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Model Development of Large-Scale DoD Systems-of-Systems

Santiago Balestrini Robinson [email protected]

2008 SCS Conference, June 17th, 2008 Edinburgh, Scotland

Guggenheim School of Aerospace Engineering Georgia Institute of Technology Atlanta, GA 30332-0150 http://www.asdl.gatech.edu

Model Development of Large-Scale DoD System-of-Systems [email protected]

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Outline ™ Motivation ƒ Design for Performance Æ Capability-based Acquisition

™ The Evolution of Science: Towards the Problems of Organized Complexity ƒ Brief historic overview of Complexity Science

™ Modeling and Simulation Techniques for Complex Systems ™ Network Modeling ™ Analyzing Large-scale Systems-of-Systems using Network Models ™ Quantifying DoD Architecture Framework Products ™ The Principal Components of a Network ™ Future Work Model Development of Large-Scale DoD System-of-Systems [email protected]

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Map the System to the System-of-system

™ Capability-Based Acquisition ƒ System requirements are implicit in the System-of-Systems (SoS) requirements Source: Courtesy of Kelly Cooper (SBE 2006 Presentation)

™ Need to assess the impact the system has on the System-ofSystems SoS Capabilities = f ( System Performance ) Image Source: http://www.clubs.psu.edu

Need the ability to estimate this transfer function

Model Development of Large-Scale DoD System-of-Systems [email protected]

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Characteristics that Complicate the Mapping ™ Interactions

™ Emergent Behavior

ƒ The overall behavior of the SoS is sometimes more dependent on how the entities interact than their individual capabilities

™ Nonlinearity ƒ Small variations in the causes produce large variations in the effects, or viceversa

™ Intelligence ƒ Cognitive processes must be modeled to capture the effects of decision making

ƒ It is often difficult to predict the behavior of the overall system from the study of the parts in isolation

™ Adaptation ƒ Systems tend to learn from their environment and surrounding agents

™ Dynamic Behavior ƒ The system’s behavior occurs over time and it must be studied as a function of time

™ Hierarchies ƒ The system tends to organize itself into hierarchies (e.g., Command and Control)

These are the characteristics of complex systems

Model Development of Large-Scale DoD System-of-Systems [email protected]

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The Evolution of Science ™ In 1947, Weaver analyzed the history of science from the 17th century and noticed a pattern ™ He recognized that until then there had been two main efforts ƒ Between the 17th and 19th centuries science focused on problems with only a handful of variables ƒ In the 20th century statistical methods were developed to handle problems with large number of variables

™ This left a considerable range of the problems faced by science without solid foundations ™ The advent of the computer enabled the study of the area in-between the two camps ™ This in-between field has come to be known as complexity science Warren Weaver’s Portrait Source: http://osulibrary.oregonstate.edu/

Methods from Ecology, Psychology, Biology, etc…

Computer Image Source: http://www.nea.com/ Figure based on Weaver, W., “Science and Complexity,” American Scientists, 36(536), 1948.

Model Development of Large-Scale DoD System-of-Systems [email protected]

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The Evolution of Science ™ In 1947, Weaver analyzed the history of science from the 17th century and noticed a pattern

ƒ Between the 17th and 19th centuries science focused on problems with only a handful of variables ƒ In the 20th century statistical methods were developed to handle problems with large number of variables

Newton

Detail of the Entities

™ He recognized that until then there had been two main efforts

Problems of Simplicity Problems of Organized Complexity

Boltzmann

™ This left a considerable range of the problems faced by science without solid foundations ™ The advent of the computer enabled the study of the area in-between the two camps ™ This in-between field has come to be known as complexity science Warren Weaver’s Portrait Source: http://osulibrary.oregonstate.edu/

Problems of Disorganized Complexity 2×100 2×102 2×104 2×106 2×108 Number of Elements Composing the System

Methods from Ecology, Psychology, Biology, etc…

Computer Image Source: http://www.nea.com/ Figure based on Weaver, W., “Science and Complexity,” American Scientists, 36(536), 1948.

Model Development of Large-Scale DoD System-of-Systems [email protected]

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Modeling Techniques for Complex Systems ™ No ideal method

Technique Complexity

ƒ ABM most suitable, but most difficult to implement and validate ƒ NM are the easiest to implement but do not capture the dynamic behaviors or intelligence

™ Methods are not exclusive, but complementary ™ Others techniques considered but not discussed: ƒ Markov Simulation, Petri Net Simulation, Dynamical Systems, Cellular Automata

System Dynamics Models

Agent-based Models

= Excellent

= Very Good

= Good

= Poor

= Very Poor

Network Models

Discrete Event Simulations

Nonlinearity

Interactions

Intelligent Agents Represent Hierarchies Emergent Behavior Adaptation

Dynamic Behavior

Ease of Creation

Ease of V&V

Legend:

Complementary: Coarse modeling can guide detailed modeling Model Development of Large-Scale DoD System-of-Systems [email protected]

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Modeling Techniques and the Paradigm of Complexity

ƒ

how many entities?

ƒ

in how much detail?

™ The modeling techniques map to a distinctive Pareto-front in the continuum ƒ

Notional plot based on literature reviews of different applications of the modeling techniques

ƒ

As more entities are modeled, the techniques require that the entities be simplified − System Dynamics is an exception, because in its pure form it assumes that there are an infinite number of entities

ƒ

Anything in the below or to the left of the line is a dominated solution

Network Models

Discrete Event Simulation

System Dynamics

Agent-based Models

Detail of the Entities

™ The question of how much can be modeled can be considered to be a question of

2×100 2×102 2×104 2×106 2×108 Number of Elements Composing the System

Large-Scale SoS Æ Network Models

Model Development of Large-Scale DoD System-of-Systems [email protected]

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Why not Start with Sea Basing? MPG

Ground Combat Element 3 Inf Bn 665 Personnel

2 Tank Co 47 M1A1

2 LAR Co 27 LAV-25

2 AA Co

ESG

47 EFV

3 Arty Btry 1 EFSS Btry 1 HIMARS Btry

CSG

2 Cbt Engr Co

Can See Can Kill

Aviation Combat Element

Can Supply Can Carry

4 AV-8B Sqdn 40 AV-8B

1 HMLA Sqdn 12 AH-1W / 12 UH-1N

CLF

Can Talk

2 F/A-18 Sqdn 24 F/A-18

1 EA Sqdn

2 CH-53 Sqdn 32 CH-53D/E

4 EA-6B

1 KC-130 Sqdn 6 KC-130

4 CH-46 Sqdn 48 CH-46E

LCAC

T-Craft?

ALDS?

HSV?

™ Too many elements, too many interactions Æ too complex ™ Difficult to demonstrate trends, computationally expensive Æ Can’t experiment ™ Currently developing a representative model Model Development of Large-Scale DoD System-of-Systems [email protected]

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Modeling Systems as Networks

Communication

Engage

ge Eng a

ure n at

S ig

Eng age

ge

Signature

Signature

Signature

r de

Ord er

Or

ga

ge ga En

En

ation unic m m Co

Order

™ Graphs are ideal for representing pairwise relations between large numbers of objects ™ One of a few modeling tools that enables truly holistic quantitative analysis ƒ Emphasis is placed on the structure of the macro system, rather that in decomposing the entities that make the system

System behavior can be inferred from studying the structure of the function-based network Model Development of Large-Scale DoD System-of-Systems [email protected]

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Simulating an Engagement with Networks ™ Proto-Engagement ƒ One of each, in matrix form it becomes the Graph Generation Matrix (GGM)

How many of each? age

Engage

Be Detected by Engage Order Communicate

En g

En g age

atu re

Sig n

Signature

Can Can Can Can

Signature

Signature

r

Ord er

de Or

ge ga En

ƒ Contains the probability that any two entities are related by the given function ƒ Depends on the individual system’s capability, the theater of operations, tactics, etc.

ge ga En

™ Graph Generation Matrix (GGM)

Communication

on icati m un Com

Order

Proto-Engagement

Graph Generation Matrix

Force Structure

Engagement Matrix

™ Force Structure ƒ Specifies how many systems of each type are involved

™ Engagement Matrix ƒ Represents a functional network in a possible engagement Model Development of Large-Scale DoD System-of-Systems [email protected]

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Incorporating Standard Architecture Products ™ OV-5 ƒ

™ SV-7

What set of activities constitute a capability

™ SV-5a ƒ

What functions constitute an activity

What systems perform which functions

Can Be Detected by Can Engage

Who can exchange non-automated data

™ SV-6 ƒ

Graph Generation Matrix

How good are the entities at communicating and performing their functions

™ OV-3 ƒ

™ SV-4 ƒ

ƒ

Who can exchange automated data (e.g., Link16)

OV-5

SV-4a

OV-3

Activity Sequences

Systems-Functions

Info Exchange

SV-5a

SV-7

SV-6

Activities-Functions

Performance Metrics

Info Exchange

Can Order Can Communicate

Model Development of Large-Scale DoD System-of-Systems [email protected]

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Testing through Experimentation ™ Create an agent-based simulation of an engagement including the same elements ƒ

Address spatial and time-domain complexities addressed explicitly

™ Plan ƒ

Modify the force structure and capabilities of the assets in both models to

ƒ

Compare force loss ratios ™

NetLogo Simulation of the Engagement between:

™ Agents intelligence not trivial ƒ

AWACS vector friendly fighters to closest detected targets

ƒ

Ground radars must be able to communicate with SAM sites

ƒ

Shooters must lock on for a given period of time before being able to shoot

Available Online at: http://sanbales.googlepages.com

Model Development of Large-Scale DoD System-of-Systems [email protected]

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The Principal Components of a Graph 20%

100%

0%

0%

0%

20%

20%

20%

0%

20%

λ1 = 0.00

λ1 = 1.00

17%

19%

14%

20%

14%

25%

ƒ Perron-Frobenius Eigenvector (PFE) ƒ Node’s contribution to the cycles 27%

23%

λ1 = 1.17

25%

19%

λ1 = 1.32

ƒ Associated Eigenvalue ƒ Number of Autocatalytic Cycles

Model Development of Large-Scale DoD System-of-Systems [email protected]

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Using the Principal Component to Measure a Capability 1.8

1

1.6

0.9

0.8

1.4

0.7

0.6

λPFE 1,net

NET

1 0.5 0.8 0.4 0.6 0.3 0.4

0.2 Blue Capability(PFE λ1,net)

0.2

0.1

Red Casualties 0

0 0.0

0.3

0.5

0.8

1.0

1.3

1.5

1.8

2.0

2.3

2.5

2.8

3.0

3.3

3.5

3.8

4.0

4.3

4.5

4.8

5.0

5.3

Time (hours)

Model Development of Large-Scale DoD System-of-Systems [email protected]

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5.5

Fraction of Red Casualties

1.2

Future Work: The rest of the Spectrum?

2.5

Blue Force 2

1.5

Imaginary

1

0.5

0

-0.5

-1

-1.5

-2

Red Force

-2.5

-8

-6

-4

-2

0

2

4

6

8

10

12

Real Model Development of Large-Scale DoD System-of-Systems [email protected]

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Conclusions ™ Agent-based Simulation ƒ Can capture many characteristics of complexity ƒ Very expensive to create, execute, and verify/validate

DoDAF Products OV-5

SV-7

Activity Sequences

Performance Metrics

SV-5a

SV-6

Activities-Functions

Info Exchange

SV-4a

OV-3

Systems-Functions

Info Exchange

Network Models

™ Network Modeling ƒ Captures fewer characteristics of complexity but allows for more encompassing modeling

0.6

Robustness

0.4

Vulnerability

0.2

0

ƒ Used in other fields successfully to infer behavior from structure

Focused Higher-Fidelity Models

Capability

-0.2

-0.4

Self-Synchronization

-0.6

0.2

0.4

ƒ Can be used to focus the higher fidelity models Model Development of Large-Scale DoD System-of-Systems [email protected]

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0.6

0.8

1

1.2

1.4

1.6

1.8

Questions?

Model Development of Large-Scale DoD System-of-Systems [email protected]

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