1. Watt Systems Technologies Copyright © 2009, Watt Systems Technologies All Rights Reserved Traditional Systems Engineering Kenneth A. Lloyd, Jr. (Is REALLY Model Based Systems Engineering)

2. Objectives of this Presentation Provide background context & research for SE? Raise awareness of models in SE practice. SE Conops are models. SE Requirements are models. SE Validation and Verification are models. All SE Documents map to models. Show Concepts are the foundation of models. Are Systems Models? No, but you can model systems. Have some fun …

3. Background Scientists [and engineers] come to their particular problem with an accepted body of knowledge behind them, and on which they expect to draw, without questioning the validity of each and every method, assumption, or set of facts that they use. If we all tried to work everything out from first principles, or even insisted on understanding every piece of the puzzle in equal detail, none of use would ever get anywhere. So to some degree we have to accept that whatever has been acknowledged by the relevant community has been done carefully and correctly, and can be relied on … But the process is far from perfect, and once in a while we are surprised to discover that a piece of knowledge we had long taken for granted is questionable or even wrong. -Duncan J. Watts, from Six Degrees: The Science of a Connected Age [p. 132]

4. Background Experts notice features and meaningful patterns of information that are not noticed by novices. Experts have acquired a great deal of content knowledge that is organized in ways that reflect a deep understanding of their subject matter. Experts knowledge cannot be reduced to sets of isolated facts or propositions but, instead, reflects contexts of applicability: that is, the knowledge is conditionalized on a set of circumstances. Experts are able to flexibly retrieve important aspects of their knowledge with little attentional effort. Though experts know their disciplines thoroughly, this does not guarantee that they are able to teach others. Experts have varying levels of flexibility in their approach to new situations. John D. Bransford, Ann L. Brown, and Rodney R. Cocking (eds.), How Experts Differ from Novices

5. 4 Major Concerns of SE Enterprise aspects Technical aspects Project aspects Agreement aspects Our focus Brief

6. Enterprise Aspects

7. Models – Steve Lehar Phenomenon Model Courtesy: Steve Lehar, PhD – Cognitive Science, Boston University Concept Concepts do not need, nor do they have the same topology as reality. They have maps.

8. Models – Roger Penrose PhenomenonConceptual Model Roger Penrose – Road to Reality

9. The Chasm – The Problem Domain Courtesy: Steve Lehar, PhD – Cognitive Science, Boston University

10. Minimal Model System A statement of what you believe you know, and what you dont Real world Phenomenon A Hypothesis or Theorem* ** Idealized at equilibrium

11. In Mathematical Language F is called a functor

12. Carnegie-Mellon Models Carnegie-Mellon University A System of Model Interaction Related to Phenomenon

13. Familiar Ground - The SE V

14. Evolutionary V

16. Background - The SE V

17. Models The Man Who Mistook His Brain For His Mind

18. Models INCOSE Handbook 3.1 p. 2.4Aussi, ceci nest pas un modéle

19. Conceptual Hierarchy Higher Abstraction

20. Alex Stepanovs Concept Model

21. Maps to Models Each level has elements of self-similarity – but not equality Petri nets UML, SysML and Textual Documents

22. Technical Aspects Requirements definition, Requirements analysis, Architectural design, Implementation, Integration, Verification, Transition, Validation, Operation, Maintenance, and Disposal Focus

23. Zia - A Real-world Example

24. Concept of Operations Zia goes here

25. Overview Overview (excerpt) Effective management and stewardship of the nuclear weapons stockpile into the future requires the ability to accurately assess the behavior of the weapons in order to ensure robust and reliable performance while maintaining the testing moratorium. These accurate assessments drive the requirements for predictive capability in weapons science, including a fine-scale numerical resolution and advanced models for physics and material behavior. – pg. 5 Model? Models? ?

26. Models – Steve Lehar Redux Phenomenon as Measureable, Meaningful Data: Requirements Model Courtesy: Steve Lehar, PhD – Cognitive Science, Boston University Concept Requirements do not need, nor do they have the same topology as reality. They have maps to models. What does this requirement mean?

27. Zias High Level Reqs.

28. Mapping Information to Models Agents, spiders and crawlers … Oh, my!

29. What does it all mean?

30. Contact Info Kenneth A. Lloyd, Jr. Director, Systems Science Watt Systems Technologies Inc. Albuquerque, NM 87114 USA kenneth.lloyd@wattsys.com