© 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
By-Invitation-Only
Symposium on Complex Systems Engineering11-12 January 2007
The Rand Corporation, Santa Monica, California
See Notes Page
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© 2007 The MITRE Corporation. All rights reserved
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
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© 2007 The MITRE Corporation. All rights reserved
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
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© 2007 The MITRE Corporation. All rights reserved
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
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© 2007 The MITRE Corporation. All rights reserved
Portion of AOC Petri Net Model – Monitor and Combat Operations
AOC: Air and Space Operations Center
“Eye chart” just suggesting a level of “complexity”
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© 2007 The MITRE Corporation. All rights reserved
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
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© 2007 The MITRE Corporation. All rights reserved
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
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© 2007 The MITRE Corporation. All rights reserved
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
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© 2007 The MITRE Corporation. All rights reserved
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
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© 2007 The MITRE Corporation. All rights reserved
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.
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© 2007 The MITRE Corporation. All rights reserved
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
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© 2007 The MITRE Corporation. All rights reserved
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
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© 2007 The MITRE Corporation. All rights reserved
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
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© 2007 The MITRE Corporation. All rights reserved
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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© 2007 The MITRE Corporation. All rights reserved
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
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© 2007 The MITRE Corporation. All rights reserved
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
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© 2007 The MITRE Corporation. All rights reserved
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
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© 2007 The MITRE Corporation. All rights reserved
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
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© 2007 The MITRE Corporation. All rights reserved
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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© 2007 The MITRE Corporation. All rights reserved
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
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© 2007 The MITRE Corporation. All rights reserved
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
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© 2007 The MITRE Corporation. All rights reserved
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
Analyze and Shape the Environment
Identify and Define Targeted Outcome Spaces
Tailor Development Methods to Specific Regimes and Scales
Characterize Continuously
Establish Rewards (and Penalties)
Judge Actual Results and Allocate Rewards
Formulate and Apply Developmental Stimulants
Formulate and Enforce Fitness Regulations (Policing)
The Regimen for Complex Systems Engineering
LegendPast
Present
Future
Source: Brian White
See Notes Page
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© 2007 The MITRE Corporation. All rights reserved
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
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© 2007 The MITRE Corporation. All rights reserved
Waypoints Help Generate Regimen Action Patterns
Analyze and Shape the Environment
Identify and Define Targeted Outcome Spaces
Tailor Development Methods to Specific Regimes and Scales
Characterize Continuously
Establish Rewards (and Penalties)
Judge Actual Results and Allocate Rewards
Formulate and Apply Developmental Stimulants
Formulate and Enforce Fitness Regulations (Policing)
a
a
e
eb
b c
c
d
d