Complicated vs. Complex: Leveraging Understanding for Systemist OntoPilot

1. Complicated vs. Complex Jack Ring Systemist OntoPilot LLC jring@amug.org Prepared for 14th Annual INCOSE Region II Fall Mini-Conference San Diego, CA 10/31/2009

2. Complicated vs. Complex • Understanding the difference • Leveraging the understanding • e = mc2 or e/k = mc2?

3. Complicated vs. Complex • Understanding the difference • Leveraging the understanding • e = mc2 or e/k = mc2?

4. plic --- as in complicated

5. plex --- as in complex

6. plic – as in Requirements Management Art: Jamcracker Go Forth and Stultify!

7. Descriptive Model of Problem System Business 2.0, 2/9/2001

8. Boardman, John, Prof. Stevens Institute of Technology, Systemitool

9. Z = model of the local system of interest = fidelity of Z to S Beneficiaries Beneficiaries Supply Supply Operators Operators DB Admin DB Admin Config Admin Config Admin In-service Eng. In-service Eng. Z(S1) Dev.Eng. Dev. Eng. Z(S2) Systems Eng. Systems Eng. plex – as in a SoS Problematique = U * P * C where; U = degree of understanding P = level of role proficiency C = level of collaboration ability

10. Simulation-Based Engineering of Complex SystemsBy John R. Clymer The OpEMCSS Software

11. And We Are! OBTW, ‘Complexer’ is better --- usually Year 1945 Scenario Every Citizen a Switchboard Operator If Navigable by humans

12. e e = entity S R = relation e = behavior =  e e e = system S = Stimulus, R = Response e = system (explicit) e e e e Entity can contain a system e e = e = system (implicit) e e e e e e e = system (soft) e e e e e e e System of Systems (promulgates ambiguity) Semiotics 4 Systemics

13. Situation Value Space Problem Space Out In  Class Type Class Value f(t) Type Type f(t) Class A Few System Types  = f(k) = ballistic  = f(O) = governor  = f(I) = anticipatory  = f(Sit, O) = homeostatic  = f(Val) = goal-seeking  = f(Pr) = self-organizing  = f(Pr, Val) = autopoietic  = f(all) = autocatalytic Pr = Problem Space Val = Value Space S = Stimulus R = Response Sit = Situation = System Transfer Function

14. Intelligent SE • Generative SE • Self-Aware SE • Work Program of Complexity • Model-based SE • System of Systems Engineering • Traditional SE Agenda • Understanding the difference • Leveraging the understanding • We are learning that we need a science of complex systems, and we are beginning to construct it. Herbert A. Simon, SDPS-IDPT 2000 Opening address

15. System Context System Problem System; Content, Process, Behavior 1. Problem Suppression System 2. 3. Problem Suppression System: Content t2 Problem Suppression System: Content, Structure, Behavior 4. t3 t1 The Essence of Systems Praxis Adapted from Science of Generic Design, John Warfield

16. System Characterized BoK Updated Community Situation Problem Discerned Value of System Quantified Problem System Understood Effects on Problem Known Focus on Value Context Adapted Solution Effect Envisioned Discover POSIWID Known Focus on Purpose Intervention Strategy Operational Results PSS S><R Specified PSS Activated Engineer Focus on System PSS Envisioned Operational Readiness PSS Designed & Architected PSS Tested Components Specified - Developed - Assembled Whole Systems Engineering Evaluate S = Stimulus R = Response PSS = Problem Suppression System

17. System Characterized BoK Updated Community Situation Problem Discerned Value of System Quantified Problem System Understood Effects on Problem Known Focus on Value Context Adapted Solution Effect Envisioned Discover POSIWID Known Focus on Purpose Intervention Strategy Operational Results PSS S><R Specified PSS Activated Engineer Focus on System PSS Envisioned Operational Readiness PSS Designed & Architected PSS Tested Components Specified - Developed - Assembled Traditional Systems Engineering Evaluate S = Stimulus R = Response PSS = Problem Suppression System

18. Beware Re-use?

19. Low Med High Extent Variety Ambiguity ‘Wicked’ Problems Essence of Complex, Adaptive Traditional SE SoSE Whole System Realization Extent: # of cognates (e’s and r’s) Variety: # of unique cognates, both temporal and semiotic Ambiguity: fog, conflicting data, cognitive overload

20. H M L Extent Variety Ambiguity Kinds of Infrastructures Thermodynamic Informatics Biomatics Teleonomics Social Dynamics Economics Ecologics Kinds of Technologies I&D Automation PSE’s Mediation eLearning Systems Praxis in Context Problematique Control Educing Expeditionary Kinds of Systems Kinds of SE Prescient Pursuit Generative Cut/Paste Anal-yzer Composer Critic Kinds of Practitioners Value Generated

21. Model-based System Engineering The truth, the whole truth, and nothing but the truth. Relevant Emergence Minimal Implicate Order Not to be confused with INCOSE MBSE/SysML

22. TTTWTANBTT has Six Facets • An input/output (I/O) requirement, IOR • A performance requirement, PR • A technology requirement, TYR • A cost requirement, CR • A tradeoff requirement and, TR • A system test requirement, STR. Model-based Systems Engineering, A. W. Wymore, CRC Press, 1993

23. Informatics Thermodynamics Biomatics Teleonomics Social Dynamics Economics Ecologics Minimal Implicate Order

24. t2 t3 t1 Relevant Emergence Context Co-align Content Content Adapt Pattern of Relationships Structure Adjust Gradients Behavior

25. Low Med High Extent Variety Ambiguity Autonomy ‘Wicked’ Problems The Autonomy Domain Traditional SE SoSE Whole System Realization Extent: # of cognates (e’s and r’s) Variety: # of unique cognates, both temporal and semiotic Ambiguity: fog, conflicting data, cognitive overload Tools < Process < The Way We Think, or Don’t

26. Opportunity Lies At Nexus of Tensions Quality of Solution depends on Capability of Envisioners Challenge Inherent in Suppressing the Symptoms (EVA) Key Success Factors Purpose Preparation Practice Persistence Patience Synergy Conflict Analogs, Archetypes, Intuition Key Techniques Separation of Concerns Elaboration of Information Decision Flow Availability of Technology FMEA: Cognitive Overload  Underconceptualization

27. Warfield’s Work Program of Complexity Interactive Management Interpretive Structural Modeling composed of Discovery Resolution Description Diagnosis Design Implementation Situation Complexity Index SCI = (N/7) (V/5) (K/10) = (1/350) NVK Where: N is Miller Index, V is Spreadthink index and K = DeMorgan index Staley, S. M. 1995, “Complexity Measurements in System Design” in Integrated Design and Process Technology, A. Ertes, et al, Editors, IDTP Volume 1, Austin, TX, 153-161

28. Semantics of Systemics System Model Algorithm Ontology Theory Functor Depends on your viewpoint 

29. Example: Autonomous Test and Evaluation Knowledge Missions 5000.02 Quick Reaction Warfighters Oversight (1 … k) Programs Programs /5a/ /7/ Adjust Adapt Co-align Descriptive Models AS(1) designs & generates /2/ /2/ /3/ /3/ /4/ /4/ Autonomous T&E System AST&E(i) Autonomous T&E Enterprise AS(2) exercises & observes Quality Parsimony Beauty (n.m) produces & conveys /6/ AS(n ) /5b/ /6/ T&E Assets Descriptive Models /1/ /1/ 090928 jring@amug.org 29

30. Debra Hurd Self-Aware Systems Engineering, SASE

31. Levels of Human Synergy Evolve to a Co-evolving Culture

32. The Reflective Practitioner Four ascending levels of behavior: 1. Know how. 2. Reflection -- on how 'know how' was applied. 3. Knowing-in-action (devising while doing) • Reflection-in-action. A Practitioner must have two kinds of knowing: • Objectivist • Constructivist -- world making Designing cannot be taught -- but can be coached Designing: knowledge-in-action, holistic, honors unspecified (unspecifiable?) design qualities (aesthetics) Methods of Coaching The Reflective Practitioner • Joint experimentation, • Follow Me! • Hall of Mirrors Educating the Reflective Practitioner, Donald Schon, Jossey Bass, 1987

33. Generative Systems Engineering When you crack open an acorn you do not find a tiny oak tree. You find a nut -- that “knows” how to become an oak tree -- and will, IFF it gets the right environment and nourishment.

34. Eprise Autocatalytic Eprise Agile/Autopoietic G(IE) Eprise Self-improving G(IE) Eprise Bureaucratic G(E) Self-Organizing Z(S) Sys 3 G(S) Z(S) Self-Regulating/Adaptive G(SE) Sys 2 G(S) Z(S) System Fixed/Programmable G(S) Locus of SE --- Folded Systems Rules Configurators State Determined Indeterminate Z = model of G = Generator of S = System

35. SE standards All models are wrong. Some are useful. George Box Static models of systems induce false confidence. Joe Skipper Use System Dynamics to understand the existing system, never to justify a design. J. Forrester For every complex question there is an answer that is clear, simple --- and wrong. H. L. Mencken Large, successful systems are made only from small, successful systems. John Gall Relevant Homilies Those who do not read the newspapers are uninformed. Those who do are misinformed. Mark Twain

36. How many of you understand --- • plic vs. plex vs. complexity? _____ • “Complex, Adaptive” = f(EVA)? _____ • TSE  MBSE  SASE  GSE  ISE? _____ How many of you want to be co-founders of Intelligent Systems Engineering?_____

37. The castle, Hawkins, besiege thecastle! Thank You Clarifications?