A Complexity Primer for Systems Engineers November 2015 Dr. Jimmie McEver Senior Systems ScienAst, JHU Applied Physics Laboratory Chair, INCOSE Complex Systems Working Group [email protected] 1 Agenda • Brief overview of Complex Systems WG • Complexity Primer for Systems Engineers 2 Complex Systems (CxS) WG Overview • Organized in 2006 from Systems Science WG • Co-chairs: Dr. Jimmie McEver, JHU/APL Mr. Michael Watson, NASA/MSFC • 46 INCOSE members parAcipated in discussions at IW 2015 https://connect.incose.org/WorkingGroups/ComplexSystems/ 3 Purpose and ObjecAves • The purpose of the Complex Systems Working Group is to enhance the ability of the systems engineering community to deal with complexity. • The CxS Working Group works at the intersecAon of complex systems sciences and SE, focusing on systems beyond those for which tradiAonal systems engineering approaches and methods were developed. • Complex Systems Working Group objecAves – Communicate the complexity characterisAcs to systems engineering pracAAoners – Provide knowledge and experAse on complex systems in support of other INCOSE working groups working in their systems engineering areas – Facilitate the idenAficaon of tools and techniques to apply in the engineering of complex systems – Provide a map of the current, diverse literature on complex systems to those interested in gaining an understanding of complexity. • Although analysis is important, the goal is to make a difference in synthesis (creaon of new systems). 4 Candidate AcAviAes • Expand on Mat French MBSE in Complexity Contexts work begun in 2014 • Expand on selected Primer topics; evolve into wiki to facilitate evoluAon and access; develop annotated complexity bibliography • Work with US Government partners to idenAfy WG iniAaves to help address current systems challenges (digital engineering, engineering resilient systems, open architecture based approaches for engineering complex systems) • IdenAfy relaonships and strengthen interacAons with other INCOSE WGs (and with complexity groups in other organizaons) • Workshop or other joint meeAng with AIAA Complex Aerospace Systems Exchange organizers • Maturity Model for the Complex SE capability of an organizaon or endeavor • Organize webinars to discuss WG iniAaves and topics of interest 5 A Primer on Complexity for Systems Engineers • The INCOSE Complex Systems Working Group has draed a primer to introduce complexity concepts and approaches to pracAcing systems engineers • Members of the primer development team – Sarah Sheard – Joseph Krupa – Eric Honour – Paul Ondrus – Jimmie McEver – Robert Scheurer – Dorothy McKinney – Janet Singer – Alex Ryan – Joshua Sparber – Stephen Cook – Brian White – Duane Hybertson 6 MoAvaon: The Implicaons of Complexity • Systems engineering has evolved to improve our ability to deal with scale and interdependency though the life cycle of engineered systems • Systems are becoming increasingly dynamic and interdependent, with growing emphasis on adapAveness Source: Sarah Sheard, Complexity and Systems Engineering, 2011 Source: Monica Farah-Stapleton, IEEE SOS conference, 2006 • BUT, complexity places new demands on systems engineers that require more than extensions of “classical” SE pracAce 7 Classes of Systems Problems: Kurtz and Snowden’s Cynefin Framework Cynefin domains Complex systems, Massively- characterized by complicated systems interdependence, present challenges of self-organization, and scale and interface emergence – new accounting – tools and approaches extensions of are needed traditional methods/ tools can help Chaotic systems Simple and can only be complicated systems reacted to; can are straightforward to attempt to deal with using transform into traditional systems another domain engineering / mgmt approaches Source: Kurtz and Snowden, “The new dynamics of strategy: Sensemaking in a complex and complicated world,” IBM Systems Journal, 42 (3), 2003. 8 What do we mean by complexity? • No easy, agreed-upon definiAon • We ocen call something complex when we cannot fully understand its structure or behavior: it is uncertain, unpredictable, complicated, or just plain difficult (see Silligo (2009): SubjecAve Complexity) • Concepts that seem essenAal – Emergence: Features/behavior associated with the holisAc system that are more than aggregaons of component properAes – MulA-scale behavior: System not describable by a single rule, structure exists on many scales, characterisAcs are not reducible to only one level of descripon. – A system with self-organizaon, analogous to natural systems, that grows without explicit control, and is driven by mulAple locally operang, socio-technical processes, usually involving adaptaon 9 Complex vs Complicated Complex system Complicated system Traffic jams exhibit: Modern transit vehicles exhibit: • Self-organization and Emergence – • Large numbers of interacting systems – local actors and decisions interact to fuel, electrical, engine, transmission, create larger patterns safety, etc. • Memory – even after an obstacle is • Aggregate properties, but not emergence removed the jam can persist for hours – e.g., range, top speed… • Counter-intuitive outcomes – e.g., • Unexpected outcomes still possible, Braess’ Paradox – adding capacity to the but origin is different network can degrade network • Transit systems may be complex performance The opposite of “complex” is “decomposable”, not “simple” Source: Alex Ryan 10 Hallmarks and Implicaons of Complexity How complexity makes it different Hallmarks of complexity Impact on Decision Maker Interdependence Cannot treat by decomposition Nonlinearities Extrapolation of current conditions à error Open boundaries Cannot focus only on processes inside boundary Multi-scalarity Have to address all relevant scales Causal & influence networks Challenge: develop ‘requisite’ conceptual model within time and information resource constraints Emergence Unknown risks and unrecognised opportunities Complex goals Goals may change, be unrealistic, vague Adaptation & innovation ‘Rules’ change, interventions stimulate adaptation Opaqueness Many possible hypotheses about causal paths, insufficient evidence to discriminate Adapted from Grisogono, Anne-Marie and Vanja Radenovic, The Adaptive Stance –Steps towards Teaching more Effective Complex Decision-Making, International Conference on Complex Systems, June 2011. 11 Sources of Complexity • System – Large number of components, many intricate interdependencies – AdapAve components, interacAons – Interfaces with human users or other complex enAAes – Evolving technology • Environment – Operaonal environment • Problem you’re trying to solve changes • CondiAons under which you’re trying to solve a problem change • Way that you elect to solve the problem changes – System environment • Changes to elements of the larger SoS • Design/management – Large number of people and organizaons involved 12 Sarah Sheard, Complexity and Systems Engineering, 2011. Used with permission System Complexity as SE Challenges System Characteristics Cognitive Characteristics (Objective Complexity) (Subjective Complexity) Many pieces Uncertain Emergent Difficult to understand Nonlinear Unclear cause and effect Chaotic Adaptive Unpredictable Tightly coupled Complexity Unstable Self-Organized Uncontrollable Decentralized Unrepairable, unmaintainable Political Open Takes too long to build Multi-Scale Costly 13 Complexity and Systems Development • You need complexity to respond to Ashby’s Law of complexity Requisite Variety – Provides degrees of freedom to needed to deal with/work in complex, volatile and uncertain environments • But developing and using complex systems have their own challenges – Less ability to plan and predict – Problems/systems not neatly decomposable – interdependencies A model system or controller between subsystems grow beyond ability can only model or control to deal with them in traditional ways something to the extent that it has sufficient internal – Uncomfortable phenomena arise, such variety to represent it. as normal accidents, black swans, catastrophic fragility 14 Candidate approaches: Selected Guiding Principles v Think like a gardener, Take an not a watchmaker adapve stance • Grow, don’t build • Enable and improve • Focus on the ecosystem adaptaon capabiliAes – Extensible substrate – Observe system behavior – Rules and feedback for – ID and create variaon adaptaon – SelecAng the best versions • Influence and intervene – Amplify the fit of the vice design and control selected versions 15 Candidate approaches: Selected Guiding Principles Think at mul4ple levels and from mulple perspecves • System behavior may be • Systems may create different fundamentally different at value at different levels and from different levels different perspecAves • The decomposed problem is a • Need to look at systems and different problem requirement from diverse perspecves 16 Candidate approaches: Selected Guiding Principles • Combine courage with • Focus on desired regions of humility the outcome space rather • Use free order than specifying detailed • IdenAfy and use paerns outcomes • See through new eyes • Understand what moAvates autonomous • Collaborate agents • Achieve balance • Maintain adapAve • Learn from problems feedback loops • Meta-cogniAon • Integrate problems 17 Candidate approaches: SE Methods Environmental Complexity Requirements Solution Elicitation and Architecture and Development Derivation Trade Studies Design Process Environment Use power laws Make Design for Resilience susceptible to to characterize dimensions