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]>
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@ , g y y@
National Security Innovations (NSI)Robert Popp <[email protected]>
WORK-IN-PROGRESS – NOT FOR DISTRIBUTION16 December 2007
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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
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• Multiple integration points for working with other PAINT efforts and use of new information
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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
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assumptions from literature and/or SME s• We consider system behavior at in both
short- and long-term cases
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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
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Weaponization
End GoalProcess (Duration, resources, dependencies)Intermediate Accomplishment
Develop, refine Test, Eval
Nanosphere
LiposomeNanosphere
Essential NanotechDevelopment
Focus ofcurrent work
Under development
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Full View of SD Model Nanotechnology Develop/Productionactivities, including human resources
Capacity of
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Capacity of physical facilities and resources Nefarious
activities
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• 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
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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
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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
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– Update model for more accurate representation based on intelligence, theories, and data
• Ability to update model based on new theory and data
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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)
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Country Specific • Adapt for Iraniancontext
Model focuses on end-to-end analysis by tying together “left” and “right” sides of nano pipeline
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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
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– and output of: production• Probes result in model changes to
parameters or structure
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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
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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
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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
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• Return to scale doesn’t necessarily exist in the development of practitioners– For example: Developing nano biologists
doesn’t make nano chemistry any easier
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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
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– 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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Active Participants4,000
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
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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
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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
Active Participants4,000
3,000
2,000
1,000
Pers
on
Establishedtechnology
Emerging
PotentialParticipants
ActiveParticipants
Mobilizing Participants
non participantcontacts
contact rateactive particpant prevalence
participant withnon participant
contacts
recruiting
DevelopingParticipants
Maturation
total particpants
Maturation Total
Adding PotentialParticipants
Leaving
Average Time asParticipant
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
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CurrentProductions
recruitingfraction
Products implied per participant
normalproduction delay
averageapplication life
total new productions
total backlog
ImpliedProductionBacklog
RevisionBacklog
new productions
revising products
obsoleteproductsimplied products
MaturationDelay
Max ProductionRate from
ParticipantsActual
ProductionProductivity
TotalProductions
Low Inflow of Eligible Participants
Long Training DelayInsight: For Emerging Technologies, People are often the key constraint
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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
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experts, scientific, research and industrial centers, and the state and private companies to go ahead with nanotechnology.”
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<Developing Participants>
Using Modeling to Identify Active Probes with Biggest Impact
1. Decrease cooperationVariable: (contact rate)
PotentialParticipants
ActiveParticipants
Mobilizing Participants
non participantcontacts
contact rate
active particpant prevalence
CurrentProductions
participant withnon participant
contacts
recruitingfraction
initial particpants
Products implied per participant
DevelopingParticipants
<normal production delay>
Maturation
<Potential Participants>
total particpants
averageapplication life
ImpliedProduction
RevisionBacklog
new productions obsolete
Initial PotentialParticipants
MaturationDelay
TotalProductions
Adding PotentialParticipants
Leaving
Average Time asParticipant
Recruiting ActiveParticipants
Recruiting Rate2. Reduce available participantsVariable: (adding Potentialparticipants)
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<Max production rate from Capacity>
normalproduction delay
total new productions
total backlog
ProductionBacklog
<Implied Production Backlog>
<total backlog>
new productions
revising products
obsoleteproductsimplied products
Max ProductionRate from
ParticipantsActual
ProductionProductivity
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
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• Do projections cross a threshold of visibility? • Or do they remain feasible to keep hidden?
Additional Policy Context:Pressure to Keep Program Secret?
Developing Participants2,000
1,500
1,000
500
00 2 4 6 8 10 12 14 16 18 20
Time (Year)
Pers
on
Active Participants6,000
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
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Developing Participants : base1Developing Participants : base6Developing Participants : base5
Developing Participants : base4Developing Participants : base3Developing Participants : base2
Active Participants : base1Active Participants : base6Active Participants : base5
Active Participants : base4Active Participants : base3Active Participants : base2
• We can forecast parameters that drop below line.• We can infer intent by the gap between the
projected development and secrecy threshold.
10
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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
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Current Knowledge
Probes and Intel.
Model Tests
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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
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Diagnostic Probes:
• Compare information from left and right sides of development
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P t ti l Active
non participantcontacts
contact rate
active particpant prevalence
participant withnon participant
contactsinitial particpants
Developing
<Potential Participants>
<Developing Participants>
total particpants
Average Time asParticipant
Recruiting ActiveParticipants
Recruiting RateDecrease cooperation
Developing Participants2,000
1,500
n
Developing Participation
Using Modeling to Identify Probes
<Max production rate from Capacity>
PotentialParticipants
ActiveParticipants
Mobilizing Participants
CurrentProductions
recruitingfraction
Products implied per participant
normalproduction delay
DevelopingParticipants
<normal on de
Maturation
averageapplication life
total new productions
total backlog
ImpliedProductionBacklog
RevisionBacklog
<Implied Prod
<total backlog>
new productions
revising products
obsoleteproductsimplied products
Initial PotentialParticipants
MaturationDelay
Max ProductionRate from
ParticipantsActual
ProductionProductivity
TotalProductions
Adding PotentialParticipants
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
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participants
Insight: Sensitivity Analysis shows that diminishing available participants is the high leverage probe (most effect per unit of change) to reduce participation
Unclassified//For Official Use Only
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
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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
Unclassified//For Official Use Only
• Analysis Options– Re-parameterize model– Create a second pathway based on
new theory of reaction to probe
Reacting to Probe
<Potential Participants>
<Developing Participants>
Recruiting Rate
External participants
Original pathway:Initial participants
New pathway:Both internal and new external participants
Developing Participants2,000
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
Active Participants4,000
3,000
New Theory:
Active Participation (total)Developing Participation
ActiveParticipants
tive particpant prevalenceinitial particpants
lopingipants
Maturation
total particpants
Leaving
Average Time asParticipant
Recruiting ActiveParticipants
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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
Active Participants : base1Active Participants : base8Active Participants : base9
Relying on External Participation
Probe Goal
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Unintended Consequence of Probe
• Possible unintended consequence: trigger lt t th
Active Participants4,000
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
Active Participants : base1Active Participants : base8Active Participants : base9
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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
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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
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right side shows gap in development• Probes would focus on discerning new
parameters or new theory
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Unclassified//For Official Use Only
non participantcontacts
contact rate
active particpant prevalence
participant withnon participant
contacts initial particpants
<Potential Participants>
<Developing Participants>
total particpants
Leaving
Average Time asParticipant
Recruiting ActiveParticipants
Recruiting Rate
Recruiting TestInput
Recruiting Height
Recruiting Time
initial malignantparticipants
Example of Modeling Theory
PotentialParticipants
ActiveParticipantsMobilizing Participants
CurrentProductions
recruitingfraction
Products implied per participant
normalproduction delay
DevelopingParticipants
<normal production delay>
Maturation
averageapplication life
total new productions total backlog
ImpliedProductionBacklog
RevisionBacklog
<Implied Production Backlog>
<total backlog>
new productions
revising products
obsoleteproductsimplied products
Initial PotentialParticipants
MaturationDelay
TotalProductions
Adding PotentialParticipants Allocating for
Malignant Production
MalignantParticipants Leaving MP
Total VisibleParticipants
MalignantParticipant Visibility
<Actual Participation inMalignant Production 0>
Average Timeas MP
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<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
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Deducing New Information From Probes
new productions400
300
Developing Participation
Probes help
Less active participants are seen in normal development. Where did they go?
Active Participants4,000
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
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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
15
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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
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• 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
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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
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nefarious production will increase because future availability is in question. Indicator is drop in benign participation.
• Operationalize probes– Impose budgetary/bureaucratic limitations on capacity
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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
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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
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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
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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
17
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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
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– 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
Unclassified//For Official Use Only
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
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Begun working with MIT Institute for Soldier Nanotechology (ISN)– Deeper understanding of nanotech and militarization
• Empirical analysis, data gathering, model validation
18
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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)
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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.”)