1. NASA Engineering and Safety Center (NESC) March 7, 2014 Thinking systemically about complex systems and decision making Patrick T. Hester, Ph.D.

2. 2 Outline  Introduction  Systems Errors  The Systems Age  Current State in Systems Engineering  Systemic Thinking  Decision Analysis  A Way Ahead

3. 3 The omnipotent nature of problems  Think about a problem that keeps you up at night, one that really eats at you  Too easy, right?  You’ve got countless, I’m sure  Work/home/life, etc. all present numerous problems at any given time  So, why haven’t you solved it? What makes it hard?

4. 4 What makes problems hard? (1/2)  Significant uncertainty  Both “known unknowns” and “unknown unknowns”  Competition  Between individuals, objectives, resources  Unpredictability  They exhibit emergent behavior

5. 5 What makes problems hard? (2/2)  Potential for loss  Conjures up images of the road not taken  Bias for action  We are an instant gratification society  The right now answer vs. the right answer  Humans-in-the-loop  Designing a mechanical system is straightforward, although still complicated  Designing the same system while accounting for ergonomics, fatigue, and operator error prevention is exponentially more complex

6. 6 What should we do?  Avoid all problems exhibiting one or more of these criteria  Boring and unrealistic  Our suggestion is adopting a novel way to reason about these persistent, complex problems  As engineers, we tend to think we have all the answers  In reality, truly complex problems require a discipline-agnostic approach  i.e., a purely systems engineering approach may be good for an engineering problem, but the real, interesting problems aren’t engineering problems only  They require us to embrace concepts of many disciplines that may be advantageous to our effort  Simply, they require us to think systemically about our problem  You should think and reason about complex problems using insight from engineering, psychology, mathematics, biology, management, and many other fields

7. 7 Outline  Introduction  Systems Errors  The Systems Age  Current State in Systems Engineering  Systemic Thinking  Decision Analysis  A Way Ahead

8. 8 Our postulation  Most complex problems can be characterized as having: (1) significant uncertainty, (2) competition, (3) unpredictability, (4) potential for loss, (5) bias for action among stakeholders, and (6) humans-in-the-loop  The way in which a problem is thought about, acted on, and observed is a major determinant of the effect of these factors.  Independent of the construct or rigor used to address a complex problem is the opportunity to commit a number of errors.  There is no agreed-upon taxonomy of these errors; thus, we’ve developed one.

9. 9 Error typology ErrorDefinitionIssue Type III (γ)Solving the wrong problem precisely.Wrong Problem Type IV (δ) Inappropriate action is taken to resolve a problem as the result of a correct analysis. Wrong Action Type V (ε) Failure to act when the results of analysis indicate action is required. Inaction Type I (α) Rejecting the null-hypothesis when the null-hypothesis is true. False Positive Type II (β) Failing to reject the null-hypothesis when the null- hypothesis is false. False Negative Type VI (  ) Inferring causation when only correlation exists. Unsubstantiated Inference Type VII (ζ)An error that results from a combination of the other five error types, often resulting in a more complex problem than initially encountered. System of Errors Adapted from Adams and Hester (2013)

10. 10 Error tree and probability Problem to Solve Type III Error (P(  )) Type IV Error (P(  )) Type I Error (P(  )) Type II Error (P(  )) Type VI Error (P(  )) No Type I, Type II, or Type VI Error (1- [P(  )+P(  )+P(  )]) Type V Error (P(  )) Type I Error (P(  )) Type II Error (P(  )) Type VI Error (P(  )) No Type I, Type II, or Type VI Error (1- [P(  )+P(  )+P(  )]) No Type IV or Type V Error (1-[P(  )+P(  )]) Type I Error (P(  )) Type II Error (P(  )) Type VI Error (P(  )) No Type I, Type II, or Type VI Error (1- [P(  )+P(  )+P(  )]) No Type III Error (1-P(  )) Type IV Error (P(  )) Type I Error (P(  )) Type II Error (P(  )) Type VI Error (P(  )) No Type I, Type II, or Type VI Error (1- [P(  )+P(  )+P(  )]) Type V Error (P(  )) Type I Error (P(  )) Type II Error (P(  )) Type VI Error (P(  )) No Type I, Type II, or Type VI Error (1- [P(  )+P(  )+P(  )]) No Type IV or Type V Error (1-[P(  )+P(  )]) Type I Error (P(  )) Type II Error (P(  )) Type VI Error (P(  )) No Type I, Type II, or Type VI Error (1- [P(  )+P(  )+P(  )]) Thinking Acting Observing Adapted from Adams and Hester (2013)

11. 11 Outline  Introduction  Systems Errors  The Systems Age  Current State in Systems Engineering  Systemic Thinking  Decision Analysis  A Way Ahead

12. 12 Historical background for complex problems (1/2)  Complexity of ill-structured, wicked, or messy problems has led to a need for more sophisticated methods  Problem solvers have been approaching complex problems using usually a predominantly technical perspective since the advent of large-scale telecommunications systems in the United States during the 1930s  Studies of these systems were interrupted by WWII, when many scientists and engineers worked with the US military to help solve complex logistical and strategic bombing problems  Many of these efforts made significant contributions to the philosophy and techniques of what was then called Operations Research

13. 13 Historical background for complex problems (2/2)  At the same time, the need for many novel types of electronic gear for airborne use gave rise to a wide variety of component devices, popularly known as black boxes  “These were ingenious devices, but their application in terms of the entire system of which they were merely parts was a matter of improvisation” (Engstrom, 1957, p. 113)  Those working on these systems were required to think about the system of which they were a part  After WWII, many companies (notably the RAND Corporation, the Bell Telephone Laboratories, and RCA) hired many of these scientists and engineers to provide services to both the government and the telecommunications industry  Much early work in modern telecommunications system development spurned systems approaches (Goode & Machol, 1957; Hall, 1962)  In many ways, this was the beginning of the systems age

14. 14 Systems and complexity Russell Ackoff (1974) used the terms machine-age and systems-age to refer to eras that were concerned with two different types of systems. The machine-age was concerned with simple systems, and the systems-age was concerned with complex systems. Characteristic Machine Age Simple Systems Systems Age Complex Systems BoundaryClosedOpen ElementsPassive partsPurposeful parts ObservableFullyPartially Method of Understanding Scientific method of reductionism Cannot use reductionism

15. 15 The Machine Age and the Systems Age (1/2)  Ackoff (1979) coined the concept of a mess and messes: Because messes are systems of problems, the sum of the optimal solutions to each component problem taken separately is not an optimal solution to the mess. The behavior of the mess depends more on how the solutions to its parts interact than on how they interact independently of each other. But the unit in OR is a problem, not a mess. Managers do not solve problems, they manage messes. (p. 100)  Presence of human activity necessitated more than a purely technical perspective (Ackoff, 1979)  Humans must be accounted for (and cannot be using a machine age paradigm)

16. 16 The Machine Age and the Systems Age (2/2)  Burrell & Morgan (1979) support Ackoff’s assessment, stating: Mechanical models of social systems, therefore, tend to be characterized by a number of theoretical considerations and are thus of very limited value as methods of analysis in situations where the environment of the subject is of any real significance. (p. 61)  In short, the methods and techniques of traditional operations research are “... mathematically sophisticated but contextually naïve and value free” (Hughes & Hughes, 2000, p. 10)

17. 17 Messes at the intersection between hard and soft systems Hard Perspective (technical factors) Soft Perspective (organizational, managerial, policy, political, and human factors) Messes Adapted from Hester and Adams (2014)

18. 18 System type versus system treatment

19. 19 Outline  Introduction  Systems Errors  The Systems Age  Current State in Systems Engineering  Systemic Thinking  Decision Analysis  A Way Ahead

20. 20 Current state in systems engineering (1/2)  Systems engineering has moved toward a more processed-focused, acquisition-based discipline; it became procedularized problem solving  Popular current SE texts include Blanchard and Fabrycky; Kossiakoff, Sweet, Seymour and Biemer; and Sage and Armstrong  Each of these texts expends substantial intellectual resources discussing the process of systems engineering  Indeed, SE had lost its roots; it became systematic engineering, where systematic connotes the methodical, process-based nature of standards for systems engineering

21. 21 Current state in systems engineering (2/2)  Practiced by many organizations such as the DoD and NASA  SE, as currently practiced, is by and large the practical application of procedural problem solving (most traditionally problems concerning acquisition)  Their thought process may be described as systematic thinking  Of course, many other systems approaches exist

22. 22 Systems Approach Major ThemePrimary Author(s) Viable System Model Diagnosis of structural system functions, relationships, and communications channels necessary for any system to maintain existence. (Beer, 1979, 1981, 1985) Sociotechnical Systems Work system analysis and redesign based on joint optimization of the social and technical subsystems for performing work. (Cherns, 1976; Pasmore, 1988; Taylor & Felten, 1993) Systems Engineering Structured formulation, analysis and interpretation of the technical, human, and organizational aspects of complex systems to address needs or resolve problems subject to cost, schedule, and operational performance constraints. (Blanchard & Fabrycky, 2011; Sage, 1992) System Dynamics Computer modeling and simulation approach to understand the relationships and underlying behavior of complex systems. (Forrester, 1961, 1969, 1971; Maani & Cavana, 2000) Soft Systems Methodology A process of inquiry focused on formulation of ill-structured problems appreciative of multiple perspectives. (Checkland, 1993) Total Systems Intervention A system problem solving approach based on creative thinking, appropriate method selection, and implementation of method based change proposals to resolve complex issues. (Flood & Jackson, 1991) Gibson's Systems Analysis Methodology Provides six iterative phases to study complex systems problems, including System Goals, Ranking Criteria, Alternative Development, Alternative Ranking, Iteration, and Action. (Gibson, Scherer, & Gibson, 2007) Systems-based approaches to complex problem solving (1/2) adapted from Keating (2012, p. 212)

23. 23 Systems-based approaches to complex problem solving (2/2)  These methodologies are certainly successful, but they have not been universally adopted  Many of them are focused on systematic approaches to gaining understanding  Not appropriate for systems age messes  A new paradigm of systemic thinking, conceptually founded in systems theory, is necessary  New paradigm must be discipline-agnostic and theoretically-driven

24. 24 Historical Roots of Systems Theory Stream of ThoughtMajor Contributor(s) with Selected References 1. General Systems Theory Bertalanffy (1949, 1950, 1968), Boulding (1956) 2. Living Systems TheoryMiller (1978) 3. Mathematical Systems Theory Mesarovic (1967), Wymore (1967), Klir (1968) 4. CyberneticsRosenblueth, Wiener & Bigelow (1943), Wiener (1948), Ashby (1947a, 1952, 1956), Forrester (1961, 1969, 1971) 5. Social Systems TheoryParsons (1970, 1979, 1991), Buckley (1967, 1998), Luhmann (1995, 2012) 6. Philosophical Systems Theory Laszlo (1972, 1973, 1996), Bunge (1979, 1997, 1999, 2004) These six systems theory streams of thought do not provide a generally accepted canon of general theory that applies to all systems (i.e., is discipline-agnostic). Table adapted from Adams, Hester, & Bradley (2013)

25. 25 Our View of Systems Theory Figure adapted from Adams, Hester, et al. (2014)

26. 26 Outline  Introduction  Systems Errors  The Systems Age  Current State in Systems Engineering  Systemic Thinking  Decision Analysis  A Way Ahead

27. 27 What is systemic thinking?  As a term, it has been gaining traction in recent literature (e.g., Boardman & Sauser, 2013; Hester & Adams, 2013; Hester & Adams, 2014; Midgley, 2012; Mingers, 2010)  Term has been used without specificity or universality  Goal is to articulate a unique perspective on systemic thinking to differentiate it from traditional systems approaches  Demonstrate utility in helping individuals to increase their understanding about problems and messes of any size, complexity, or discipline  Founded on systems theory

28. 28 Systematic vs. Systemic Thinking Systematic ThinkingSystemic Thinking AgeMachineSystems Unit of AnalysisProblemMess (System of problems) Stopping Criteria OptimizationSatisficing GoalProblem SolutionIncreased Understanding Underlying Philosophy Reductionism Constructivism and Reductionism EpistemologyAnalysisSynthesis and Analysis Discipline Scope Multidisciplinary and Interdisciplinary Transdisciplinary ApproachPrescriptiveExploratory adapted from Hester and Adams (2013)

29. 29 A methodology for systemic thinking (1/3)  Key is consideration of the “5 W's and How?”  Who is relevant to understanding our mess?  Stakeholders  What are we trying to achieve in understanding our mess further?  Outputs, outcomes  Why are we interested in this mess?  Motivation  Where does our situation reside?  Context and boundaries  How do we achieve improved understanding of our mess?  Mechanisms for achieving understanding  When do we want to have increased mess understanding by?  Stability and maturity

30. 30 A methodology for systemic thinking (2/3) Adapted from Hester and Adams (2014)

31. 31 A methodology for systemic thinking (3/3) Adapted from Hester and Adams (2014)

32. 32 Outline  Introduction  Systems Errors  The Systems Age  Current State in Systems Engineering  Systemic Thinking  Decision Analysis  A Way Ahead

33. 33 Decision Analysis Decision analysis directly supports the what and how of systemic thinking (among others):  What are we trying to achieve?  How are we going to increase our understanding?

34. 34 What is decision making? A process where: 1) There is more than one possibility (a choice) 2) The decision maker can form expectations concerning future events and outcomes associated with each choice 3) Consequences contingent on the choice can be evaluated on a subjective scale with respect to the decision maker’s values and goals

35. 35 Sub-disciplines  Decision Theory—You vs. nature  Normative (Prescriptive) Decision Theory: How humans should make decisions Expected utility theory  Descriptive Decision Theory: How humans actually make decisions Prospect theory, biases and heuristics  Game Theory—You vs. others  How humans make decisions in time of competition and cooperation

36. 36 Why does decision making matter?  As practitioners in engineering (and in life) we are forced to make decisions every day  So, we must know how to make these decisions and how to differentiate between alternatives  In some cases, we must be able to defend our choices

37. 37 Why is rigorous decision making important?  If we make the right decision, it’s not  At least, to a point  No one asks questions (unless they want to repeat our successes)  If we make the wrong decision, there are huge consequences  We must be able to answer for our decisions Hyatt Regency Walkway collapse Ford Pinto recall Tacoma Narrows Bridge Etc….

38. 38 So what makes a good decision?  If we reflect back on the circumstances under which we made the decision a day, week, month, etc. later, would we make the same decision?  If so, it was a good decision.  If not, it was not.  Remember, this is independent of what you now know  Not fair to say “I wouldn’t have bought that lottery ticket” after you know the numbers didn’t win After all, who wouldn’t say this?

39. 39 Methods for decision making How do we pick a method to increase our understanding? Adapted from Hester and Adams (2014)

40. 40 Choosing a method  Recall our earlier graphic:  We need a more detailed framework for classifying systems and matching up appropriate techniques for them: 1. Could choose based on popularity of usage 2. Can use a sensemaking framework such as Cynefin

41. 41 Identify the appropriate technique (1/2) MethodAdvantagesDisadvantagesAreas of Application Multi-Attribute Utility Theory (MAUT) Takes uncertainty into account; can incorporate preferences. Needs a lot of input; preferences need to be precise. Economics, finance, actuarial, water management, energy management, agriculture Analytic Hierarchy Process (AHP) Easy to use; scalable; hierarchy structure can easily adjust to fit many sized problems; not data intensive. Problems due to interdependence between criteria and alternatives; can lead to inconsistencies between judgment and ranking criteria; rank reversal. Performance-type problems, resource management, corporate policy and strategy, public policy, political strategy, and planning. Case-Based Reasoning (CBR) Not data intensive; requires little maintenance; can improve over time; can adapt to changes in environment. Sensitive to inconsistent data; requires many cases. Businesses, vehicle insurance, medicine, and engineering design. Data Envelopment Analysis (DEA) Capable of handling multiple inputs and outputs; efficiency can be analyzed and quantified. Does not deal with imprecise data; assumes that all input and output are exactly known. Economics, medicine, utilities, road safety, agriculture, retail, and business problems. Fuzzy Set TheoryAllows for imprecise input; takes into account insufficient information. Difficult to develop; can require numerous simulations before use. Engineering, economics, environmental, social, medical, and management. Simple Multi- Attribute Rating Technique (SMART) Simple; allows for any type of weight assignment technique; less effort by decision makers. Procedure may not be convenient considering the framework. Environmental, construction, transportation and logistics, military, manufacturing and assembly problems. Adapted from Velasquez and Hester (2013)

42. 42 Identify the appropriate technique (2/2) MethodAdvantagesDisadvantagesAreas of Application Goal Programming (GP) Capable of handling large-scale problems; can produce infinite alternatives. It’s ability to weight coefficients; typically needs to be used in combination with other MCDM methods to weight coefficients. Production planning, scheduling, health care, portfolio selection, distribution systems, energy planning, water reservoir management, scheduling, wildlife management. ELECTRETakes uncertainty and vagueness into account. Its process and outcome can be difficult to explain in layman’s terms; outranking causes the strengths and weaknesses of the alternatives to not be directly identified. Energy, economics, environmental, water management, and transportation problems. PROMETHEEEasy to use; does not require assumption that criteria are proportionate. Does not provide a clear method by which to assign weights. Environmental, hydrology, water management, business and finance, chemistry, logistics and transportation, manufacturing and assembly, energy, agriculture. Simple Additive Weighting (SAW) Ability to compensate among criteria; intuitive to decision makers; calculation is simple does not require complex computer programs. Estimates revealed do not always reflect the real situation; result obtained may not be logical. Water management, business, and financial management. Technique for Order Preferences by Similarity to Ideal Solutions (TOPSIS) Has a simple process; easy to use and program; the number of steps remains the same regardless of the number of attributes. Its use of Euclidean Distance does not consider the correlation of attributes; difficult to weight and keep consistency of judgment. Supply chain management and logistics, engineering, manufacturing systems, business and marketing, environmental, human resources, and water resources management. Adapted from Velasquez and Hester (2013)

43. 43 Cynefin domains Adapted from Kurtz and Snowden (2003) Complex Complicated Chaotic Simple Disorder

44. 44 Outline  Introduction  Systems Errors  The Systems Age  Current State in Systems Engineering  Systemic Thinking  Decision Analysis  A Way Ahead

45. 45 A way ahead  Systems engineering needs additional research which appreciates the complexity of modern messes and provides guidance for choosing a supporting decision analysis method appropriately  We are working on an appropriate mapping between the Cynefin sensemaking framework and appropriate decision techniques

46. 46 Contact information Patrick Hester Old Dominion University Department of Engineering Management and Systems Engineering Office: (757) 683-5205 Email: pthester@odu.edupthester@odu.edu

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