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1 Introduction Systemic Models and CLDs

The title of this short conceptual paper is intended to make immediately clear that, in my opinion, Systems Thinking is one of the most powerful tools of knowledge and understanding because it teaches us to devise coherent and sense-making models of the world, which are among the most effective for improving our intelligence, modifying our world and constructing our existence [1, 2].

Intelligent persons are those who learn quickly and effectively; they have the ability (innate or acquired) to construct, utilize and modify models; they can understand their interconnections and changes and always “know” what is happening and could happen in order to control events and successfully face the various situations in life, deciding in the most rational way how to solve problems.

My opinion is based on three premises:

  • intelligence is the ability to develop a system of coherent and meaningful models that allow us not only to survive in a dynamic world that is continually evolving but also to improve our intelligence and make progress;

  • the most powerful and effective models are the systems ones that view reality as a set of connected and dynamic variables forming a whole;

  • the most interesting and useful connections among the variables that make up reality are not the linear ones—characterized by chains of causes and effects—but the circular ones, the feedbacks and loops, which make the elements not only connected but also interconnected, not only dynamic but also interactive. The relations of cause and effect between variables can be simply represented using arrows that unequivocally correlate their variations. The cause (input) variables are written in the tail of the arrow; the effect (output) variables at the head of the arrow. Two variables have the same direction of variation (“s”) if increases or decreases in the former result in corresponding increases or decreases in the latter (first arrow). They have the opposite direction (“o”) if increases or decreases in the former result in corresponding decreases or increases in the latter (second arrow).

figure a

A closed causal chain, that is a loop, is formed by a circular link between two or more variables which can be linked in two opposite directions with respect to the causal link. Loops can be basic, when there are only two variables, or compound, when more than two variables are joined in a circular link. There are only two basic types of loop:

  1. 1.

    Reinforcing loops [R], which produce a reciprocal increase or reduction—in successive repetitions of the system’s cycle—in the values of the two variables having identical direction of variation: “s and s” or “o and o”.

figure b
  1. 2.

    Balancing loops [B], which maintain relatively stable the values of the connected variables, which are connected by a different direction of variation: “s and o” or “o and s”.

figure c

There is no limit to the number of interconnected variables. A system of loops in which all variables are linked by arrows, without there being an initial and final variable, is defined as a Causal Loop Diagram [3].

Figure 55.1 represents a very simplified model of a struggle-for-life system. Figure 55.2 connects the main variables that drive the marketing strategy.

Fig. 55.1
figure 1

Struggle for life

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Fig. 55.2
figure 2

Marketing strategy

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2 The Five Rules of Systems Thinking

Systems Thinking was presented, in a way that could be understood by a broad readership, by Peter Senge in his important work, The Fifth Discipline: The Art and Practice of the Learning Organization [1].

But what does Systems Thinking consist of? What are its logical and theoretical bases?

Systems Thinking, a Systems Approach, Systems Dynamics, Systems Theory and just plain “Systems” are but a few of the many names commonly attached to a field of endeavor that most people have heard something about, many seem to feel a need for, and few really understand. […] As I prefer the term “Systems Thinking,” I’ll use it throughout as the single descriptor for this field of endeavor.

In his masterly book, Peter Senge presents Systems Thinking in an intuitive way, but he does not provide the logical principles behind it. In my recent book, “Systems Thinking. Intelligence in action”, I have tried to recognize the fundamental rules and principles as well as the cultural background of this discipline. I believe that the logical structure of systems thinking can be summarized in five fundamental rules which the systems thinker must follow at all times.

First rule: if we want to describe and understand the world we must be able to “see the trees and the forest”; we must develop the capacity to “zoom” from the whole to the parts, from systems to components, and vice-versa.

This rule, which is at the basis of systems thinking, can be translated as follows: “reality” is permeated with systems, increasingly vaster in scope, which form a global structure that creates a global process that cannot be understood only from the outside or inside. If we want to broaden our intelligence we must develop the capacity to “zoom” from parts to the whole, and from entities to components. In this sense we can say that this first rule of Systems Thinking “operationalizes” the holonic view, in that it not only specifies how far the observation of the whole/part relationship should extend but above all tries to identify the links and constraints that make the whole and its parts interdependent. This rule leads to an important corollary: to describe and understand the world we must always be aware of, or specify, our point of view; that is, the level of observation in which we choose to place ourselves.

Second rule: we must not limit our observation to that which appears constant but “search for what varies over time”; it is the variables over time that interest the systems thinker.

This rule is perhaps more important than the preceding one, and requires even more intense discipline, since Systems Thinking tells us to shift from a “world of objects”—whether trees or forests—to a “world of variables” that connote those objects. This seems easier than it really is. One needs sensitivity and experience to select the truly significant variables. However, we must not limit ourselves to explicitly stating the variables we consider useful but must be able to measure the “variations” they undergo over time. The objects must be “seen” as vectors of variables. This seems easier than it really is. One needs sensitivity and experience to select the truly significant variables. Systems Thinking also requires us to select the relevant variables and restrict their number so as to consider only those most relevant for the construction of models.

Third rule: if we truly wish to understand reality and change we must make an effort “to understand the cause of the variations in the variables we observe” by forming chains of causal relationships among the connected variables while identifying and specifying:

  1. 1)

    the processes that “produce” the dynamics in the variables, and the machines (or systemic structures) that “produce” those processes;

  2. 2)

    the variables that “carry out” those processes (causes or inputs), and those that “derive” from the processes (effects or outputs)

For simplicity’s sake we could even call the input and output variables “causes” (causal variables) and “effects” (caused variables), respectively.

However, we must always remember that the processes—to the extent they are considered black boxes—always play the role of producer of the effects, given the causes and more or less broad set of “initial” and “boundary or surrounding” conditions. Systems Thinking admits that the processes that produce variations can be conceived of as a black box whose internal structure and functioning may not even be known. What is truly indispensable is to understand the rules (laws, functions, operations) by which the variations in the input variables cause variations in the output variables.

Fourth rule: it is not enough to search for the causes of the variations we observe; we must also link together the variables in order to “specify the loops among all the variations”.

Systems Thinking states: if we really want to “understand” the world and its changes, it is not enough to reason in terms of chains of causes and effects between variables. We must recognize that the effects can, in turn, become the causes of their causes, thereby creating a loop, a circular connection; we must make an effort to link together the variables until we obtain a loop among their variations. In other words, we must move from the causal chains to the systemic interconnections and from the linear variations to the systemic interactions among the variables of interest. In brief, we must see the world in terms of circular processes, or feedback loops, abandoning the “linear thinking” (“laundry list thinking”) that only considers chains of causes and effects and becoming accustomed to “circular thinking” (loops and Causal Loop Diagrams), identifying the loops that interconnect the variables.

[…] If you took the time to record your thoughts, I’ll bet they took the form of a […] “laundry list”. I like to refer to the mental modeling process that produces such lists as laundry list thinking. I believe it to be the dominant thinking paradigm in most of the Western world today. […] Notice that the implicit assumptions in the laundry list thinking process are that (1) each factor contributes as a cause to the effect, i.e., causality runs one way; (2) each factor acts independently; (3) the weighting factor of each is fixed; and (4) the way in which each factor works to cause the effect is left implicit (represented only by the sign of the coefficients, i.e., this factor has a positive or a negative influence). The systems thinking paradigm offers alternatives to each of these assumptions. First, according to this paradigm, each of the causes is linked in a circular process to both the effect and to each of the other causes. Systems thinkers refer to such circular processes as feedback loops.

Fifth rule: when we observe the world we must always “specify the boundaries of the system” we wish to investigate.

In fact, the first rule of Systems Thinking obliges us to zoom inside a system—thereby identifying increasingly smaller subsystems—as well as outside a system, to identify ever larger super systems. Are we thus destined (or “condemned”) to having a holistic view without limits? Certainly not! Systems Thinking is the art of “seeing” the world, and in order for what we see to have a true meaning it must depend on our cognitive interests. We cannot have a forest without limits. To understand this let us briefly review the rules that make up the logical structure behind Systems Thinking, using the figure as an aid.

By connecting a number of variables and determining the direction of variation we can build models of every dynamic system, keeping in mind that we must zoom in order to analyze the processes in more detail, in order to identify and connect other important variables.

Systems Thinking, precisely because it is a tool for developing our intelligence, must be learned gradually through practice and continual improvement. It is a discipline that requires the systems thinker to have a deep knowledge and to constantly apply its rules, as well as to have the willingness to continually improve: A discipline is a developmental path for acquiring certain skills or competencies. […] To practice a discipline is to be a lifelong learner. You “never arrive”; you spend your life mastering disciplines [2, p. 10].

3 System Dynamics

It is important to clarify which systems Systems Thinking examines and what types of models can thereby be obtained. Due to its intrinsic logic, which observes a world of variables and of variations, Systems Thinking defines “system” as a unitary set of interconnected variables possessing its own autonomy—capable of producing emerging macro-dynamics that do not coincide with any of the micro-dynamics of the individual variables or their partial subsystems—whose logical structure is examined and represented by Systems Thinking. This discipline considers dynamic systems of any kind in any field, building models of a world of incessant movement in continual transformation and evolution.

The construction of the Causal Loop Diagram is a crucial step for “seeing” and understanding the systems that operate around us and interact with our behaviour. Since Systems Thinking by nature considers systems to be dynamic, it is natural to develop simulation techniques to try to numerically and graphically represent the succession of values generated by the system under examination, as in the attempts made in sect. 55.5. Systems Thinking, when quantitatively expressed in simulations, is commonly known as the study of the dynamics of dynamic systems, or (Dynamic) System Dynamics, a discipline that all agree goes back to Jay Forrester and his fundamental book Industrial Dynamics. In a recent article the founder of this discipline defines it in this way:

By “systems thinking” I mean the very popular process of talking about systems, agreeing that systems are important, and believing that intuition will lead to effective decisions. … “System dynamics” is a professional field that deals with the complexity of systems. System dynamics is the necessary foundation underlying effective thinking about systems. System dynamics deals with how things change through time, which covers most of what most people find important. System dynamics involves interpreting real life systems into computer simulation models that allow one to see how the structure and decision-making policies in a system create its behaviour.

Since System Dynamics and Systems Thinking are disciplines that cover the same field of knowledge, there is a question as to which can be considered the original one and which the derived one. Is Systems Thinking a generalization of System Dynamics or is System Dynamics a specialized operational application of Systems Thinking?

The discipline considers not only dynamic but also repetitive systems, which are able to repeat their processes over time, as well as recursive systems, capable of interacting with themselves in the sense that their output, entirely or in part, becomes their own inputs. Even if we are not used to observing them, repetitive and recursive systems are all around us; they are the typical essence of nature; life itself is recursive in its typical process of birth, reproduction and death, which is destined to repeat itself again and again.

Systems Thinking is particularly sensitive to systems with memory; it forces us to consider the connections between variables, always zooming between high-level variables, which accumulate variation over time, and more detailed (state) variables, which cause variations over time; it forces us to observe the dynamics of recursive processes and not only individual pairs of values, to consider the loops and not only the pure causal connections.

4 Two Fundamental Systems Thinking Laws

The Systems Thinking models are certainly not the only ones capable of increasing our knowledge of the world, but in my view their cognitive effectiveness owes to their ease of construction and communication. The only skills they require are perspicacity and insight; they use elementary techniques; they are understandable even to non-experts; and they can be easily communicated, examined and improved. They allow us to learn together to collectively improve our understanding of the world and can be easily translated into quantitative simulation models.

These definitions allow us to present a first fundamental law of Systems Thinking:

  • on the one hand, the behavior of a variable depends on the system in which it is included;

  • on the other hand, the behavior of the entire system depends on its structure; that is, on the interconnections among its component variables.

This law has two corollaries:

  • it is useless to try to modify the values of a variable if first we do not understand the systemic structure of which it is a part, since the balancing loops will restore its value and the reinforcing loops will increase it;

  • in observing a dynamic world, the “ceteris paribus” assumption is never valid.

Connected to the preceding law is a second fundamental law of Systems Thinking, which I shall name as:

law of dynamic instability: expansion and equilibrium are processes that do not last forever; they are not propagated ad infinitum. Sooner or later stability is disturbed. Sooner or later the dynamics are stabilized.

5 The General Law of Dynamic Instability

Operationally speaking, the law of dynamic instability affirms in a simple way that, though we are unaware or unable to observe this, every expansion loop is always associated with a balancing loop that dampens the expansion dynamics; and vice-versa, every balancing loop is associated with some type of expansion loop that counters the balancing effect.

Moreover, external disturbances can come from the environment in the form of braking variables that counter the expansion, or acceleration variables that eliminate the balancing effect, as shown in the model in Fig. 55.3.

Fig. 55.3
figure 3

The general law of dynamic instability

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Paraphrasing Newton’s first law of mechanics: “Every object remains in its state of rest or uniform motion in a straight line unless a force intervenes to modify this state”, Systems Thinking could instead state: “Every repetitive system does not endlessly produce its own reinforcing or balancing processes because other processes intervene to reverse the dynamics”. It seems impossible to respect the wise motto of Plato: “Mota quietare, quieta non movere!”.

A simple and interesting numerical simulation—in one of its many possible variants—is shown in Fig. 55.4. Several simulation tests are presented in Fig. 55.5. The diagram in Fig. 55.5, Test 1, shows that the curve in bold, which depicts the dynamics of the stabilized variable Yt, has a cyclical dynamics precisely due to the reinforcement from the curve for variable Xt and the balancing effects from the curve for variable Zt. By modifying the values in the control panel (directly shown in the CLD at the top of the table) we obtain the explosive dynamics of growth in Yt if variable Xt prevails (see Test 4 in Fig. 55.5), or the explosive decreasing dynamics if Zt prevails. By introducing outside disturbances the dynamics can become quite irregular. By adopting the simulation model behind Fig. 55.5 we can simulate other dynamics, introducing new parameters or even disturbance elements for one or even all three of the variables. I will not add any comments since the loops produced by the Systems Thinking technique are clearly representative and understandable.

Fig. 55.4
figure 4

The law of dynamic instability. A numerical simulation

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Fig. 55.5
figure 5

The law of dynamic instability. Simulation tests (the bold line represents the stabilized Variable Y)

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The first three tests shown in Fig. 55.5 reveal the relative stability in the dynamics of variable Y (the thicker curve), which is stabilized even in the presence of disturbances; in fact, the latter are “neutralized” by the joint action of the variables X and Z. Test 4 shows that by deactivating loop [B] we get only the reinforcing effect of loop [R].

6 Final Remark

Organizations and men are linked to the world through their behaviour, and are thus part of the world they interact with and intend to change. They are subject to and produce the fundamental variables of their existence and that of other entities, and for this reason managers and organizations as cognitive systems must develop a systemic and pro-active learning for observing (understanding), judging (deciding) and identifying opportunities and solving problems. Systems Thinking is not the panacea for solving all problems related to man’s and organizations’ behaviour involving knowledge, judgment and decisions, but it nevertheless is an instrument that broadens our way of thinking, one which allows us to avoid the error of ignoring the structures and isolating the variables. Peter Senge indicates to managers the “superiority” of systems thinking not in order to propose a new form of thinking but to emphasize the need for organizations to think systematically. Precisely for this reason, he considers systems thinking as a discipline—the Fifth Discipline—everyone should follow in order to get accustomed to “seeing the trees and the forest”, the only kind of thinking that can grasp the properties of the whole without, however, forgetting those of the parts. I hope this study adds some ideas for observing the world through Systems Thinking.

In this regard, we can paraphrase Lucius Annaeus Seneca (“Ignoranti quem portum petat, nullus suus ventus est”, VIII, Ep. 71) and conclude that: “No wind is favorable for those who know not what port they are making for, but for the man who knows where he is headed, in the sea of knowledge, even the breeze of Systems Thinking is sufficient to navigate”.