Understanding the Human Network Martin Kruger LCDR Jodie Gooby November 2008

Program Goal Develop and then translate models of “who, where, how, when, why” to suggestion of action

Areas of Emphasis (1/2) Advanced Sensors – development of high information content sensors that can detect, classify, identify and/or geolocate actors or behaviors of interest at the sensor. Biometrics – development of capabilities that enable person identity dominance 24/7 over large areas. Tagging, Tracking and Location (TTL) – development of capabilities enabling the persistent tracking of actors or objects of interest and the classification of collected track data. Urban Situational Awareness – development of sensors to enable through structure situational awareness and the development of capabilities enabling structure classification. Wide Area Surveillance – the development of a tier-2 wide area surveillance payload, with supporting communications and the development of processing services that can translate persistent surveillance to actionable intelligence Sensor Planning and Management – development of decision aids that help the warfighter understand what to look for, where to look and how to look for it.

Areas of Emphasis (2/2) Determining Intent – development of software applications that expose the intent of irregular actors in clutter (who/where). Expose Enemy Structure & Decision Modeling – development of capabilities that can automatically expose enemy modus operandi with respect to their network structure or decision processes (how, why, when). Cultural Intelligence – development of the capabilities required to make cultural intelligence tactically actionable. Cognitive Information Operations– development of warfighter decision aids based on high resolution models, which enable decision space shaping or exploitation (proactive action). ISR to C2 – development of capabilities related to the complete integration of ISR into current tactical operations.

The Domain of Irregular Warfare Analysis approaches utilized against conventional forces have in large part been transferred to the domain of security and stabilization with limited success. –Case management (cannot be scalable) –Static What we believe we need to do better: –Expose at risk subpopulations –Efficiently and effectively act Disrupt Influence Stimulate

Capability Needs Develop persona and network signatures –The “signal” needed to support classification Map signatures to behaviors –Relevance of the signature Develop persona and network models that are causal to outcomes of interest – Actionable intelligence Analyze personas and networks as tracks –Time as an independent variable Understand how to classify subject tracks –Likelihood ratios (suspicion, clutter)

Persona, Human Network Fingerprints Persona (Persona discovery involves clustering entities by their attributes) Static Signature Persona = f (network stability, communication patterns, interactions with other humans and networks, proximity to values, proximity to goals, proximity to themes, proximity to places, material or events, observed behaviors/actions) or metadata Dynamic Signature Persona (X n, t+x) = Persona (X n, t) * IO( n ) Network Static Signature Human Network = f (membership, persona composition, structure, stability, communication patterns, interactions with other human networks, proximity to values, proximity to goals, proximity to themes, proximity to other entities or events, observed behaviors/actions) or metadata Dynamic Signature Human Network (Xn, t+x) = Human Network (Xn, t) * IO(n)

Communication Patterns Research questions: –What are the set of terms that uniquely describe the identity of a persona or a network –What are the set of terms that describe the function of a persona or a network Present state of the art: –Largely frequency based metrics Required capability: –What collective set of measurements can be made that uniquely describe the network, allow for network decomposition to nodes, shed light on its function and provide a predictive capability? Frequency, duration, track heading, likelihood of a match to a template of interest, likelihood of a match to a clutter model, other

Key Technologies Clustering in N-dimensional space Similarity measures across N-dimensional space Change detection Causality “Fusion” of soft sciences with computational techniques

Other Research Challenges Poor data environments –Networks are undersampled Reliance on second hand data –Source reliability Persona, human network or decision model resolution –How good is good enough Model definition –Normalization of terms –Mapping to behaviors Human network models –Aggregation in N-dimensions Decision models –Actionable via time as a independent variable Closeness measures –Calculation of the closeness between N-dimensional, un-normalized vectors Transfer learning –Moving models in space and time Non linear response –Low input, big output

Individuals Trending Toward Persona Types 11 #3 #306 #397 Persona A #300 #360 #200 Persona B

Network Behavior Over Time 12

Proprietary & Confidential13 Behavioral Trajectories Enabling Action HUMINT SIGINT Incident Data Signature Analyst Signature Analyst Existing Past Event Data IMINT MASINT ID threat locations Intel Feeds 4 Detect Change in TTPs Force Action Optimization

Uncertainty Reduction - Variable Reduction

Uncertainty Reduction with Cultural Models Analysis Challenge:  How to track the movement of networks through time/space Determine locale of the leadership cell, based on: Persona signature Infrastructure factors Causally relevant cultural factors SPADAC Proprietary15

Summary Human network and persona signatures can be used to understand the human terrain and to model IO effects. Higher resolution decision modeling enables own force action optimization