center title, click to go to center home page .
click for Air University home page research and theory

home
about us
methodology
gov. resources
analysis tools
questions
research
links
search

see also
io research
influence ops
info-ops ref
culture
language
psy of terr

.

  • see also analysis & assessment tools page

    graph of adversary behavioral origins
    Adversary Behavior Origins
    (click on image for larger version)



    • please see disclaimer about links, and privacy and security notice ... contact us
    page reviewed/updated 15 Dec 2011

    Overview ___return to top
    • Ministry of Defence Joint Doctrine Pub 04, Understanding, Dec 2010
      • Understanding provides the context for the decision-making process which informs the application of national power. The purpose of understanding is to equip decision-makers at all levels with the insight and foresight required to make effective decisions as well as manage the associated risks and second and subsequent order effects.
      • The human domain concerns the interaction between human actors, their activity and their broader environment. It is defined as the totality of the human sphere of activity or knowledge. This broad environment is shaped by 4 principal factors: the culture that affects how they interpret and orient themselves towards that environment; the institutions which embody cultural ideas as practices; the technology and infrastructure that people assemble to survive in their environment; and the physical environment in which people live. The human domain framework considers these 4 areas as environments (cultural, institutional, technological and physical) to capture the interaction between human actors and their wider environment. The framework takes the approach that considering the role of people as actors on the global stage - as states, non-state actors, populations, organisations, groups and individuals – provides insufficient depth to develop effective understanding. Actors must be set within their cultural, institutional, technological and physical environments to provide the appropriate context for developing understanding.

    • Behavioural Conflict: From General to Strategic Corporal: Complexity, Adaptation and Influence, by Mackay and Tatham, Shrivenham Paper number 9, Defence Academy of the United Kingdom, Dec 2009

    • Behavioral Modeling and Simulation: From Individuals to Societies, edited by Zacharias et al, Committee on Organizational Modeling from Individuals to Societies, National Research Council, 2008
      • The National Research Council was asked by the U.S. Air Force to review relevant IOS [individual, organizational, and societal] modeling research programs in the various research communities, evaluate the strengths and weaknesses of the programs and their methodologies, determine which have the greatest potential for military use, and provide guidance for the design of a research program to effectively foster the development of IOS models useful to the military.
      • Chapter 3 - Verbal Conceptual Models and Verbal Cultural Models
      • Chapter 9 - State of the Art with Respect to Military Needs
      • Chapter 11 - Recommendations for Military-Sponsored Modeling Research
      • Appendix B - Exemplary Scenarios and Vignettes to Illustrate Potential Model Uses
      • Appendix C - Candidate DIME/PMESII Modeling Paradigms

    • Information Research/Collection Guide for Analysis and Behavioral Assessment of Adversary/Other Actors, by Jannarone, Sep 2007

    • What is a computational social model anyway?: A Discussion of Definitions, a Consideration of Challenges, and an Explication of Process (local copy), by Turnley and Perls, Galisteo Consulting Group, for the Defense Threat Reduction Agency (DTRA), Sep 2008

    • Introduction to Causal Modeling, Bayesian Theory and Major Bayesian Modeling Tools for the Intelligence Analyst (local copy), by Anthony, USAF National Air and Space Intelligence Center (NASIC)

    • Computational Models for Belief Revision, Group Decisions and Cultural Shifts (local copy of slides), 2006 - examines methods, models, goals for research, and proposed projects
      • MURI Objective: models that forecast patterns of behavior when social networks respond to external pressures.

    • Combating Terrorism: Research Priorities in the Social, Behavioral and Economic Sciences (local copy), by National Science and Technology Council (NSTC), 10 Feb 2005

    • Operational Net Assessment: A Framework for Social Network Analysis (local copy), by Hannan, in IO Sphere, Fall 2005
      • Social network analysis tools cannot be honestly sold as the sole determinant for success. Ideas, systems, and metrics are moving in the right direction, but gaps remain. While analysts cannot fully eliminate preconceptions and error, they can leverage effort to tamp it down. One must select the models that best fit and ignite the white heat of analysis.
        Joint Forces Command, in concert with the Intelligence Community, must engage Centers of Excellence to develop more adaptive social network research capabilities. We do not yet have reliable “devil’s advocate” analytical systems, and work is needed to improve analytical tools for military decision-making and planning.

    Air Force Institute of Technology (AFIT) ___return to top

    • Social Network Analysis Research at AFIT , briefing by Deckro, Perry, Geffre, Herbranson, and Seder, at the March 2007 BIAC conference on analysis tools

    • A System Dynamic Model of Leader Emergence (local copy), by Wever, AFIT, Mar 2008
      • The purpose of this thesis is to develop a system dynamics model of leader emergence. Longitudinal social network and personality data were collected in a class of enlisted military professionals attending a six week leadership development course. Findings support known relationships in existing leadership research. This thesis demonstrates the applicability of system dynamics toward the complex social phenomena of leader emergence.

    • Predicting Group Performance Using Cohesion and Social Network Density: a Comparative Analysis (local copy), by Peterson, AFIT, Mar 2007
      • Group performance has been an important topic as evidenced by an extensive literature review that has supports a positive relationship between group cohesion and performance. Social network researchers have also found similar relationships between cohesion and group performance using social network density as a proxy for cohesion. The traditional cohesion construct is measured using an attitudinal instrument that relies on member perceptions that are aggregated at the group level. The density construct, on the other hand, is based on social network relations which are based on behaviors and actual member interactions and relationships. Considering these differences, although both cohesion measures have been shown to predict group performance, it is important to understand their subtle differences in order for leaders to accurately understand how to influence each.

    • A Layered Social and Operational Network Analysis (local copy), by Geffre, AFIT, Mar 2007
      • To provide maximal disruption to a clandestine/terrorist network’s ability to conduct missions, we must develop a means to determine the individuals’ importance to the network and operations. In a network centric world, this importance is represented as an additive value of their criticality across the convergence of multiple layers of network connections. The connections layers of the network are comprised of social layers (Acquaintance, Friendship, Nuclear Family, Relatives, Student-Teacher, and Religious Mentors, Reverent Power and others), as well as layers representing interactions involving Resources, Knowledge/Skills and Temporal Local. The social criticality of an individual is measured by centrality.

    • Analysis of Layered Social Networks (local copy), by Hamill, AFIT, Sep 2006
      • Prevention of near-term terrorist attacks requires an understanding of current terrorist organizations to include their composition, the actors involved, and how they operate to achieve their objectives. To aid this understanding, operations research, sociological, and behavioral theory relevant to the study of social networks are applied, thereby providing theoretical foundations for new methodologies to analyze non-cooperative organizations, defined as those trying to hide their structure or are unwilling to provide information regarding their operations.

    • Theory of Effectiveness Measurement (local copy), dissertation by Bullock, AFIT, Sep 2006
      • Another assumption of this research is that observations are based strictly on outward behavioral system attributes. This passive approach will always have more error and uncertainty associated with it since the time between the observed behavior and the action that produced the observed behavior will rarely be instantaneous. If the effectiveness measurement framework developed in this research could be linked with internal models of the system of interest, not only could the error and uncertainty be significantly mitigated, but system state changes from a given action could be forecasted.

    • Gauging the Commitment of Clandestine Group Members (local copy), by Downs, AFIT, March 2006
      • This thesis uses Decision Analysis principles, specifically a Value-Focused Thinking-like approach, to develop an initial hierarchal model of significant factors influencing an individual’s commitment to a terrorist organization, or any clandestine group of violent extremists. Individuals are evaluated and scored according to the model to identify exploitable vulnerabilities in their commitment level. This information is then used to identify fissures of the entire organization that can be used to diminish the cohesion of the group.

    • Modeling and Analysis of Clandestine Networks (local copy), by Clark, AFIT, March 2005
      • This thesis develops a new proxy measure of pair-wise potential influence between members of a network, a Holistic Interpersonal Influence Measure (HIIM). The HIIM considers the topology of the multiple formal and informal networks to which group members belong as well as non-network characteristics such as age and education level that may indicate potential influence. The HIIM, once constructed results in a network of pair-wise potential influence between group members.
      • In addition to an overall measure of influence, the HIIM methodology provides important intermediate results such as the development of operational group profiles.
      • The methodology is applied to open source data on both Al Qaeda and the Jemaah Islamiah (JI) terrorist networks. Key leaders are identified, and leadership profiles are developed. Further, a parametric analysis is performed to compare influence based on individual characteristics, network topology characteristics, and mixtures of network and non-network characteristics.

    • Aggregation Techniques to Characterize Social Networks (local copy), by Sterling, AFIT, Sep 2004
      • Social network analysis focuses on modeling and understanding individuals of interest and their relationships. Aggregation of social networks can be used both to make analysis computationally easier on large networks, and to gain insight in subgroup interactions.

    • Modeling and Analysis of Social Networks (local copy), by Renfro, AFIT, Dec 2001
      • Social networks depict the complex relationships of individuals and groups in multiple overlapping contexts. Influence in a social network impacts behavior and decision making in every setting in which individuals participate. This study defines a methodology for modeling and analyzing this complex behavior using a Flow Model representation. Multiple objectives in an influencing effort targeted at a social network are modeled using Goal Programming. Value Focused Thinking is applied to model influence and predict decisions based on the reaction of the psychological state of individuals to environmental stimuli.

    • A Social Network Analysis of the Iranian Government (local copy), by Renfro and Deckro, AFIT, June 2001

    • Modeling Transnational Terrorists' Center of Gravity: an Elements of Influence Approach (local copy), by Hetherington, AFIT, Mar 2005
      • This research effort suggests a single COG, Public Support as the transnational terrorists’ key driver. An influence diagram-like approach was used to collect, organize, and display the COG and its key elements of value. These qualitative influence diagrams serve as a basis to develop a system dynamics model where quantitative measures were applied to the interactions. A prototype model capable of capturing and utilizing time-persistent and higher order effects that provides insight to decision makers regarding alternative strategic policies and courses of action (COA) against transnational groups has been developed and illustrated against a notional transnational terrorist group
      au/awc/awcgate/afit/sylvester.pdf">Influence of Anonymity in a Group Problem-Solving Environment (local copy), by Sylvester, AFIT, Mar 2000
      • Overall, the conclusion of this study suggests that process anonymity has a weak detrimental effect on problem-solving groups in terms of improvement in decision quality and satisfaction with the group outcome.

    Air Force Research Laboratory(AFRL) ___return to top

    • see also analysis & assessment tools page

    • Air Force Research Laboratory(AFRL)
      • AFRL, Human Effectiveness Directorate, Warfighting Interface Division, Cognitive Systems (AFRL/HECS)
      • AFRL, Information Directorate, Command and Control Engineering (AFRL/IFSA)
        • Java Causal Analysis Tool (JCAT)

    • FY10 Air Force SBIR Research Topics (AFRL is responsible for the implementation and management of the Air Force SBIR Program)
    • FY07 Air Force SBIR Research Topics (AFRL is responsible for the implementation and management of the Air Force SBIR Program)
      • AF071-039
      • TITLE: Behavior Signatures
      • OBJECTIVE: Develop a new class of multi-attribute behavior signatures to enable the anticipation of enemy activities.
      • DESCRIPTION: Asymmetric warfare and operations against transnational terrorist groups requires new methods and techniques to better anticipate potential adversaries actions. New classes of multi-attribute behavior signatures are required to predict adversary intent and anticipate their likely courses of actions (COA). These behavior signatures could potentially draw upon multiple streams on intelligence data, possibly over long temporal durations, to provide direct and indirect indicators, or fingerprints, of activities of interest. From a conceptual perspective, behavior signatures can be thought of as schemas or frames whose attributes delineate a set and arrangement of characteristics, or patterns of activity, that define the behavior of potential threat entities to include individuals, groups, organizations, societies, and nations/states. Behavior signatures can be developed from focused knowledge about the identity of interest; they will define the entities methods of operations. The activation of a behavior signature is not all or none. Rather, a partial activation, where some but not all of the attributes are matched, might trigger a request for additional surveillance or a reprioritization of ongoing analyses of intelligence data. Behavior signatures could, for example, be implemented as intelligent agents or a case-based reasoning system that monitors streams of intelligence data. Research is needed to define select initial signature libraries, to explore what type of architecture would be required to instantiate behavior signatures as a computational system, and identify which Air Force systems would benefit most from behavior signatures technology. Initial libraries could focus on, for example, insurgents’ activities within a theater of operations or terrorists’ activities within an urban (Western) environment. Potential systems behavior signatures could be embedded in distributed ground control system (DGCS) or a command center, such as those employed in uninhabited air vehicle operations.

    • AFRL/HE Modeling Program, 4 May 06 (local copy) - briefing slides
      • Agent-Based Modeling and Behavioral Representation (AMBR)
      • DMSO Human Performance Modeling Program
      • Behavior Signatures and Models
      • Research on Culture, Personality, and Society

    • Asynchronous Chess: a Real-Time, Adversarial Research Environment, by Lawton et al, AFRL, Mar 2006
      • Asynchronous Chess (AChess) is a platform for the development and evaluation of real-time adversarial agent technologies. It is a two-player game using the basic rules of chess with the modification that agents may move as many pieces as they want at any time. Modifying chess in this way creates a new robust, asynchronous, real-time game in which agents must carefully balance their time between reasoning and acting in order to out-perform their opponent. As a fast-paced adversarial game, many challenges relevant to real-word application arise which give it merit for study and use.

    • Intent Driven Adversarial Modeling (local copy), by Gilmour et al, presented at the 10th International Command and Control Technology Symposium (ICCRTS), 14 June 2005 (slides)
      • The increase in limited conflict warfare has created new challenges in mission planning and simulation. New approaches to warfare planning, such as effects based operations and predictive battlespace awareness, have also increased the need for improved simulations. An important part of simulation for mission planning is the creation and exercise of realistic adversary responses to friendly force actions. Typical “flipped” response approaches, while adequate when facing a doctrine based opponent are no longer sufficient with the types of less predictable, less organized adversary forces commonly faced in modern battlefield scenarios. The Emergent Adversarial Modeling System, or EAMS, is under development to address this shortfall. EAMS is being developed by a team of researchers from Securboration and the University of Connecticut under the direction of the Information Directorate of the Air Force Research Laboratory.

    • Real-Time Course of Action Analysis (local copy), by Gilmour et al, presented at the 10th International Command and Control Technology Symposium (ICCRTS), June 2005 (slides - 12 Mb file)
      • A significant research challenge for wargaming is predicting and assessing how friendly actions result in adversary behavioral outcomes, and how those behavioral outcomes impact the adversary commander’s decisions and future actions. The focus of this research is to develop technologies to assist decision makers in assessing friendly COAs against an operational-level adversarial environment.

    • Commander's Predictive Environment (CPE)-Understand the Battlespace - synopsis posted at FBO.GOV of solicitation Reference-Number-BAA-06-07-IFKA, 18 Aug 2006 modification
      • The objective of the Commander's Predictive Environment (CPE) program is to provide a decision support environment that enables the Joint Force Commander / Joint Force Air Component Commander (JFC/JFACC) to better anticipate and shape the future battlespace. Key objectives of CPE are to design, build, test, integrate, and evaluate tools to support and enhance the Joint Intelligence Preparation of the Battlespace (JIPB) and Joint Air Estimate Process (JAEP) processes.
      • Air Force Research Laboratory (AFRL) is soliciting white papers for developing innovative and critical technologies necessary to make the CPE capability viable. White papers are sought addressing any or all of the following four areas:
        1. Defining and Understanding the Operational Environment
          • To assist in understanding the operation environment, a System-of-Systems Analysis (SoSA) is employed, treating the battlespace as an interrelated system across Political, Military, Economic, Social, Information, and Infrastructure (PMESII) dimensions. This process attempts to 1) Model and analyze adversaries, self, and neutrals as a complex adaptive system; 2) Understand key relationships, dependencies, and vulnerabilities of adversary/self/neutrals; and 3) Identify leverage points that represent opportunities to influence capabilities, perceptions, decision making, and behavior. The objective is to develop computer-based modeling and simulation capabilities that describe and project the complex dynamics of the operational environment (across PMESII dimensions) to better understand adversary/neutrals/self strengths, capabilities, vulnerabilities, and critical gaps. Technology needs include behavior models, model integration frameworks, and model development environments.
          • Behavior Signatures: AFRL is interested in developing behavior signatures which can be thought of as schemas or frames whose attributes delineate a set and arrangement of entity characteristics and patterns of activity that define the behavior of potential threat entities. The entity may be individuals, groups, organizations, societies, and/or nations/states. The behavior signatures are envisioned as being dynamic, active monitors drawing upon multiple streams of intelligence data, possibly over long temporal durations, to provide direct and indirect indicators, or "fingerprints", of activities of interest. Behavior signatures can be developed from focused knowledge about the identity of interest; they will define the entities methods of operations. Research is needed to define signature libraries; develop analytic methodologies for assessing adversaries; provide a sensemaking support environment for the creation, interpretation, and exploitation of signatures and models; explore what type of architecture would be required to instantiate behavior signatures as a computational system; and identify which air force systems would benefit most from behavior signatures technology.
        2. Course of Action Development, Analysis, Comparison, and Selection.
        3. Forecasting and Predictive Planning.
        4. Experimentation and Evaluation.

    Multidisciplinary Research Program of the University Research Initiative (MURI) ___return to top

    • About MURI

    • FY2009 MURI competition topics included
      • Grounding Language Understanding in Cognitive Architecture
      • Multi-Scale Fusion of Information for Uncertainty Quantification and Management in Large-Scale Simulations
      • Information Dynamics In Networks
      • Network-based Hard/Soft Information Fusion
      • Cyber Situation Awareness
    • FY2008 MURI competition topics included
      • Real-Time Methods for the Analysis of Networks
      • Socio-Cultural Modeling for Understanding Asymmetric Threat Environments
      • Assured Information Sharing
    • FY2007 MURI Program Award Winners included
      • Building Bridges between Neuroscience, Cognition, and Human Decision Making
    • FY2006 MURI Program Award Winners included
      • Cognitive Architecture for Reasoning About Adversaries, by Dana Nau, U. of Maryland-College Park - MURI category: Dynamic, Adaptive Techniques for Adversary Behavior Modeling

    • Computational Models for Belief Revision, Group Decisions and Cultural Shifts (local copy of slides), 2006 - examines methods, models, goals for research, and proposed projects
      • MURI Objective: models that forecast patterns of behavior when social networks respond to external pressures.

    • Belief Dynamics & Decision Making, MURI at MIT

    • Integrating Intelligent Assistants into Human Teams , host site at Carnegie Mellon U.

    • MURI research thrust areas, U. of Arizona
      • Connections between Mathematical and Behavioral Decision-Making Models
      • Modeling and Exploiting Decision-Making Weaknesses in Enemy Behavior
      • Application Study on Sequential Search Problems
      • Software Models for Human Decision-Making

    • News story at U. of Maryland-College Park, about the Cognitive Architecture research project above
      • The three-year project is funded at $3.4 million, with an option for two additional years. The project is within the “Dynamic, Adaptive Techniques for Adversary Behavior Modeling” MURI category and will be funded by the Air Force Office of Scientific Research (AFOSR).
      • The project will develop theory and algorithms for a cognitive architecture for reasoning about adversaries. This architecture will initially estimate adversary models, then automatically modify and continuously update them for a variety of DOD customers and client programs. The researchers will develop ways to access and integrate diverse databases; dynamically learn what adversaries think; create an adversary modeling language; learn, calibrate, and maintain adversary models; construct a strategy development architecture; and develop an adversarial game testbed.
      • The cross-disciplinary team includes Professor Michael Fu (BGMT/ISR) and Barry Silverman (UPenn), who are experts in operations research; Nau, a game-tree search and planning specialist; Philip Resnik (Linguistics); political scientist Jonathan Wilkenfeld (Government and Politics); machine learning and data mining researchers Subrahmanian and Lise Getoor (CS/UMIACS); and Marvin Weinbaum, a cultural expert on Afghanistan and Pakistan.

    Carnegie Mellon University ___return to top

    • see also analysis & assessment tools page

    • Carnegie Mellon University, Center for Computational Analysis of Social and Organizational Systems (CASOS)
      • CASOS brings together computer science, dynamic network analysis and the empirical study of complex socio-technical systems. Computational and social network techniques are combined to develop a better understanding of the fundamental principles of organizing, coordinating, managing and destabilizing systems of intelligent adaptive agents (human and artificial) engaged in real tasks at the team, organizational or social level. Whether the research involves the development of metrics, theories, computer simulations, toolkits, or new data analysis techniques advances in computer science are combined with a deep understanding of the underlying cognitive, social, political, business and policy issues.
      • CASOS Projects

    Dartmouth College ___return to top

    • Dartmouth College, Thayer School of Engineering, Distributed Information and Intelligence Analysis Group (DI2AG)
      - research areas include
      • Adversary Intent Inferencing (Adversarial Modeling)
        • Emergent Adversary Modeling System
        • War-gaming
      • Deception Detection and Deception Intent Modeling
        • Insider Threat
      • Cultural Behavior Modeling
      • Efficient Distributed Computational Biology (Protein Folding)
      • User Modeling and User Adaptive
        • Intent Inferencing
      • Distributive and Adaptive Information Retrieval
        • Geospatial Information Systems
        • Information Extraction
      • Multi Document and Technology Summarization
      • Real World Cognitive Multitasking's and Problem Solving
        • Modeling Insight and Intuition

    • Laboratory for Human Terrain
      • The Laboratory for Human Terrain at Dartmouth College is focused on the foundational science and technology for modeling, representing, inferring and analyzing individual and organizational behaviors.
    George Mason University ___return to top

    Georgia Tech ___return to top

    University of Maryland ___return to top

    Virginia Tech ___return to top

    • Computer Science Research, Virginia Tech - includes

      • Laboratory of Computation, Information & Distributed Processing (LCID)
        • An Effective Anytime Anywhere Parallel Approach for Centrality Measurements in Social Network Analysis, by Santos et al, 2006, IEEE International Conference on Systems, Man and Cybernetics
          • With the broad application of electronic communication monitoring tools and data-sharing techniques, the size of networks to be studied by social network analysis (SNA) has grown rapidly. However, current SNA techniques are not particularly scalable. For example, even centrality, which is one of the most frequently used SNA parameters, cannot be measured by most current SNA software when the network is large. This paper presents the design of an effective and scalable anytime anywhere parallel methodology for SNA with large-scale networks emphasizing centrality measurement algorithms. The efficiency and effectiveness of the methodology is validated by experiments of centrality analysis for large networks. [from IEEE summary]

      • Effective and Efficient Methodologies for Social Network Analysis, by Long Pan, PhD dissertation, 11 Dec 2007

    University of Pittsburgh ___return to top

    • Center for the Extraction and Summarization of Events and Opinions in Text (CERATOPS)
      • "We will create easily trainable learning algorithms that can automatically create domain-specific patterns to identify facts and relations associated with relevant events...."
      • "We will develop trainable learning algorithms that can distinguish factual assertions from subjective (non-factual) assertions, identify beliefs that are held by an entity, and assess the intensity, polarity, and motivation and attitude types of those beliefs."
      • "We will create methods for understanding event and belief progressions over time."

    University of Southern California ___return to top

    • Center for Knowledge Integration and Discovery (CKID) - Research focuses on three themes:
      • discovery and extraction of relevant information from diverse media, including text (email, news, blogs, etc.), speech, geospatial sources (maps, satellite images, etc.),
      • integration and storage of this information in standardized homogeneous format,
      • automated discovery of trends and patterns from the integrated information, across the media, in order to find interesting knowledge that may not be apparent in any single medium alone.

    University of Illinois ___return to top

    • Multimodal Information Access and Synthesis Center (MIAS), University of Illinois at Champaign-Urbana - Research directions include
      • Developing fundamental theories, computational models, algorithms, and tools for information access and synthesis.
      • Enabling intelligence analysts to access a variety of data formats, transforming raw data into useful and understandable information.
      • Integrating these technologies with existing resources.

    Other University Research ___return to top

    Sandia National Laboratories ___return to top

    Defense Advanced Research Projects Agency (DARPA) ___return to top

    • DARPA home page

    • Tools for Real-Time Anticipation of Enemy Actions in Tactical Ground Operations (local copy), by Kott and Ownby, DARPA, presented at the 10th International Command and Control Technology Symposium (ICCRTS), 14 June 2005 (slides)
      • DARPA has recently undertaken a research project titled Real-time Adversarial Intelligence and Decision-making (RAID), which provides in-execution predictive analysis of probable enemy actions. A particular focus of the program is tactical urban operations against irregular combatants – an especially challenging and operationally relevant domain. The RAID program leverages novel approximate game-theoretic and deception-sensitive algorithms to provide real-time enemy estimates to a tactical commander. In doing so, the RAID program is addressing two critical technical challenges: (a) adversarial reasoning: the ability to continuously identify and update predictions of likely enemy actions; (b) deception reasoning: the ability to continuously detect likely deceptions in the available battlefield information.

    National Academies ___return to top

    Australian Department of Defence ___return to top

    Other Research ___return to top

    • Influencing Violent Extremist Organizations Pilot Effort: Focus on Al Qaeda in the Arabian Peninsula (AQAP) (local copy), by DoD Strategic Multi-Layer Assessment (SMA) Office, Fall 2011

    • Guide to the Drivers of Violent Extremism (local copy), USAID, 2009

    • Psychological Strategies for the Defence Against Terrorism (local copy), by Koltko-Rivera and Hancock, NATO paper, 25 Oct 2004

    • Eli Pariser: Beware online "filter bubbles", a talk from TED.com - if TED site is blocked, you may be able to watch it on YouTube or one of the other sites
      • "As web companies strive to tailor their services (including news and search results) to our personal tastes, there's a dangerous unintended consequence: We get trapped in a "filter bubble" and don't get exposed to information that could challenge or broaden our worldview. Eli Pariser argues powerfully that this will ultimately prove to be bad for us and bad for democracy."

    • Persuasive Speech: The Way We, Um, Talk Sways Our Listeners, Science Daily, 16 May 2011
      • "Interviewers who spoke moderately fast, at a rate of about 3.5 words per second, were much more successful at getting people to agree than either interviewers who talked very fast or very slowly," said Jose Benki, a research investigator at the University of Michigan Institute for Social Research (ISR).
      • ... They found that males with higher-pitched voices had worse success than their deep-voiced colleagues. But they did not find any clear-cut evidence that pitch mattered for female interviewers.
      • The last speech characteristic the researchers examined for the study was the use of pauses. Here they found that interviewers who engaged in frequent short pauses were more successful than those who were perfectly fluent.
      • ... If interviewers made no pauses at all, they had the lowest success rates getting people to agree to do the survey. We think that's because they sound too scripted.

    • The Unexpected Influence Of An Uncertain Expert, by Martin, Inside Influence Report, 11 May 2011
      • ... But in an information saturated world where so many claim to be experts, what does the latest persuasion research tell us about which expert we should pay particular attention to? And how could such insights help when attempting to persuade others?
      • ... A series of new studies conducted by Stanford Business School’s Zak Tormala and Uma Karmarkar and published recently in the Journal of Consumer Research suggest that rather than the most confident sounding expert being the most persuasive it is often the recommendations and advice from experts that are themselves uncertain, that will be more compelling.
      • Their series of studies found that an experts’ influence over others increases when that expert expresses minor doubts about their advice and opinions. They found that this effect was particularly acute when an expert’s advice concerned subjects or situations where there was no one single clear or obvious answer.
      • ... In explaining these counter intuitive findings the researchers point out that because people generally expect experts to be certain of their opinions, when that expert signals potential uncertainties about their message people become more intrigued and drawn in to what they are saying. In effect the incongruity between the source’s expertise and their level of uncertainty makes his or her message appear more intriguing. As a result, assuming that the arguments in a message are reasonably strong, this drawing in of an audience leads to more effective persuasion.
      • ... And when it comes to persuading others about the merits and benefits of the products and proposals we have to offer, assuming our case is a strong one, it would seem sensible that rather than hide or cover up minor drawbacks and weaknesses in our case, we instead embrace them in the knowledge that they can actually make us more persuasive.