A Decadal Survey of the Social and Behavioral Sciences A Research Agenda for Advancing Intelligence Analysis (2019) / Chapter Skim
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5 Sensemaking: Emerging Ways to Answer Intelligence Questions
5 Sensemaking: Emerging Ways to Answer Intelligence Questions
Pages 85-140
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From page 85... ...
: 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.
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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.
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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.
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. The Study of Social Networks Understanding the complex and dynamic relationships among and within social and institutional networks is an essential facet of intelligence analysis.
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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)
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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.
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; 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.
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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 7These methods include general algorithmic assessments (Flum and Crohe, 2006)
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Emotions serve intrapersonal, interpersonal, and sociocultural functions that all have implications for intelligence analysis (Hwang and Matsumoto, 2016; Keltner and Haidt, 1999; Levenson, 1999)
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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. APPLYING SOCIAL AND BEHAVIORAL SCIENCE RESEARCH TO SENSEMAKING FOR CORE ANALYTIC PROBLEMS 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.
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, 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.
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, 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)
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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)
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For example, social network analysis has built on traditional understanding of international relations (Hafner-Burton et al., 2009)
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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.
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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)
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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
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It is, moreover, not practical to solicit network data from covert adversarial actors. Network analysis of digital data.
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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.
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; • how online influence operations might be identified based on high- imensional d network behaviors; • the aspects of social networks that facilitate or impede the viral spread of ideas; • the specific emotions and sentiments underlying narratives that emerge from social network analysis that make those narratives powerful in different ways and drive changes in attitudes, values, and beliefs; and • the way networks and individuals' network positions are reshaped by emotional messaging and online influence operations. 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)
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, simulation (Carley et al., 2001) , mixed network analysis and simulation (Tsvetovat and Carley, 2005)
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. Specific areas such research could help illuminate include • the functioning, viability, and vulnerability of groups and organizations, such as terrorist groups, social movements, and leadership networks involving state actors; • the emergent properties of social networks, particularly dark networks, that make them resilient or susceptible to disturbances and direct interventions; • the role of redundancy in the functioning, adaptation, evolution, and resiliency of multidimensional networks (e.g., when multiple actors have access to the same resources, tasks, or other actors)
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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)
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Specific benefits to the IC might include improved understanding of • the influence of inter- and intrastate economic and political networks on the behaviors of strategic state and nonstate actors; • indicators and warnings based on changes in positive and negative network relations among nation-states that can help predict shifts in state actors' posi tions with respect to weapons of mass destruction; • the economic and political factors that affect how connected one state is to others (centrality) , as well as changes in those connections; • factors that contribute to the declining network dominance and influence of a nation-state; • the development and adaptation of global criminal and terrorist networks and the associated regional constraints on these networks; and • how shifts in such factors as international trade alliances, pacts, crypto-cur rency payments from one country to another, and trade routes (because of climate change)
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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)
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• How do such emotions as hatred and their verbal and nonverbal signals combine with other verbal and linguistic markers of threat, such as integrative complexity (the degree to which the reasoning of a writer or speaker integrates multiple perspectives and/or solutions to problems)
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A related possibility is to integrate research on integrative complexity and other social and behavioral sciences approaches (e.g., identifying affective signals of hatred) with technologies for automated flagging.
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. Red Hen Lab, a consortium that conducts commu nication research, has compiled a vast collection of data consisting of approxi mately 4 billion words and 360,000 hours of audiovisual broadcasts that can be searched using natural language processing and optical character recognition software.12 Researchers in such fields as linguistics, social networks, and machine learning are collaborating to develop ways to search such databases -- for exam ple, using linguistic forms and grammar patterns, semantic patterns, and topic i dentification -- to detect significant events or changes in discourse.
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Corporations are also affected by insider threat or industrial espionage (Crane, 2005)
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. Insider threat is also usefully viewed as a complex sociotechnical problem for which simulation models have been developed.
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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.
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Research Directions Conduct transdisciplinary research that brings together the disparate threads involved in the study of insider threat. No single approach has proven sufficient to explain insider threat, so a multidis ciplinary research approach is most likely to yield breakthroughs in the ability to identify potential insider threats and deal with this risk.
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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)
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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.
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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)
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These dangers can contribute to conflict when the relationships form criminalized power structures (Dziedzic, 2015)
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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)
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CONCLUSIONS 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
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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.
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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.
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In 2009 International Conference on Advances in Social Network Analysis and Mining. Institute of Electrical and Electronics Engineers.
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. Dynamic network analysis.
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. Integrating social networks and human social motives to achieve social influence at scale.
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Presentation sub mitted to the Committee on a Decadal Survey of Social and Behavioral Sciences and Applications to National Security, National Academies of Sciences, Engineering, and Medicine. Available: https://vimeo.com/258003972 [February 2019]
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. Dynamic network analysis in information operations: A process for near real-time assessment of atmospherics, socio-political landscape and leverage points.
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. Network analysis for interna tional relations.
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Galaskiewicz (Eds.) , Advances in Social Network Analysis (pp.
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. Social media, network heterogeneity, and opinion polarization.
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Presentation submitted to the Committee on a Decadal Survey of Social and Behavioral Sciences and Applications to National Security, National Academies of Sciences, Engineering, and Medicine. Available: https://vimeo.com/258004014 [February 2019]
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. A longitudinal social network analysis of peer influence, peer selection, and smoking behavior among adoles cents in British schools.
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. Social networks and organizations.
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. Social network analysis in the study of terrorism and political violence.
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. Network analysis of narrative content in large corporations.
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. Behavioral and policy issues in information systems security: The insider threat.
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. Internationalization and the performance of born global SMEs: The mediating role of social networks.
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