Dynamic - Counter-Intelligence Simulation Lab (MIT D-CISL) (MIT...

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Unclassified//For Official Use Only

Dynamic - Counter-Intelligence Simulation Lab (MIT D-CISL)

ProPro--Active Intelligence (PAINT)Active Intelligence (PAINT)

Simulation Lab (MIT D CISL)Massachusetts Institute of Technology (MIT)

PI’s: Stuart Madnick <[email protected]>, Nazli Choucri<[email protected]>, Michael Siegel <[email protected]> Research Team: Daniel Goldsmith <[email protected]>, Dan Sturtevant <[email protected]>, Dinsha Mistree<[email protected]>, Douglas Matty <[email protected]>

Unclassified//For Official Use Only 1

@ , g y y@

National Security Innovations (NSI)Robert Popp <[email protected]>

WORK-IN-PROGRESS – NOT FOR DISTRIBUTION16 December 2007


MIT System Dynamics Contribution to PAINT

• Model and theory to identify most effective active probes to detect nefarious activities

• Methodology and management approach for dynamic tracking of pathways and plan developmentM lti l i t ti i t f ki ith


• Multiple integration points for working with other PAINT efforts and use of new information

2


Caveats• This is work-in-progress• We are specifically responding to guidance

ffrom– BAE (Ed Waltz)– Peter Brooks– Bill Vanderlinde (Nanotechnology Subject Matter Expert)

• We intend to support all our model assumptions from literature and/or SME’s


assumptions from literature and/or SME s• We consider system behavior at in both

short- and long-term cases


Guidance from BAE (Ed Waltz)- Pathway Framework

2005 2006 2007 2008 2009 2010 2011 2012 2011 2012 2013

Commit Agent Review Critical Review Method Select Deploy

Test alternative encapsulation,storage, dispersion methods

RefineProductionProcesses

Final OperationEvaluation

Train

LogisticSupport

Plan

Operational Capability

RefineDeployProcess

Refine Production Process

Weaponization

g p y

Mass Produce

Test, Eval

Pilot TestProduction

Storage Testing

Mass ProduceEvaluation

Develop, refine

Characterization-nanotoxicology

- stability-dosage

Neurotoxin DevelopmentAnd Selection

Performance Simulation

Encapsulation Simulation

Nerve AgentDevelopment

Develop, refine Test, Eval

Nanotube

GlassNanosphere

Carbon nanotube processdevelopment


Weaponization

End GoalProcess (Duration, resources, dependencies)Intermediate Accomplishment

Develop, refine Test, Eval

Nanosphere

LiposomeNanosphere

Essential NanotechDevelopment

Focus ofcurrent work

Under development

3


Full View of SD Model Nanotechnology Develop/Productionactivities, including human resources

Capacity of


Capacity of physical facilities and resources Nefarious

activities


• Nanotechnology program production rate (rate of accomplishment) is

Base Case(no probe)

Benign

Observable Production Rate of Nanotechnology Program

200

Where We Are Going

observable

• The effect of probe lowers production in the benign case...

• ...but if a nefarious program is active the benign development

gModel Probe Results

Nefarious Model Probe Results(as leaders focus the nanotech

150

100

50

0

Year 2 Year 3

Probe Occurs

This increasing


benign development is lowered even further – a clear and measurable signature

the nanotech program toward the nefarious activity with fewer researchers)

Key: Probe response can distinguish benign vs. nefarious cases

This increasing difference is an

observable diagnosticeffect – evident within

six months of the probe

4


SD Model Based Process Enhancements

• Enable integration with other PAINT participants th h li kthrough linkages– E.g., Exchange parameters with leadership models

• Focus on identification of high-leverage probes– Policy levers differ in control of outcomes

• Establish methodology for analyzing probes


– Update model for more accurate representation based on intelligence, theories, and data

• Ability to update model based on new theory and data


Basic Research R & D Manufacture Deployment

Modeling Methodology

• Start with established

Left Right

Nano Specific R & D Structure

R & D Structure• Start with established

theories on R&D and development

• Adapt for emerging technology (e.g., nano)


Country Specific • Adapt for Iraniancontext

Model focuses on end-to-end analysis by tying together “left” and “right” sides of nano pipeline

5


Assumptions• At any point in time, the model is the best

representation of the way we think the world works

• The model is based on the dynamic interactions among – inputs of: people and capacity (resources)

and output of: production


– and output of: production• Probes result in model changes to

parameters or structure


Current Productions

Production Experiments• Insight: People and Capacity are interdependent• More People may not mean much more production if

Capacity is constrainedActive ParticipantsPeople ProductionBase case

2,000

1,500

1,000

500

00 2 4 6 8 10 12 14 16 18 20

Time (Year)

Gad

get

Current Productions : test1Current Productions : test2

Active Participants4,000

3,000

2,000

1,000

00 2 4 6 8 10 12 14 16 18 20

Time (Year)

Pers

on

Active Participants : base

p

People and capacity constrain production, and expand at different rates

Capacity Constraint MetricCapacity to Support Production4,000

Constrained case

Capacity4,000


Capacity is constrained when below black line

3,000

2,000

1,000

00 2 4 6 8 10 12 14 16 18 20

Time (Year)

Gad

get/Y

ear 2,900

1,800

700

-4000 2 4 6 8 10 12 14 16 18 20

Time (Year)

Gad

get/Y

ear

6


Guidance from Bill Vanderlinde(Laboratory for Physical Sciences)

• Nanotechnology developmentThe generic “nanotechnologist” doesn’t exist– The generic nanotechnologist doesn t exist

– People are taken from different fields, depending on desired application area

– For example: development of nanoscale bio-structures requires (a) PhD biologist with (b) training in nano techniques

R t t l d ’t il i t


• Return to scale doesn’t necessarily exist in the development of practitioners– For example: Developing nano biologists

doesn’t make nano chemistry any easier


Emerging Technology(Nano-ization of Process)

• Development processes for Established Technology (e.g., Software) are characterized by:gy ( g ) y– a moderately high inflow of potential participants due to

low barriers to entry (undergraduate equivalent)– relatively short training delays– high numbers of current practitioners

• Development processes for Emerging Technology(e.g., Nanotechnology) are characterized by:

l i fl f t ti l ti i t d t hi h b i


– a low inflow of potential participants due to high barriers to entry (PhD equivalent)

– long training delays (i.e. chemistry PhD and nano training)

– low numbers of current practitioners

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3,000

2,000

1,000

Pers

on

Process for Established TechnologyPotential Participants

6,000

4,500

3,000

1,500

Pers

onDeveloping Participants

4,000

3,000

2,000

1,000

Pers

on

00 2 4 6 8 10 12 14 16 18 20

Time (Year)Active Participants : cs2

PotentialParticipants

ActiveParticipants

Mobilizing Participants

non participantcontacts

contact rateactive particpant prevalence

participant withnon participant

contacts

recruiting

DevelopingParticipants

Maturation

total particpants

MaturationD l

TotalProductions

Adding PotentialParticipants

Leaving

Average Time asParticipant

Interested College Eligible Population

Master’s Level Training Active Practitioners

00 2 4 6 8 10 12 14 16 18 20

Time (Year)Potential Participants : base1

00 2 4 6 8 10 12 14 16 18 20

Time (Year)Developing Participants : base1

Ability to SupportLong Term Development


CurrentProductions

gfraction

Products implied per participant

normalproduction delay

averageapplication life

total new productions

total backlog

ImpliedProductionBacklog

RevisionBacklog

new productions

revising products

obsoleteproductsimplied products

Delay

Max ProductionRate from

ParticipantsActual

ProductionProductivity

Productions

High Inflow of Eligible Participants

Relatively ShortTraining Delay


Process for Emerging TechnologyPotential Participants

4,000

3,000

2,000

1,000

Pers

on

Developing Participants2,000

1,500

1,000

500

Pers

on


3,000

2,000

1,000

Pers

on

Establishedtechnology

Emerging


ActiveParticipants



contact rateactive particpant prevalence


contacts

recruiting


Maturation

total particpants

Maturation Total


Leaving


PhD PopulationAdvanced Nano Training Active Nano

Developers

Long Training Delay

00 2 4 6 8 10 12 14 16 18 20

Time (Year)Potential Participants : base1

00 2 4 6 8 10 12 14 16 18 20

Time (Year)Developing Participants : base1

Declining Ability to SupportDevelopment

00 2 4 6 8 10 12 14 16 18 20

Time (Year)Active Participants : cs1Active Participants : cs2

g gtechnology


CurrentProductions

recruitingfraction





total backlog


RevisionBacklog

new productions

revising products


MaturationDelay


ParticipantsActual


TotalProductions

Low Inflow of Eligible Participants

Long Training DelayInsight: For Emerging Technologies, People are often the key constraint

8


Iran Context of Development Process

• Special Office of Nanotechnology Development – Stated aims include: “Institutionalization of sustainable andaims include: Institutionalization of sustainable and dynamic development of science, technology and nano-industry.”

• Ahmadinejad “advised First Vice-President Parviz Davoudi to organize national headquarters for development of nanotechnology….[expectation is] to adopt necessary strategies to give incentives for experts scientific research and industrial centers and


experts, scientific, research and industrial centers, and the state and private companies to go ahead with nanotechnology.”


<Developing Participants>

Using Modeling to Identify Active Probes with Biggest Impact

1. Decrease cooperationVariable: (contact rate)


ActiveParticipants



contact rate

active particpant prevalence

CurrentProductions


contacts

recruitingfraction

initial particpants



<normal production delay>

Maturation

<Potential Participants>

total particpants


ImpliedProduction

RevisionBacklog

new productions obsolete

Initial PotentialParticipants

MaturationDelay

TotalProductions


Leaving


Recruiting ActiveParticipants

Recruiting Rate2. Reduce available participantsVariable: (adding Potentialparticipants)


<Max production rate from Capacity>



total backlog

ProductionBacklog

<Implied Production Backlog>

<total backlog>

new productions

revising products



ParticipantsActual


3. DecreaseNano AttractivenessVariable: (recruiting fraction)

Q: Which option (1,2,3) do you think might have biggest impact?

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Live Vensim Simulation

• Ability to rapidly test o Various ranges of the option variableso Various ranges of the option variableso and combinations of option variables



• Do projections cross a threshold of visibility? • Or do they remain feasible to keep hidden?

Additional Policy Context:Pressure to Keep Program Secret?


1,500

1,000

500

00 2 4 6 8 10 12 14 16 18 20

Time (Year)

Pers

on


4,500

3,000

1,500

00 2 4 6 8 10 12 14 16 18 20

Time (Year)

Pers

on

Active Participants : base1 Active Participants : base4

ParticipantsDeveloping Participants


Developing Participants : base1Developing Participants : base6Developing Participants : base5

Developing Participants : base4Developing Participants : base3Developing Participants : base2

Active Participants : base1Active Participants : base6Active Participants : base5


• We can forecast parameters that drop below line.• We can infer intent by the gap between the

projected development and secrecy threshold.

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Methodology for analyzing probes

• Active probes and their responses represent opportunities for model changes based on newopportunities for model changes based on new parameter values or structure

• Analyze gaps between model and new data– Actual and Expected– Perception and Reality


Current Knowledge

Probes and Intel.

Model Tests


Examples of Operationalized Probes

• Probe to modify the “people pipeline” (link toInfluence Probes:

Probe to modify the people pipeline (link to leadership model)– Restrict access to U.S. institutions, limit visas

• Probe to modify capacity– Impose capacity limitations

Diagnostic Probes


Diagnostic Probes:

• Compare information from left and right sides of development

11


P t ti l Active


contact rate



contactsinitial particpants

Developing



total particpants



Recruiting RateDecrease cooperation


1,500

n

Developing Participation

Using Modeling to Identify Probes

<Max production rate from Capacity>


ActiveParticipants


CurrentProductions

recruitingfraction




<normal on de

Maturation



total backlog


RevisionBacklog

<Implied Prod

<total backlog>

new productions

revising products



MaturationDelay


ParticipantsActual


TotalProductions


Leaving

Reduce available participants

DecreaseNanoAttractiveness

1,000

500

00 2 4 6 8 10 12 14 16 18 20

Time (Year)

Pers

on

Developing Participants : base1127Developing Participants : test11273Developing Participants : test11272Developing Participants : test11271


participants

Insight: Sensitivity Analysis shows that diminishing available participants is the high leverage probe (most effect per unit of change) to reduce participation


INPUT: Leadership model impacts the

number and fragmentation of OUTPUT

System View of Probe Inputs and Outputs PROBE:

Restrict nanotech participation

fragmentation of research participants

OUTPUT: Measure the overall program production rate


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• Nanotechnology program production rate (rate of accomplishment) is

Base Case(no probe)

Benign

Observable Production Rate of Nanotechnology Program

200

Calculated Observable Effects

observable

• The effect of probe lowers production in the benign case...

• ...but if a nefarious program is active the benign development

gModel Probe Results

Nefarious Model Probe Results(as leaders focus the nanotech

150

100

50

0

Year 2 Year 3

Probe Occurs

This increasing


benign development is lowered even further – a clear and measurable signature

the nanotech program toward the nefarious activity with fewer researchers)

Key: Probe response can distinguish benign vs. nefarious cases

This increasing difference is an

observable diagnosticeffect – evident within

six months of the probe


• Analysis Options– Re-parameterize model– Create a second pathway based on

new theory of reaction to probe

Reacting to Probe



Recruiting Rate

External participants

Original pathway:Initial participants

New pathway:Both internal and new external participants


1,500

n

y p(increased outside participation)

– Now have two models: • Active participation goes down• Move towards external participation

Probe:Lowers the

t f


3,000

New Theory:

Active Participation (total)Developing Participation

ActiveParticipants

tive particpant prevalenceinitial particpants

lopingipants

Maturation

total particpants

Leaving




1,000

500

00 2 4 6 8 10 12 14 16 18 20

Time (Year)

Pers

on

Developing Participants : baseDeveloping Participants : test3

amount of projectedinternal participation

2,000

1,000

00 2 4 6 8 10 12 14 16 18 20

Time (Year)

Pers

on


Relying on External Participation

Probe Goal

13


Unintended Consequence of Probe

• Possible unintended consequence: trigger lt t th


Active Participation

alternate pathway.• External participation may

drive total participation to a higher level

• We have to look at something else in the model to make sure we are not on

3,000

2,000

1,000

00 2 4 6 8 10 12 14 16 18 20

Time (Year)

Pers

on



to make sure we are not on the “green path”

• May see overlap with social network models (see new outside contacts)

Management of probes over several years is required to maximize value and limit unintended consequences


Current Productions600

450

Case of Conflicting Informationnew productions

400

300

Developing Participation Development

450

300

150

00 2 4 6 8 10 12 14 16 18 20

Time (Year)

Gad

get

Current Productions : base1

200

100

00 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Time (Year)

Gad

get/Y

ear

new productions : base1

ObservedDevelopment

• While left side of model tracks with probes, right side shows gap in development

InformationProbes


right side shows gap in development• Probes would focus on discerning new

parameters or new theory

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contact rate



contacts initial particpants



total particpants

Leaving



Recruiting Rate

Recruiting TestInput

Recruiting Height

Recruiting Time

initial malignantparticipants

Example of Modeling Theory


ActiveParticipantsMobilizing Participants

CurrentProductions

recruitingfraction




<normal production delay>

Maturation


total new productions total backlog


RevisionBacklog

<Implied Production Backlog>

<total backlog>

new productions

revising products



MaturationDelay

TotalProductions

Adding PotentialParticipants Allocating for

Malignant Production

MalignantParticipants Leaving MP

Total VisibleParticipants

MalignantParticipant Visibility

<Actual Participation inMalignant Production 0>

Average Timeas MP


<Max production rate from Capacity>Max Production

Rate fromParticipants

ActualProductionProductivity

MalignantProduction

StartsCurrent

MalignantProductionsImplied Malignant

ProductsProducingMalignant

Retiring MalignantProductions

Max MalignantProduction from

Participants

<Max production ratefrom Capacity>

Malignant productsper participant initial MPS

initial CMP

Average MP life

<MS Height>

Production Gap fromCapacity andParticipants

NefariousProductionpipeline


Deducing New Information From Probes

new productions400

300

Developing Participation

Probes help

Less active participants are seen in normal development. Where did they go?


3,000

2,000

1,000

0

Pers

on

Current Productions1,000

750

200

100

00 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Time (Year)

Gad

get/Y

ear

new productions : base1

pdetermine gap in active participation

Active Participation (visible)

Development

y g

ParticipationOriginally


0 2 4 6 8 10 12 14 16 18 20Time (Year)

Active Participants : base1127maligActive Participants : test1127malig

500

250

00 2 4 6 8 10 12 14 16 18 20

Time (Year)

Gad

get

Current Productions : base1127maligCurrent Productions : test1127malig

Originallyexpected

Participation based on possible alternate nefarious pathway

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Establishing Probes from Modeling Analysis

• We have established two pathways competing for the active participation resource– Benign development– Nefarious development

• This competition helps identify possible probes/strategies– Influence movement between benign and nefarious pathways– Better understand commitment towards nefarious plan– Stimulate internal pressures between military and economic

development• Long-term modeling of probe approaches


• Long-term modeling of probe approaches– Some lead to one-time effects (e.g. limit capacity, stimulate

economic sector)– Others lead to endogenous change (e.g. shift the economic/military

balance)• May lead to tipping point in favorable direction


MIT Approach to Identify ProbesIdentify:• Conditional Test to confirm nefarious behavioral,

influence behavior and sustain effectinfluence behavior, and sustain effect• Theory or multiple theories that connect to goals

– There will be behavioral responses to changes in the availability of capacity

• Indicators for analysis of hypotheses– Hypothesis: If capacity is constrained, the pace of

nefarious production will increase because future


nefarious production will increase because future availability is in question. Indicator is drop in benign participation.

• Operationalize probes– Impose budgetary/bureaucratic limitations on capacity

16


Capacity toSupport

Productioninvestment reductions

capacity life

desired research capacity

time to adjustcapacity

capacity adjustmentinitial capacity to

support production

Active Probe Results:Limiting Capacity

E.g. Limit the amount of Capacit

Capacity to Support Production6,000

total implied productions

AverageImplied

ProductionRate

potential production ratedesired production rate

desired backlog

p y

backlog correction

TotalProductionBacklogtarget delivery

delay Max production rate from Capacity

time to correct production backlog

time to averageproduce

production delay

adding implied productions

Capacity(i.e. budgetary pressures, bureaucratic shifts)

Capacity


4,500

3,000

1,500

00 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Time (Year)

Gad

get/Y

ear

Capacity to Support Production : test 11-202

Model

Probe Goal


Capacity to Support Production6,000

4,500

3,000

Gad

get/Y

ear

• Analysis– Capacity drops from probe

in the short-term

CapacityReactions to Probe

ProbeResult

1,500

00 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Time (Year)Capacity to Support Production : test 11-202Capacity to Support Production : test 11-201

– If nefarious exists, pressure increases nefarious production

– However, there is limited long-term benefitCurrent Malignant Productions

2,000

1,500

Nefarious DevelopmentBut we really


How can we determine a probe or set of probes that will meet all of our goals?

1,000

500

00 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Time (Year)

Gad

get

Current Malignant Productions : test 11-201Current Malignant Productions : test 11-202

really want the red line to “tip” in the right direction

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MIT System Dynamics Contribution

• Model and theory to identify probes and various activities – Identify high-leverage probes

M i ff t• Maximum effect • Limit unintended consequences

– Identify pathways to nefarious plans• Multiple coexisting paths• Probable paths and levels of activities

• Methodology and management approach for dynamic tracking of pathways and plan development

M d l d l ti b d b lt d


– Model develops over time based on probe results and new theories

– Management of multiple pathways and optimal probe definition • Multiple integration points for working with other

modeling efforts and new information


Additional Activities• Begun working with MIT Security Studies Program

– Owen Cote, Military doctrine, WMD– Harvey Sapolsky, Civil/military relations, Weapon

i iti li iacquisition policies – Barry Posen, Organization and employment of military

force, Military innovation• Specific focus team effort on weaponization

– Doug Matty (LTC U.S. Army), Dan Sturtevant, Dinsha Mistree

• Begun working with MIT Institute for Soldier


Begun working with MIT Institute for Soldier Nanotechology (ISN)– Deeper understanding of nanotech and militarization

• Empirical analysis, data gathering, model validation

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Some Interesting Long-term Possibilities and Extensions

1. WMD + Terrorism - The role of terrorist groups gand state/terrorism collaboration on WMD (in both directions)

2. Deterrence of pursuit of WMD – Mitigation strategies to discourage WMD development (by both state and non-state actors)


3. Impacts of cyber-terrorism and WMD – Both as a type of WMD as well as a means of organizing and recruiting terrorists (e.g., "messaging.”)

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