Chapter 1 Lines or Circles: The Basics of Systems Thinking Systems thinking is needed more than ever because we are becoming overwhelmed by complexity. by seeing wholes we learn how to foster health. Senge 1 There are many ways to solve problems. Our normal mode of thinking causes us to isolate the problem, search for causes, and find solutions. The logic behind this approach follows a straight line: problem- cause- solu- tion. Often called event-oriented or linear thinking, this method is highly effective when problems are simple or effects are fairly singular. However, in today’s complex world, neither condition is true. Our socioeconomic envi- ronment is changing rapidly—often too rapidly for us to see or to understand the implications of important events. A more effective approach, called systems thinking, views this environ- ment as a group or system of elements, and then determines how these ele- ments “interact with each other to function as a whole.” 2 This big picture perspective originates in the concept of holism, from the Greek word for “whole” or “entire.” A holistic or systems perspective means that behaviors cannot be explained by looking only at separate parts or solitary events, but rather by considering how these parts work together. 3 In systems thinking, cause and effect do not always follow a straight line whose end is set apart from its beginning. Instead, actions can be circular; their effects fold back to become a cause. Thus, a solution can actually exacerbate rather than resolve a problem. Another relevant feature of systems thinking is that it considers human actions. British professor Ralph Stacey describes this aspect: “Systems thinking is a holistic way of thinking that respects profound 1. Senge, 2006. 2. Lewis, 1998. 3. Smuts (1926) coined the term holism as a “fundamental factor operative towards the creation of wholes in the universe.” 3 Financial Whirlpools. © 2013 Elsevier Inc. All rights reserved. interconnectedness and puts people, with their different beliefs, purposes, evaluations and conflicts, at the center of its concerns.” 4 With its many interrelated elements and a purpose to promote stability and growth, an economy easily meets the criteria for a system that involves people. 5 Thus the global economic crisis is a perfect candidate for using sys- tems thinking. With these characteristics in mind, we now review the history and fundamentals of systems thinking. 1.1 A BRIEF HISTORY OF SYSTEMS THINKING In western civilizations, the philosophical roots of systems thinking lie deep in Aristotle’s recognition of a whole that is something besides the parts. 6 The origin of modern-day systems thinking, however, reaches back to the late 1700s when Thomas Malthus expressed his philosophy on population dynamics. 7 Then in the late 1800s, Herbert Spencer described evolution as the combined development of the physical world, biological organisms, human mind, and human culture. 8 These concepts of emergent evolution and holism were revived in the 1920s by psychologist C. Lloyd Morgan, 9 states- man Jan Smuts, 10 and others. In the 1930s and 1940s, the holistic perspective reappeared as systems theory. 11 During these decades, a group of scholars including Bertalanffy, Boulding, and Ashby 12 created a new paradigm that defined a system as a collection of subsystems and considered that collection to be part of an even larger system. 13 These scientists and engineers shifted academic focus from understanding elements that make up a system to understanding how these elements work together: a holistic view. This new systems model deviated from the popular reductionist approach that breaks a problem apart and analyzes features of each part. Particularly after Descartes formalized it in the mid-1600s, 14 reductionism was immensely effective. The disciplines of physics, biology, chemistry, and medicine progressed using a reductionist method. Imagine breaking this ana- lytic mold to use synthesis instead—to understand how the whole operated not only by understanding each part but also by recognizing their interac- tions. New insights were possible. Even today on the forefront of 4. Stacey, 2010. See also Jackson, 2000, cited by Stacey. 5. See Meadows (2008) for the definition of a system. 6. Sachs, 2002. 7. Richardson, 1999; see Malthus, 1798. 8. Spencer, 1890. 9. Morgan, 1927. 10. Smuts, 1926. 11. Corning, 1998. 12. Bertalanffy, 1968; Ashby, 1958; Boulding, 1956. 13. Stacey, 2010. 14. Descartes, 2008 (1637). 4 PART | I Foundations neurobiology, this same integration, or “linkage of differentiated parts of a system—is at the heart of well-being.” 15 Embraced by diverse disciplines such as biology and engineering, sys- tems thinking became the subject of intense interest in the 1950s and 1960s. From this foundation, researchers and practitioners built three branches of systems theory: general systems theory, 16 cybernetics, 17 and system dynam- ics. 18 General systems theory and cybernetics regard systems as mechanisms that seek order and stability (homeostasis) or as goal-directed processes that adapt themselves to their environment. Biologists and those in related fields led the way in general systems theory, while engineers explored cybernetics. Engineers also developed system dynamics. This third branch is grounded in concepts of “dynamics and feedback control developed in mathematics, physics, and engineering.” 19 Unlike the other branches, system dynamics applies systems theory to national and social problems of large scope and complexity. By modeling organizational and economic behaviors, it showed “how policies, decisions, structure, and delays are interrelated to influence growth and stability.” 20 The distinction between the first two and this third branch is important for our application. Unlike general systems theory or cybernetics, system dynamics recognizes that not all systems reach stability; internal factors may prevent them from attaining specific goals. In this view, a system no longer regulates itself. Instead, it influences itself; the effects of its actions come back to shape future behaviors. Thus, it can sustain or destroy itself. 21 Because the economy can certainly deviate from a desired goal and because its outputs such as prices or unemployment do influence what happens in the future, this third path of system dynamics is more suited for understanding the 2008 crisis. 1.2 APPLICATION AND RELEVANCE OF SYSTEMS THINKING Yet, for our purposes, system dynamics in its pure form also has limitations. Often called hard systems thinking, system dynamics is quantitative by nature and investigates behavior using engineering equations and compute r models, 22 neither of which is easily applied to a problem as complex or as human-centric as the crisis. However, an offshoot of system dynamics, called 15. Siegel, 2012. 16. See Bertalanffy, 1968. Concurrently, Bogdanov, a Russian scientist, also explored general systems concepts in the 1920s. See Strijbos, 2010, and Capra, 1996. 17. See Ashby, 1958; Wiener, 1948. 18. MIT professor Jay Forrester founded system dynamics in 1956; see Forrester, 1961. 19. Sterman, 2000. 20. Forrester, 1961. 21. Systems thinking history and branches of thought derived from Stacey, 2010. 22. Jackson, 2000. 5Chapter | 1 Lines or Circles: The Basics of Systems Thinking soft systems thinking, is appropriate for our analysis. Like system dynamics, this category acknowledges interactions, but unlike system dynamics, it uses data and trends in a qualitative manner and does not apply rigorous model- ing. Soft systems thinkers promote a systems perspective as a beneficial way to consider interconnections and influences, and to expand individuals’ per- spective and improving decision-making skills. These goals perfectly com- plement the book’s objectives, thus we will view the economic crisis using soft systems thinking 23 or what we simply refer to as systems thinking. 1.3 LINEAR THINKING AND SYSTEMS THINKING To appreciate the benefits of systems thinking, consider a typical business situation. Suppose a company’s goal is to make a profit in a highly competi- tive industry. Next, suppose that a competitor introduces a popular new product, and suddenly the company’s profit decreases. To save money, the company dismisses its customer-support staff. It now believes the problem is solved; lower expenses should increase profit. Figure 1.1 shows that this approach to the problem is linear. 24 It places cause and effect in a straight line without looking for other factors that may indirectly create larger issues. Alternatively, using systems thinking, the company would expand its investigation to see if the solution ignored critical factors. Figure 1.2 shows Increase competition Decrease profit Reduce staff Save money Increase profit FIGURE 1.1 Linear thinking example. Frustrate customers Reduce trust; undermine reputation Lose customers Increase competition Decrease profit Reduce staff File bankruptcy?? Save money FIGURE 1.2 Systems thinking example. 23. See Richmond, 1994. 24. Sterman (2001) refers to this type of thinking as an “event-oriented view of the world” and introduces the notion of dynamic complexity to describe unintended consequences. 6 PART | I Foundations that, indeed, it missed important aspects. By reducing service, the company frustrated customers and diminished trust. Lack of trust under mined the com- pany’s reputation. This sequence caused customers to leave, which decre ased rather than increased profit—not at all the intent. If the company continues this strategy, perhaps the end result will be bankruptcy. Systems thinking suggests that it should have considered a different solu tion. 1.4 COMPLEXITY ECONOMICS AND SYSTEMS THINKING Big picture views are not new to economics. To compensate for the draw- backs of analyzing an economy from its individual components, the disci- pline of macroeconomics appeared in the early 1900s as a way to understand collective economic behavio r. Some economists expanded this view with complexity theories. These theories consider how “individual behaviors col- lectively create an aggregate outcome” and what the reactions are to that out- come. One complexity theory known as emergence has been applied to stock market behavior 25 and to business cycle research. 26 This concept has long history; it was reco gnized in 1875 when Lewes defined “an emergent” as the effect that comes from actions that combine in ways that don’t reveal their individuality. 27 Another more recent approach, agent-based modeling, assesses actions of individual elements relative to their effects on the larger economic system in which they operate. These theories regard the economy as a system that is in constant motion. They recognize that “behavior creates pattern; and pattern in turn influences behavior.” 28 Financial economist Eric Beinhocker uses the umbrella term complexity economics to describe these lines of thinking. He links this “gen- uinely new approach to economics” to a “long and rich intellectual history” that extends back to the mid-1900s and to notables such as mathematician John von Neumann and economists Herbert Simon and Friedrich Hayek. 29 In fact, parts of modern complexity economics evolved from the same 1950sÀ60s general systems theory that fost ered systems thinking. 30 These applications recognize that individual elements combine to produce unin- tended patterns of behavior and that these patterns cannot be predicted from their individual elements. 31 By the 1990s, some theorists touched the systems realm more deeply, adopting “a view of the economy based on positive feedbacks .” One technol- ogist describes the effects of societal pressures on behaviors using feedback 25. Arthur et al., 1996; Corning, 2002. 26. Gatti et al., 2008. 27. Lewes, 1875. 28. Arthur, 2006; see also Arthur et al., 1997. 29. Beinhocker, 2006. 30. Corning, 1998. 31. Stacey, 1996; this is a definition of emergence. 7Chapter | 1 Lines or Circles: The Basics of Systems Thinking loops. 32 Experts in system dynamics use nonlinear modeling to better under- stand aspects of economic behavior. 33 Some suggest that in accepting the idea of feedback, economists “are beginning to portray the economy as process-dependent, organic and always evolving.” 34 By recognizing the tre- mendous complexity and dynamics of an economy, these theorists are lead- ing the way to view economic event s differently. Although it does not expressly use systems thinking, complexity econom- ics closely parallels its tenets. As we will discover, the feed back concept is a mainstay of systems thinking. Furthermore, complexity economics recog- nizes that the economy depends on networks of relationships and assumes that larg e-scale patterns (such as economic health) emerge from microlevel behaviors (such as monetary theory and human expectations) and adapt over time. 35 While complexity economics “is still more of a research program than a single, synthesized theory,” 36 it does provide a niche in economic the- ory that accommodates systems thinking. The recent economic crisis exemplifies various types of systems behav- ior. Certainly single indicators could not have predicted the housing bubble or the subsequent gutting of the financial industry. Parts of the economy, it seems, behaved differently than expected; traditional government interven- tions were less effective than in the past and repercussions mushroomed beyond all experience. Something else was happening that would require a deeper understanding. So whether we call it systems thinking, emergence theory, or complexity economics, the idea of interdependent and dynamic relationships is a valuable viewpoint from which to discuss the crisis. 1.5 SYSTEMS THINKING CONCEPTS This book uses four basic systems constructs: loops, lags, limits, and levers. These constructs have roots in system dynamics, but have been adapted for the qualitative application of systems thinking. The first of these, loops,emerged from the engineering background of system dynamics founder Jay Forrester, who applied information-feedback theory to management and social topics. Loop behavior is a foundational principle for both system dynamics and sys- tems thinking; loops exist “whenever the environment leads to action which affects the environment and thereby influences future decisions.” 37 For the study of complex systems, systems thinking also recognizes the importance of a second construct: lags or time delays between decision and 32. Schneier, 2012. 33. Sterman, 2000. 34. This and previous quote from Arthur, 1990. 35. See Beinhocker, 2006. 36. Beinhocker, 2006. 37. Forrester, 1961. 8 PART | I Foundations action. The third construct, limits, is built on the principle that natural sys- tems such as an economy cannot grow unbounded, but have inherent limits. The final construct, levers, identifies areas where constructive change would be most effective. These four—lo ops, lags, limits, and levers—comprise the systems frame- work we will use to portray the recent economic crisis in the U.S. and its global implications. The following sections describe these constructs and translate them into the visual language of behavior-over-time graphs (BOTs) and causal loop diagrams (CLDs). 1.6 LOOPS Like Neapolitan ice cream, systems loops for our purpos es come in three fla- vors: balancing feedback, reinforcing feedback, and reinforcing feed forward. Although each is important for describing a particular phenomenon or rela- tionship, various combinations of the three are required to portray interac- tions and dynamics in the economic crisis. 1.6.1 Feedback Processes When we hear the word feedback we usually think about someone correcting our behavior or paying us a compliment. If we are receptive, feedback in this sense helps us improve our behavior. However, in systems thinking, feedback is a continuous process rather than a comment; its definition is much broader. Instead of straight line arrows or linear cause-effect chains, systems thinking uses two types of feedback processes: reinforcing and balancing. 38 “Reinforcing (or amplifying) feedback processes are the engines of growth” or “accelerating decline.” 39 In other words, reinforcing feedback pushes “a system the way it is going.” 40 Alternatively, balancing (or stabilizing) feed- back tries “to bring things to a desired state (or goal) and keep them there.” 41 By itself, balancing feedback is neither good nor bad, “it just means the sys- tem resists change.” 42 Multiple reinforcing and balancing feedback processes were present in the economic crisis. 1.6.1.1 Reinforcing Feedback A reinforcing feedback loop can be beneficial, leading to a virtuous circle, or detrimental, resulting in a vicious circle. Compound interest exemplifies a 38. Reinforcing feedback is also called positive feedback and balancing feedback is also called negative feedback. Because this nomenclature (positive and negative) is easily confused with effects of the loops we will not use it. 39. Senge, 2006. 40. O’Connor and McDermott, 1997. 41. Anderson and Johnson, 1997. 42. O’Connor and McDermott, 1997. 9Chapter | 1 Lines or Circles: The Basics of Systems Thinking beneficial reinforcing feedback loop. Putting mone y into a compounding sav- ings account earns interest. Over time, if we do not withdraw funds, our account grows from the interest earned. That larger balance earns more inter- est, which in turn earns still more interest and continues to grow until we withdraw our money. 43 Figure 1.3 shows this virtuous circle of saving. The opposite case of compounding debt becomes the detrimental reinfor- cing feedback loop or vicious circle in Figure 1.4. This situation occurs when a consumer borrows money at some interest rate but does not repay the debt. When interest accrues each month, the debt builds on itself and can become unmanageable. Vicious circles were also prominent in our economic crisis framework. Reinforcing loops may involve exponential growth, or the “process of doubling and redoubling and redou bling again” 44 as we saw in the com- pounding interest and compounding debt examples. Alternatively exponential decay is the reverse process of being divided in half again and again. For an economic system, this type of growth or decay can quickly produce astound- ing and often unexpected effects. Account Balance: investment plus interest earned Interest Earned Interest Rate Interest earned is reinvested Initial Investment FIGURE 1.3 Compound interest as a beneficial reinforcing feedback loop (virtuous circle). Debt Balance: debt plus interest charged Interest Charged Interest Rate Interest charged is added to debt Initial debt FIGURE 1.4 Compounding debt as a detrimental reinforcing feedback loop (vicious circle). 43. A fixed amount of money invested at 7 percent a year would double in about 10 years. 44. Meadows et al., 2004. Thus, “a quantity grows exponentially when its increase is propor- tional to what is already there.” 10 PART | I Foundations 1.6.1.2 Balancing Feedback Balancing feedback has an altogether different nature than reinforcing feed- back. Its goal-seeking behavior tries to stabilize a situation or guide it toward a desired outcome. Balancing feedback processes are everywhere in day-to- day life—from steering our cars, to using the thermostat in our homes, to our body healing a cut. In these cases, a desired goal is compared with the actual condition to determine what action will bring us closer to that goal. Trying to lose weight is an example of a balancing feedback loop. Here we compare our current weight with desired weight; if we weigh too much, we exercise or diet. After a time, we weigh again to determine our next action. Figure 1.5 shows how this feedback/corrective action cycle repeats. If all goes well, we reach our goal. Meeting organizational goals is a form of balancing feedback that existed during the crisis. As an example, a lending organization will set a goal for its loan officers and then measure their performance against this goal. If agents meet the goal, they are rewarded. If they do not, the company consid- ers other options. These options may be so enticing (big bonuses) or distres- sing (loss of job) that employees make irrational decisions to meet the goals—sometimes causing unintended consequences. The simple principle here is that the company wants to guide employees toward desired outcomes and employees are motivated to achieve them. Culture is a more subtle example of balancing feedback. Often without conscious intent, we behave in ways that are consistent with the beliefs and values of the culture in which we live or work. In this case, cultural norms are the goal of a balancing feedback loop; we compare our behaviors to this goal and make decisions that put us more in line with the culture. We will see this type of feedback when we explore human values and beliefs. 1.6.2 Feed-Forward Processes In a special type of reinforcing loop, the feed-forward loop, the anticipation of an outcome determines behavior. In 1848, economist John Stuart Mill identified a feed-forward loop (although he did not call it that) when he found that “a ten- dency for the price to rise feeds back to produce a still greater tendency for the Compare actual weight with desired weight Take action if actual weight is different from desired weight Desired weight Actual weight FIGURE 1.5 Dieting as a balancing feedback loop. 11Chapter | 1 Lines or Circles: The Basics of Systems Thinking price to rise.” 45 A hundred years later, sociologist Robert Merton called this same phenomenon a self-fulfilling prophecy 46 in which hopes, fears, expecta- tions, and beliefs “lead us to act in ways that fundamentally change the world we observe” 47 and create the very future we had anticipated. 48 In economics, the feed-forward loop aptly represents speculation on an asset. If for some real or imagined reason people expect its price to rise, they invest in that asset regardless of its worth. Hearing of these investments, others expect prices to rise so they, too, invest and add to the demand. In doing so, they unwittingly create a cycle that reinforces their original expec- tation of rising prices. The feed-forward loop is a powerful force. Its presence generated the financial panic of 1893 that became one of the worst depressions in U.S. his- tory. 49 In this case, as concern about the economy increased and confidence decreased, people withdrew their money from b anks fearing it would lose value. Rumors circul ated. Lines formed in front of the banks as more tried to claim their money. When others heard the rumors and saw the lines, they panicked and ran to pull out their money until the banks’ cash reserves were depleted. Banks sought cash everywhere and even recalled loans they had made to businesses. Interest rates soared because the demand for money was perilously high. Businesses went bankrupt, banks failed, and depositors lost everything. Indeed the cycle was self-fulfilling: people did lose their money, but not for the reasons they had imagined. Figure 1.6 shows this self-fulfilling Expectation of money loss Banks fail: Money lost Belief that money in banks will lose value Pull money out of bank Actions feed rumors FIGURE 1.6 Expectations form a reinforcing feed-forward loop. 45. Richardson, 1999; reference to John Stuart Mill Principles of political economy (1848). 46. Merton (1948) referred to it as “a false definition evoking a new behavior which makes the originally false conception come true.” 47. Gilovich, 1991; Gilovich references Merton, 1948. 48. O’Connor and McDermott, 1997. 49. Discussion of this period in Akerlof and Shiller, 2009; Lauck, 1907; Kindleberger and Aliber, 2005. (There was a fear that the U.S. would not maintain the gold standard for money. Note that the interest rate for money lent to stockbrokers for overnight transactions at one point reached 74 percent.) 12 PART | I Foundations [...]... period (measured in years for the crisis) In the generic graph of Figure 1.7, time falls on the horizontal axis and the factor of interest (such as the price of housing) lies on the vertical axis A BOT graph may depict the behavior of a single variable such as housing prices or delinquency rates, or it may compare the behavior of different variables In either case, examining the shape of the curves allows... or more above prime rates.64 Interest rates on subprime loan interest rates are also generally higher than on Alt -A loans.65 2.3.2 Types of Loans Prime and nonprime markets use fixed rate mortgage (FRM) and adjustable rate mortgage (ARM) types of loans FRM interest rates are based on the prevailing prime rate; they remain the same over the life of the loan While ARM interest rates may be initially lower... include the Federal National Mortgage Association (FNMA/Fannie Mae) and the Federal Home Loan Mortgage Association (FHLMC/Freddie Mac) The FHA insures home loans made by approved lenders Fannie Mae, Freddie Mac, and Ginnie Mae expand the secondary mortgage market45 by purchasing loans from original lenders and reselling them as securities Ginnie Mae 43 Chart derived from Historical Inflation, 2012; federal... Federation of America, 2010) 38 PART | I Foundations generate business, lenders had to compete in the less familiar area of nonprime loans So, in addition to relaxing standards, they offered these loans to people who could not meet prime loan criteria Within nonprime loans, there are two main options: Alt -A (lower-risk) loans and subprime (higher-risk) loans.58 Alt -A loans are usually for those who have... investigates events surrounding the 2008 economic crisis as they pertain to the United States and expands that investigation in the final chapter (Chapter 12) to include the greater global economy Chapter 2 As the Gears Turn: Policies, Practices, Markets, and Risk The forces that hit financial markets in the U.S in the summer of 2007 seemed like a force of nature, something akin to a hurricane, or an earthquake,... using BOT and CLD visualization tools.65 1.10.3 Balancing Feedback Loop Figure 1.9 describes a factor that approaches its desired goal with passing time Although the BOT graph on the left shows that the original value of the factor of interest is above the goal, it could also be below the goal In the balancing feedback CLD on the right, the desired goal is continuously compared with the actual condition... federal funds rate from Board of Governors of the Federal Reserve System, 2011b; average annual unemployment from U.S Bureau of Labor Statistics, 201 2a 44 See http://portal.hud.gov/portal/page/portal/HUD 45 The primary market provides the actual loan to a borrower the secondary market channels liquidity into the primary market by purchasing packages of loans from lenders” and reselling them as securities... 2.3.1.1 Relaxed Standards Most lenders relaxed traditional criteria that rejected loans or limited loan amounts and set loan terms The Federal Reserve Bank of San Francisco remarked on the prevalence of looser standards, including an “increase in loan-to-value ratios, less stringent debt-to-income requirements, and a willingness on the part of lenders to accept limited or no documentation of borrowers’... dysfunction Many complex behaviors seen during the crisis reflect a combination of these structures This chapter also introduced tools (causal loop diagrams and behavior-over-time graphs) that translate behaviors into patterns and facilitate visual understanding These pictorial representations are mainstays in later chapters Using the loops, lags, limits, and levers of systems thinking, this book sequentially... to as “circles of causality” or “circle diagrams.” Sterman (2000) refers to BOTs as “modes of dynamic behavior.” Chapter | 1 17 Lines or Circles: The Basics of Systems Thinking 1.10.2 Causal Loop Diagrams In its portrayal of feedback structures, system dynamics uses stock-and-flow diagrams to identify specific quantities of an element that accumulate over time (called stock) and the rate at which a . have been. The aftermath of Japan’s immense earthquake and tsunami in March 2011 illustrates a more extended lag. In addition to ongoing cleanup of the devastation in Japan, over a year after- ward. organization will set a goal for its loan officers and then measure their performance against this goal. If agents meet the goal, they are rewarded. If they do not, the company consid- ers other. view. This new systems model deviated from the popular reductionist approach that breaks a problem apart and analyzes features of each part. Particularly after Descartes formalized it in the mid-1600s, 14 reductionism