M&S SUPPORT IN THE OPERATIONS PROCESS NATO MSG-143 Symposium "Ready for the Predictable, Prepared for the Unexpected - M&S for Collective Defence in Hybrid Environments and Hybrid Conflicts " | Guido Veldhuis, Nico de Reus
MEANWHILE... SOMEWHERE ELSE ON THE PLANET
21 October 2016 2 | M&S support in the operations process Credits: Sean Swan (ITN)
Tactical
Operational
Military-strategic
Political-strategic
M&S SUPPORT TO OPERATIONS
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THE OPERATIONS PROCESS
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Conceptual planning
Detailed planning
Execute
MILITARY OPERATIONS THE CHALLENGE
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CURRENT APPROACH EXAMPLES FROM TASK FORCE URUZGAN
(Local) Leadership
Existing industries
Local intelligentia
Natural resources
Security forces presence
IO/G
O/N
GO
activity
Insurgency
Organized crim
e
1. Governance2. Rule of Law3. Security Apparatus4. Education5. Healthcare6. Agriculture and Rural Development7. Infrastructure8. Energy and Water9. Social Protection
Local conflicts
Illegal logging
National program
mes
Factors to exploit
Media
Employm
ent
Narcotics
Piracy
10. Finance and Banking
11. Trade, Commerce and Tourism12. Sports, Culture and Media
Developm
ent themes
Corruption and N
epotism
Factors to mitigate
Economy
Public opinion
Politics
Factors to influence
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MODELLING & SIMULATION SUPPORT AS A SOLUTION DIRECTION
M&S enables a user to: Simulate the behaviour of systems over time Gain insight into the system behaviour and relations between factors/actors The modelling process itself stimulates deep reflection on the problem situation. Especially when done in a group it gives insight into each others views. Models as knowledge repository
M&S applications for training and concept development and experimentation have been successful -> A project was started to investigate the possibilities of M&S for operations
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WHERE CAN M&S OFFER SUPPORT?
Design Create insight in PMESII-PT (f)actors
Planning Develop and analyse Courses of Action
Assessment Define relevant MOEs/MOPs Investigate and update assumptions about MOEs/MOPs and PMESII-PT (f)actors
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KEY POINTS OF APPROACH Other frameworks and models exist, such as: COMPOEX, NOEM,… are/contain BIG models, (COMPOEX models typically contain 10,000 state variables and thousands of relations.
Our approach:
Use of smaller models and visual diagrams More manageable, easier to build Accessibility, ownership
Process is just as important as product: Build with(by) end-user, involved in Group Model Building sessions
Getting mission area insight from the model building process Focus on including uncertainty in analysis
No point predictions but embrace uncertainty Insight, Robustness and traceability.
Support continuous updating Enable the (re)-use of model parts: ‘building blocks’
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MEMPHIS CONCEPT
Design
Conceptual framework Assignment/mission
Objectivesguidance
Analysis of situation and historic developments
MEMPHIS model maker, model container, simulator
Planning
CoA-developmentand comparison
CoA analysis
Assessment
Advice + argumentationProgress and possible
interventionsModel update
Analysis situation,Progess/impact interventions
(visualisation/structuring)
Current Opsdata
Design data
Planningdata
Assessmentdata
Environmentdata
Other data sources
Supportdata
data transformation
Military Environment Modelling with Physical and Human terrain Information Services
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A NOTE ON PROCESS
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Data collection
Model refinement
Simulation Analysis
Reporting
ARCHITECTURE
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Susceptible and Infected Population1.5 M
750,000
00 10 20 30 40 50
Time (day)
Pers
on
Susceptible Population : EpidemicInfected Population : Epidemic
MODEL BUILDING METHODOLOGY: SYSTEM DYNAMICS
‘Structure drives behaviour’
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SusceptiblePopulation
InfectedPopulation
infections
rate that people contact other people
fraction of population infected
total population
contacts between infected and unaffected
fraction infected from contact
initial infectedinitial susceptible
EXPLORATORY MODELLING AND ANALYSIS
(Deep) Uncertainty will remain about: Appropriate models Relationships amongst factors Correct parameters/probabilities How to value outcomes Exploratory modelling and analysis
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EXAMPLE CASE: COUNTER-INSURGENCY
EXAMPLE CASE Goal
Evaluate developing a model based on knowledge available within an operational unit;
GMB sessions: Operational analysts, Intelligence analysts, Staff chiefs, Soldiers
Use the model as a testbed to develop and evaluate EMA techniques; Develop example case to demonstrate the added value of the approach to project stakeholders and receive feedback in an early stage of development.
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FULL MODEL
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The model and results presented are intended for demonstration purposes only
MODEL OVERVIEW
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Faction A
Population Combatants
Resources
Satisfaction with others
Needs fulfilment
Faction A
Faction B
Faction C
Regime Coalition
Security forces
Security forces
Public services
Perception of regime legitimacy
DIME Interventions
SIMULATION
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25 Uncertainties 4 Courses of action
VE
NS
IM
JMP
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COMMUNICATING THE RESULTS
Multi-layered approach needed to effectively use model output
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Analyst
Plans/ Environment cell
Commander
RESULTS – BASE CASE
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Tota
l vio
lent
act
s P
opul
atio
n lo
ss
Some conflict almost inevitable
Potential large impact on population
ANALYTICS
Techniques used Screening Clustering Partitioning Discriminant But most importantly: Visualization
Accessible for wide group of users Support building understanding
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CLUSTERING TO IDENTIFY PLAUSIBLE FUTURE BEHAVIOUR
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Minor escalation ‘No’ escalation
Escalation and sustained violence
Rapid escalation
Total violent acts
MOE FOR EACH SCENARIO
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Minor escalation
‘No’ escalation
Escalation and sustained violence
Rapid escalation
Total violent acts
Humanitarian situation
Population lost
(CIVCAS + IDP)
Combatant casualties
Legitimacy of host nation
government perceived by population
MOE FOR EACH SCENARIO
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Minor escalation
‘No’ escalation
Escalation and sustained violence
Rapid escalation
WHICH UNCERTAINTIES MOST STRONGLY INFLUENCE ESCALATION?
Planning delay Combatants needed per violent act Time to mobilize Average resources targeted per act Fraction population influenced per act
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WHICH UNCERTAINTIES MOST STRONGLY INFLUENCE ESCALATION?
Planning delay Combatants needed per violent act Time to mobilize Average resources targeted per act Fraction population influenced per act
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Perceived regimelegitimacy
Perceivedcombatant gap
OrdinaryPopulation
(Part-time)Combatant
(De)Mobilizing
ViolentActs Target priority
Combatant gapwith target
Violent Actsavailable
Available forces
Dissatisfaction towards faction
Perceived threat
Economic motivesPlanning delay
Combatants neededper violent act
Time to mobilize
Fraction populationinfluenced per act
Average resourcestargetted per act Seizing resources
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Perceived regimelegitimacy
Perceivedcombatant gap
OrdinaryPopulation
(Part-time)Combatant
(De)Mobilizing
ViolentActs Target priority
Combatant gapwith target
Violent Actsavailable
Available forces
Dissatisfaction towards faction
Perceived threat
Economic motives
Dissatisfaction C -> A
Perceived legitimacy
Perceived threat by C
Violence A -> C A <- C Violence
COURSE OF ACTION ANALYSIS
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Courses of action Base case: ‘None’ ‘Combat’ ‘Policing, PRT, InfoOps’ ‘All with balanced focus’
Perceived regimelegitimacy
Perceivedcombatant gap
Ordinary Population
(Part-time) Combatant
(De)Mobilizing
ViolentActs Target priority
Combatant gapwith target
Violent Actsavailable
Available forces
Dissatisfaction towards faction
Perceived threat
Economic motives
Seizing resources
Combat
PolicingPRT
InfoOps
COURSE OF ACTION ANALYSIS
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Courses of action Base case: ‘None’ ‘Combat’ ‘Policing, PRT, InfoOps’ ‘All with balanced focus’
Total violent acts Median result (per COA, per Cluster)
Minor escalation ‘No’ escalation
Escalation and sustained violence Rapid escalation
POLICY ANALYSIS
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Policies Base case: ‘None’ ‘Combat’ ‘Policing and PRT’ ‘All with balanced focus’
SOME ADVANTAGES OF EMA BEYOND POLICY ANALYSIS* *IN ADDITION TO OTHER SD / M&S BENEFITS
Insight Clustering for scenario identification
Force structuring, commitment of resources
Which events (e.g. threat) can always/never be countered with a given set of capabilities? Which uncertainties should be reduced (by for example tasking intelligence units) for a maximum decrease in outcome uncertainty?
Course of action Which COA’s are most effective under deep uncertainty? Which assumptions must hold true for a policy to be effective?
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EVALUATION & WAY AHEAD
Very positive reception by project stakeholders OA & Intel analysts: “This is the future”
Extension
Develop ‘building block’ approach to modelling Extend modelling methodology: ABM, Hybrid, GIS Process: Integration in operational process, multi-layered communication of insights, operational requirements (DOTMLPFI)?
Way-ahead
November 2016: Operational test during NLD Vigorous Vector wargame focus on hybrid warfare scenario (new model)
Research and concepts aside, where are we now in the ‘real world’?
December 2016: Qualitative system modelling tool ‘MARVEL’ delivered to Intel unit together with modelling training
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THANK YOU FOR YOUR ATTENTION
SIMILAR IDEAS: PLANNING FRAMEWORKS
COMPOEX: COnflict Modeling, Planning, and Outcomes Experimentation; DARPA includes
Model building tool, Model library, Data tool, OptionExploration tool, Campaign planning tool
NOEM: National Operational Environment Model; ARL includes
Adaptable models for analysis and assessment of interventions NATO TOPFAS: Tool for Operational Planning, Force Activation and Simulation
Operational planning support tool Others frameworks and models exist
Campaign Planning Tool
Option Exploration Tool
Conflict Space Modeling
Tool
COMPOEX
Lines of Effort
DIME Actions
Generic PMESII Model
Library
ONA SIPR NATO OSC
Plans Effects
Over 40 Generic Models of PMESII
Systems
A typical 3-level simulation has over 2500 dynamic casual relationships changing on one-week
time steps for a 6-12 month simulation period.
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2
4
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