicit ways to address both personal frustration and social, political, or religious aims. Some work suggests that for many lone wolves, ideas of justice and empathy are more critical than association with a radical group in determining their choices (Moskalenko and McCauley, 2011).
Insider threat is also usefully viewed as a complex sociotechnical problem for which simulation models have been developed. For example,
Sokolowski and colleagues (2016) developed a model of the likelihood that each employee in an organization would become an insider threat, focusing in particular on how individuals become disgruntled. The model includes both individual-level factors (e.g., affect, risk tolerance, reward, threat) and organization-level factors (e.g., rate of change, organizational culture).15Casey and colleagues (2016) modeled insider threat as a compliance game. Their findings suggest characteristics of settings that are relatively protected from insider threat: organizational responses and policies with respect to trusting employees and checking their activities are difficult to predict, and employees are continually learning in response to changing circumstances. Such simulations could be used to assess the potential effects of complex interacting influences—in Manning’s case, for example, tensions between her religious upbringing and her change in gender identification, or between the disciplined culture of the army and the rebellious appeal of her Internet contacts.
In related work, the National Consortium for the Study of Terrorism and Responses to Terrorism (START) at the University of Maryland has assessed means of deterring insider threat using safety, security, and other systems.16 Other related areas of interest include identifying attributes most associated with individuals who choose to report possible threat behavior they witness and possibilities for promoting such choices (Bradley et al., 2017).
Another related issue is trust, although the term lacks a universally agreed-upon definition. In economics, trust is understood in the context of a cost-benefit calculation of material gain (or loss), a calculation of the likelihood of defection or mutual cooperation in a bargaining situation. In psychology, by contrast, the concept of trust implies a willingness to be vulnerable to another actor—a specific trustee—whose behavior cannot be controlled or monitored (Dunning, 2018; Mayer, 2018). In general, trust connotes a presumption that the other party will adhere to certain ethical or moral guidelines, whether explicit or implicit. Perceptions of ability, benevolence, and integrity all contribute to a decision about whether to trust an individual, group, organization, or information source (Mayer et al., 1995).
A sizable body of research has examined the dynamics of interpersonal trust, as well as the dynamics of trust within small groups and large organizations (Colquitt et al., 2007). Deception can occur only to the extent that its target—whether an individual or members of a group—is receptive to
15 This model was validated using comparison with measures of workforce ethics derived from a survey and observed level of misconduct. The model suggests that more than 40 percent of actors have the potential to be threats and that organizational climate can affect their decisions in this regard.
the false, misleading, or even partially true but selectively tailored information provided by the deceiver. That receptivity, in turn, is a function of the target’s trust in the information or the information source.
The potential for insider threat is one reason analysts are concerned with deception and trust, but of course external actors that analysts follow also deceive. They may manipulate intelligence by intentionally introducing information that is false, misleading, or partially true but selectively tailored; they may also introduce errors in logic into information collection channels with the objective of influencing an adversary’s judgments and actions (Lowenthal, 2016).17 The ability to recognize that deception may
17 The use of misinformation as part of an information operation is sometimes referred to as the “four Ds”: dismissing a current narrative, sowing distrust or dismay, or distracting the audience with a new narrative (Snegovaya, 2015).
be occurring, to evaluate truthfulness, and to assess credibility is critical for intelligence analysts (and collectors), especially when they are analyzing human-generated source data (e.g., videos, interviews, interrogations) (Heuer, 1999).
Many recent developments related to deception are occurring in the context of cyber activity, the subject of Chapter 6. Here we briefly review the traditional framework for the study of deception and the foundation it provides for intelligence analysis applications. For example, evidence supports the idea that understanding of emotion and nonverbal communication and behavior can be used in detecting deception and assessing credibility (Matsumoto and Hwang, 2018b; Matsumoto et al., 2014c; Novotny et al., 2018; Vrij et al., 2008; Warren et al., 2009). The effects of false information on such phenomena as hate crimes or violent protest have been studied, although the accuracy of predictions has been mixed (Alden and Parker, 2005; Hossain et al., 2018).
Other work points to possibilities for developing markers associated with both veracity and deception that could be used as indicators. Possibilities include verbal (Guitart-Masip et al., 2012), linguistic (Bachenko et al., 2008), and grammatical (Burgoon et al., 2012) markers, as well as eye and body movements (Zhang et al., 2013). Machine learning models are key tools for the development of such indicators: computational modeling is used to identify signals of veracity and deception in narratives, documents, images, videos, and other forms of communication (see, e.g., Bachenko et al., 2008; Burgoon et al., 2012; Hauch et al., 2012, 2015; Matsumoto and Hwang, 2018b; Zhang et al., 2013). It is important to note, however, that cultural understanding is critical to the development of effective indicators, and that pure machine learning models on their own have not been effective at identifying specific emotions that are important for motivation, judgment, and decision making (Hauch et al., 2015; Matsumoto et al., 2015a). More precise analysis of emotions, intentions, and preferences could be valuable to the IC. Similarly, SBS approaches such as the use of discourse analysis to evaluate the linguistic content of speech are needed to provide reliable indicators of tone, honesty, audience, formality, and cognitive decline.
In this chapter, complexity is discussed both as a discipline—the study of complex systems—and as a core challenge for the analyst. Almost by definition, analysts are responsible for making sense of sometimes extraordinarily complex circumstances. Many—perhaps most—of the actors and entities monitored by intelligence analysts are functioning as elements of a system, and perhaps multiple systems; isolated individuals rarely hold sig-
nificant power, and even seemingly lone actors may be subject to influences not immediately apparent. As discussed earlier in this chapter, as well as in Chapter 4, many of the problems analysts seek to understand are so-called “wicked” problems—ones that are unusually challenging and difficult to define and solve, and that involve complex competing factors that are difficult to reconcile. An example in international affairs is the collapse of the Soviet Union in the 1990s: analysts had no precedents to help them analyze what happens when a Communist superpower disintegrates. Under such volatile circumstances, reliance on previous models, even successful ones, can be particularly dangerous (see Box 5-10).
The general contributions of the study of complex systems were discussed earlier. Here we look more closely at developments in modeling and simulation and in human–systems integration that offer possibilities for intelligence analysis.
Modeling and Simulation
Work in the fields of complexity theory and network systems has clear applications to the analytic challenge of understanding complexity, offering the possibility of measuring, modeling, and interpreting complex phenomena and developments, as well as forecasting change.18 Scalable high-level network algorithms, for example, have made possible advances in modeling of the relationships between processes and networks. This type of modeling can support analysts in understanding the dynamic nature of systems, and in monitoring the emergence of new phenomena and move-
ments in the international system. Simulations developed in the context of computational social science are supported by data (quantitative, text) from real-world contexts but are also grounded in theory.19 Such simulations can be tested with real data and modified on the basis of their own results. Simulation has been used in the IC in a number of ways, including forecasting Soviet defense spending (Firth and Noren, 1998), predicting a subject’s decision making (Sticha et al., 2005), reasoning about a leader’s personality (Sticha et al., 2009), and predicting insider threat (Moore et al., 2011).
Tools for modeling and simulation can help analysts see connections among people, ideas, corporations, and other entities that are components of complex systems (see Box 5-11). These data networks are dynamic and often scale to billions if not trillions of nodes. Analyzing such networks is essential to understanding nonstate actors involved in global affairs, such as terrorist organizations, which often lack fixed organizational structures and constantly adapt to their environment; the emergence of new leaders and changes in power structure; vulnerability in nation-state alliances; and trade and hostility structures (see Box 5-12).
Tools that can be used in mining, analyzing, and visualizing high-dimensional network data (data that capture, for example, not only the substance of discussion but also where and when the discussion occurred, what changes it reflects, or how it is structured) are already available to the IC (Carley et al., 2015). These tools, which include those that can be used for
19 A model is a representation of a complex phenomenon, such as a system, in which mathematical procedures are used to represent the phenomenon’s parameters as accurately as possible; simulation is the use of a model to explore possible outcomes in the case of hypothetical changes.
both modeling and simulation,20 have been applied to such security-related challenges as deterrence of violent behavior (Davis, 2014), the settlement of Syrian refugees (Hattle et al., 2016), and the relationship between European Union policies on immigration and subsequent refugee crises (Melis, 2001).
Despite advances in this area and the increasing use of simulation by the Department of Defense and the IC, however, no one approach has yet proven adequate to the challenge of modeling complex real-world situations. For example, the Defense Advanced Research Project Agency’s (DARPA’s) Integrated Crisis Early Warning System did lead to a series of integrated, geographically based models for forecasting crises (Ward et al., 2012). But the strength of these models was that, using machine algorithms, they could assess change in the level of a critical event and the likelihood that another, similar event would occur in a region; the models could not forecast entirely unusual events, such as the Syrian refugee crisis. One of the key outcomes of this modeling effort, however, was an event database that can support future modeling efforts (Ward et al., 2013).
20 These include agent-based (Bonabeau, 2002; Davidsson, 2002; Van Dam et al., 2012) and agent-based dynamic-network models (e.g., Morgan et al., 2017); event-history models (Box-Steffensmeier, 2004); and system dynamic models (Mohaghegh et al., 2009; Sterman, 2001).
Society-level modeling is not yet fully developed for several reasons. First, this type of modeling is resource-intensive: it typically requires very large datasets that require careful cleaning and archiving. Thus the development of such models is quite time-consuming. At a theoretical level, moreover, existing models cannot readily handle multilevel systems, such as systems relevant to the IC in which individuals, organizations, and states interact, each affecting the behavior, learning, and adaptation of the others. In general, the more tuned to a specific problem a model is, the more costly it is to develop but the more informative are its results. Thus, models that accurately depict circumstances at the cognitive and task levels do not scale to the community or even large-group level. The reverse is also true: models that are informative about populations are inaccurate at the cognitive level.
Dynamic network modeling helps bridge this gap, as does modeling of social cognition (Morgan et al., 2017). However, models offering true multilevel accuracy and scalability do not yet exist; most simulation models are one-off. Efforts to develop testbeds that can support reuse and model integration have failed for reasons that include overly constraining components; premature ontologies; lack of support for multiple timeframes, spatial frames, and procedural frames; forced validation using inappropriate validation theory; and difficulties in creating and maintaining a comprehensive database for all component models.
Another issue is that validation methods for simulations of social systems are still in their infancy.21 Methods for modeling uncertainty and validating results developed in other contexts (Schefzik et al., 2013; Slotte and Smørgrav, 2008) do not readily transfer to social simulation models, which must include hundreds of variables and have many sources of nonlinearity. Many societal-level simulations, moreover, are centered on events for which there are no historical analogs, so that past data cannot readily be used for validation purposes. Thus while the science of validation is well developed for models of physical systems (Sargent, 2013), such is not the case for social simulation models.
In response to these challenges, researchers in the field are moving toward hybrid modeling and a “system of systems” approach that makes use of interoperable models (models designed to interface with one another). A key advantage of these models is that they make gaps in the underlying theories explicit; support comparison, integration, and development of theories; and allow users to create a framework with which they can rapidly reason about alternative explanations (useful forensically) or alternative
21 We note that the DARPA SocialSim program has the potential to promote improved models of social behavior and new testbeds and integration platforms in which diverse models can be linked together, tested, and evaluated; see https://www.darpa.mil/program/computational-simulation-of-online-social-behavior [October 2018].
courses of action (useful for planning) (Gilbert and Troitzsch, 2005). Yet while hybrid models are increasingly being used to link micro-, meso- and macro-level processes (such as those at the individual, organization, and state levels), such models are still difficult to reuse, take a long time to develop, may require massive amounts of data that need to be cleaned and fused. Generating the data needed for realistically sized, complex sociotechnical systems also takes a long time.
Computer simulation requires extensive resources, person power, and time. The Pentagon has those prerequisites, and so has been able to make good use of computer simulations over the years to predict the military responses of adversaries and to reason about other state and nonstate actors (National Research Council, 2008). Because of the rate of change in problems faced by the IC and the resources available, however, the IC’s use of computer simulation has been more limited. Before the IC can use computer simulation more extensively, the field will have to mature, the requirements will have to become less onerous, and the IC will have to invest in the needed research.
Application of Human–Systems Integration
Research in the field of human–systems integration (discussed in greater detail in Chapter 7) considers the integration of humans with technology in the context of a rich sociotechnical system (National Research Council, 2007). This research relies on methods for understanding a system at multiple levels and the interactions within and between those levels. Research in this area has led to the development of methods for understanding and modeling complex sociotechnical systems (Waterson et al., 2015). Agent-based models are another example of this approach (see, e.g., Sun, 2006). Methods developed to understand particular system levels (e.g., the team level) can be extended to the analysis of other levels (Cooke and Gorman, 2009). Nonetheless, these methods are often tedious and time-consuming, and research is therefore needed to develop human–systems integration methods that allow for real-time monitoring and automatic analysis of a system at all levels (Gorman et al., 2012). This research direction is promising not only for work on human–systems integration but also as an aid to the IC in understanding complex sociotechnical systems, subtle interactions within those systems, and possible unintended consequences.
The work explored in this chapter demonstrates the potential of SBS research to deepen, strengthen, and enhance the accuracy of intelligence analysis. We have examined a large and growing body of work that is
interdisciplinary, draws on multiple methods, and makes use of new types of data (especially data captured from cyberspace).
The examples we have discussed illustrate how basic research provides a theoretical and empirical foundation for the development of sophisticated analysis methods. For example, accurate computer modeling of complex, sophisticated sociopolitical systems rests on foundational understanding of the nature of status and power, socioemotional processes, and linguistic structures. The potential for developing such tools and approaches rests on the marriage of technological advances and insights from SBS fields. Examples include the use of digital trace data to assess the importance of network nodes that pose potential security threats or to track the trajectory of political ideas, the use of understanding of how nonverbal cues can enhance the power and influence of political messages to assess such messages, and the application of traditional techniques of narrative analysis to machine computational analysis of discourse among social media groups.
Sophisticated methods such as computational analysis of large datasets would make little sense without theoretical frameworks to guide the development of algorithms, such as those for classification of narrative structures or analysis of the functioning of social networks. Similarly, insights from SBS research are important to guide the development of implementable indicators of, for example, potentially consequential emotional states or changes in leaders or other powerful actors, the developing strength of a minority group’s message, or the cohesiveness of networks in which toxic narratives are spreading.
CONCLUSION 5-1: Developing research on narratives, social networks, complex systems, and affect and emotion can enhance understanding of primary targets of intelligence analysis, the potential impact of actions taken by the Intelligence Community, and individual and social processes relevant to security threats. This research offers possibilities for new tools, including but not limited to
- indicators for use in monitoring and detection of key security-related developments;
- algorithms for extracting meaning from large quantities of open-source information; and
- models for reasoning about the potential implications of various interventions or activities.
There is considerable variation in how directly SBS research has been applied to questions of interest to the IC, in how close it is to providing the basis for practical application for analysts, and in the aspects of intelligence analysis it could potentially support. Tools for social network analysis,
for example, are close to being operational, and simulation groups within the IC are using models to address complex situations. Research on other tools, such as those based on understanding of unconscious behavior and nonverbal cues, is still emerging. Nonetheless, existing SBS research provides the basis for a forward-looking program of interdisciplinary research aimed directly at leveraging developments in theory, understanding, and technology to support the work of the intelligence analyst at a time when the nature of threats to security is evolving.
Further progress in the development of applications that can serve the IC’s needs will depend on interdisciplinary collaboration. For example, the integration of recent advances in analysis of narratives, networks, and affect would provide a framework for supporting dramatic advances in the assessment of narratives and counternarratives, early detection of polarization, assessment of group vulnerability to disinformation, and detection and mitigation of diverse information maneuvers. Similarly, applying multidisciplinary, multimethods research to IC issues from a complexity perspective—using modeling and simulation, representation, and understanding of human factors—would yield significantly stronger methods of forecasting surprising events or developments.
Advances in the use of large-scale data are likely to be at the heart of significant developments for the IC in the coming decade, but new technologies will be only as strong as the understanding of the human behaviors they are used to model or explain. The committee anticipates progress in the development and validation of computational models, the reuse of simulation modes, and the integration of social networks with computational models, advances with the potential to enable near-real-time assessment of competing actors, messages, or groups and the interventions that influence them.
CONCLUSION 5-2: Interdisciplinary, multimethod approaches to integrating insights from social and behavior sciences fields with sophisticated technological developments will be essential to support the development of new tools for the analysis and interpretation of data and intelligence. The Intelligence Community would benefit from pursuing a portfolio of such research focused on the development of operational methods and tools.
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