ModelBased Design of HighPerformance Command

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Model-Based Design of High-Performance Command
Control Organizations

• Daniel Serfaty
• Aptima, Inc.
• Modeling of C2 Decision Processes Workshop
• Vienna, VA, Jul 31-Aug 2, 2001
• Serfaty_at_aptima.com
• www.aptima.com

12 Gill Street, Suite 1400 Woburn, MA 01801
[phone] Ext. 211
1030 15th St NW, Suite 400 Washington, DC 20005
[phone] Ext. 211
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Objective

• Demonstrate the potential of advanced
  organizational and team modeling techniques and
  tools to support
• Model-based experimentation
• C2 Design decisions

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Outline of Presentation

• Value of modeling C2 Organizations
• Prescriptive vs. descriptive modeling
• Model-Based Experimentation
• TIDE modeling approach
• C2 Design Example
• How to use TIDE products

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Understanding Command Teams From the Lab to
the Field... And Back

• Rigorous human modeling
• Algorithm-based team design
• Human decision maker models
• Simulation-based assessment
• Simulation-based experimentation
• Team-in-the-loop simulation
• Partnerships with academia and industry
• Technologies support data collection
  analysis
• Live Performance Assessment Human Engineering
• Computer-based observer tools
• Results inform training, performance, display
  design

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Why Design C2 Teams?

• Engineering the interplay between the command
  organizations systems procedures and its human
  decision-makers to optimize the quality of
  decisions
• Complex human-system design issues
• How many operators/decision-makers?
• How to partition command roles?
• How to distribute tasks among operators?
• What is the optimal team structure?
• How should operators proceed within it?
• How will new technology and missions impact an
  evolutionary team design?

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Goal Leverage C2 Research to Improve Teams
Technology
Assess/diagnose team performance
Design teams procedures
Design systems interfaces
Design team training
Team Research
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The Challenge (JTF example)
How would you design a command team organization
for this mission?
How would you derive human requirements for the
organization?
How would you evaluate its performance for this
mission?
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Model-Based ExperimentationDesign-Model-Test-Mod
el
Design
Model
Test
Define Mission, Objectives, Resources
Scenario
Organization Design Process
Conduct Experiment
Analyze Data
Experiment Design
Refine Design
Pre-Exp. Model
Validate Model
Propose Hypotheses for Next Experiment
Post Exp. Model
Adaptive Architectures for Command and Control
(A2C2) Project Integration of Modeling,
Simulation, and Experiments A Paradigm for Future
Joint Experimentation?
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Example JTF Model-Based Optimized Command
Teams
A1-4 4 nodes
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Can Model-Based Designs Improve on Common Sense?
Design
59.7
Ad-hoc
79.7
Model- based
85.1
Overall Mission Outcome
Observers Overall Rating
78.1
Model- reduced
68.5
76.2

• Experiments validate model-based team
  organizational design approach

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Improved Team Process Performance
Findings -- Model-based architectures required
less communication -- Engineered capabilities at
each command node reduced wasteful inter-node
coordination -- Better and more timely use of
communication channels supported anticipatory
behavior (a performance predictor)
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7
6
5
Comm Rate msgs/min
4
3
2
1
0
Ad-hoc
Model-based
Model- reduced
3
4
2.5
2
3
Coordination Actions /min
Anticipation Ratio
1.5
2
1
1
0.5
0
0
Model- reduced
Model-based
Ad-hoc
Model-reduced
Model-based
Ad-hoc
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Limited-Objective, Model-Based Experimentation
(A2C2/GLOBAL 99)
Network Centric Warfare
GLOBAL Wargame
Adaptive Architectures for Command and Control
(A2C2)
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Model-Based Team Architectures
Phase I
Phase II
Adaptation
FLAG
FLAG
ALPHA
CHARLIE
BRAVO
CHARLIE
BRAVO
command or supported/supporting
ALPHA
coordination
ALPHA, BRAVO, and CHARLIE cells are
multi-functional, multi-service sub-teams
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Bridge Example Phase I Architecture
FLAG
BRAVO
Tasks
CHARLIE
ALPHA

• Surface Surveillance of ERS,
• YSEA, and island O
• Defense vs. CDCM Attack
• USW in ERS, YSEA, and island O
• ASuW in TSUS area
• MIW in TSUS Strait
• CVBG penetrates ERS
• In addition, Bravo is/can be involved
• in the following tasks
• Attack Naval Bases from ERS
• Attack Red C2 Nodes from ERS
• (together with Charlie)
• Attack Red IADS from ERS
• (together with Charlie)
• Attack Red Missile Bases from ERS
• (together with Charlie)

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Team Modeling for C2 Organizations
Current Responsibilities
Operational Challenges
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Designing an Organization
Questions
Who does what? Who controls what? Who sees
what? Who knows what? Who talks to whom? Who
gives orders? Who makes decisions? Who overrides
decisions?
Structure
Who is responsible for what? Who is tasked with
what? Who backs-up whom? Who talks with whom?
Who coordinates with whom?
Process
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Defining Organizational Design
Organizational Constraints
Optimal Structure
Can the structure sustain the C2 process?
What effect the process has on the organization?
Optimal Process
Mission Structure
Problem Formulation as Multi-Objective Structural
and Process Optimization
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Team Organizational Design
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Mission-Based Methodology for Modeling
Organizations
Mission
Multi-Dimensional Task Decomposition
FEED-BACK
Organizational Design Process
1) Task to Resource Allocation 2) DM to
Resource Allocation 3) Organizational Structure
Iterative Design Process
FEED-BACK
Organizational Structure
1) Taskwork Strategy 2) Teamwork Strategy 3)
Physical Lay-Out
Performance Measures
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Outline of Presentation

• Value of modeling C2 Organizations
• Prescriptive vs. descriptive modeling
• Model-Based Experimentation
• TIDE modeling approach
• C2 Design Example
• How to use TIDE products

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TIDE Organizational EngineeringTeam Integrated
Design Environment

• Mission-driven organizational design process
• A novel, formal, and quantitative way to model
  teams and organizations
• Prescriptive methodology
• New technology/automation tradeoffs
• Cost and risk reduction
• Multiple optimization criteria
• Error minimization, workload balance, speed, etc

TIDE is capable of supporting the design of both
revolutionary and evolutionary organizational
structures to support optimized mission
performance
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Examples of C2 Organizational Design Objectives

• Speed of Command
• Organization must meet mission objectives at
  maximum speed of command (--gt op tempo)
• Staff Reduction
• Reduce Command Staffing by X
• Acceptable Workload
• Minimize Peak Workload/Balance Workload
• Total Workload Accumulation
• Effective Team Coordination
• Optimize inter-node synchronization
• Minimize communication message queues

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TIDE Five-Phase Process
Phase A Mission Representation
Event-Task Mapping
Phase B Task-Resource Mapping
Optimized Task Scheduling
Operator Role Definition/ Info Requirements
Phase C Clustering Tasks into Roles
Team-Level Assignments
Phase D Design Team Interactions
Team Design Structures Processes
Phase E Organizational Structuring
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Phase A Mission Representation
Phase A Mission Representation
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Representing the Mission

• Task interdependencies
• External triggering events
• Multiple scenarios that represent extremes for
  mission performance
• Resources required for each task effectiveness
  of resource packages
• Duration and workload associated with tasks

Subject matter experts define the mission
Note In this phase, TIDE can take advantage of
an existing Mission model (IDEF, Task Network,
Petri Net, Simulation model, etc)
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Event-to-Task Mapping
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Phase B Optimal Task Scheduling

• Meet time constraints
• Maximize effectiveness
• Resolve resource contentions

Multi-objective optimization algorithms to
develop optimal schedule

• 1. Optimal branch-and-bound algorithm
• 2. Dynamic Programming algorithm
• 3. Dynamic List Scheduling (DLS)
• 4. Pair-wise task exchange

Note Roles for individuals not yet considered
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Phase C Cluster Tasks Into Roles
Multi-dimensional multi-objective clustering
algorithms leading to task-resource pairs

• Typically optimize for
• balanced workload across individuals
• minimize need for coordination and communication
• Constrain using individual workload ceilings
• Results are fed back to task scheduling

Team size is either given or optimized
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Multi-dimensional Clustering AnalysisEx
Cluster on Information

• Goal Maximize information within watchstanders
  minimize info overlap when unnecessary
• Conduct_DCA Control_DCA use similar info
  Review_ID Conduct_engage-ment do not.

Info Task
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Phase D Engineering Team Interactions

• Uniquely assign tasks when possible to minimize
  routine communications between team members
• May need to split tasks if individuals are
  overloaded
• Detailed Modeling Tool

Note Splitting tasks introduces new
communication workload
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Intra- and Inter-Task Analysis
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Example Objective Balancing Workload in the
Team
Instant Workload
Threshold
0
Objective 1
Workload Accumulation Balance
DM1
DM2
DM3
DM4
DM5
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Notional Workload Analysis
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Outline of Presentation

• Value of modeling C2 Organizations
• Prescriptive vs. descriptive modeling
• Model-Based Experimentation
• TIDE modeling approach
• C2 Design Example AWACS
• How to use TIDE products

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AWACS Design Challenge
How do you design an optimal command control
teams for complex, variable AWACS missions to
take advantage of advanced information fusion
technology?

• TIDE Design Approach
• Mission analysis
• What needs to be done
• What information is available
• Task analysis
• How is it done
• What information is used
• Organizational analysis
• How is information shared
• Who does what

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Example AWACS Crew Optimization

• Human-Centered Re-Engineering of AWACS Command
  and Control Teams (REAC2T)
• Phase III SBIR Project funded by AWACS System
  Program Office ESC, Hanscom AFB
• Demonstrate proven, scientific approach to C2
  team design in AWACS domain
• Team Integrated Design Environment (TIDE)
• Present ACC/Wing with proof-of-concept for crew
  optimization
• Evaluate impact of information fusion on mission
  performance and operator functions
• Introduce optimized team structures to enhance
  mission performance

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AWACS Example Inputs to Mission Model

• Mission decomposition and evaluation
• Work with operational community to define current
  approach to mission completion
• CONOPS, tactics, roles, and responsibilities
• Red Flag Spin-Up Training
• Tinker AFB
• Live Fly Red Flag Exercises
• Five flights, Nellis AFB
• Cognitive Task Analysis
• Wing Tactics Office, Tinker AFB
• SD instructors Fighter Weapons School, Nellis AFB

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TIDE Prototype Software

• Mission task graphs are converted into data
  tables to serve as input for optimization
  algorithms

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Preliminary Results Baseline 14 Operator Task
Distribution
Max Workload 1400
Colors represent unique operational tasks
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Impact of Technology (MSI) InsertionNon-Optimize
d 14 Operator Configuration
Max Workload 950
Colors represent unique operational tasks
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Impact of Technology (MSI) InsertionOptimized
14 Operator Configuration
Max Workload 750
Colors represent unique operational tasks
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Impact of Technology (MSI) InsertionOptimized
12 Operator Configuration
Max Workload 800
Colors represent unique operational tasks
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Internal Communication Outgoing Messages
Baseline
MSI Non-Optimized 14
160
500
1. Technology Insertion
2. Optimal Team
MSI Optimized 14
MSI Optimized 12
3. Manning Optimization
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Summary Model-based Re-Engineering of AWACS
Command Control Teams (REAC2T)
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Outline of Presentation

• Value of modeling C2 Organizations
• Prescriptive vs. descriptive modeling
• Model-Based Experimentation
• TIDE modeling approach
• C2 Design Example
• How to use TIDE products

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TIDE Model Products

• Detailed Specification of Team Roles
• When Tasks are performed
• How Decision are made
• What Resources are used
• What Information is used
• What Communications are required

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Multiple Applications of TIDE Model
Interface Design
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Model-driven Measurement Process
Success in Meeting Training Objectives
How well are training objectives met?
Learning Objectives
Success at JTF Certification
in JTF Environment
Measurement
Improve by X
Challenges
Competencies
KSA Assessment
Knowledge, Skills,
Individual
What
KSAs
do learners have/lack?
and Abilities
Team
Diagnose individuals needs for additional
training
Team-of-Teams
Stimulated or Trained by...
Performance Measures by Task
Tasks
How well did learners perform?
Theories of
Performance Link
Skills to Behaviors to
Tasks
Put together into vignettes...
Scenario
Stories and
TIDE Model
MOPs Measurement Tools
events
Subject Matter Experts
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TIDE Integrated Toolset
Task Network Simulation
SIMULATION-BASED EVALUATION
ANALYSIS
DESIGN
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Some Current Military Applications of the TIDE
Modeling Methods and Tools

• Joint Task Force Adaptive Architectures for
  Command and Control (A2C2)
• Next Generation Navy Surface Ships (SC-21/DD-21)
• Human-Centered Re-Engineering of AWACS Command
  and Control Teams (REAC2T)
• Uninhabited Combat Air Vehicle (UCAV) Control
  Center
• Kwajalein Radar/Missile Control Center (ATIDS)
• Air Operations for Time Critical Targets (JFACC)
• Time Critical Targeting Cell in Air operations
  (CAOC)
• Effects-Based Operations in Operations Center
  (EBO)
• Army Future Combat Systems (FCS)
• Global Wargame JTF Org. Design and Assessment
• .

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Summary Why Model C2?

• TIDE is a method to optimize decision-making
  organizations to capitalize on advanced
  technology
• Model-based organizational structures are
  congruent with mission needs
• Modeling guides experimentation and performance
  assessment
• Analysis and design tool for system designers
• Cost and risk reduction
• New technology payoffs
• Mission/organization model serves multiple
  purposes
• Organizational design Provide alternative,
  optimized organizations
• Team training Highlight areas for team training
• Synthetic tasks Develop environments to train
  and evaluate teams
• Interface design Functional definition of GUI