A Decadal Survey of the Social and Behavioral Sciences A Research Agenda for Advancing Intelligence Analysis: Digest Version (2019) / Chapter Skim
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2 Opportunities for the Intelligence Community
2 Opportunities for the Intelligence Community
Pages 5-22
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From page 5... ...
. They identified significant research opportunities in four key areas: • sensemaking: emerging ways to answer intelligence questions; • enhancing security in cyberspace; • supporting the design of a human–machine ecosystem; and • strengthening the analytic workforce for future challenges For each of these areas the committee identified specific ways the IC could benefit from research developments that can be reasonably expected in the coming decade, if priority is placed on supporting this work.
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Members of the IC specifically expressed interest in improved ways to deter mine the usefulness of and analyze information and data; effectively communicate findings to decision makers; monitor and measure evolving events; model and understand complex, multiple-actor phenomena; and avoid errors and biases in decision making. The committee identified research that is on the cusp of sup porting significant progress with those objectives, highlighting work that has high potential for impact on urgent national security priorities; has a strong supporting evidence base; is somewhere along the research continuum from basic research, to field testing and evaluation, to applied research; and offers the potential to use or develop emerging data sources, methods, or other technical advances.
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7 SOURCE: Generated by the Committee on a Decadal Survey of Social and Behavioral Sciences for Applications to National Security.
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Machine learning techniques have opened up possibilities for developing effective indicators of growing extremism or potential for violence in narrative streams. The Study of Social Networks Social network analysis is a structural approach to understanding the world based on the interdependencies among actors and their influences on behavior.
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The study of these kinds of phenomena, including the verbal and nonverbal signals of affective states, can provide insights into the mindsets, personalities, motivations, and intentions of the actors whom intelligence analysts seek to understand; help explain people's actions, judgments, and decisions; and support more nuanced and sophisticated understanding of communication. Research has provided strong support for the validity and reliability of interpretations of nonverbal expressions of specific emotional states and signals of other cognitive and emotional states.
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Box 6 Potential Application: Using Dynamic Network Analysis to Track Security-Related Developments Dynamic network techniques can support understanding of, for example, how groups or regions transition from stability to instability and how factions form. These techniques may also provide indicators and metrics of reductions or increases in the power of key actors, help identify emergent groups, and aid in identifying anomalous network activity.
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Similarly, insights from SBS research are important guides for the development of indicators that could be used to track, for example, significant 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 1: Developing research on narratives, social net works, complex systems, and affect and emotion can enhance understanding of primary targets of intelligence analysis, the poten tial impact of actions taken by the IC, and individual and social processes relevant to security threats.
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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 phenomena they are used to model or explain. The committee expects that there will be progress in the development and validation of computational models, the reuse of simulation modes, and the integration of social networks with computational models.
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However, available machine learning techniques and standard computer science methods are of limited utility for answering nuanced questions about developing situations. Nor are traditional social science methods sufficient to address complex issues in today's information environment.
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These insights could support development of the capacity to, for example, detect bots and malicious online actors and track the impact of social cyberattacks. CONCLUSION 4: The IC could strengthen its capacity to safe guard the nation against social cyber-mediated threats by support ing research with the objectives of developing • generally applicable scientific methods for assessing bias in online data, drawing conclusions based on missing data, and triangulating to interpolate missing or incorrect data using multiple data sources; and • new computational social science methods that would simul taneously consider change in social networks and narra tives within social media–based groups from a geotemporal social-cyber perspective; and operational computational social science theories of influence and manipulation in a cyber- mediated environment that simultaneously take into account the network structure of online communities, the types of actors in those communities, social cognition, emotion, cogni tive biases, narratives and counternarratives, and exploitable features of the social media technology.
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and machine learning in conjunction with social network analysis, which is likely to be an increasingly important component of analysis. This work could also support the development of an ecosystem for intelligence analysis composed of human analysts and semiautonomous AI agents, operating on and through diverse social media and supported by other technologies.
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provide information to the human and AI agents for a number of different purposes, from monitoring and analyzing data pertinent to intelligence analysis to collecting and processing data from interactions between analysts and AI to improve performance. SOURCE: Generated by the Committee on a Decadal Survey of Social and Behavioral Sciences for Applications to National Security.
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CONCLUSION 6: An SBS research agenda to support the devel opment of technologies and systems for effective human–machine teams for intelligence analysis should include, but not be limited to, the following goals: • Apply methodologies from the vision sciences, the behavioral sciences, and human factors to advances in data visualization to improve understanding of how people extract meaning from visualizations and the functionality of tools designed to present information from large datasets. • Use techniques from social network analysis to better under stand how information can be transmitted effectively, as well as filtered among distributed teams of humans and machines, and how the need to use AI to search and filter information can be balanced with the need to restrict access to certain information.
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CONCLUSION 7: The design, development, and implementation of a system of human–technology teams, which would include autonomous agents, for use in intelligence analysis raise import ant ethical questions regarding access to certain types of data; authority to modify, store, or transmit data; and accountability and protections when systems fail. The IC could best ensure that such systems function in an ethical manner and prepare to address unforeseeable new ethical issues by • from the start, incorporating into the design and development process collaborative research, involving both members of the IC and the SBS community, on the application of ethical prin ciples developed in other human–technology contexts to the IC context; • ensuring that all research supported by the IC adheres to the standards for ethical conduct of research; and • establishing a structure for ongoing review of ethical issues that may arise as the technology develops and new circum stances arise.
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To take advantage of these opportunities, the analytic workforce will need new skills: developments in such areas as network science, complex systems models, statistics, and data analytics of all kinds will likely add new methods and tools to the analyst's toolbox. In areas in which intelligence analysts are expert -- qualitative analysis of text and narrative, for example -- new developments such as improved quantitative methods for text analysis, including methods for analyzing social media, offer possibilities that may not yet have been integrated into common practice within the IC.
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CONCLUSION 9: A large body of SBS research identifies indi vidual and organizational factors linked to employee retention, including employees' attitudes and engagement, unit cohesiveness, and leader quality, but these factors have not been examined in the IC context. Translational work examining the role of these poten tial influencing factors could aid in managing retention in the IC.
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Translating key insights about selection, training, retention of, and support for the IC analytic workforce will in itself require a team approach in which members of the IC, SBS researchers, applied scientists, and others collaborate to help translate the approaches discussed here for the IC context and assess their effectiveness.
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