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Analytical Tradecraft. “ Solid intelligence on terrorism is not easy to develop…

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analytical tradecraft
Analytical Tradecraft

“ Solid intelligence on terrorism is not easy to develop…

I would like to salute the unsung heroes of the struggle against terrorism. These heroes are the intelligence analysts. Often they have little to go on: a photograph, a fragment of an overheard conversation, the text of a communiqué, the summary of a meeting, a used airline ticket. Sometimes, it is like piecing together a gigantic jigsaw puzzle, but it is a puzzle that can save lives.”

George Shultz

Former Secretary of State

slide2

ANALYSTS

The Challenge

Analyst Work Environment

expanding world of data

Velocity

Volume

IMINT

HUMINT

SIGINT

IC Collection

Sources

MASINT

Variety

(@ Signal /

Media Level)

OSINT

Volatility

Expanding

World of Data

Expanding World of Data

Variability

(@ Content

Level)

turning data into intelligence

S

E

N

S

O

R

P

H

Y

S

I

C

A

L

D

A

T

A

O

B

J

ECTS

“Information Spectrum”

B

I

T

S

S

I

GNALS

  • Types of
  • Intelligence
  • Current
  • Estimative
  • Warning
  • Scientific &
  • Technical
  • Research

Intel

Report

IC Customers

… INT

Part of the “INTELLIGENCE CYCLE

Processing

Reporting

Collection

Analysis

Raw Data

Intelligence

Turning Data into Intelligence

IM …

SIG …

HUM …

MAS …

OS ...

Single Discipline Analysis

All Source Analysis

Information

data information intelligence synthesis fusion of observables within a given context

Events /

Activities /

Processes

Relationships

Analytic

Knowledge

Information Retrieval

Content Data Mark-up

Assessment

& Interpretation

Properties /

Attributes

Presentation & Visualization

Time / Temporal Issues

Location / Spatial Issues

Reporting & Dissemination

Data Filtering

& Selection

Instances

Content Data Transformation

Synthesis& Fusion

IC Analysts

Humans / Organizations

Physical Objects

Information

Understanding

Information Discovery

Expanding

World of Data

Data  Information  IntelligenceSynthesis & Fusion of Observables within a Given Context

“Observables”

Who, What,

When, Where, How

Time / Temporal

Issues

Location /

Spatial Issues

data information intelligence assessments interpretations judgments predictions

Intentions

Motivation

Attitudes /

Perspectives

Meaning

Analytic

Knowledge

Information Retrieval

Content Data Mark-up

Assessment

& Interpretation

Presentation & Visualization

Time / Temporal Issues

Location / Spatial Issues

Values /

Beliefs

Goals /

Objectives

Reporting & Dissemination

Data Filtering

& Selection

Content Data Transformation

Synthesis& Fusion

IC Analysts

Humans / Organizations

Physical Objects

Information

Understanding

Information Discovery

Expanding

World of Data

Data  Information  Intelligence Assessments, Interpretations, Judgments & Predictions

Location /

Spatial Issues

Time / Temporal

Issues

“Behavioral Factors”

Why?

Location /

Spatial Issues

slide7

Active

Cross-

Fertilization

Analytic

Knowledge

Information Retrieval

Content Data Mark-up

Assessment

& Interpretation

Presentation & Visualization

Reporting & Dissemination

Data Filtering

& Selection

Content Data Transformation

Synthesis& Fusion

IC Analysts

Strong

Interaction

Information

Understanding

Information Discovery

Location / Spatial Issues

Time / Temporal Issues

Humans / Organizations

Physical Objects

Breadth of Information ExploitationApplied across all of the “INTs”

Data  Information  Intelligence “Observables” & “Behavioral Factors” are NOT Independent Activities

“Observables”

“Behavioral

Factors”

data information intelligence combined observables behavioral factors

Analytic

Knowledge

Information Retrieval

Content Data Mark-up

Assessment

& Interpretation

Presentation & Visualization

Time / Temporal Issues

Location / Spatial Issues

Reporting & Dissemination

Data Filtering

& Selection

Content Data Transformation

Synthesis& Fusion

IC Analysts

Humans / Organizations

Physical Objects

Information

Understanding

Information Discovery

Expanding

World of Data

Data  Information  Intelligence Combined “Observables” & “Behavioral Factors”

Events /

Activities /

Processes

Observables

Relationships

Time / Temporal

Issues

Properties /

Attributes

Goals /

Objectives

Values /

Beliefs

Instances

Attitudes /

Perspectives

Meaning

Intentions

Motivation

Behavioral

Factors

Location /

Spatial Issues

universal similarities do exist intelligence community analysts

IC Analysts

Universal Similarities do ExistIntelligence Community Analysts
  • They are far more than just casual users of information
  • They work in an information rich environment where they have access to large quantities of heterogeneous data
  • They are almost always subject matter experts within their assigned task areas
  • They track and follow a given event, scenario, problem, or situation for an extended period of time
  • They frequently have extensive collaboration with other analysts
  • They are focused on their assigned task or mission and will do whatever it takes to accomplish it
  • The end product that results from their analysis is often judged against the standards of:

Timeliness Accuracy Usability

Completeness Relevance

major differences do exist between agencies and within agencies
Major Differences Do Exist(between agencies and within agencies)
  • Between single source and all source analysts (data formats, degree of closeness to the raw information, accessibility to contextual information)
  • Between analytical domains (counter terrorism, WMD, regional analysis, weapons systems, etc)
  • Between types of intelligence produced (e.g. current intelligence, estimative intelligence, etc.)
  • Experience level in the art of analysis and ability to understand the analytic process
  • Understanding and use of analytic networks
  • Customer requirements (strategic, operational, tactical)
  • Unique individual differences
sample of an analytic work flow
Sample of an Analytic Work Flow

Collaborate with Colleagues

Library Research

Tasking

Thinking

Completed Report

Copy/Paste Annotate

Internet Search

Compose/Write

Compose/Write

Revisions

Time

fusion of multilayer analysis
Fusion of Multilayer Analysis

Personal

Relationships

Organizational

Relationships

Technical

Processes/Flows

Transportation

Networks

Currency/Financial

Transfers

Electronic Connectivity/

Information Flows

the challenge of time in analysis

Collector 5 picks up event planning material in a raid

Collector 4 observe event-related info & reports

Collector 1 observes event planning & reports

Collectors 1,2,3 observe event

Collector 1 reports

Collector 2 reports

Collector 3 reports

H-n

H+1

H-1

H-n

Event

Event Planning

Aftermath of the Event

H Hour

The Challenge of Time in Analysis
  • Different sources do not report simultaneously on an event.
  • Data from different sources may be near real-time or take years to arrive.
  • The hypothesis of today may be thrown out by new data arriving next week.
  • Data must be visualized over time as patterns which change in time as updates occur
the challenge of credibility in analysis
The Challenge of Credibility in Analysis

What do we look for in a source?

  • Credibility
  • Reliability
  • Relevance
  • Can be confirmed

What standards are we held to in reporting

  • Accurate
  • Timely
  • Actionable
  • Complete
  • Relevant
slide18

THE SOCIAL SCIENCES ARE, IN FACT, THE “HARD” SCIENCES.

GROWING ARTIFICIAL SOCIETIES

EPSTEIN AND AXTELL

nobody knows questions

“NOBODY KNOWS” QUESTIONS

WHAT IS GOING ON INSIDE THE IRAQI/SERB/NORTH

KOREAN REGIMES?

WHAT WOULD COLLAPSE OF THESE REGIMES

LOOK LIKE?

HOW STIFF A RESISTANCE WILL THE FEDAYEEN AND OTHER SADDAAM SUPPORTERS PUT UP?

WHAT IS GOING ON IN ANY COUNTRY, AND WHERE IS IT GOING?

WHAT WILL THE MIDEAST LOOK LIKE IN 2010?

WHAT ARE THE ROOTS OF TERRORISM & HOW CAN WE AFFECT THOSE ROOTS

slide20

CHARACTERISTICS OF “NOBODY KNOWS” QUESTIONS

  • COMPLEXITY
    • MANY INDEPENDENT ACTORS
    • MULTIPLE VARIABLES
    • DYNAMIC/ADAPTIVE BEHAVIOR
    • EMERGENT OUTCOMES
  • HUMAN BEHAVIOR
    • INDIVIDUAL AND GROUP PROCESSES
slide21

NEW APPROACHES

INFORMATION

INTELLIGENCE

KNOWLEDGE

UNDERSTANDING

TO MOVE FROM PROVIDING INTELLIGENCE

TO PROVIDING UNDERSTANDING

NEW

APPROACHES

TRADITIONAL

ANALYSIS

FACTS

DATA

PROCESSES/

OUTCOMES

CONNECTIONS/

RELATIONSHIPS

potential new approaches
Potential New Approaches
  • NATURAL EXPERIMENTATION
  • COMPLEXITY SCIENCE
  • MODELING AND SIMULATION
  • WHAT ELSE?
some other ideas
SOME OTHER IDEAS
  • EXPERIMENT WITH AND INTEGRATE ONGOING
  • EFFORTS (IC Test Nets)
  • PUSH THE SCIENCE - PARTNER WITH NSF AND DARPA
  • INCREASE FUNDING FOR EXPLORATORY PROGRAMS
      • SMALL GROUP MODELS
      • SOCIETAL MODELS
  • SUBSIDIZE SELECT WARGAME DEVELOPERS (EXISTING GAME ENHANCEMENTS)
  • “LIBRARY” OR CROSS-REFERENCE FOR
  • INTELLIGENCE RELATED SIMULATIONS
slide24

ANALYSIS AND COMPLEXITY

“THE RULES OF THE GAME: LEARN EVERYTHING, READ EVERYTHING, INQUIRE INTO EVERYTHING… WHEN TWO TEXTS, OR TWO ASSERTIONS, OR PERHAPS TWO IDEAS, ARE IN CONTRADICTION, BE READY TO RECONCILE THEM RATHER THAN CANCEL ONE BY THE OTHER; REGARD THEM AS TWO DIFFERENT FACETS, OR TWO SUCCESSIVE STAGES, OF THE SAME REALITY, A REALITY CONVINCINGLY HUMAN JUST BECAUSE IT IS COMPLEX.”

Marguerite Yourcenar, Memoirs of Hadrian

non linear dynamics of human behavior ndhb advanced r d program
Non-Linear Dynamics of Human Behavior (NDHB) Advanced R&D Program
  • Can we achieve a better understanding of Human Dynamics; Individual and Small Group Behavior; Leadership Decision Making; Large Group Dynamics? Can we model it?
  • Can we use modeling approaches to influence the present or to forecast potential future events/activities? What approaches are best for which intelligence problems?
  • Can we use models and simulations for knowledge discovery?
  • Can we model across missing data?
  • Can we use models and simulations as a method for training analysts in hypothesis generation and argumentation?
slide26

POSSIBLE OPERATIONAL PROBLEMS TO BE SOLVED IN MODELING HUMAN BEHAVIOR

  • Anticipating Surprise – Asymmetric threats & tactics
    • Avoiding technology surprise (novel ways of using existing technology and use of emerging technologies)
    • Anticipating political instability (including ethnic strife and state collapse)
    • Identifying plans & Intentions for threat operations against national US interests
      • Conventional military operations
      • Terrorism & other acts of violence
      • Information operations
      • WMD/CBNRE
    • Anticipating attacks designed to disrupt the economy
slide27

POSSIBLE OPERATIONAL PROBLEMS TO BE SOLVED IN MODELING HUMAN BEHAVIOR (cont.)

  • Leadership Analysis/Decision-making (both state and non-state)
    • Formation/Transformation (regime change/succession/etc)
    • Coalition Dynamics
    • Influences (external/internal)
    • Plans/Intentions/Policies/Strategies
  • Environmental Issues
    • Spread of diseases or C/B agents through human interactions and their associated social impact
    • Spread of diseases through livestock and plants and impact on society (e.g. associated economic upheavals, starvation)
    • Environmental disasters (either man-made or natural) and impact on society
  • Ethnic/Cultural/Religious/ Societal constraints on courses of action and decision-making (US, Allies, Neutrals, Enemies)
possible applications for modeling human behavior
Possible Applications for Modeling Human Behavior
  • Development of Threat Models (using various modeling applications) and analysis of results from those models
  • Fusion of intelligence from different disciplines/domains/experts with support from modeling and simulation
  • Integration of modeling with traditional analytic methods.
    • Training analysts to use models as part of their cognitive toolset
    • Understanding what approaches work best with different intelligence problems
      • Comparative case studies
      • Definitions of modeling approaches
    • Development of new analytic strategies
      • Discovery of previously unknown data/patterns
      • “what if”
      • Modeling missing data and uncertainty
      • Comparative analysis
      • Anticipating surprise
      • Development of new patterns/trends
      • Comparative case studies
  • What else?
slide29

Possible Technical Needs for Modeling Human Behavior

Some Modeling Approaches

Individual Small Large

Group Group

Agent-Based

Modeling

System Dynamics

Reaction-Diffusion

Social Network

Analysis

Game Theory

Multiscale Analysis

Coupled Oscillators

Neural Nets

What else?

slide30

Possible Technical Needs for Modeling Human Behavior

Cross-cutting technologies

  • Natural Language Processing
  • Semi-automated and automated data loading of the model(s)
  • Integration of modeling approaches
  • Intuitive visualization of modeling data
  • Tools to manage and analyze modeling data
  • Library of models (how each model works)
  • Architecture that permits sharing
  • Hypothesis Generation tools
  • Collaborative wargaming tools that can retain fidelity of data
slide31

CENTER FOR COMPLEX INTELLIGENCE ISSUES (CCII)

?

?

?

COMPLEX ISSUES

ANALYTIC

CORE

GROUP

TOOLS

GROUP

ACADEMIC

SUPPORT

GROUP

CONCEPTS

APPROACHES

TOOLS

ANSWERS

CONSUMERS

slide32

HUMAN ANALYSIS

“...THE REPRESENTATION OF HUMAN CHARACTER AND PERSONALITY REMAINS ALWAYS THE SUPREME LITERARY VALUE, WHETHER IN DRAMA, LYRIC, OR NARRATIVE.”

BLOOM: SHAKESPEARE: THE INVENTION OF THE HUMAN

intelligence community the intelligence cycle

1

Intelligence

Requirements

2

Planning &

Direction

7

Dissemination

6

Reporting

5

Analysis

Intelligence Community & The Intelligence Cycle

3

Collection

4

Processing

sources of novel intelligence

Known

Unknown

You Know

What

You Know

You Know

What

You Don’t Know

You Know

You Don’t Know

What

You Should Know

You Don’t Know

What

Can Be Known

You Don’t

Know

Sources of Novel Intelligence

Information Sources

Analytic Knowledge

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