© 2007 the mitre corporation. all rights reserved multi-scale modeling of the air and space...

© 2007 The MITRE Corporation. All rights reserved

Multi-Scale Modeling of the Air and Space Operations Center

Brian E. White, Ph.D.(781) 271-8218

[email protected]

By-Invitation-Only

Symposium on Complex Systems Engineering11-12 January 2007

The Rand Corporation, Santa Monica, California

See Notes Page

2


Outline

Context– Multi-scale analysis of complex system– Combining agent based model (ABM) with systems dynamics (SD)– 3-way hybrid including Petri net model– Petri net model focuses on processes that communicate and need

synchronization– Current results involve only Petri net and SD models– ABM portion will be exercised in near future

Figures from paper Backup charts

– Definitions of complexity, system, and engineering terms– Enterprise Systems Engineering (ESE) ProfilerTM

– Regimen for Complex Systems Engineering (CSE)– Regimen “Slider” Template under development

See Notes Page

3


Systems Dynamic Model Outcome Spaces

Time (units)

Variable

Time 2Time 1

“Behavior mode”: Time-series change of a desired variable

Time (days)

Adversary TBM Launches

Day 30Day 1

Adversary Theater Ballistic Missile (TBM) Launches

See Notes Page

4


AOC Petri Net Process Model

Monitor Operations

Combat Operations

Dynamic Targeting

Confirm Target

Update TCT Chief

Target Validation

Determine Engagement Options

Recommend Target for DTL

Request for Information

Merge withJIPTL

Evaluate CurrentAssets

CoordinateAirspace

Assess ThreatEnvironment

JAG Approval

Final ApprovalGet

Approvals

PackageMission

Task Assets

Re-ValidateAssessments

More Info?

Request Battle Damage

Assessment

Time SensitiveTargets

Observed

Assets forTarget

Assessment

YES NO

AOC: Air and Space Operations Center

5


Portion of AOC Petri Net Model – Monitor and Combat Operations

AOC: Air and Space Operations Center

“Eye chart” just suggesting a level of “complexity”

6


Systems Dynamics Model of DTC Operations for TST of TBMs

J FC and J FACC Objectiveto Defeat Adversary

Gap to JointForce Objectives

degree ofmilitary

operations

Strategic Objectiveprotect from TBM

attack

effect of gap onmilitary actions

+

closeness ofassets to target

effectiveness ofDTC cell operation

Priority ofTBM Target

TBM SpecificOperatorsstaffing operators

Tactical Objectiveto prosecute TBM

+

response time toprocess TBM

-+

effect of operators onaverage response time

effect of priority onaverage response time

normal TBMSpecific Operators

normal priority

effect of tacticalobjective TBM onoperator staffing

+

desired staffing TBM

staffing gap

normal staffingchange delay

+

effect of tacticalobjective TBM onpriority of task

+

averageresponse

time

++

Estimated TBMCapability

-

total TBM observationseffect of TBM

observations on degreeof military actions +

++

+Use SD Model

Feedback or Msim avgresponse time data

<impact of politicalwill on military

operations>

desired degree ofeffort by military+

+

MilitaryObjectives

ControlLoop

<initial estimate>

staffing change delay

PlannedATO

Operations change inoperations

gap inoperations time to change

amount ofoperations

normal military effort

DTC: Dynamic Targeting CellTST: Time Sensitive TargetingTBM: Theater Ballistic Missile

7


U.S. and World Public Support and Their Effect on Political Will

World Supportof US Operation

Scale from zero (no support) to 100 (full support)

initial World Supportinitial US Public Support

world supportchanging

US Political Willchanging political will

Scale from zero (no will) to 100 (full political will)

factor importanceof US public

total current supportfor operation

necessary total support

gap to acceptablepolitical will

normal time to change will

thing going badly

-time to change will

effect of major erroron changing World

support

impact of political willon military operations

ratio current toacceptable political

will

direction of changein military action

political will onmilitary actionlookup function

normal worldsupport change

<Rate of MajorErrors in

Prosecution>

US PublicSupport US support changing

effect of major erroron changing US

support

normal USsupport change

8


Response Times for Each Type Event for 1 Day

0

2

4

6

0 5 10 15 20 25

Time during day (hours)

Res

po

nse

tim

e (h

ou

rs)

All Event types tbm_launch

sam csar

tbm_detect choke_pt

Pilot Down has higher priority

9


Systems Dynamics Model Outputs for 9 Days

10


Response Times for All Events

0

1

2

3

4

5

6

7

8

9

10

0 6 12 18 24

Time (hours)

Res

po

nse

Tim

e (h

ou

rs)

Day 2

Day 9

11


Summary We may be breaking some new ground with this hybrid modeling

approach to complex systems.– Bringing in the agent based modeling aspect should be exciting– Results may capture the attention of practitioners and lead to better

opportunities for Trying out hypotheses for action Training in looking at the “big picture”

We are looking forward to modeling more of the Regimen for complex systems engineering (CSE) to learn how much the activities can be “validated” and/or improved.– Fundamentally we’re building on the idea of accelerating processes of

natural evolution in complex environments

Interactions we’re having at this symposium will be invaluable in furthering our understanding and stimulating future process in applying complex systems to practice.

12


References

[Kuras and White, 2005] Kuras, M. L., and B. E. White, “Engineering Enterprises Using Complex-System Engineering,” 11 July 2005, Proceedings INCOSE 2005 Symposium, 10-15 July 2005, Rochester, NY

[Kuras and White, 2006] Kuras, M. L., and B. E. White, “Complex Systems Engineering Position Paper: A Regimen for CSE,” 7 April 2006, Fourth Annual Conference on Systems Engineering Research (CSER), 7-8 April 2006, Los Angeles, CA

[White, 2005] White, B. E., 26 October 2005, “A Complementary Approach to Enterprise Systems Engineering,” National Defense Industrial Association, 8th Annual Systems Engineering Conference, 24-27 October 2005, San Diego CA

[White, et al., 2006] White, B. E., J. J. Mathieu, J. Melhuish, and M. L. Kuras, 26 July 2006, “Modeling and Simulation of Data Sharing at Multiple Scales: An Application of the Regimen of Complex-System Engineering,” System of Systems (SoS) Engineering Conference, 25-26 July 2006, Defense Acquisition University (DAU), Fort Belvoir, VA

[White, 2006] White, B. E., 26 October 2006, “Fostering Intra-Organizational Communication of

Enterprise Systems Engineering Practices,” National Defense Industrial Association, 9th

Annual Systems Engineering Conference, 23-26 October 2006, San Diego CA

[White, 2007] White, B. E. April 2007, “On Interpreting View (aka Scale) and Emergence in Systems Engineering,” 1st Annual IEEE Systems Conference, 9-12 April 2007, Honolulu, HI

13


Back-Up Charts

14


Some Definition DependenciesSee Notes Page

15


Complexity Terms: Scale and Complexity

Scale: A human conceptualization consisting of scope, granularity, mindset, and timeframe– Examples of the first three qualitative factors are field of view

(FoV), resolution, and cognitive focus Note: In a future paper [White, 2007], “scale” will be changed to “view”

Complexity: Description of the ultimate richness of an entity that – Continuously evolves dynamically through self-organization of

internal relationships – Requires multi-scale analysis to perceive different non-

repeating patterns of its behavior – Defies methods of pre-specification, prediction, and control

Note: Complexity as really a continuum extending from its lowest degree, complication, say, to its higher degree, intended here.

See Notes Page

16


Complexity Terms (Concluded): Order, Fitness, and Emergence Order: A qualitative measure of the instantaneous nature

and extent of all specific internal relationships of an entity.– Notes: If something has only a few relationships, i.e., patterns

of attributes defined by values, it has a small order. Fitness: The orthogonal combination of complexity and

order. – Note: Both aspects of fitness (order: what currently is;

complexity: what could be) are a part of perceiving an entity. Emergence: Something unexpected in the collective

behavior of an entity, not attributable to any subset of its parts, that appears at a given scale which is not present at the comparative scale.– Notes: Some people employ a broader definition where things

that emerge can be expected as well as unexpected. Emergence can have benefits or consequences.

See Notes Page

17


System Terms: System, SoS, and Megasystem

System: An interacting mix of elements forming an intended whole greater than the sum of its parts.– Features: These elements may include people, cultures,

organizations, policies, services, techniques, technologies, information/data, facilities, products, procedures, processes, and other human-made or natural) entities. The whole is sufficiently cohesive to have an identity distinct from its environment.

System of Systems (SoS): A collection of systems that functions to achieve a purpose not generally achievable by the individual systems acting independently.– Features: Each system can operate independently (in the same

environment as the SoS) and is managed primarily to accomplish its own separate purpose.

Megasystem [or Mega-System]: A large, man-made, richly interconnected and increasingly interdependent SoS.

See Notes Page

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System Terms (Concluded): Complex System, CAS, and Enterprise Complex System: An open system with continually

cooperating and competing elements. – Features: Continually evolves and changes according to its

own condition and external environment. Relationships among its elements are difficult to describe, understand, predict, manage, control, design, and/or change.

Notes: Here “open” means free, unobstructed by artificial means, and with unlimited participation by autonomous agents and interactions with the system’s environment.

Complex Adaptive System (CAS): Identical to a complex system.

Enterprise: A complex system in a shared human endeavor that can exhibit relatively stable equilibria or behaviors (homeostasis) among many interdependent component systems.– Feature: An enterprise may be embedded in a more inclusive

complex system.

See Notes Page

19


Engineering Terms: Engineering, Enterprise Engineering, and Systems Engineering Engineering: Methodically conceiving and implementing

viable solutions to existing problems. Enterprise Engineering: Application of engineering efforts

to an enterprise with emphasis on enhancing capabilities of the whole while attempting to better understand the relationships and interactive effects among the components of the enterprise and with its environment.

Systems Engineering: An iterative and interdisciplinary management and development process that defines and transforms requirements into an operational system.– Features: Typically, this process involves environmental,

economic, political, social, and other non-technological aspects. Activities include conceiving, researching, architecting, utilizing, designing, developing, fabricating, producing, integrating, testing, deploying, operating, sustaining, and retiring system elements.

See Notes Page

20


Engineering Terms (Concluded): TSE, ESE, and Complex Systems Engineering

Traditional Systems Engineering (TSE): Systems engineering but with limited attention to the non-technological and/or complex system aspects of the system.– Feature: In TSE there is emphasis on the process of selecting and

synthesizing the application of the appropriate scientific and technical knowledge in order to translate system requirements into a system design.

Enterprise Systems Engineering (ESE): A regimen for engineering “successful” enterprises. – Feature: Rather than focusing on parts of the enterprise, the

enterprise systems engineer concentrates on the enterprise as a whole and how its design, as applied, interacts with its environment.

Complex Systems Engineering (CSE): ESE that includes additional conscious attempts to further open an enterprise to create a less stable equilibrium among its interdependent component systems.– Feature: The deliberate and accelerated management of the natural

processes that shape the development of complex systems.

See Notes Page

21


Enterprise Systems Engineering ProfilerTM

Stable mission

Mission evolves slowly

Mission very fluid,

ad-hoc

Single function

Single enterprise

Extended enterprise

Single user class

Many different

users

Single program,

single system

Single program, multiple systems

Multiple programs, multiple systems

Similar users

Improve existing

capability

Build fundamentally new capability Change

existing capability

Stake-holders concur

Agree in principle; Some not involved

Multiple equities; distrust

Known system

behavior

System behavior

fairly predictable

System behavior will

evolve

Relationships stableNew

relationships

Resistance to changing

relationships

Strategic Context

Implementation Context

Stakeholder Context

System Context

Typical program domain– Traditional systems engineering

– Chief Engineer inside the program; reports to program manager

Transitional domain– Systems engineering across

boundaries

– Work across system/program boundaries

– Influence vs authority

Messy frontier– Political engineering (power,

control…)

– High risk, potentially high reward

– Foster cooperative behaviorSource: Renee Stevens

See Notes Page

22


The Regimen for Complex Systems EngineeringSee Notes Page

Analyze and Shape the Environment

Characterize Continuously

Formulate and Apply Developmental Stimulants

Judge Actual Results and Allocate Rewards

Establish Rewards (and Penalties)

Tailor Developmental Methods to Specific Regimes and Scales

Identify or Define Targeted Outcome Spaces

Formulate and Enforce Fitness Regulations (Policing)

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What Can One Do to Engineer a Complex Systems Environment? Analyze and shape the environment: Guide the

complex-system's self-directed development. This depends on the nature of the system and its environment. None of the environment can be directly controlled in a persistent fashion.

Tailor developmental methods to specific regimes and scales: Any complex-system operates in multiple regimes and at multiple scales. The operational regime is directly associated with the purposes or mission of the whole system. The developmental regime and it is associated with changes in the system. These two regimes cannot be sufficiently isolated for a complex-system.

Identify or define targeted outcome spaces: Outcome spaces are large sets of possible partial outcomes at specific scales and in specific regimes. The complex-system itself will choose the exact combinations of partial outcomes that it realizes.

Establish rewards (and penalties): Establish rewards (and penalties) that are intended to influence the behavior of individual (but not specific) autonomous agents at one or more scales and regimes to influence agent outcomes.

See Notes Page

24


What Can One Do to Engineer a Complex Systems Environment? (Concluded) Judge actual results and allocate rewards: Consider

and judge the actual outcomes in many or all of the regimes and scales in terms of targeted outcome spaces. Then allocate rewards to the most responsible agents, whether they were pursuing those rewards or not. Do this in ways that preserve or even increase the opportunity for more new results.

Formulate and apply developmental stimulants: Use methods that increase the number of, or the intensity and persistence of, interactions among autonomous agents. Specific forms of this method depend on the phase of the developmental cycle of a capability that is being addressed.

Characterize continuously: Aim at gathering information at multiple scales and in multiple regimes pertinent to Outcome Spaces and making it available to the autonomous agents.

Formulate and enforce fitness regulations (policing): For example, initiate procedures aimed at detecting and screening changes so that fitness is maintained; that monitor characteristic periods; and that inhibit or negate changes that increase characteristic periods.

See Notes Page

25


Evaluating How One Acts Within the TSE to ESE Continuum

Influence Authoritative Policies

Innovate With Users

Emphasize Mission Capabilities

Leverage Personal Motivations

Pay for Desired Results

Continually “Stir the Pot”

Embrace ESE “Dashboards”

Enable Future Change

Tend Your Program

Develop Off-Line

Focus on Requirements

Expect Best Behaviors

Invest in Uncertainty

Stay With the Plan

Protect Information

Manage Risk


Identify and Define Targeted Outcome Spaces

Tailor Development Methods to Specific Regimes and Scales






The Regimen for Complex Systems Engineering

LegendPast

Present

Future

Source: Brian White

See Notes Page

26


Suggested Intermediate Slider “Waypoints”

Regimen Activity a-Left End of Slider

b-1st Intermediate Point

c-2nd Intermediate Point

d-3rd Intermediate Point

e-Right End of Slider

1-Analyze and Shape the Environment

Tend Your Program

Work with Other Programs

Integrate with Other Programs

Recommend Policy Changes

Influence Authoritative Policies

2-Tailor Development Methods to Specific Regimes and Scales

Develop Off-Line

Participate in Exercises Work Directly with Users

Develop in Operational Environment

Innovate With Users

3-Identify and Define Targeted Outcome Spaces

Focus on Requirement

s

Define Mission Impacts Curtail Non-Mission Activities

Ensure Mission Capabilities

Emphasize Mission Capabilities

4-Establish Rewards (and Penalties)

Expect Best

Behaviors

Encourage Personal Risk Taking

Reward Informed Failures

Improve Reward Structure

Leverage Personal Motivations

5-Formulate and Apply Development Stimulants

Invest in Uncertainty

Encourage Competition Increase Requisite Variety

Select Promising Paths Continually "Stir the Pot"

6-Judge Actual Results and Allocate Rewards

Stay with the Plan

Enforce Exit Criteria Consider Alternative Paths

Reward Group Achievements

Reward Desired Results

7-Characterize Continuously

Protect Information

Own Data Act as Data Custodian Share Information with Others

Broker, Publish, and Subscribe

8-Formulate and Enforce Fitness Regulations (Policing)

Manage Risk Consider Opportunities Manage Uncertainties Employ Real Options Enable Future Change

27


Waypoints Help Generate Regimen Action Patterns


Identify and Define Targeted Outcome Spaces

Tailor Development Methods to Specific Regimes and Scales






a

a

e

eb

b c

c

d

d

© 2007 the mitre corporation. all rights reserved multi-scale modeling of the air and space...

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