Strategic competition with near peer threats such as Russia and China replaced a focus on non-state extremist threats. Competition "success" is measured by domestic stability, strong alliances, networks and partners(1); the goal is greater influence in order to shape international norms, institutions.(2) In the current information saturated age, manipulation of truth is common and propaganda can be used to weaponize information in order to compete.(3) Diplomatic and economic strategies (e.g., Belt Road) are also important.(4) Competitive advantage requires national ambition, unified identity, will, effective institutions.(5) Analytics and modeling for strategic competition must characterize competitors across multiple information vectors (culture, cyber, diplomatic, social-cyber, economic, etc.) with "emic" (1st person), "etic" (3rd person) perspectives, leveraging expert-AI to contextualize information for situation awareness, planning, developing tactics for behavior change without war. This paper highlights the state-of-the-knowledge/art re: behaviors and assessment of strategic competition, spotlighting gaps, issues (e.g., inappropriate/outdated assumptions) and identifies future research areas.

Cognitive warfare is not new. Weaker parties in an asymmetric conflict have manipulated information and ideas to convince stronger opponents to not fight (e.g., the Trojan Horse). What is new is the extent to which technologies enable cognitive warfare – resulting in the delegitimization of governments by sowing discord and creating division in order to compel acceptance of political will Information sharing tools enable adversaries to interfere more directly than ever with national political processes as well as citizens minds3. Cognitive warfare is considered a new domain of warfare, along with land, maritime, air, space and cyber (technical). The goal of cognitive warfare attacks is to alter or mislead the thoughts of leaders and operators, of members of entire social or professional classes, of the men and women in an army, or on a larger scale, of an entire population in a given region, country or group of countries and impact territory, influence, service interruptions, transportation, etc. The means could be social cyber, cyber technical, electronic warfare, and broadcast, etc. Senior officers and strategists in the Chinese People’s Liberation Army (PLA) claim that AI, neuroscience, and digital applications (e.g., social media) will be able to influence enemies by affecting human cognition directly, Russia’s Gerasimov doctrine talks of the “battlespace of the mind”, Pocheptsov provides examples including creation of fake events and objects and organizing protest actions in Ukraine. Dr Giordano stated, “the brain is the battlefield of the future”. This paper will highlight current examples of cognitive warfare, touch on enabling technologies and relevant social science principles of influence (cognitive and social), highlight existing analytics, introduce the “House model” which identifies pillars of relevant fields of knowledge as well as operationally relevant aspects related to the pillars as potentially helpful framework for thinking about cognitive warfare and identifying needed research.

Incidents related to insider threats are steadily increasing, especially technology thefts.(1) Dr. Larry Ponemon wrote, "Insider threats are not viewed as seriously as external threats, like a cyber-attack. But when companies had an insider threat, in general, they were much more costly than external incidents. This was largely because the insider that is smart has the skills to hide the crime, for months, for years, sometimes forever."(2) Insider threat is a relatively rare occurrence, often perpetuated by revenge seeking employees with a grievance against their employer. The capitol assault was an eye-opening event in that it clearly demonstrated that insiders, in this case military and ex-military, were willing to engage in violence against the government. Detection of insider threat is a difficult problem as data is limited and the factors surrounding insider threat are highly contextual. Previous research tends to focus on theoretical perspectives and threat mitigation, frequently emphasizing cyber-technical indicators of insider threat versus focusing on the human behind the screen.(3) More behavior focused research has identified several psychosocial, individual-level risk factors for insider threat (e.g., disgruntlement, poor work performance, etc.) or has conducted personality assessments on known insiders post-hoc.(4,5) However, future directions in this line of research need to address early detection of mobilization as part of the defensive strategy against these threats. Subsequently, this paper will summarize the state-of-the-knowledge in terms of research on behavioral factors and approaches for insider threat detection, highlighting methods for assessing social-cyber information to enable early detection of insider threat.

Often after an act of violence, a forensic analysis of what the responsible individual(s) or group(s) said or wrote would reveal “signals” that would have foreshadowed the event. Although these signals frequently occur well in advance; they are often nuanced, requiring a different lens to find and interpret discursive patterns and practices related to social identity, affect, integrative cognitive complexity, trustworthiness, and worldview. Threat narrative is the behavioral (actions/words) manifestation of subjective reality regarding threat. These lenses help an analyst reason about how an individual or group sees themselves and others, their perception of threat and propensity to negotiate, cooperate or engage in violence. The result is a tomographic view, albeit imperfect one, of the threat narrative.The Air Force Research Laboratory (AFRL) has been engaged in research aimed at enabling meaning making from discourse regarding threat narratives for several years. Previous research developed multi-lingual methodologies (Arabic and Pashto), documented in primers transitioned to operational customers, including the National Air and Space Intelligence Center (NASIIC), which enable the detection and interpretation of discourse related to social identity (in-group/out-group) (Fenstermacher et. al. 2012). This paper will focus on two projects designed to enable meaning making from the analysis of discourse, one employing a systematic approach to creating codebooks for automated analysis, and another employing taxonomies for automated analysis of identity and intent.A grounded theory approach, using human coders, was used to identify relevant discursive practices and patterns (themes and rhetorical devices), including intensifiers used to express trust, trustworthiness or distrust in Farsi. Key themes were identified such as Islam, positive virtues, and advanced age and/or experience. Association with a trusted individual, expert citation, language related to intimacy and poetry were typically associated with trust. Conversely, distrust was conveyed in themes related to negative virtues and government agendas and by use of figurative language such as metaphors and allusions.An automated approach focused on understanding the link between affect and behaviors using quantitative models of the effects of emotions (eight classes coded: trust, fear, surprise, sadness, disgust, anger, anticipation and joy) on behaviors of competing actors in Syria, Egypt and the Philippines (e.g., a dissident group, government and population). This approach highlights similarities and differences in resulting behaviors. For example, in both Egypt and the Philippines, societal fear, anger and disgust toward dissidents resulted in increases in dissident hostility. Conversely, in Egypt, government hostility increased in response to societal disgust whereas in Philippines it decreased.This research effort identified several apparently independent features: idea density and vocabulary diversity (proxies for integrative cognitive complexity) and affect expressed regarding in-group and out-group. Preliminary results indicate that the combination of these features would enable accurate forecasting of Naxalite bombings (.92 in sample, .8 out of sample correlation between model and actual bombings). These results are promising but preliminary; the generalization and robustness of these factors relative to different groups and languages will be assessed in a newly started research effort.The coding methodologies and the text analytic algorithms are a significant step forward in assisting analysts to systematically interpret threat narrative related language, characterize sources and reason about future behaviors and influence as well as helping to mitigate information overload by cueing analyst attention to potentially relevant documents and important events.

Many scholars of contentious politics claim there is no such thing as a group that uses only one tactic, yet scholars, pundits, and the public routinely use single-minded terms like protestors, dissidents, and terrorists. Other scholars and research programs suggest that some groups are specialists who tend to stick to a single tactic to achieve their goals, such as non-violence, violence, or specific kinds of violence, like terror. We make the claim that both sides of the debate are empirically valid and that both types of group exist. That is, some groups tend to specialize in a single tactic while others use a variety of tactics. This paper examines the empirical distribution of group types by examining the mix of tactics that groups employ. The analysis helps resolve part of the debate and pushes scholarly thinking in new directions about how often, why, and when groups operate across this spectrum.

Abstract In terms of methods, researchers working in nationalism, ethnicity, and migration have used everything from broad historical narratives to automated coding and event data analysis. Traditionally, narratives were the dominant methodological approach implemented to study these areas. The narrative approach allowed for explication of groundbreaking theoretical arguments generating testable hypotheses, the deep inspection of particular areas of the world or particular issues with richness of detail and process, and the investigation of a small number of cases. In addition, the use of formal theory to explore issues related to nationalism, ethnicity, and migration also has a long tradition. Formal theory allows for the construction of concise decision making models that force the researcher to be explicit about key assumptions made regarding preferences and the political structure involved. The formal theory approach has encouraged greater specificity from the arguments formed by scholars of nationalism, ethnicity, and immigration and has generated important theoretical insights. Finally, the most rapidly expanding approach to the study of nationalism, ethnicity, and immigration over the past two decades has been statistics. Statistical analyses offer the advantage of being able to bracket confidence intervals around the causal inferences one makes and to more formally control for a variety of competing factors. As statistical technology and training have become more common, the use of statistics has grown substantially.

This project employs data extracted from unstructured text and quantitative behavioral models to understand, forecast, and mitigate US adversaries' aggressive actions against the US and our allies. We use a combination of quasi-experimental causal modeling and counterfactual assessment techniques to assess the effectiveness of US courses of action (COAs) to quell aggressive states’ hostile activities. Results illustrate actions may yield unintended consequences through their impacts on other contextual factors. Additional analyses employ forecasting and ensemble techniques to examine the likely anticipated consequences of various US COAs in future scenarios and cases. Ultimately, the data, methods, and results provide a useful decision-support tool for planners and analysts faced with how best to mitigate against unfavorable outcomes.

Automated event data extraction techniques have revolutionized the study of conflict dynamics through the ability of these techniques to generate large volumes of timely data measuring dynamic interactions among actors around the world. In this paper, we describe our approach for adapting these techniques to extract data on sentiments and emotions, which are theorized to crucially contribute to escalating and de-escalating conflict. Political scientists view political conflict as resulting from a series of strategic interactions between groups and individuals. Psychologists highlight additional factors in political conflict, such as endorsements and condemnations, the public's attitude toward its leaders, the impact of public attitudes on policy, and decisions to engage in armed conflict. This project combines these two approaches to examine hypotheses regarding the effects that different emotional impulses have on government and dissident decisions to escalate or de-escalate their use of hostility and violence. Across the two cases examined-the democratic Philippines and authoritative Egypt between 2001 and 2012-we found consistent evidence that intense societal fear of dissidents and societal disgust toward the government were associated with increases in dissident hostility. Conversely, societal anger toward dissidents was associated with a reduction in dissident hostility. However, we also found noticeable differences between the two regimes. We close the article with a summary of these similarities and differences, along with an assessment of their implications for future conflict studies.

Why does a dissident group go through phases of violence and nonviolence? Many studies of states and dissidents examine related issues by focusing on structural or rarely changing factors. In contrast, some more recent work focuses on dynamic interaction of participants. We suggest forecasting state–dissident interaction using insights from this dynamic approach while also incorporating structural factors. We explore this question by offering new data on the behavior of groups and governments collected using automated natural language processing techniques. These data provide information on who is doing what to whom at a directed-dyadic level. We also collected new data on the attitudes or sentiment of the masses using novel automated techniques. Since obtaining valid and reliable time-series public opinion data on mass attitudes towards a dissident group is extremely difficult, we have created automated sentiment data by scraping publicly available information written by members of the population and aggregating this information to create a pollof opinion at a discrete time period. We model the violence and nonviolence perpetrated by two groups: the Tamil Tigers in Sri Lanka and the Moro Islamic Liberation Front in the Philippines. We find encouraging results for predicting future phase shifts in violence when accounting for behaviors modeled with our data as opposed to models based solely on structural factors.

Why does a dissident group go through phases of violence and nonviolence? Many studies of states and dissidents examine related issues by focusing on structural or rarely changing factors. In contrast, some more recent work focuses on dynamic interaction of participants. We suggest forecasting state-dissident interaction using insights from this dynamic approach while also incorporating structural factors. We explore this question by offering new data on the behavior of groups and governments collected using automated natural language processing techniques. These data provide information on who is doing what to whom at a directed-dyadic level. We also collected new data on the attitudes or sentiment of the masses using novel automated techniques. Since obtaining valid and reliable time-series public opinion data on mass attitudes towards a dissident group is extremely difficult, we have created automated sentiment data by scraping publicly available information written by members of the population and aggregating this information to create a pollof opinion at a discrete time period. We model the violence and nonviolence perpetrated by two groups: the Tamil Tigers in Sri Lanka and the Moro Islamic Liberation Front in the Philippines. We find encouraging results for predicting future phase shifts in violence when accounting for behaviors modeled with our data as opposed to models based solely on structural factors.

This study uses counterfactual methodologies and empirical data to analyze the impacts of specific United States counter-insurgent (COIN) strategies on violent political conflict. The authors design a quasi-experiment in which they match cases across US Diplomatic, Information, Military, and/or Economic - DIME- actions (i.e., treatment variables) and then statistically analyze the impacts of such DIME actions on levels of political violence. For the purposes of this study the authors focus on the effects of military training in India and the impacts of other various DIME indicators on violence in the Philippines and Indonesia. The results reveal that some DIME actions have the expected effects while others have counterintuitive effects.

While some of the intrastate war literature calls for the disaggregation of civil conflict, most of those studies focus on the geography of civil conflict failing to take into account the various actors involved in such conflicts. This study addresses the multi-actor nature of civil conflict by examining whether 'actor aggregation' affects the inferences drawn from quantitative studies of civil conflict. Using two cases, Cambodia (1980-2004) and Indonesia (1980-2004), the authors examine how multiple dissident groups' behavior aggregated together can affect the inferences drawn from quantitative studies of government-dissident interactions. The results demonstrate that researchers may draw different inferences and commit both Type I and Type II errors using different actor aggregations. The results have myriad implications for the study of civil conflict and conflict processes.

This article describes a new machine-coded event data set specifically designed to study the spatially, temporally, and tactically disaggregated actions of multiple state and nonstate actors in a systematic fashion. The project develops an extensive set of dictionaries for multiple actors and employs a new coding scheme to organize information on such actors and their behavior. The author describes the machine content-analysis methods used to collect the data and the newly developed coding scheme.

This article explores data relating to public support for Iran’s nuclear program, using data from 2006 collected by Fair working with a consortium of institutional partners. The authors evaluate a few general hypotheses from the literature regarding support for nuclear programs using these new data from Iran. Before presenting empirical results, they first provide some background to this poll and some of the challenges that the team encountered. This discussion illuminates both the strengths and weaknesses of the data that undergrid this study. Second, they address some of the questions about the relevance and integrity of data collected by the consortium and used in this analysis. They address forthrightly whether or not public opinion matters in a country like Iran and whether polling of Iranians is a useful exercise given the degree of coercion which is ascribed to the regime. They present the top-line results of questions germane to Iranian support for its country’s program and some reasons cited for this support as well as results from logistic regressions, which focuses on key outcome measures which allows them to identify independent variables that explain variation in selected dependent variables. They proffer several analytical lessons that can be drawn from this exercise.

Iran's nuclear programme has brought ever-sharpening conflict with Israel, the United States, and the European Union. The Iranian public has been actively drawn into this debate, as the Iranian government cultivates support for its actions and by foreign appeals for change (including Bush administration support for regime change). This article explores data relating to public support for Iran's nuclear program. We utilize data from a nationally representative, face-to-face poll fielded in Iran in late 2006. The poll (n = 1,000) queried respondents about numerous domestic and external security concerns, including Iran's ‘full nuclear fuel cycle’ program. We present data on Iranian beliefs about Iran's nuclear program and the determinants of those beliefs. After discussing poll methodology and data integrity, the paper presents summary statistics on key variables about the nuclear program. We estimate three logit models to explain respondent beliefs about the program. The dependent variables address support for the program, the economic importance of the nuclear program, and beliefs that Iran will weaponize. Iranians’ support for the program correlates with perceived status and deterrence benefits conferred by the program and opinions of the United States. Respondents’ concerns about Israel do not drive support for the program. The paper concludes with a discussion of lessons learned from this study for future work of this type in Iran or other coercive environments.