Multi-Scale Modeling of the Air and Space Operations Center

1. See Notes Page Multi-Scale Modeling of the Air and Space Operations Center By-Invitation-Only Symposium on Complex Systems Engineering 11-12 January 2007 The Rand Corporation, Santa Monica, California Brian E. White, Ph.D. (781) 271-8218 bewhite@mitre.org

2. See Notes Page 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

3. See Notes Page Adversary Theater Ballistic Missile (TBM) Launches Systems Dynamic Model Outcome Spaces “Behavior mode”: Time-series change of a desired variable

4. AOC Petri Net Process Model AOC: Air and Space Operations Center

5. “Eye chart” just suggesting a level of “complexity” Portion of AOC Petri Net Model – Monitor and Combat Operations AOC: Air and Space Operations Center

6. Systems Dynamics Model of DTC Operations for TST of TBMs DTC: Dynamic Targeting Cell TST: Time Sensitive Targeting TBM: Theater Ballistic Missile

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

8. Response Times for Each Type Event for 1 Day Pilot Down has higher priority

9. Systems Dynamics Model Outputs for 9 Days

10. Response Times for All Events

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. See Notes Page Some Definition Dependencies

15. See Notes Page 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.

16. See Notes Page 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.

17. See Notes Page 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.

18. See Notes Page 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.

19. See Notes Page 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.

20. See Notes Page 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.

21. See Notes Page • 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 behavior Enterprise Systems Engineering ProfilerTM Strategic Context System Context System Behavior Mission Environment Mission very fluid, ad-hoc System behavior will evolve System behavior fairly predictable Mission evolves slowly Desired Outcome Build fundamentally new capability Scope of Effort Known system behavior Extended enterprise Stable mission Change existing capability Single enterprise Improve existing capability Single function Relationships stable Single user class New relationships Similar users Single program, single system Stake-holders concur Many different users Resistance to changing relationships Scale of Effort Stakeholder Relationships Agree in principle; Some not involved Single program, multiple systems Multiple programs, multiple systems Multiple equities; distrust Acquisition Environment Stakeholder Involvement Stakeholder Context Implementation Context Source: Renee Stevens

22. See Notes Page The Regimen for Complex Systems Engineering

23. See Notes Page 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.

24. See Notes Page 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.

25. See Notes Page The Regimen for Complex Systems Engineering Analyze and Shape the Environment Tend Your Program Develop Off-Line Focus on Requirements Expect Best Behaviors Invest in Uncertainty Stay With the Plan Protect Information Manage Risk 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 Tailor Development Methods to Specific Regimes and Scales Identify and Define Targeted Outcome Spaces Establish Rewards (and Penalties) Judge Actual Results and Allocate Rewards Formulate and Apply Developmental Stimulants Characterize Continuously Formulate and Enforce Fitness Regulations (Policing) Legend Past Present Future Evaluating How One Acts Within the TSE to ESE Continuum Source: Brian White

26. Suggested Intermediate Slider “Waypoints”

27. a b c d e a c e b d Waypoints Help Generate Regimen Action Patterns Analyze and Shape the Environment Tailor Development Methods to Specific Regimes and Scales Identify and Define Targeted Outcome Spaces Establish Rewards (and Penalties) Judge Actual Results and Allocate Rewards Formulate and Apply Developmental Stimulants Characterize Continuously Formulate and Enforce Fitness Regulations (Policing)