COINS:

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• COINS
• Counter Insurgency Simulation
• Institute for Revolutionary and Insurgency
  Studies
• (IRIS)

Preston Dunlap Mike Collins Class IPR 13 April
2006 http//mason.gmu.edu/pdunlap
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Institute for Revolutionary and Insurgency
Studies (IRIS)

• SynthesisPreston Dunlap (project lead,
  website, insurgency framework, analysis)Wesley
  Wood (output requirements, simulation, analysis)
• SME/Parameter DevelopmentPierre Jutras
  (missions, special forces, SME)
• DevelopmentMike Collins (code, survey)Mark
  Donahey (requirements, Powersim)
• SponsorDr. Al SweetserPh.D., Operations
  ResearchDirector, Simulation and Analysis
  CenterOffice of the Secretary of Defense
• Faculty AdvisorProf. Andrew Loerch,
  Ph.D.Professor, George Mason University
• IRIS Website
• http//mason.gmu.edu/pdunlap/

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Senior IRIS Consultants

• Mike Ottenberg, Sr. Irregular Warfare Analyst
• John Collier, Sr. Special Forces Analyst
• Mike Williams, OR Analyst
• LTC Neil Fitzpatrick, SOCOM J8
• LTC Steve Knight, Joint Staff J8

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Project Schedule Update
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Outline

• Objective
• Background
• Assumptions Limitations
• Methodology
• Road to Victory

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Objective

• Develop and demonstrate a methodology to assess
  the effectiveness of various courses of action to
  mobilize indigenous popular support at the
  provincial scale in a counterinsurgency scenario

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Outline

• Objective
• Background
• Assumptions Limitations
• Methodology
• Road to Victory

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Sponsors Vision for COINS

• Currently no known comprehensive and
  community-accepted tools exist that provide the
  capability to analyze irregular warfare at the
  campaign level
• COINS has the potential to add significant
  capability to analyze insurgencies
• Results of the baseline study could inform
  current OIF strategic assessments

• Integrate COINS into the Simulation and Analysis
  Centers Irregular Warfare and Special Forces
  Analysis division tool suite

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Related Efforts

• Dynamics of Insurgency (Naval Postgraduate
  School)
• Understanding Modeling State Stability
  (Massachusetts Institute of Technology)
• Modeling and Analysis of Post-Conflict
  Reconstruction (Air Force Institute of
  Technology)
• DARPA Pre-Conflict Anticipation and Shaping /
  Integrated Battle Command
• Synthetic Environments for Analysis and
  Simulation (Purdue University)
• Center for Army Analysis ERIS Concept

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System Dynamics

• Methodology pioneered at MIT using differential
  equations to model complex feedback systems
• Feedback refers to a situation where A affects B,
  and B in turn affects A
• Business and social systems are popular examples
  of complex feedback systems

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System Dynamics and Insurgency
The Dynamics of Insurgency Gordon H.
McCormick Frank R. Giordano

• Competitive struggle of organizational growth
• Effective support is reasonable proxy for
  organizational growth
• Each side has a favorable operating space
• Two sides attempt to affect game parameters in
  order to maximize their operating space

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Outline

• Objective
• Background
• Assumptions Limitations
• Methodology
• Road to Victory

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Assumptions

• The objective function of an insurgent model is
  the level of popular support enjoyed by the two
  sides.
• An insurgency can be modeled as a deterministic
  event in which the appropriate dynamic
  relationships and system characteristics can
  represent the behavior of a contested population
  and thus the success or consequences of an
  insurgent campaign.
• The population in an area under an insurgency
  struggle can be characterized as belonging to one
  of five groups core counterinsurgency,
  supporting counterinsurgency, undecided,
  supporting insurgency, and core insurgency.
• The contested population consists of all but the
  core supporters of the counterinsurgency and/or
  insurgency.
• The only way to diminish the core support of
  either side is to attrite them through killing or
  detainment.

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Assumptions

• Popular support levels can be represented by a
  single number for all insurgent and
  counter-insurgent units.
• Coalition forces (US, UK, etc) can be represented
  as a single entity with similar effectiveness at
  counterinsurgency operations.
• Missions depicted in the COINS model are believed
  to be representative of the actual missions being
  exercised by the counterinsurgent and insurgent
  forces.
• Strategic level decisions and tendencies can
  accurately represent the dynamics of an
  insurgency campaign.

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Limitations

• There are no generally accepted metrics which can
  effectively and comprehensively measure popular
  support.
• Specific individual unit capabilities and
  characteristics are not modeled.
• Data used to parameterize the COINS model was
  collected only from unclassified sources.
• Data sources utilized particularly subject
  matter experts whom have directly participated in
  the Iraqi conflict do not necessarily all have
  experience with the same province within Iraq.
• Current version of COINS limits analysis to a
  single region or province.

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Outline

• Objective
• Background
• Assumptions Limitations
• Methodology
• Road to Victory

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Methodology

• Develop model
• Incorporate SME-derived data
• Verify and validate model
• Analyze test scenario
• Explore results

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COINS Model

• Accounts for changes in Core Support left
  unaddressed in McCormick insurgency model
• Incorporates training delay for establishment of
  local ISF and Police forces
• Compact architecture conforms to constraints of
  software platform

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COINS Model

• Fully integrated Commanders Control Panel
• Accepts number of units performing each type of
  mission
• Panel output is linked to hidden layer for
  translation to model parameters

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COINS Model

• Model is ready to accept results from SME survey
  analysis
• Each model parameter is adjusted using weighted
  values for each Unit/Mission combination

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Data

• Insurgent input variables
• x(t) the size of the insurgent organization at
  time t
• kx the upper limit of the insurgents popular
  support
• kx size of the core insurgent organization,
  unrecruitable by the regime
• a the insurgents growth intensity (ability to
  win public support) IRIS Survey
• n the insurgents combat effectiveness (ability
  to draw down the regime organization) IRIS
  Survey
• Regime input variables
• y(t) the size of the regimes organization at
  time t
• ky the upper limit of the regimes popular
  support
• ky size of the regimes core support,
  unrecruitable by the insurgents
• b the regimes growth intensity (ability to win
  public support) IRIS Survey
• m the regimes combat effectiveness (ability to
  draw down the insurgent organization) IRIS
  Survey

Starting conditions derived from Brookings
Institute analysis
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Validation

• Develop a set of scenarios which includes
• Range of starting conditions
• Range of simulated outcomes
• Run programmed scenarios for SMEs
• Gather comments about performance of the model
• Expected or unexpected results?
• Can unexpected results be explained?
• Adjust parameters to reflect SME experience
• Obtain SME validation of revised model

Plan approved by sponsor and faculty advisor
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Analyze Scenario Explore Results

• Identify victory condition
• Identify operational profiles (units and
  missions) to test
• Run simulation for each profile and record time
  to victory
• Regress results to determine the impact of
  unit/mission profiles on the time required to
  achieve victory
• Develop a relative assessment of mission
  portfolios based on regression results
• Provide recommendations to commander

Plan approved by sponsor and faculty advisor
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Outline

• Objective
• Background
• Assumptions Limitations
• Methodology
• Road to Victory

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Road to Victory

• Incorporate survey results (4/17)
• Validate model (4/20)
• Perform analysis and explore results (4/23)
• Propose follow-on options (4/24)
• Complete final brief (4/27)
• Complete final paper (5/4)
• Brief sponsor, faculty, consultants (5/5)
• Present final brief at GMU (5/12)

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• Questions?