Exploring Critical Thinking vs. Systems Thinking
What is “critical thinking”, steps to the critical thinking process, how is critical thinking applied in life, what is “systems thinking”, steps to the systems thinking process, how is systems thinking applied in life, critical thinking vs. systems thinking (in a nutshell), critical thinking vs. systems thinking: the main differences, can critical thinking and systems thinking work together.
Yes, Critical Thinking and Systems Thinking can work together. Concise thinkers utilize Critical Thinking and Systems Thinking together when addressing many problems, often without knowing it!
Systems Thinking, Critical Thinking, and Personal Resilience
The “Thinking” in Systems Thinking: How Can We Make It Easier to Master?
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What is systems thinking, taking a systems thinking approach to problem solving.
- Environment
Systems Thinking, Critical Thinking, and Personal Resilience
- May 24, 2018
As a writer focused on the global sustainability crisis, I’m often asked how to deal with the stress of knowing—knowing, that is, that we humans have severely overshot Earth’s long-term carrying capacity, making a collapse of both civilization and Earth’s ecological systems likely; knowing that we are depleting Earth’s resources (including fossil fuels and minerals) and clogging its waste sinks (like the atmosphere’s and oceans’ ability to absorb CO2); knowing that the decades of rapid economic growth that characterized the late 20 th and early 21 st centuries are ending, and that further massive interventions by central banks and governments can’t do more than buy us a little bit more time of relative stability; knowing that technology (even renewable energy technology) won’t save our fundamentally unsustainable way of life.
In the years I’ve spent investigating these predicaments, I’ve been fortunate to meet experts who have delved deeply into specific issues—the biodiversity crisis, the population crisis, the climate crisis, the resource depletion crisis, the debt crisis, the plastic waste crisis, and on and on. In my admittedly partial judgment, some of the smartest people I’ve met happen also to be among the more pessimistic. (One apparently smart expert I haven’t had opportunity to meet yet is 86-year-old social scientist Mayer Hillman, the subject of this recent article in The Guardian .)
In discussing climate change and all our other eco-social predicaments, how does one distinguish accurate information from statements intended to elicit either false hope or needless capitulation to immediate and utter doom? And, in cases where pessimistic outlooks do seem securely rooted in evidence, how does one psychologically come to terms with the information?
Systems Thinking
First, if you want to have an accurate picture of the world, it’s vital to pay attention to the connections between things. That means thinking in systems. Evidence of failure to think in systems is all around us, and there is no better example than the field of economics, which treats the environment as simply a pile of resources to be plundered rather than as the living and necessary context in which the economy is grounded. No healthy ecosystems, no economy. This single crucial failure of economic theory has made it far more difficult for most people, and especially businesspeople and policy makers, to understand our sustainability dilemma or do much about it.
Unsurprisingly, perhaps, the field in which systems thinking is most highly developed is ecology—the study of the relationships between organisms and their environments. Since it is a study of relationships rather than things in isolation, ecology is inherently systems-oriented.
Systems thinking has a pre-history in indigenous thought ( Mitákuye Oyás’i ŋ , or “All are related,” is a common phrase in the Lakota language). But as a formal scientific pursuit it emerged only during the latter part of the twentieth century. Previously, Western scientists often assumed that they could understand systems just by analyzing their parts; however, it gradually became clear—in practical fields from medicine to wildlife management to business management—that this often led to unintended consequences.
In medicine, it is understood that treating diseases by managing symptoms is not as desirable as treating the disease itself; that’s partly because symptomatic treatment with pharmaceuticals can produce side effects that can be as distressing as the original disease symptoms. Take a pill and you may feel better for a while, but you may soon have to deal with a whole new slew of aches, rashes, sleep problems, mood swings, or digestive ailments. Further, truly curing a disease often involves addressing exposure to environmental toxins; or lifestyle choices including poor nutrition, smoking, lack of exercise, or job-related repetitive stress injuries—all of which are systemic issues that require treating the whole person and their environment, not just the symptoms, or even just the disease in isolation.
In order to address systemic problems we need to understand what systems are, and how to intervene in them most effectively.
All systems have:
- Boundaries , which are semi-permeable separations between the inside and outside of systems;
- Inputs of energy, information, and materials;
- Outputs , including work of various kinds, as well as waste heat and waste materials;
- Flows to and from the environment;
- Stocks of useful nutrients, resources, and other materials; and
- Feedbacks , of which there are two kinds: balancing or negative, like a thermostat; and self-reinforcing or positive, which is the proverbial vicious circle. Systems need balancing feedback loops to remain stable and can be destabilized or even destroyed by self-reinforcing feedback loops.
The human body is a system that is itself composed of systems, and the body exists within larger social and ecological systems; the same could be said of a city or a nation or a company. A brick wall, in contrast, doesn’t have the characteristics of a system: it may have a boundary, but there are few if any meaningful ongoing inputs and outputs, information flows, or feedbacks.
The global climate is a system, and climate change is therefore a systemic problem. Some non-systems thinkers have proposed solving climate change by putting chemicals in the Earth’s atmosphere to manage solar radiation. Because this solution addresses only part of the systemic problem, it is likely to have many unintended consequences. Systems thinking would suggest very different approaches—such as reducing fossil fuel consumption while capturing and storing atmospheric carbon in replanted forests and regenerated topsoil. These approaches recognize the role of inputs (such as fossil fuels), outputs (like carbon dioxide), and feedbacks (including the balancing feedback provided by soil carbon flows).
In some cases, a systemic approach to addressing climate change could have dramatic side benefits: regenerative agriculture would not just sequester carbon in the soil, it would also make our food system more sustainable while preserving biodiversity. Interventions based in systems thinking often tend to solve many problems at once.
Donella Meadows, who was one of the great systems thinkers of the past few decades, left us a brilliant essay titled “ Leverage Points: Places to Intervene in a System .” There are places within every complex system where “a small shift in one thing can produce big changes in everything.” Meadows suggested that these leverage points have a hierarchy of effectiveness. She said that the most powerful interventions in a system address its goals, rules, and mindsets, rather than parameters and numbers—things like subsidies and taxes. This has powerful implications for addressing climate change, because it suggests that subsidizing renewable energy or taxing carbon is a fairly weak way of inducing systemic change. If we really want to address a deeply rooted, systemic problem like climate change, we may need to look at our society’s most fundamental paradigms—like, for example, the assumption that we must have continual economic growth.
We intuitively know that systems are more than the sum of their parts. But digging deeper into the insights of systems theory—going beyond the basics—can pay great dividends both in our understanding of the world, and in our strategic effectiveness at making positive change happen. A terrific resource in this regard is Meadows’s book Thinking in Systems .
Systems thinking often tends to lead to a more pessimistic view of our ecological crisis than thinking that focuses on one thing at a time, because it reveals the shortcomings of widely touted techno-fixes. But if there are truly useful strategies to be found, systems thinking will reveal them.
Critical Thinking
Human thought is rooted partly in words, partly in emotions, and partly in the body states (whether you feel alert, sleepy, hungry, agitated, etc.) that may accompany or give rise to emotions; another way of saying this is that our thought processes are partly conscious but mostly unconscious. In our conscious lives we are immersed in a soup of language, which often simply expresses judgments, intuitions, and observations that emerge from unconscious thought. But thought that’s expressed in language has great potential. Using language (including mathematics), we can assess the validity of statements about the world, then build upon proven statements until we ultimately achieve comprehensive scientific understandings and the capacity to manipulate reality in new ways (to build a bridge, for example, or land a probe on a distant asteroid, or update an app).
Of course, language can be powerful in another way. Some of us use language to persuade, confuse, or mislead others so as to gain social or economic power. Appeals to unconscious prejudices, including peer group-think, are frequently employed to sway the masses. The best protection against being the subject of verbal manipulation is the ability to use language to distinguish logic from illogic, truth from untruth. Critical thinking helps us separate information from propaganda. It can help us think more clearly and productively.
One way to approach critical thinking is through the study of logic—including formal logic (which builds conclusions almost mathematically, using syllogisms), informal logic (which also considers content, context, and delivery), and fuzzy logic (which recognizes that many qualities are subjective or matters of degree). Most of our daily thinking consists of informal and fuzzy logic.
The study of formal logic starts with learning the difference between deductive reasoning (which proceeds from a general principle to a special case, sometimes referred to as “top-down reasoning”) and inductive reasoning (which makes broad generalizations from specific observations, also called “bottom-up reasoning”).
Both deductive and inductive forms of reasoning can be misapplied. One might deduce from the general rule “human history is a grand narrative of progress” that therefore humanity will successfully deal with the ecological challenges of the 21 st century and emerge smarter, wealthier, and more virtuous than ever. Here the problem is that the general rule is laden with value judgments and subject to many exceptions (such as the collapse of various historical civilizations). Inductive reasoning is even more perilous, because there is always the danger that specific observations, from which one is drawing general conclusions, are incomplete or even misleading (economic growth has occurred in most years since World War II; therefore, economic growth is normal and can be expected to continue, with occasional brief setbacks, forever).
My favorite book on logic and its fallacies is Lean Logic by the late David Fleming, a British economist-philosopher who cofounded what eventually became the Green Party in the UK, and who originated the idea of Tradable Energy Quotas . There’s no simple way to sum up Fleming’s book, which is organized as a dictionary. Among many other things, it explores a wide range of logical fallacies—especially as they relate to our sustainability crises—and does so in a way that’s playful, artful, and insightful.
One of my favorite sections of the book is a four-page collection of ways to cheat at an argument. Here are just a few of the entries, chosen mostly at random:
Absence. Stop listening. Abstraction. Keep the discussion at the level of high-flown generality. Anger. Present it as proof of how right you are. Blame. Assume that the problem is solved when you have found someone to blame. Bullshit. Talk at length about nothing. Causes. Assume that an event which follows another event was therefore caused by it. Evil motive. Explain away the other side’s argument by the brilliance of your insight about their real intentions. False premise. Start with nonsense. Build on it with meticulous accuracy and brilliance. Old hat. Dismiss an argument on the grounds that you have disregarded it before.
Critical thinking should not necessarily elevate reason above intuition. Remember: most thought is unconscious and emotion-driven—and will continue to be, no matter how rigorously we analyze our verbal and mathematical expressions of thought. Just as we seek coherence and consistency in our conscious logic, we should seek to develop emotional intelligence if we hope to contribute to a society based on truth and conviviality. Lean Logic reveals on almost every page its author’s commitment to this deeper concept of critical thinking. Here’s one illustrative entry:
Reasons, The Fallacy of. The fallacy that, because a person can give no reasons, or only apparently poor reasons, her conclusion can be dismissed as wrong. But, on the contrary, it may be right: her thinking may have the distinction of being complex, intelligent and systems-literate, but she may not yet have worked out how to make it sufficiently clear and robust to objections to survive in an argument.
As politics becomes more tribal, critical thinking skills become ever more important if you want to understand what’s really going on and prevent yourself from becoming collateral damage in the war of words.
Personal Resilience
Let’s return to the premise of this essay. Suppose you’ve applied systems thinking and critical thinking to the information available to you about the status of the global ecosystem and have come to the conclusion that we are—to use a technical phrase—in deep shit. You want to be effective at helping minimize risk and damage to ecosystems, humanity, yourself, and those close to you. To achieve this, one of the first things you will need to do is learn to maintain and use your newfound knowledge without becoming paralyzed or psychologically injured by it.
Knowledge of impending global crisis can cause what’s been called “ pre-traumatic stress disorder .” As with other disorders, success in coping or recovery can be enhanced through developing personal or psychological resilience. Fortunately, psychological resilience is a subject that is increasingly the subject of research.
Some people bounce back from adversity relatively easily, while others seem to fall apart. The reason doesn’t seem to have much to do with being more of an optimist than a pessimist. Research has shown that resilient people realistically assess risks and threats; studies suggest that in some ways pessimists can have the advantage . What seems to distinguish resilient people is their use of successful coping techniques to balance negative emotions with positive ones, and to maintain an underlying sense of competence and assurance.
Researchers have isolated four factors that appear critical to personal psychological resilience:
- The ability to make realistic plans and to take the steps necessary to follow through with them;
- A positive self-concept and confidence in one’s strengths and abilities;
- Communication and problem-solving skills; and
- The ability to manage strong impulses and feelings.
An important question: To what degree is psychological resilience based on inherited or innate brain chemistry, or childhood experiences, versus learned skills? We each have a brain chemistry that is determined partly by genetic makeup and partly by early life experience. Some people enjoy a naturally calm disposition, while others have a hair-trigger and are easily angered or discouraged. In seeking to develop psychological resilience, it’s important to recognize and deal with your personal predispositions. For example, if you find that you are easily depressed, then it may not be a good idea to spend hours each day glued to a computer, closely following the unraveling of global ecological and social systems. Don’t beat yourself up for getting depressed; just learn to recognize your strengths and limits, and take care of yourself.
Nevertheless, research suggests that, regardless of your baseline temperament, you can make yourself more psychologically resilient through practice. The American Psychological Association suggests “10 Ways to Build Resilience,” which are:
- Maintain good relationships with close family members, friends and others.
- Avoid seeing crises or stressful events as unbearable problems.
- Accept circumstances that cannot be changed.
- Develop realistic goals and move towards them.
- Take decisive actions in adverse situations.
- Look for opportunities of self-discovery after a struggle with loss.
- Develop self-confidence.
- Keep a long-term perspective and consider the stressful event in a broader context.
- Maintain a hopeful outlook, expecting good things and visualizing what is wished.
- Take care of your mind and body, exercise regularly, and pay attention to your needs and feelings.
These recommendations are easier said than done. Learning new behaviors, especially ones that entail changing habitual emotional responses to trigger events, can be difficult. The most effective way to do so is to find a way to associate a neurotransmitter reward with the information or behavior being learned. For example, if you are just beginning an exercise regimen, continually challenge yourself to make incremental improvements that are just barely within your reach. This activates the dopamine reward circuits in your brain.
Psychological resilience may also entail learning to deal with grief . Awareness of species extinctions, habitat destruction, and the peril to human beings from climate change naturally evokes grief, and unexpressed grief can make us numb, depressed, and ineffective. It’s helpful therefore to find a safe and supportive environment in which to acknowledge and express our grief. Joanna Macy , in her “work that reconnects,” has for many years been hosting events that provide a safe and supportive environment for grief work.
Personal resilience extends beyond the psychological realm; developing it should also include identifying and learning practical skills (such as gardening, small engine maintenance, plumbing, cooking, natural building, primitive technology, and wilderness survival skills). Knowing practically how to take care of yourself improves your psychological state, as well as making you more resilient in physical terms.
Further, your personal resilience will be greatly enhanced as you work with others who are also blessed (or burdened) with knowledge of our collective overshoot predicament. For many years we at PCI have been assisting in the formation of ongoing communities of reflection and practice such as Transition Initiatives . If that strategy makes sense to you, but you don’t have a Transition group close by, you might take the Think Resilience course and then host a discussion group in your school, home, or public library.
Systems thinking, critical thinking, and personal resilience building don’t, by themselves, directly change the world. However, they can support our ability and efforts to make change. The key, of course, is to apply whatever abilities we have—in community resilience building, ecological restoration, or efforts to resist the destruction of nature and the exploitation of human beings. As we remain open to learning, action presents opportunities for still more learning, in the form of what systems thinkers would call balancing feedback. We test what we think we know, and discover new things about the world and ourselves. It’s a life-long process.
Even if we do all we can, there is no guarantee that problems will be solved, extinctions prevented, collapse forestalled. But paralysis only guarantees the very worst outcome. In the words of the Bhagavad Gita, “The wise should work, without attachment to results, for the welfare of the world.” Act from love with the best understanding you have, and always seek to improve your understanding. It’s all that any of us can do.
Feature Image Credit: Renaud Camus , c/o Flickr: https://www.flickr.com/photos/renaud-camus/8434440715/
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The Thinking in Systems Thinking: Eight Critical Skills
I have been writing and re-writing this guide (Introduction to Systems Thinking with STELLA, 1985–2000) for 15 years. I always begin by reeling off a litany of serious challenges facing humanity. And, you know what? The list has remained pretty much the same! There’s homelessness and hunger, drug addiction and income distribution inequities, environmental threats and the scourge of AIDS. We’ve made precious little progress in addressing any of these issues over the last couple of decades! Indeed, you could make a strong case that, if anything, most (if not all) have gotten worse! And, some new challenges have arisen.
So what’s the problem? Why do we continue to make so little progress in addressing our many, very pressing social concerns?
My answer is that the way we think, learn, and communicate is outdated. As a result, the way we act creates problems. And then, we’re ill-equipped to address them because of the way we’ve been taught to think, learn, and communicate. This is a pretty sweeping indictment of some very fundamental human skills, all of which our school systems are charged with developing! However, it is the premise of systems thinking that it is possible to evolve our thinking, learning, and communicating capacities. As we do, we will be able to make progress in addressing the compelling slate of issues that challenge our viability. But, in order to achieve this evolution, we must overcome some formidable obstacles. Primary among these are the entrenched paradigms governing what and how students are taught. We do have the power to evolve these paradigms. It is now time to exercise this power!
I will begin by offering operational definitions of thinking, learning, and communicating. Having them will enable me to shine light on precisely what skills must be evolved, how current paradigms are thwarting this evolution, and what systems thinking can do to help. In the course of this chapter, I will identify eight systems thinking skills. They are:
- 10,000-meter thinking
- system-as-cause thinking
- dynamic thinking
- operational thinking
- closed-loop thinking
- scientific thinking
- empathic thinking
- generic thinking
The processes of thinking, learning, and communicating constitute an interdependent system, or at least have the potential for operating as such. They do not operate with much synergy within the current system of formal education. The first step toward realizing the potential synergies is to clearly visualize how each process works in relation to the other.
Thinking is something we all do, but what is it? The dictionary says it’s “to have a thought; to reason, reflect on, or ponder.” Does that clear it up for you?
It didn’t for me.
I will define thinking as consisting of two activities: constructing mental models and then simulating them in order to draw conclusions and make decisions. We’ll get to constructing and simulating in a moment. But first, what the heck is a mental model?
It’s a “selective abstraction” of reality that you create and then carry around in your head. As big as some of our heads get, we still can’t fit reality in there. Instead, we have models of various aspects of reality. We simulate these models in order to “make meaning” out of what we’re experiencing, and also to help us arrive at decisions that inform our actions.
For example, you have to deal with your kid, or a sibling, or your parent. None of them are physically present inside your head. Instead, when dealing with them in a particular context, you select certain aspects of each that are germane to the context. In your mind’s eye, you relate those aspects to each other using some form of cause-andeffect logic. Then, you simulate the interplay of these relationships under various “what if” scenarios to draw conclusions about a best course of action, or to understand something about what has occurred.
If you were seeking to understand why your daughter isn’t doing well in arithmetic, you could probably safely ignore the color of her eyes when selecting aspects of reality to include in the mental model you are constructing. This aspect of reality is unlikely to help you in developing an understanding of the causes of her difficulties, or in drawing conclusions about what to do. But, in selecting a blouse for her birthday? Eye color probably ought to be in that mental model.
As the preceding example nicely illustrates, all models (mental and otherwise) are simplifications. They necessarily omit many aspects of the realities they represent. That statement is a paraphrase of something George Box once uttered: “All models are wrong; some models are useful.” It’s important to dredge this hallowed truth back up into consciousness from time to time to prevent yourself from becoming “too attached” to one of your mental models; nevertheless, despite the fact that all models are wrong, you have no choice but to use them—no choice, that is, if you are going to think. If you wish to employ non-rational means (like gut feel and intuition) in order to arrive at a conclusion or a decision, no mental model is needed. But, if you want to think, you can’t do so without a mental model!
Constructing Mental Models
Whether the mental model being constructed is of an ecosystem, a chemical reaction, a family, or a society, three fundamental questions must always be answered in constructing it. They are: (1)What elements should be included in the model—or, the flip side—what elements should be left out? (2) How should the elements you decide to include be represented? (3) How should the relationships between the elements be represented?
Selecting Activities. Deciding what to include in a mental model, in turn, breaks into two questions. How broadly do you cast your net? This is a “horizontal” question. And, how deeply do you drill? This is a “vertical” question. Developing good answers to these two questions requires skill. And, like any skill, this one must first be informed by “good practice” principles, and then honed through repeated practice.
Systems thinking offers three thinking skills that can help students to become more effective in answering the “what to include” question. They are: 10,000-meter thinking, systems-ascause thinking, and dynamic thinking.
The first systems thinking skill, 10,000-meter thinking, was inspired by the view one gets on a clear sunny day when looking down from the seat of a jet airliner. You see horizontal expanse, but little vertical detail. You gain a “big picture” but relinquish the opportunity to make fine discriminations.
The second systems thinking skill, system-ascause thinking, also works to counter the vertical bias toward including too much detail in the representations contained in mental models. System-ascause thinking is really just a spin on Occam’s Razor; that is, the simplest explanation for a phenomenon is the best explanation. It holds that mental models should contain only those elements whose interaction is capable of self-generating the phenomenon of interest. They should not contain any so-called “external forces.” A simple illustration should help to clarify the skill that’s involved.
Imagine you are holding a slinky, as shown in part a of “Slinky Does Its Thing.” Then, as shown in part b, you remove the hand that was supporting the device from below. The slinky oscillates as illustrated in part c. The question is: What is the cause of the oscillation? Another way to ask the question: What content would you need to include in your mental model in order to explain the oscillation?
SLINKY DOES ITS THING
The oscillatory behavior of a slinky is latent within the structure of the slinky itself.
The third of the so-called “filtering skills” (systems thinking skills that help to “filter” out the nonessential elements of reality when constructing a mental model) is called dynamic thinking. This skill provides the same “distancing from the detail” that 10,000-meter thinking provides, except that it applies to the behavioral—rather than the structural—dimension.
Just as perspectives get caught-up in the minutiae of structure, they also get trapped in “events” or “points,” at the expense of seeing patterns. In history, students memorize dates on which critical battles were fought, great people were born, declarations were made, and so forth. Yet in front of and behind each such “date” is a pattern that reflects continuous build-ups or depletions of various kinds. For example, the United States declared its independence from England on July 4, 1776. But prior to that specific date, tensions built continuously between the two parties toward the ensuing conflict. In economics, the focus is on equilibrium points, as opposed to the trajectories that are traced as variables move between the points.
Dynamic thinking encourages one to “push back” from the events and points in time to see the pattern of which they are a part. The implication is that mental models will be capable of dealing with a dynamic, rather than only a static, view of reality.
TWO KINDS OF MENTAL MODELS
This figure shows the difference between the “divide and conquer”–inspired viewpoint and the systems thinking–inspired perspective in terms of the resulting content of a mental model.
“Two Kinds of Mental Models” should help make clearer the difference between the “divide and conquer”– inspired viewpoint and the systems thinking–inspired perspective in terms of the resulting content of a mental model.” Two Kinds of Mental Models” makes the contrast between mental models constructed using the alternative perspectives look pretty stark. That’s an accurate picture. Yet there is nothing to prevent models from being forged using both perspectives from co-existing within a single individual.
Until the average citizen can feel comfortable embracing mental models with horizontally-extended/vertically-restricted boundaries, we should not expect any significant progress in addressing the pressing issues we face in the social domain. And until the measurement rubrics on which our education system relies are altered to permit more focus on developing horizontal thinking skills, we will continue to produce citizens with predilections for constructing narrow/deep mental models. The choice is ours. Let’s demand the change!
Representing Activities. Once the issue of what to include in a mental model has been addressed, the next question that arises is how to represent what has been included. A major limit to development of students’ skills in the representation arena is created by the fact that each discipline has its own unique set of terms, concepts, and, in some cases, symbols or icons for representing their content. Students work to internalize each content-specific vocabulary, but each such effort contributes to what, in effect, becomes a content-specific skill.
Systems thinking carries with it an icon-based lexicon called the language of “stocks and flows.” This language constitutes a kind of Esperanto, a lingua franca that facilitates cross-disciplinary thinking and, hence, implementation of a “horizontal” perspective. Mental models encoded using stocks and flows, whatever the content, recognize a fundamental distinction among the elements that populate them. That distinction is between things that accumulate (called “stocks”) and things that flow (called “flows”). Stocks represent conditions within a system—i.e., how things are. Flows represent the activities that cause conditions to change. Some examples of accumulations are: Water in Clouds, BodyWeight, and Anger. The associated flows are: evaporating/precipitating, gaining/losing, and building/venting. “Distinguishing Between Stocks and Flows” should help you to develop a clearer picture of the distinction between a stock and a flow.
To gain a quick idea of why the distinction matters, consider the illustration in part b of “Distinguishing Between Stocks and Flows.” Suppose a person whose weight has been increasing decides to take some action to address the situation. First, they successfully eliminate all junk food snacks from their diet, and do not eat more at regular meals to compensate for doing so. Second, they implement a rigorous aerobic exercise program—to which they religiously adhere. This means the person will have lowered the volume of the gaining flow (i.e., reduced caloric intake) and increased the volume of the losing flow (increased caloric expenditure). So what happens to this person’s Body Weight?
Did your answer include the possibility that it would still be increasing? It should have! Look at part b of the illustration. The reason the person may still be gaining weight is because decreasing the rate of gaining (the inflow), and increasing rate of losing (the outflow), will only cause BodyWeight (the stock) to decrease if gaining actually drops below losing. Until this occurs, the person will continue to gain weight— albeit at a slower rate! Take a moment to make sure you understand this reasoning before you proceed.
DISTINGUISHING BETWEEN STOCKS AND FLOWS
This figure distinguishes between stocks and flows, using the STELLA software tool.
When the distinction between stocks and flows goes unrecognized—in this example, and in any other situation in which mental simulations must infer a dynamic pattern of behavior—there is a significant risk that erroneous conclusions will be drawn. In this case, for example, if the inflow and outflow volumes do not cross after some reasonable period of time, the person might well conclude that the two initiatives they implemented were ineffective and should be abandoned. Clearly that is not the case. And, just as often, the other type of erroneous conclusion is drawn:”We’re doing the right thing, just not enough of it!”Redoubling the effort, in such cases, then simply adds fuel to the fire.
DEVELOPING CONTENT-REPRESENTATION SKILLS
Because the concepts of accumulation and flow are content-independent, their use contributes to building students’ general content-representation skill, regardless of the specific content arena in which they are applied.
In addition to helping increase the reliability of mental simulations, using stocks and flows in representing the content of a mental model has another very important benefit. The benefit derives from the fact that the concepts of accumulation and flow are contentindependent. Therefore, in whatever specific content arena they are used, their use contributes to building the general content-representation skill! “Developing Content-Representation Skills” seeks to capture this idea via the links that run from each of four specific content-representing activities to the building of a general contentrepresentation skill.
There’s a second important idea illustrated in this illustration. Note the two Rs. They stand for the word “Reinforcing”— which is the type of feedback loop they designate. The loops work like this . . .
As general content-representation skills build, they facilitate each specific contentrepresenting activity— though, to keep the picture simple, the link to only two of the specific arenas is illustrated. Then, as students engage in specific content- representation activities, because they are using a contenttranscendent language to do so, they develop general content-representation skills—a virtuous learning cycle! The cycle creates synergy because all content arenas benefit from activities that go on in any one of them! Now, instead of one content arena interfering with learning in another, each helps to accelerate learning in each of the others. (This is an example of another of the systems thinking skills, closed-loop thinking, discussed a little later in this chapter.)
To be able to “speak/write” effectively in the language of stocks and flows requires that students build a fourth systems thinking skill, a very important one: operational thinking. Teaching the language of stocks and flows, and the associated operational thinking skills, at an early point in the formal education process (e.g., fourth, fifth, and sixth grade) would be a huge step toward enabling students to develop a better set of representing skills. It would, at the same time, leverage development of students’ horizontal thinking skills. And the good news is that, at the lower grade levels, there still remains sufficient flexibility in many curricula to permit taking this step. Carpe diem!
Representing Relationships. The final question we must answer in constructing a mental model is how to represent the relationships between the elements we decide to include. In answering this question, we must necessarily make assumptions about the general nature of these relationships. Among the most sacred of all the covenants that bind members of a society together is the implicit agreement about how such relationships work. InWestern cultures, the implicit agreement is that reality works via a structure of serial causeand- effect relationships. Thus-and-such happens, which leads this-and-such to occur, and so forth. Not all cultures “buy” serial cause-and-effect (some subscribe to perspectives such as “synchronicity” and “God’s hand”). But Western culture does.
I have no beef with serial causeand- effect. It’s a useful viewpoint; however, when I look more closely at the assumptions that characterize the particular brand of it to whichWestern culture subscribes, I discover that these assumptions seriously restrict learning! Let’s see how.
LAUNDRY-LIST THINKING MENTAL MODEL
This figure depicts a laundry-list thinking mental model.
The name I use for theWestern brand of serial cause-and-effect is laundry-list thinking (another name would be critical success factors thinking). Laundry-list thinking is defined by a set of four meta assumptions that are used to structure cause-and-effect relationships. I use the term meta because these assumptions are content-transcendent. That is, we use them to structure cause-and-effect relationships whether the content is literature, chemistry, or psychology, and also when we construct mental models to address personal or business issues. Because we all subscribe to these meta assumptions, and have had them inculcated from the “get go,” we are essentially unaware that we even use them! They have become so obviously true, they’re not even recognized as assumptions any more. Instead, they seem more like attributes of reality.
But as you’re about to see, the meta assumptions associated with laundry-list thinking are likely to lead to structuring relationships in our mental models in ways that will cause us to draw erroneous conclusions when we simulate these models. I will identify the four meta assumptions associated with laundry-list thinking, and then offer a systems thinking alternative that addresses the shortcomings of each. Here’s a question that I’ll use to surface all four assumptions:
What causes students to succeed academically? Please take a moment and actually answer the question.
If you did produce a laundry list, it probably included some of the variables shown on the left-hand side of “Laundry-ListThinking Mental Model” on p. 5. This figure belies four meta assumptions about cause-and-effect relationships implicit in the laundry-list framework. Let’s unmask them!
EFFECT IS ALSO CAUSE
Once circular cause-and-effect enters the picture, the so-called “effect” variable also loses its “dependent” status.
The first meta assumption is that the causal “factors” (four are shown in the illustration) each operate independently on “the effect” (“Academic Success”). If we were to read the story told by the view depicted in the figure, we’d hear,” Good teachers cause Academic Success; Good parenting causes …” Each factor, or independent variable, is assumed to exert its impact independently on Academic Success, the dependent variable.
To determine how much sense this “independent factors” view really makes, please consult your experience.
Isn’t it really a “partnership” between teachers and parents (good open lines of reciprocal communication, trust, etc.) that enables both parties to contribute effectively to supporting a student’s quest for academic success? And don’t good teachers really help to create both high student motivation and a positive classroom environment? Isn’t it the case that highly motivated students and a positive classroom environment make teaching more exciting and enjoyable, and as a result cause teachers to do a better job? I could continue. But I suspect I’ve said enough to make the point. The four factors shown in “Laundry-ListThinking Mental Model” aren’t even close to operating independently of each other! They operate as a tightly intertwined set of interdependent relationships. They form a web of reciprocal causality! The picture that emerges looks much more like “Effect Is Also Cause” than “Laundry-List Thinking Mental Model”!
So, there goes the first meta assumption associated with laundry-list thinking (i.e., that the causal “factors” operate independently). Now let’s watch the second laundry-list meta assumption bite the dust! The second assumption is that causality runs one way. Look back at “Laundry-ListThinking Mental Model.” Notice that the arrows all point from cause to effect; all run from left to right. Now steal another glance at “Effect Is Also Cause.” Notice anything different?
That’s right, the arrows linking the “causes” now run both ways! Causeand- effect comes in loops! As “Effect Is Also Cause”shows, once circular causeand- effect enters the picture, the socalled “effect” variable also loses its “dependent” status. It, too, now “causes”—which is to say that academic success stimulates student motivation and a positive classroom environment, just as much as they drive it. Academic success also causes teachers to perform better—it’s easier to teach students who are doing well—just as much as good teachers create academic success. And so forth.” Academic Success” is just as much a cause of any of the four “factors” as they are a cause of it! And so, independent and dependent variables become chickens and eggs. Everybody becomes a co-conspirator in a causal web of interrelationships.
The shift from the laundry-list causality runs one way—view, to system thinking’s two-way, or closed-loop, view is a big deal! The former is static in nature, while the latter offers an “ongoing process,” or dynamic, view. Viewing reality as made up of a web of closed loops (called feedback loops), and being able to structure relationships between elements in mental models to reflect this, is the fifth of the systems thinking skills. It’s called closed-loop thinking. Mastering this skill will enable students to conduct more reliable mental simulations. Initiatives directed at addressing pressing social issues will not be seen as “one-time fixes,” but rather as “exciting” a web of loops that will continue to spin long after the initiative is activated. Developing closed-loop thinking skills will enable students to better anticipate unintended consequences and short-run/long-run tradeoffs. These skills also are invaluable in helping to identify high-leverage intervention points. The bottom line is an increase in the likelihood that the next generation’s initiatives will be more effective than those launched by our “straight-line causality”–inspired generation.
The third and fourth meta assumptions implicit in laundry-list thinking are easy to spot once the notion of feedback loops enters the picture. The causal impacts in laundry lists are implicitly assumed to be “linear,” and to unfold “instantaneously” (which is to say, without any significant delay).
Feedback loops, as they interact with waxing and waning strength, create non-linear behavior patterns—patterns that frequently arise in both natural and social systems. Such patterns cannot arise out of simulations of mental models whose relationships are linear.
The fourth implicit meta assumption associated with laundry-list thinking is that impacts are felt “instantaneously.” For example, when we look at the factors impacting academic success, the implicit assumption is that each exerts its influence “right now.” Take “Positive classroom environment.” The idea here is that a good classroom environment— i.e., physical factors like space, light, good equipment, etc.—will encourage students to achieve high levels of academic success. Boost the quality of the physical environment and you boost academic success. Sounds reasonable, but when you draw a more operational picture, the cause-and-effect is not quite so straight-forward. Take a look at “Non-InstantaneousView of Academic Success.”
Instead of words and arrows— Positive Classroom Environment -> Academic Success—to show causality, “Non-InstantaneousView of Academic Success” depicts the associated causal relationships operationally. In particular, the figure includes the potentially significant delay between initiating improvements to a classroom environment and the “arrival” of those improvements. Such delays have been known to stretch out for months. In the meantime, it’s possible that student and teacher morale might suffer. This, in turn, could stimulate an outflow from the Level of Academic Success before the arrival of the new lab has a chance to stimulate the associated inflow!
Delays are an important component of how reality works. Leaving them out when structuring relationships in mental models undermines the reliability of simulation outcomes produced by those models. Building the operational thinking skills that enable students to know when and how to include delays should be a vital part of any curriculum concerned with development of effective thinking capacities.
Okay, it’s been a long journey to this point. Let’s briefly recap before resuming. I asserted at the outset that our education system was limiting the development of our students’ thinking, learning, and communicating capacities. I have focused thus far primarily on thinking capacities. I have argued that the education system is restricting both the selecting and representing activities (the two sub-processes that make up constructing a mental model). Where restrictions have been identified, I have offered a systems thinking skill that can be developed to overcome it. Five systems thinking skills have been identified thus far: 10,000- meter, system-as-cause, dynamic, operational, and closed-loop. By developing these skills, students will be better equipped for constructing mental models that are more congruent with reality. This, by itself, will result in more reliable mental simulations and drawing better conclusions. But we can do even more!
Simulating Mental Models
We’re now ready to examine the second component of thinking: simulating. The first component of thinking is constructing mental models. The second component is simulating these models. Throughout the discussion thus far, I’ve been assuming that all simulating is being performed mentally. This is a good assumption because the vast majority of simulating is performed mentally. Simulating is key to the learning process.
NON-INSTANTANEOUS VIEW OF ACADEMIC SUCCESS
Significant delays can occur between initiating improvements to a classroom environment and the “arrival” of those improvements.
Learning is depicted in “Map of the Learning Process” on p. 8. It’s a pretty elaborate picture, and a good example of one that should be unfurled one chunk at a time. The first type of learning was identified in the discussion of the thinking process. Call it self-reflective learning. It comes about when simulation outcomes are used to drive a process in which a mental model’s content, and/or representation of content, is changed. I’ve also just alluded to a second type of learning, one that’s driven by the communicating process. Call it otherinspired learning. As the illustration suggests, the raw material for this type of learning is: the mental model itself, the simulation outcomes associated with that model, and/or the conclusions drawn from simulating. How much learning occurs depends upon both the quality of the feedback provided—where “quality” includes both content and “packaging” —as well as the willingness and ability to “hear” the feedback.
“Map of the Learning Process” also adds a fourth source of raw material for learning: the impacts of one’s actions. As the figure suggests, often it is difficult to perceive the full impact because ramifying takes a long time, and spreads out over a great distance. To reflect this fact, the information for this type of learning is shown as radiating off the “conveyor” named Ramifying, rather than the stock called Realized Impacts. (Note: Conveyors are used to represent delays.)
MAP OF THE LEARNING PROCESS
How much learning occurs depends upon both the quality of the feedback provided—where “quality” includes content and “packaging”—as well as the willingness and ability to “hear” the feedback.
It’s useful to spend a little time digesting “Map of the Learning Process”—which shows the thinking, learning, and communicating system. An important thing to note about this illustration is that all roads ultimately lead back to learning—which is to say, improving the quality of the mental model. Learning occurs when either the content of the mental model changes (via the selecting flow), or the representation of the content changes (via the representing flow). By the way, to make the figure more readable, not all wires that run to the representing flow have been depicted.
There are two important takeaways from this figure. First, the three processes—thinking, learning, and communicating— form a self-reinforcing system. Building skills in any of the three processes helps build skills in all three processes! Second, unless a mental model changes, learning does not occur!
If you refer to “Map of the Learning Process,” you will see that simulating is a key part of the self-reflective learning loop. Reflecting on the simulation outcomes we generate is an important stimulator of change in our mental models. But what if those outcomes are bogus? What if we are not correctly tracing through the dynamics that are implied by the assumptions in our mental models? That’s right. The self-reflective learning loop will break down. In addition, because simulation outcomes are one of the raw materials being made available for scrutiny by others in the communicating process, a key component of the other-inspired loop will break down, as well. So, it’s very important that our simulation results be reliable in order that the associated learning channel can be effective.
Detailing the reasons for our shortcomings (as a species) in the simulation sphere is beyond the scope of this chapter; however, part of the issue here is certainly biological. Our brains simply have not yet evolved to the point where we can reliably juggle the interplay of lots of variables in our heads. There is, however, growing evidence to suggest that people can hone this capacity.
Systems thinking can offer a couple of things that can help in this arena. The first is the language of stocks and flows. Because the language is both visual and operational, it facilitates mental simulation. STELLA maps really do facilitate mental simulation! But the other nice thing about them is that they are readily convertible into models that can be simulated by a computer. And if you follow good practice in doing your STELLA simulations, they will serve as an excellent sanity check on your mental simulation. Think of the software as a fitness center for strengthening mental simulation muscles. In order to take full advantage of the exercise facility, it’s important to acquire the habit of making explicit a guess about what dynamics a particular model will generate before actually using STELLA to generate them. Experience has shown that it is far too easy to back-rationalize that you really knew the model was going to produce that pattern. The collection of rigorous simulation practices is called scientific thinking, the sixth of the systems thinking skills.
Currently, in the formal education system, very little attention is paid to developing simulation skills. This means that a very important set of feedback loops for improving the quality of mental models is essentially being ignored. The STELLA software is a tool that can play an important role in helping to develop these skills.
Communicating
The next process in the thinking/ learning/communicating system is communicating.
GENERIC STRUCTURE OF A DISSIPATION PROCESS
The stock-flow Esperanto associated with systems thinking can play an important role in raising students’ level of both comfort and confidence in moving more freely across disciplinary boundaries.
The communicating I’m talking about must become a vital part of every class! It’s the feedback students provide after scrutinizing each other’s mental models and associated simulation outcomes (refer to “Map of the Learning Process”).
The current formal education system provides few opportunities for students to share their mental models and associated simulation outcomes. Well-run discussion classes do this (and that’s why students like these classes so much!). Students sometimes are asked to critique each other’s writing or oral presentations, but most often this feedback is grammatical or stylistic in nature.
The empathic thinking capacity for both giving and receiving feedback on mental models is vital to develop if we want to get better at bootstrapping each other’s learning! Many skills are involved in boosting this capacity, including listening, articulating, and, in particular, empathizing capabilities. Wanting to empathize increases efforts to both listen and articulate clearly. Being able to empathize is a skill that can be developed—and is, in some ways, the ultimate systems thinking skill because it leads to extending the boundary of true caring beyond self (a skill almost everyone could use more of). By continually stretching the horizontal perspective, systems thinking works covertly to chip away at the narrow selfboundaries that keep people from more freely empathizing.
However, even with heightened empathic skills, we need a language that permits effective across-boundary conversations in order for communication to get very far. And this is where the issue of a content-focused curriculum resurfaces as a limiting factor. Even if time were made available in the curriculum for providing studentto- student feedback on mental models, and empathy were present in sufficient quantity, disciplinary segmentation would undermine the communication process. Each discipline has its own vocabulary, and in some cases, even its own set of symbols. This makes it difficult for many students to master all of the dialects (not to mention the associated content!) well enough to feel confident in, and comfortable with, sharing their reflections. The stock-flow Esperanto associated with systems thinking can play an important role in raising students’ level of both comfort and confidence in moving more freely across disciplinary boundaries.” Generic Structure of a Dissipation Process” illustrates this notion.
“Generic Structure of a Dissipation Process” shows the accumulation of strength in a personal relationship, the accumulation of electrostatic charge on a capacitor, and the accumulation of facts in human memory. Each is represented by the same symbol. As stocks, each performs an analogous function— albeit in quite different contexts— which is to report the status of a condition. In addition, as illustrated in “Generic Structure of a Dissipation Process,” the logic by which one or more of the associated flows operate is generic. This is, at the very least, a comforting discovery in a world generally perceived to be growing more complex and unfathomable on a daily basis, and in a curriculum rife with detail-dense, dialect-specific content bins. But it also holds the wonderful potential for creating cross-curricular learning synergies. What’s being learned in physics could actually accelerate (rather than impede) learning in literature or psychology (and vice versa)! And by building their capacity for seeing “generic structures,” students will be simultaneously boosting their capacity for making “horizontal” connections in the real world. This last systems thinking skill is generic thinking.
Teaching the generic, operational, and empathic thinking skills needed to “speak/write it” effectively can go a long way toward improving the student communication capacities needed to realize the synergies latent within a multi-discipline curriculum.
This chapter identified eight systems thinking skills that leverage all three processes of thinking, learning, and communicating. Each skill can be readily implemented into today’s school systems. The primary barrier to doing so is the view that the mission of an education system is to fill students’ heads with knowledge. This view leads to sharp disciplinary segmentation and to student performance rubrics based on discipline-specific knowledge recall. Changing viewpoints especially when they are supported by a measurement system and an ocean of teaching material—is an extremely challenging endeavor. But the implications of not doing so are untenable. The time is now.
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Critical Issues in Systems Theory and Practice pp 61–71 Cite as
What Is This Thing Called CRITICAL Systems Thinking?
- Gerald Midgley 3
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This paper is about Critical Systems Thinking (CST), a research perspective which is said to embrace three fundamental commitments. These are commitments to:
critical awareness —examining and re-examining taken-for-granted assumptions, along with the conditions which give rise to them;
emancipation —ensuring that research is focused upon ‘improvement’, defined temporarily and locally, taking issues of power (which may affect the definition) into account; and
methodological pluralism —using a variety of research methods in a theoretically coherent manner, becoming aware of their strengths and weaknesses, to address a corresponding variety of issues.
- Critical System
- Critical Awareness
- System Methodology
- System Practice
- Ethical Critique
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Midgley, G. (1995). What Is This Thing Called CRITICAL Systems Thinking?. In: Ellis, K., Gregory, A., Mears-Young, B.R., Ragsdell, G. (eds) Critical Issues in Systems Theory and Practice. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-9883-8_7
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Critical Systems Thinking and the Management of Complexity
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From the winner of the INCOSE Pioneer Award 2022 The world has become increasingly networked and unpredictable. Decision makers at all levels are required to manage the consequences of complexity every day. They must deal with problems that arise unexpectedly, generate uncertainty, are characterised by interconnectivity, and spread across traditional boundaries. Simple solutions to complex problems are usually inadequate and risk exacerbating the original issues.
Leaders of international bodies such as the UN, OECD, UNESCO and WHO — and of major business, public sector, charitable, and professional organizations — have all declared that systems thinking is an essential leadership skill for managing the complexity of the economic, social and environmental issues that confront decision makers. Systems thinking must be implemented more generally, and on a wider scale, to address these issues.
An evaluation of different systems methodologies suggests that they concentrate on different aspects of complexity. To be in the best position to deal with complexity, decision makers must understand the strengths and weaknesses of the various approaches and learn how to employ them in combination. This is called critical systems thinking. Making use of over 25 case studies, the book offers an account of the development of systems thinking and of major efforts to apply the approach in real-world interventions. Further, it encourages the widespread use of critical systems practice as a means of ensuring responsible leadership in a complex world. The INCOSE Pioneer Award is presented to someone who, by their achievements in the engineering of systems, has contributed uniquely to major products or outcomes enhancing society or meeting its needs. The criteria may apply to a single outstanding outcome or a lifetime of significant achievements in effecting successful systems.
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Preface xvii
Introduction xxv
Part I Systems Thinking in the Disciplines 1
1 Philosophy 3
1.1 Introduction 3
1.3 Hegel 8
1.4 Pragmatism 9
1.5 Husserl and Phenomenology 10
1.6 Radical Constructivism 11
1.7 Conclusion 12
2 The Physical Sciences and the Scientific Method 15
2.1 Introduction 15
2.2 The Scientific Method and the Scientific Revolution 16
2.3 The Physical Sciences in the Modern Era 19
2.4 The Scientific Method in the Modern Era 21
2.5 Extending the Scientific Method to Other Disciplines 24
2.6 Conclusion 25
3 The Life Sciences 27
3.1 Introduction 27
3.2 Biology 27
3.3 Ecology 35
3.4 Conclusion 40
4 The Social Sciences 43
4.1 Introduction 43
4.2 Functionalism 44
4.3 Interpretive Social Theory 49
4.4 The Sociology of Radical Change 52
4.5 Postmodernism and Poststructuralism 56
4.6 Integrationist Social Theory 59
4.7 Luhmann’s Social Systems Theory 62
4.8 Action Research 67
4.9 Conclusion 68
Part II The Systems Sciences 71
5 General Systems Theory 75
5.1 Introduction 75
5.2 von Bertalanffy and General System Theory 75
5.3 von Bertalanffy’s Collaborators and the Society for General Systems Research 79
5.4 Miller and the Search for Isomorphisms at Different System Levels 80
5.5 Boulding, Emergence and the Centrality of “The Image” 82
5.6 The Influence of General Systems Theory 85
5.7 Conclusion 86
6 Cybernetics 89
6.1 Introduction 89
6.2 First‐Order Cybernetics 91
6.3 British Cybernetics 95
6.4 Second‐Order Cybernetics 102
6.5 Conclusion 108
7 Complexity Theory 111
7.1 Introduction 111
7.2 Chaos Theory 112
7.3 Dissipative Structures 117
7.4 Complex Adaptive Systems 119
7.5 Complexity Theory and Management 125
7.6 Complexity Theory and Systems Thinking 136
7.7 Conclusion 144
Part III Systems Practice 147
8 A System of Systems Methodologies 151
8.1 Introduction 151
8.2 Critical or “Second‐Order” Systems Thinking 152
8.3 Toward a System of Systems Methodologies 155
8.3.1 Preliminary Considerations 155
8.3.2 Beer’s Classification of Systems 155
8.3.3 The Original “System of Systems Methodologies” 157
8.3.4 Snowden’s Cynefin Framework 160
8.3.5 A Revised “System of Systems Methodologies” 162
8.4 The Development of Applied Systems Thinking 166
8.5 Systems Thinking and the Management of Complexity 169
8.6 Conclusion 169
Type A Systems Approaches for Technical Complexity 171
9 Operational Research, Systems Analysis, Systems Engineering (Hard Systems Thinking) 173
9.1 Prologue 173
9.2 Description of Hard Systems Thinking 175
9.2.1 Historical Development 175
9.2.2 Philosophy and Theory 177
9.2.3 Methodology 179
9.2.4 Methods 182
9.2.5 Developments in Hard Systems Thinking 184
9.3 Hard Systems Thinking in Action 188
9.4 Critique of Hard Systems Thinking 191
9.5 Comments 196
9.6 The Value of Hard Systems Thinking to Managers 197
9.7 Conclusion 197
Type B Systems Approaches for Process Complexity 199
10 The Vanguard Method 201
10.1 Prologue 201
10.2 Description of the Vanguard Method 203
10.2.1 Historical Development 203
10.2.2 Philosophy and Theory 206
10.2.3 Methodology 209
10.2.4 Methods 211
10.3 The Vanguard Method in Action 212
10.3.1 Check 213
10.3.2 Plan 215
10.3.3 Do 216
10.4 Critique of the Vanguard Method 220
10.5 Comments 224
10.6 The Value of the Vanguard Method to Managers 225
10.7 Conclusion 226
Type C Systems Approaches for Structural Complexity 227
11 System Dynamics 229
11.1 Prologue 229
11.2 Description of System Dynamics 231
11.2.1 Historical Development 231
11.2.2 Philosophy and Theory 233
11.2.3 Methodology 241
11.2.4 Methods 244
11.3 System Dynamics in Action 247
11.4 Critique of System Dynamics 249
11.5 Comments 258
11.6 The Value of System Dynamics to Managers 258
11.7 Conclusion 259
Type D Systems Approaches for Organizational Complexity 261
12 Socio‐Technical Systems Thinking 263
12.1 Prologue 263
12.2 Description of Socio‐Technical Systems Thinking 264
12.2.1 Historical Development 264
12.2.2 Philosophy and Theory 268
12.2.3 Methodology 276
12.2.4 Methods 279
12.3 Socio‐Technical Systems Thinking in Action 280
12.4 Critique of Socio‐Technical Systems Thinking 281
12.5 Comments 288
12.6 The Value of Socio‐Technical Systems Thinking to Managers 289
12.7 Conclusion 289
13 Organizational Cybernetics and the Viable System Model 291
13.1 Prologue 291
13.2 Description of Organizational Cybernetics 296
13.2.1 Historical Development 296
13.2.2 Philosophy and Theory 299
13.2.3 Methodology 311
13.2.4 Methods 317
13.3 Organizational Cybernetics in Action 320
13.4 Critique of Organizational Cybernetics and the Viable System Model 325
13.5 Comments 337
13.6 The Value of Organizational Cybernetics to Managers 339
13.7 Conclusion 340
Type E Systems Approaches for People Complexity 341
14 Strategic Assumption Surfacing and Testing 343
14.1 Prologue 343
14.2 Description of Strategic Assumption Surfacing and Testing 346
14.2.1 Historical Development 346
14.2.2 Philosophy and Theory 348
14.2.3 Methodology 353
14.2.4 Methods 355
14.3 Strategic Assumption Surfacing and Testing in Action 357
14.4 Critique of Strategic Assumption Surfacing and Testing 360
14.5 Comments 365
14.6 The Value of Strategic Assumption Surfacing and Testing to Managers 366
14.7 Conclusion 367
15 Interactive Planning 369
15.1 Prologue 369
15.2 Description of Interactive Planning 371
15.2.1 Historical Development 371
15.2.2 Philosophy and Theory 375
15.2.3 Methodology 379
15.2.4 Methods 382
15.3 Interactive Planning in Action 384
15.4 Critique of Interactive Planning 388
15.5 Comments 394
15.6 The Value of Interactive Planning to Managers 395
15.7 Conclusion 395
16 Soft Systems Methodology 397
16.1 Prologue 397
16.2 Description of Soft Systems Methodology 401
16.2.1 Historical Development 401
16.2.2 Philosophy and Theory 404
16.2.3 Methodology 411
16.2.4 Methods 420
16.3 Soft Systems Methodology in Action 427
16.4 Critique of Soft Systems Methodology 431
16.5 Comments 441
16.6 The Value of Soft Systems Methodology to Managers 442
16.7 Conclusion 443
Type F Systems Approaches for Coercive Complexity 445
17 Team Syntegrity 447
17.1 Prologue 447
17.2 Description of Team Syntegrity 449
17.2.1 Historical Development 449
17.2.2 Philosophy and Theory 450
17.2.3 Methodology 455
17.2.4 Methods 458
17.3 Team Syntegrity in Action 459
17.4 Critique of Team Syntegrity 462
17.5 Comments 468
17.6 The Value of Team Syntegrity to Managers 470
17.7 Conclusion 470
18 Critical Systems Heuristics 471
18.1 Prologue 471
18.2 Description of Critical Systems Heuristics 473
18.2.1 Historical Development 473
18.2.2 Philosophy and Theory 476
18.2.3 Methodology 479
18.2.4 Methods 484
18.3 Critical Systems Heuristics in Action 485
18.4 Critique of Critical Systems Heuristics 490
18.5 Comments 502
18.6 The Value of Critical Systems Heuristics to Managers 508
18.7 Conclusion 509
Part IV Critical Systems Thinking 511
19 Critical Systems Theory 515
19.1 Introduction 515
19.2 The Origins of Critical Systems Theory 516
19.2.1 Critical Awareness 517
19.2.2 Pluralism 519
19.2.3 Emancipation or Improvement 522
19.3 Critical Systems Theory and the Management Sciences 524
19.4 Conclusion 528
20 Critical Systems Thinking and Multimethodology 531
20.1 Introduction 531
20.2 Total Systems Intervention 540
20.2.1 Background 540
20.2.2 Multimethodology 541
20.2.3 Case Study 545
20.2.4 Critique 553
20.3 Systemic Intervention 558
20.3.1 Background 558
20.3.2 Multimethodology 559
20.3.3 Case Study 562
20.3.4 Critique 565
20.4 Critical Realism and Multimethodology 568
20.4.1 Background 568
20.4.2 Multimethodology 570
20.4.3 Case Study 572
20.4.4 Critique 572
20.5 Conclusion 576
21 Critical Systems Practice 577
21.1 Prologue 577
21.2 Description of Critical Systems Practice 579
21.2.1 Historical Development 579
21.2.2 Philosophy and Theory 581
21.2.3 Multimethodology 593
21.2.4 Methodologies 601
21.2.5 Methods 604
21.3 Critical Systems Practice in Action 607
21.3.1 North Yorkshire Police 607
21.3.2 Kingston Gas Turbines 617
21.3.3 Hull University Business School 621
21.4 Critique of Critical Systems Practice 632
21.5 Comments 637
21.6 The Value of Critical Systems Practice to Managers 638
21.7 Conclusion 638
Conclusion 641
References 645
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Systems Thinking, Critical Thinking, and Personal Resilience
| May 25, 2018 | Leave a Comment
Image by Saad Faruque | Flickr | CC BY-SA 2.0
Item Link: Access the Resource
Date of Publication: May 24, 2018
Year of Publication: 2018
Publication City: Santa Rosa, CA
Publisher: Post Carbon Institute
Author(s): Richard Heinberg
Richard Heinberg discusses how systems thinking, critical thinking, and personal resilience are central to dealing with “knowing —knowing, that is, that we humans have severely overshot Earth’s long-term capacity making a collapse of both civilization and Earth’s ecological systems likely…”
In discussing climate change and all our other eco-social predicaments, how does one distinguish accurate information from statements intended to elicit either false hope or needless capitulation to immediate and utter doom? And, in cases where pessimistic outlooks do seem securely rooted in evidence, how does one psychologically come to terms with the information?
For Heinberg, systems thinking provides the most accurate picture, critical thinking helps us think more clearly and productively, and personal resilience aids us in maintaining and using knowledge of the world without becoming paralyzed or psychologically injured by it.
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There are many differences between Critical Thinking vs Systems Thinking. Critical Thinking involves examining and challenging thoughts or ideas
Critical systems thinking (CST) is a systems approach designed to aid decision-makers, and other stakeholders, improve complex problem situations that cross
Systems Thinking, Critical Thinking, and Personal Resilience · The ability to make realistic plans and to take the steps necessary to follow
Critical Thinking: Thinking about problems in depth and holding preconceived notions to scrutiny with new evidence and understanding. · Systems Thinking: The
The Seven Critical Thinking Skills · EXTENSIVE AND INTENSIVE MODEL BOUNDARIES · TWO REPRESENTATIONS OF THE LEARNING PROCESS · A STRAIGHT-LINE VS.A CLOSED-LOOP VIEW
Five systems thinking skills have been identified thus far: 10,000- meter, system-as-cause, dynamic, operational, and closed-loop. By developing these skills
This paper is about Critical Systems Thinking (CST), a research perspective which is said to embrace three fundamental commitments.
Systems thinking and one of its subsets-system dynamics-are important for developing effective strategies to close this gap.
They must deal with problems that arise unexpectedly, generate uncertainty, are characterised by interconnectivity, and spread across traditional boundaries.
For Heinberg, systems thinking provides the most accurate picture, critical thinking helps us think more clearly and productively, and personal