A Complexity Primer for Engineers

November 2015

Dr. Jimmie McEver Senior Systems Scienst, 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 WG • Co-chairs: Dr. Jimmie McEver, JHU/APL Mr. Michael Watson, NASA/MSFC • 46 INCOSE members parcipated in discussions at IW 2015

https://connect.incose.org/WorkingGroups/ComplexSystems/

3 Purpose and Objecves

• 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 intersecon of complex systems and SE, focusing on systems beyond those for which tradional systems engineering approaches and methods were developed. • Complex Systems Working Group objecves – Communicate the complexity characteriscs to systems engineering praconers – Provide knowledge and experse on complex systems in support of other INCOSE working groups working in their systems engineering areas – Facilitate the idenficaon 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 Acvies

• Expand on Mat French MBSE in Complexity Contexts work begun in 2014 • Expand on selected Primer topics; evolve into wiki to facilitate evoluon and access; develop annotated complexity bibliography • Work with US Government partners to idenfy WG iniaves to help address current systems challenges (digital engineering, engineering resilient systems, open architecture based approaches for engineering complex systems) • Idenfy relaonships and strengthen interacons with other INCOSE WGs (and with complexity groups in other organizaons) • Workshop or other joint meeng with AIAA Complex Aerospace Systems Exchange organizers • Maturity Model for the Complex SE capability of an organizaon or endeavor • Organize webinars to discuss WG iniaves 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 praccing 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 Movaon: 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 adapveness

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 pracce

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 – 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 definion • We oen call something complex when we cannot fully understand its structure or behavior: it is uncertain, unpredictable, complicated, or just plain difficult (see Sillio (2009): Subjecve Complexity) • Concepts that seem essenal – Emergence: Features/behavior associated with the holisc that are more than aggregaons of component properes – Mul-scale behavior: System not describable by a single rule, structure exists on many scales, characteriscs 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 mulple 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 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 – Adapve components, interacons – Interfaces with human users or other complex enes – Evolving • Environment – Operaonal environment • Problem you’re trying to solve changes • Condions 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 – 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

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Think like a gardener, Take an not a watchmaker adapve stance • Grow, don’t build • Enable and improve • Focus on the ecosystem adaptaon capabilies – Extensible substrate – Observe system behavior – Rules and for – ID and create variaon adaptaon – Selecng 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 mulple 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 perspecves • 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 • Idenfy and use paerns outcomes • See through new eyes • Understand what movates autonomous

• Collaborate agents

• Achieve balance • Maintain adapve • Learn from problems feedback loops • Meta-cognion • 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 resilience to analysis unpredictable relevant of agility key “beyond- but high- phenomena trade-space design- Enterprise consequence attributes envelope” development: events and/or Focus elicitation events to study how recursive on agility vice Use trades to provide enterprises or complexity optimizing to ID aspects of robustness societies particular the problem and timely survive assumptions space that recovery to a catastrophes. will drive the minimally system functional architecture state.

18 Candidate approaches: SE Methods System/Soluon Complexity

Requirements Solution Elicitation and Architecture and Development Derivation Trade Studies Design Process Complexity in Use multi-scale modeling Emphasize Use Systems- System (linking macro- and micro- selection of of-Systems Design & level models), including robust and methodologies Development exploratory analysis and adaptive to synchronize (General) agent-based modeling, and elements and constituent experimentation: structures over systems; • To generate insight into the optimizing to incentivize implications of derived meet current collaboration. requirements requirements • As the basis for trade Ensure studies and to inform trade- prototyping and off decisions experimentation are used.

19 Applicability of Generalized Analyc Methods: The Cook Matrix

• Lists and briefly describes modeling and analysis methods from a wide range of disciplines • Candidate approaches to consider in modeling complex systems problems

Analyze Diagnose Model Synthesize Data Mining Algorithmic Complexity Uncertainty Modeling Design Structure Matrix Splines Monte Carlo Methods Virtual Immersive Modeling Architectural Frameworks Fuzzy Logic Thermodynamic Depth Funconal / Behavioral Models Simulated Annealing Neural Networks Feedback Control Models Arficial Immune System Classificaon & Regression Trees Informaon Theory Dissipave Systems Parcle Swarm Opmizaon Kernel Machines Stascal Complexity Game Theory Genec Nonlinear Time Series Analysis Graph Theory Cellular Automata Mul-Agent Systems Markov Chains Funconal Informaon System Dynamics Adapve Networks Power Law Stascs Mul-scale Complexity Dynamical Systems Social Network Analysis Network Models Agent Based Models Mul-Scale Models

20 Candidate approaches: Analyc tools for the Complex SE Toolkit? • Some are emerging for “massively complicated” SE – MBSE tools and methods – Emerging thinking on T&E in large test spaces • For complex SE, some exist – but are not designed for or aligned with SE community • Complex SE (or CxSE) requires more than just tools – Not a “run the tool, get the answer” problem – Requires understanding of the nature of complexity and how to deal with volality and deep uncertainty • Mindset, experience (and educaon) – Tools needed are those that will enable this understanding • A key role of the systems engineer is to facilitate this understanding across his/her stakeholder communies

21 Next Steps (for the Primer and the WG)

• Present Primer for INCOSE • Leverage primer (and other) technical review approaches to address (or • Connue to socialize primer at least generate new as a resource for INCOSE insight into) community members “hard problems” • Expand on selected Primer – Cybersecurity topics; evolve into wiki to – Evoluonary Acquision facilitate evoluon and – Systems of systems access; develop annotated • Engage with other working complexity bibliography groups to idenfy – Construct Concept Map for intersecon topics we can complex systems knowledge work together

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