System dynamics

SYSTEM DYNAMICS We are going to describe in the following pages an approach to interpret the reality. It is likely not to exist what we might call the correct or the best way to observe the reality, given that it is impossible to point at a certain direction as the best or more adequate. However, this is a new concept for many people. In my opinion, it‟s a useful way to deal with the problems and challenges we are facing at the turn of the millennium: hunger, poverty, the degradation of the environment, war, etc. It would seem that the traditional ways of dealing with such issues haven‟t improved matters significantly. The viewpoint presented here has several names. Here, we will call it system dynamics. I‟m aware that “systems” could mean various things, and I will attempt to clarify my interpretation of the word through discussion and examples further on. As an introduction, we will look at the characteristics of models that represent the world as a whole; as a global system. We will then describe the characteristics of the situation of the world today from this viewpoint. System Dynamics We are increasingly aware that we live in a very complex and constantly changing reality, and that it‟s more so every year. In order to make the decisions that are continually asked of us, we use mental models. However, these models don‟t always bring us closer to solving the problem, as the solution may be, as Jay Forrester calls it, counter-intuitive, even in the simplest of cases. For instance, during a visit to the Science Museum with our children, we may have to explain why the hole in a water tank which is nearest the ground spouts water further. We may also have to explain why the image in a magnifying glass is inverted after a certain distance, instead of growing indefinitely. As Ludwig von Bertalanffy notes: for those that wish to study science and only science, any posterior question makes no sense. "Quod non est in formula non est in mundo". Such is the only legitimate position of science. Despite this, if we wish to further our understanding, there is only one analogy that can explain that which is irrelevant to the physicist, the analogy of the only reality we know directly: the reality of our immediate experience. All interpretations of reality, to use Kant‟s expression, are an adventure of reason. There are therefore only two alternatives to choose from: either we reject all interpretations of the essence of things, or (if we do attempt an interpretation) we must remain conscious of its analogous nature, since we don‟t have the slightest proof that true reality is the same as that of our own internal experience. When faced with the common occurrence of a reality with a limited number of parameters, especially when these are quantifiable, we employ formal models which allow us to act with a reasonably high probability of succeeding. However, when faced with complex situations with an uncertain number of parameters that are difficult to quantify, we can resort to less formal models that provide a more structured view of the problem, its key aspects, and possible solutions. Lynda M. Applegate states that computers today are designed to treat information sequentially, instruction after instruction. This works well if the problem can be structured and divided into a series of stages. It doesn‟t work well with complicated, unstructured tasks which require intuition, creativity and discernment. The main application of System Dynamics is in this kind of complex and loosely defined environments, where a human being‟s decisions tend to be guided by logic. We must remember that science is currently based on measurable and reproducible phenomena. As specialists in marketing know, people also behave according to certain rules, which are fairly easy to measure and reproduce: market law (more demand pushes up prices, etc.). With reference to this, Javier Aracil states in his book Introducción a la Dinámica de Sistemas (Introduction to System Dynamics) that computer models can provide information not attainable via mental models: they can show the dynamic consequences of interactions between components of a given system. When assessing the consequences of certain actions, the use of mental models means running the risk of obtaining erroneous conclusions. Intuition isn‟t reliable when the problem is complex. One possible reason for this is that we tend to think in terms of one-way cause-effect relationships, forgetting the structural feedback which almost certainly exists in such a system. When preparing a computer model, we must consider each step separately. The mental image we have of the system must be developed and expressed in a language that can be used to program the computer. Normally, any consistent and explicit mental image of any system can be expressed in this way. The mental images that we have of real systems are the result of experiences and observations. The explicit formulation of these experiences in a computer program forces us to examine, formalise and focus our mental images, thus providing us with a greater understanding through several perspectives. Mathematical models, which are programmable, are explicitly expressed. The mathematical language used to describe the model leaves no room for ambiguity. A System Dynamics model is more explicit than a mental model, and can therefore be expressed without ambiguity. The hypotheses upon which the model is built and the relationships between its constituent elements are present in complete clarity and are subject to discussion and revision. For this reason, a model‟s forecasts for the future can be studied in a completely precise way. It‟s important to differentiate between the following two kinds of model: predictive models are designed to offer precise information of the future status of the modelled system, whereas management models are basically designed to decide whether option X is better than Y. Management models don‟t require as much precision, since comparisons are equally useful. System Dynamics models are of the latter type. As explained above, I understand the word system to mean a set of independent elements that interact with each other in a stable way. The first step towards understanding the behaviour of a system would be to define its constituent elements, and their possible interaction. The notion of Aristotle that the whole is more than the sum of its parts takes on a special meaning here. The standpoint of System Dynamics is radically different to other existing techniques for the construction of socio-economic system models such as econometrics. Econometric techniques, which are based on behaviourism, use empirical data such as statistical calculus in order to determine the meaning and correlation between the various factors involved. The model is developed from the historical evolution of variables that are declared independent, and statistics is applied in order to determine the parameters of the system of equations that link them to other independent variables. These techniques can establish the behaviour of the system without the need for information regarding its internal functioning. This is how stock market models analyse the upward and downward trends in the values of shares, the rising and falling cycles, etc. They are designed in order to minimise the risk of losses, etc. They don‟t attempt to gain any detailed knowledge of the internal workings of the firms, as the value of a given company rises and falls according to its new products, new competitors, etc. The basic objective of System Dynamics is different. It aims to gain understanding of the structural causes of a system‟s behaviour. This implies increased knowledge of the role of each element of the system, in order to assess how different actions on different parts of the system accentuate or attenuate its behavioural tendencies. One characteristic that sets it apart from other methods is that it doesn‟t aim to give a detailed forecast of the future. Using the model to study the system and test different policies, we will deepen our knowledge of the real world, assessing the consistency of our hypotheses and the effectiveness of each policy. Another important characteristic is its long-term perspective, meaning that the period studied is long enough for all significant aspects of the system to evolve freely. Only with a sufficiently broad time scale can the fundamental behaviour of a system be observed. We mustn‟t forget that the results of certain policies are sometimes not the most appropriate, if the time horizon of the decision-making process was too short, or if there was a lack of perspective when the problem was addressed. In these cases, it would be useful to know the long-term consequences of actions taken in the present, and this can be more tangibly attained if we use a suitable model. The long-term development will be understood only if the main causes of any possible changes are identified. This process is facilitated if the appropriate variables are chosen. Ideally, the limits of the system should include the whole set of mechanisms that are responsible for any important alterations in the main system variables over a broad time horizon. System Dynamics allows the construction of models after a careful analysis has been conducted of the elements of a system. This analysis allows the internal logic of the model to be extracted. Knowledge may then be gained of the long-term evolution of the system. It should be noted that the adjustment of the model according to historical data is of secondary importance, the analysis of the internal logic and the structural relationships within the model being the key issues involved in its construction. Note: All teaching material, and that includes this text, should be objective. This text aims to be so, but the author admits he hasn‟t always succeeded. For this he must apologise. Readers are invited to make their own assumptions as to what is an exposure of methodology, and what amounts to personal opinion. Identifying the Problem What is the problem? We are going to learn a method for constructing simulation models that help us determine the best solution for a given problem. These are therefore management models, not predictive models. Firstly, we have to identify the problem clearly, and give a precise description of the aims of the study. It may be obvious, but it‟s very important that the definition of the problem be correct, since all further steps depend on this. This is also very useful when establishing the amount of time and money that will be spent creating the model. Once the core of the problem is defined, a description must be completed, based on the knowledge of experts on the subject, basic documentation, etc. The result of this phase should be a preliminary perception of the elements that have a bearing on the problem, the h-y-p-o-t-h-e-t-i-c-a-l relationships between them, and their historical behaviour. The historical reference of a system is a record of the historical behaviour of the main elements that are believed to influence the problem. Where possible, they should be quantified. This is the graphical and numerical representation of the verbal description of the problem. It‟s a good idea to ask ourselves whether it is necessary to construct a simulation model in order to find an efficient solution to the problem. This is an important question. The construction of a model is a long and costly process. It can‟t be justified if there are other more simple ways of obtaining the same results. There are essentially two other ways: statistics and intuition. - Statistics, or numerical calculus methods, are very useful for solving problems where there is an abundance of historical data, or when we can assume reality will remain stable. For example, if you want to find out how many cars will drive past your house today, all you need is sufficient historical data, and assuming the street hasn‟t changed, you‟ll get a good approximation. - Intuition has got you where you are today, so don‟t underestimate it. For many problems, intuition provides the right answer, drawing on our experience and knowledge. Intuition is cheap and fast. Keep using it as often as possible. Only when we can‟t apply one of these two options with certainty must we resort to constructing a simulation model. Once the problem is defined, we will see that there are many directly or indirectly related aspects, or elements, which are also interrelated. They needn‟t be clearly or obviously interrelated. These elements constitute the system. We will now study reality as a system. Defining the System What is a system? A system is a set of interrelated elements, where any change in any element affects the set as a whole. Only elements directly or indirectly related to the problem form the system under study here. In order to study a system, we must know the elements that make it up, and the relationships between them. When we analyse a system we usually focus merely on the characteristics of its constituent elements. However, in order to understand the functioning of a complex system, we must focus also on the relationships that exist between the elements which form the system. It is impossible to understand the essence of a symphony orchestra by merely observing the musicians and their instruments. It is the coordination that exists between them that produces beautiful music. The human body, a forest, a country, and the ecosystem of a coral reef are all examples of systems that are far more than the sum of their parts. An ancient Sufi saying can illustrate this: You can think, because you understand “one”, and you can understand “two”, which is “one” plus “one”. However, you must also understand “plus”. For example, in a traffic problem many related elements converge: number of inhabitants, number of cars, the price of petrol, parking spaces, alternative transport, etc., and it‟s often easier and more effective to attempt to solve the relationships between the elements (“plus”) than the elements themselves. A good method to begin defining a system is to write the main problem down in the middle of a blank page, surrounding it with the directly related elements. The elements which affect the main problem indirectly go around the appropriate direct elements. This will be the system that we will study in order to consider possible solutions to a given problem. The Boundaries of a System Where does the system end? We have all heard the theory that a butterfly fluttering its wings in China could cause a tornado in the Caribbean. In our study, however, we will include only elements with a reasonable influence on the behaviour of a system. We mustn‟t lose sight of the objective: to propose practical action towards effectively solving the problem at hand. The system must contain as few elements as possible, while providing a simulation that will truly allow us to decide which of the possible courses of action studied is the most effective solution to the problem. The models are generally small to begin with, with few elements. They are then expanded and perfected. Later on, elements which don‟t play a decisive part in the problem are eliminated. During the construction of a model, there are several extension and simplification phases, in which elements are added and subtracted. We can‟t ignore the relationship between the consumption of petrol and lung health. When we analyse the carbon combustion process in an electric power plant, we can see that, apart from energy, the following is produced: ash, suspended particles, SO 2, CO2, etc. We can also see that there is no barrier between the desired product (electricity) and the by-products. Sometimes, the so-called side-effects are as real and as important as the main effects. The beauty of a system in nature is that the waste produced by one process serves to feed the next. Perhaps this is the model to follow for industrial design in the future. The final size of the model must be such that its main aspects can be explained in ten minutes. Any model larger than this will fail. The Causal Diagram How do we represent the system? The set of elements that bear relation to the problem, and that account for the observed behaviour, along with the relationships that exist between these constituent elements (which often involve feedback) form the system. The causal diagram represents the key elements of the system and the relationships between them. As discussed above, it‟s important to draft versions that will bring us increasingly closer to the final complex model. The minimum set of elements and relationships that serves to reproduce the historical reference of a system is that which forms the basic structure of the system. Once the variables of a system and the hypothetical relationships between these variables are known, we move on to produce a graphical representation. This diagram shows the relationships as arrows between the variables. These arrows are marked with a sign (+ or -) which indicates the kind of influence one variable exerts over the other. A "+" means a change in the influencing variable will produce a change of the same direction in the target variable. A "-" means the effect will be the opposite. So, when an increase in A results in an increase in B, or a fall in A causes a fall in B, this is a positive relationship, as shown below: When an increase in A results in a fall in B, or a fall in A causes an increase in B, this is a negative relationship, which is expressed as follows: Feedback What is a loop? A closed chain of relationships is called a loop, or a feedback loop. When we turn on the tap to fill a glass with water, the amount of water in the glass increases. The amount of water in the glass, however, also has an effect on the speed at which it is filled. We fill it more slowly when it is fuller. Therefore, a loop exists. The system formed by us, the tap and the glass is a negative loop, because it is designed to achieve a goal (fill the glass without spilling). Negative loops act as stabilising elements in systems designed to reach a given goal, like when a thermostat in a heating system guides the temperature towards the level specified by the user. When we construct a model, loops appear. For example, those formed by ABEDA, DBED and ABECA in the following causal diagram. Loops are defined as „positive‟ when the number of negative relationships is even. If the number of negative relationships is odd, the loop is „negative‟ (just as -3 multiplied by +3 gives -9). Negative loops tend to stabilise the model, while positive loops tend to destabilise it, independently of the basic problem at hand. Positive loops Negative loops Time Time Real-life systems contain both types of loops, and the ultimate behaviour will depend on the dominant type at any particular moment. When a country acquires more arms it makes its neighbours feel threatened, and causes them too to acquire more arms. This is a positive loop, also called a vicious circle, and grows more and more as it feeds itself. Positive loops cause growth, evolution, and also the collapse of systems. Naturally, socioeconomic and ecological systems are made up of hundreds of interconnected positive and negative loops, and its ultimate behaviour isn‟t obvious. The concept of the loop is very useful, because it enables us to start from the structure of the system that we are analysing and work towards its dynamic behaviour. If a system fluctuates persistently, or remains in equilibrium, or drops off rapidly, we can identify the structural reasons and decide how to go about modifying the causal loops that are going to influence it. This procedure can be applied to anything from the control of an industrial process to the monitoring of diabetes or cancer, fluctuations in the price of raw materials, or economic growth. Yet the most important use of this concept is in understanding how the structure of systems affects their behaviour. In the same market and in the same year, various firms that offer the same product present very different economic results. The less competent managers put this down to causes beyond their control – the cost of labour, competitors, customers‟ habits, and so on – when in fact they should study why the systems they control (their businesses) have a less competitive structure than those that show better results. Country A perceives that the arms race was caused by country B and vice versa. But in reality we can also say that country A has caused its own rearmament by acquiring arms, as this causes the rearmament of country B. Similarly, the rise in oil prices is due both to the concentration of production in a few countries and to excessive consumption in developed countries of a product that‟s limited, inasmuch as it isn‟t renewable. Identifying the cause of a problem as being something that‟s not external to the system doesn‟t tend to be very popular, as it‟s easier to blame external factors beyond our control. The trouble is, if the exponents of the argument of the external cause really believe what they‟re saying, they‟ll be unable to identify the true cause of the problem – inside the system – and obtain the desired results. If the system contains the elements that cause the problem, it also has those that can be used to solve it. For example, a product‟s life curve can be said to be regulated initially by a positive loop that permits rapid exponential growth, followed by a steady state dominated by a negative loop involving the saturation of the market, and finally a usually sudden drop caused by the appearance of fast-growing substitute products. Lastly, note that the causal diagram is very important for explaining the final model to the user, if he or she isn‟t familiar with this technique, as is usually the case. The Limiting Factor The limiting factor is the element of the system that is limiting the growth of the system at this moment in time. There‟s only one limiting factor at any given moment, although over a period various different elements of the system can act as limiting factors. Maize can‟t grow without phosphates, no matter how much nitrogen we add to the soil. Although this fact is quite elementary, it‟s often ignored. Agronomists assume that they know how the soil should be fertilised as they know the 20 main substances that plants require for nutrition… but how many elements are they unaware of? Attention is often focused on the more voluminous substances, but seldom on the truly important parameter: the limiting factor. In order to understand reality we have to appreciate not only that the limiting factor is essential, but furthermore that changes also modify the elements that make up the system. The relationship between a growing plant and the soil, or between economic growth and the resources that sustain it, is dynamic and constantly changing. When a factor ceases to be limiting, growth occurs and the proportion between the factors changes until one of them becomes the limiting factor. If we can direct our attention towards the next limiting factor we can advance towards real understanding and efficient control over the evolution of systems. The limiting factor is dynamic; in the growth of a plant, today it might be lack of water, whereas tomorrow this might be solved and the limiting factor might be lack of nutrients, and so on. There‟s never more than one limiting factor. The Key Factors Also called leverage points, as it‟s here that pressure or influence is exerted. A system includes several key factors, and they tend not to vary over time. We can use them to bring about major changes in the system with minimum effort. They can unleash violent behaviour in the system. Each system has a number of key factors, and they‟re neither obvious nor easy to identify. A normal person‟s key factors will be related to their health, the family and (hopefully) their education. They‟re the driving force behind their acts in their daily life. We also have to take into account that these key factors can unleash violent behaviour. Sometimes people tolerate all kinds of humiliation, publicly and privately alike, yet a derogatory remark about their parents can be fatal. This is, then, a key factor. Key factors can be physical (we can stick a finger in somebody‟s ear without making them unduly angry, but not in their eye) or psychological (a minor accident in a car can make some people react extremely violently). In order to attain a goal, huge efforts are sometimes made in the wrong direction. This is especially true in the personal, social, business and ecological fields. In an attempt to avoid this, Jay Forrester proposed a set of guidelines for the business world that can easily be extrapolated to other areas. 1) Whatever the problem is that has arisen, it‟s necessary to know the inner workings of the system, how it takes its decisions, how it operates. Don‟t be led astray by indications that point towards momentary or superficial factors, however visible they may be. 2) Often a small change in one or very few policies can solve the problem easily and definitively. 3) The key factors tend to be ruled out, or judged to be unrelated to the problem at hand. They‟re rarely an object of attention or discussion, and when they‟re identified nobody can believe that they‟re related to the problem. 4) If somebody happens to have already identified a key factor, it‟s not unusual for action to have been taken in the wrong direction, thus seriously magnifying the problem. Models enables us to conduct sensitivity studies and see which of the system‟s elements can have a decisive bearing on its behaviour; in other words, they enable us to identify the key factors. However, that doesn‟t mean we can‟t advance without their help. The peculiarity of these key factors is that they are located in unexpected points or aspects that provoke counter-productive actions. This is difficult to illustrate with a causal diagram. The phenomenon seems to be attributable to the difficulty of interpreting the behaviour of a system that‟s already defined, rather than to any specific structure, as the effect of the interrelationships is beyond our capacity for analysis (for me this means that the system has more than four loops). This inability to perceive and interpret the nature of the system and the identity of its key factors makes for counter-intuitive behaviour by the system, with the result that our actions are in the wrong direction. Let‟s take a look at some examples. a) A car engine manufacturing firm suffered a constant loss of market share. Every four years there was a major loss of customers who seldom came back afterwards. According to the firm‟s analyses, the problem lay in their policy on stocks of finished products. The company was reluctant to keep a large number of engines in stock waiting for orders to arrive, due to the high financial cost. The policy was to keep stocks of finished products low. This policy saved a great deal of money. But whenever there was an upturn in the economic cycle, the firm was overwhelmed with orders that they were forced to attend to with long delays. The customers then went to the competitors, who supplied the engines more quickly. The firm responded to the loss of sales with a programme of cost-cutting measures, including further reductions in stocks of finished products. b) Dairy farms are steadily disappearing. Measures are proposed to combat this, including tax cuts, soft loans and subsidies. There‟s plenty of incentive for anybody wanting to start up a small farm. However, the main reason why farms close is expansion. Farmers try to increase their income by producing more milk. When all the farmers do the same the market is flooded with milk and prices fall (as there‟s no intervention or guaranteed price; if there were, the burden would be shifted to the external factor). When the prices have dropped, each farmer has to produce more milk in order to maintain his or her earnings! Some manage to do so and others don‟t, and of the latter those that are in the weakest position give up farming. c) One of the key factors in any economy is the useful life of the installed capital. The best way to encourage the sustained growth of the economy is to stretch this useful life as long as possible. Yet the policy that‟s practised is one of accelerated obsolescence, or priority is given to replacing existing equipment with machinery designed to provide shortterm economic growth. d) The right way to revitalise the economy of a city and ease the problem of depressed areas occupied by people without economic resources isn‟t to build more subsidised housing. The solution is to demolish the abandoned factories and houses, and create space to set up new businesses, thus allowing the balance between jobs and population to restore itself. Ideally, we‟d have a set of simple rules to find the key factors and know which direction to act in. It‟s not always possible to find these points by simply observing the system, and this is where computer simulation models really come into their own. Classification of Systems Stable and Unstable Systems A system is stable when it consists of or is dominated by a negative loop, and is unstable when the loop is positive. That is, when the dominant loop contains an odd number of negative relationships, we have a negative loop, and the system will be stable. The basic structure of stable systems is as follows: Here we can see that the system has a “desired state” and a “real state”; these two states are compared (“difference”), and on the basis of this value the system takes “action” to move the “real state” towards the “desired state”. In this case the initial parameters are of relatively little importance, since the system will act according to the environmental conditions it encounters, so if it‟s hungry it‟ll look for food, and when it finds it, it‟ll deal with its next objective, and so on. It‟s important to note that in stable systems the structure that generates the behaviour is always the same: there‟s an odd number of negative relationships, and the loop is negative. This means that the system permanently compares its real state with the desired state, and when there‟s a difference, it takes action to bring its real state closer to the desired one. Once these two states coincide, any change in the real state will result in action (proportional to the difference) to regain the desired state. This is how we usually find systems. By the time we get close to them, they are in a position of stability. If a system is unstable we‟re unlikely to be able to study it, as it will have disintegrated before we can analyse it. However, if we‟re designing a totally new system, we should take the trouble to find out whether it‟s going to be a stable one. And if we‟re designing a change in a stable system, we have to ensure that we‟re not changing it into an unstable one. Examples of systems that are not in an optimum situation but carry on over the years – i.e., stable systems – can be found in many fields: government, workers and bosses together produce the inflation that‟s harmful to all. Rich countries and poor countries trade with raw materials, each with a different political and economic objective, and the result is permanent price instability. Let‟s suppose that the government intervenes in the system with a particular policy that puts the state of the system where it wants it. This will cause major discrepancies between the other elements of the system, which will intensify their efforts until, if they succeed, the system is back very close to the initial position, after each element has made a huge effort. For example, think of the work that‟s gone into improving the traffic in Barcelona over the last 10 years; the traffic improved for a few years after the opening of the Ring Roads, but now we‟re faced with the same problems as before – except that they affect many more cars. The most effective way of combating the natural resistance of the system is to persuade each element to change its objectives, in the direction in which we want to lead the system. Then the efforts of all the elements will be directed towards the same goal and the effort will be minimum for all, as they won‟t have to resist the tide going the other way. When this can be achieved the results are spectacular. The commonest examples of this are the mobilisation of the economy in wartime and the recovery after wars or natural disasters. A less warlike example can be found in the birth rate policy in Sweden in the 1930s, when the birth rate fell below the rate of natural replacement. The government made a careful assessment of its objectives and those of the population, and found that an agreement could be reached on the basis of the principle that the important thing isn‟t the size of the population but its quality. Every child should be wanted and loved, preferably in a strong, stable family, and have access to excellent education and health care. The Swedish government and citizens agreed on this philosophy. The policies that were introduced included contraceptives and abortion, education on sex and the family, unhindered divorce, free gynaecological care, aid for families with children in the form of toys, clothes, etc., rather than cash, and increased spending on education and health. Some of these policies seemed strange in a country with such a low birth rate, yet they were introduced, and since then the birth rate has risen, fallen and risen again. Some systems lack feedback, and the models we build must show the fact. For instance, if we know the initial parameters of a clam (type, weight, etc.) and we control the environmental conditions in which it will live, we can safely predict its weight after 6 months. There‟s a “transfer function” between the start and end values, and we have to find it, but that‟s all. Other examples: God is someone who gets his real state to coincide with his desired state instantly. Suicide is the response of those who perceive that they will never get their real state to coincide with their desired state, and that therefore all action is pointless. Please note: The more intelligent a system is (i.e., the clearer its vision of its objectives), the more stable it will be. This is applicable to people. Hyperstable Systems When a system consists of several negative loops, any action taken to modify one of its elements is offset not only by the loop in which that element is located but also by the whole set of negative loops, which act to support it, thus superstabilising the system. An analysis of the system can be helpful. Any complex system, whether social or ecological, is made up of hundreds of elements. Each element is only linked to a limited number of variables that are important to it, and which it permanently compares with its objectives. If there‟s a discrepancy between the state of these variables and its objectives, the element acts in a particular way to modify the system. The greater the discrepancy, the more energetic the action taken by the element on the system. The combined action of all the elements that attempt to fit the system to their objectives leads the system to a position that none of the elements actually wants, but in which all of them find the smallest gap between the parameters that are meaningful for them on the one hand and their objectives on the other. Why do many problems persist despite continual efforts to solve them? As we‟ve just seen, systems base their stability on the actions of all its elements in pursuit of different objectives, trying to get the rest of the system as close as possible to its desired position. From this moment on, if an element of the system or an external agent attempt to modify its stability, the other elements will take action to go back to the initial situation, thus neutralising the action that altered its stability. So the answer‟s simple: systems resist any change we try to introduce because its present configuration is the result of many previous attempts like ours (unsuccessful ones, otherwise the system would be different today) and an internal structure that renders it stable and capable of neutralising changes in its surroundings, such as the one we made with our action. The system achieves this as a whole, by rapidly adjusting the internal relationships between its elements in such a way that each continues to pursue its own goal, and together they neutralise the action exerted on them from outside. Oscillating Systems We will see later, in the case studies, that for a system to display oscillating behaviour it has to have at least two stocks, which are elements of the system that produce accumulations. Sigmoidal Systems These are systems containing a positive loop that acts as the dominant feature at the beginning, causing the system to undergo an exponential take-off. Subsequently, control of the system is taken over by a negative loop that cancels out the affects of the earlier positive one and provides the system with stability, setting it to a particular value asymptotically. It‟s important to keep sight of the fact that in this case we‟re dealing with the same system all the time, dominated by one part of it in one period, and by a different part later on. So in order to regulate its behaviour, we‟ll have to find a way to play up or down the part of the system we‟re interested in. We also have to be aware that in the mid-term the negative loop will stabilise the system at its target value. All we can do is regulate the time scale and the way in which the system reaches its objective. Generic Structures In complex systems, we can observe the same structure: desired state - real state difference - action, over and over again in very different contexts. On top of this base structure, generic structures have been identified that tend to appear regardless of the object of study. There‟s always the same “intelligent” structure that seeks to bring the real state closer to the desired state. Resistance to Change When new managers joins a firm, usually with new objectives, they often find that its employees put up resistance to everything they propose: “they already tried that, that won‟t work here, our customers like it the way it‟s always been, that proposal is very risky...” In short, the company acts as a system that has managed to survive innumerable economic crises in the past, and as a structure is capable of neutralising any change, whether from inside or outside, due to the multiple relationships between its members. Each pursues a different objective, yet as a whole they have succeeded in endowing the firm with stability, although that doesn‟t mean its position is necessarily the most efficient. For this reason it‟s often wise for new managers to seek the commitment of the general manager for their new objectives as a way of achieving a certain amount of strength and aligning the other elements in the company towards these objectives. Many systems are not only resistant to new policies designed to improve their state (greater productivity, lower costs, etc.) but also show a persistent tendency to worsen, despite the efforts to improve the situation. Examples abound in the business world: productivity, market share, quality of service, etc. And on a personal level, we all know somebody with a tendency towards obesity in spite of repeated diets. Erosion of Objectives The action required to shift the real state towards the desired state always demands an effort. And this effort in turn requires a consumption of time, energy, money, etc. It‟s normal for the real state to “contaminate” the desired state, that is, for the system to try and avoid the consumption of energy required to take the action. The desired state is initially reconsidered, since if it coincided with the real state no action would be necessary. The diagram below shows this “contamination”. If contamination occurs, the desired state is modified until it‟s the same as the real state. The difference is then zero and therefore there‟s no action to be taken. And so the real state of the system doesn‟t change. There are only two ways of avoiding this process: 1.- Find a “hero” system. That is, convince the system that it doesn‟t matter how much effort is required to reach the desired state, it just has to be reached. (Personally, I can assure the student that this way of avoiding the contamination process doesn‟t tend to get results in the 21st century.) 2.- Get an “external element” to serve as a reference or anchor for the desired state, so that it can‟t be altered by pressure exerted on the system, and so that the system has no capacity to alter the “external element”. In Spain, when secondary school students consider the possibility of carrying on studying at a public university, they already have a fairly accurate idea of the minimum mark they have to get in their schoolwork and the entrance exams. Their desired state is that minimum entrance mark. It‟s not negotiable. Their real state tends to be a lower mark than the entrance mark in the early years of secondary education, so they perceive a difference, which leads them to take action (studying harder) in order to get their real state to match the desired state. If students know what it is they want to study, their family don‟t need to push them at all. The system isn‟t contaminated because the desired state (the minimum entrance mark) isn‟t alterable. Later, when they start at university, if you ask they‟ll say they want to be a great professional, and that they‟re going to get an average mark of 10 in their degree. With the first exams come the first fails, which make them: 1) study harder than they‟d anticipated, and 2) tone down (contaminate) their desired state, from the desired 10 to the nonnegotiable minimum of 5. The structure that brings about this behaviour is based on the idea that the system includes a particular objective (e.g., desired weight) that is compared with reality (real weight), and the discrepancy between these two values triggers an action, which is proportional to the size of the gap. This is the usual pattern, seen up to now as a negative loop that tends to gradually pull the system towards its objective if it encounters some discrepancy. However, sometimes the state of the system can condition or modify the desired state; either because the real state is very long-lasting, or because the action taken involves a great effort, or indeed for some other reason, the initial goal shifts towards the real state of the system. This relieves the need to take action, as the discrepancy has been reduced, not because the system has approached the objective, but because the objective has approached the real state. As a result, the action taken is smaller. In the case of the weight of obese people, this occurs when they accept that the target weight was too ambitious, and that a more realistic target (a higher weight) is better. This argument serves as an excuse to follow a less strict diet. When they see their weight doesn‟t fall, they reconsider the ultimate target once again… and so on until they think that actually their real weight is best, at which point they don‟t have to follow any sort of diet (this would have involved a sacrifice). There are plenty of examples of this pathology in environmental pollution, law and order, traffic accidents, etc. In all of them, a poor performance becomes the standard in the face of the effort required to do something effective. A system that bases its objectives on reality and intends no more than to improve on it is permanently drawn towards poor results. A system that gets its targets from outside itself is immune to this type of process. It may seem paradoxical, but if a student is convinced that he must pass all his subjects in July because his father has imposed it as an immovable objective, for whatever family reasons, it‟ll be easier for him than if he himself had made that decision. If it‟s a personal decision it can be reconsidered when some of the subjects prove to be too difficult. He can accept to leave one or two for next time, which means less studying. However, if the objective is non-negotiable, this risk doesn‟t arise, and he has to study as hard as necessary to reach the objective. Economics provides any number of examples. In Spain nobody remembers such low rates of inflation as we have now. Any government would be satisfied, and would be happy to give up on reducing inflation further, as that would mean taking very unpopular measures (a wage freeze for civil servants). If the target for inflation were in the hands of the government, corrective measures would have been less strict in the past and the present, since they would have meant less public spending and therefore lost votes. However, the target for inflation was imposed as a condition for entering the euro zone and as such was beyond the control of the government, who pulled out all the stops and took all the unpopular measures they deemed necessary, because there was a fixed goal with a deadline, and it was non-negotiable. The obvious antidote to this pathology is to fix absolute objectives for the system, that aren‟t based either on the past or the present situation, and take corrective measures depending on the difference. An absolute objective loses credibility if it is raised or lowered, and it won‟t get it back. We see this sometimes when an objective is raised because the initial objective has been reached; when this happens everybody expects the initial objective to be changed again (but this time downwards) when the results are lower than the initial objective. Addiction Sometimes the real state of the system matches the desired state not as the result of action but due to support from outside the system. This support may or may not be permanent, and may or may not be disinterested, but the net effect is to bring the real state into line with the desired state, resulting in zero difference, and therefore action by the system is now unnecessary. This phenomenon occurs when there‟s an objective that serves as a point of comparison with the state of the system. On the basis of the discrepancy observed, corrective measures are taken proportionally, but in this case the action taken doesn‟t serve to bring the system‟s real state closer to its desired one but rather to create the perception that the real system is close to the desired one, whereas in fact this action has no such effect. The lack of clear perception of the real system leads to a situation in which the necessary corrective measures aren‟t taken, because the state of the system is perceived as being closer to the objective than it really is. When the immediate or short-term effect of the action disappears, the problem (i.e., the discrepancy between the real state and the desired one) reappears, often with greater intensity, so the system reapplies some measure that appears to solve the problem whenever the effect of the previous measure starts to fade. Alcohol, nicotine and caffeine are obvious examples of addictive substances. Another case that springs to mind is the use of pesticides, which eliminate, together with the pest in question, the natural control mechanisms. As a result, the pest will reappear as soon as the effect of the pesticide abates, but this time without any natural control. In cases of addictive systems it‟s difficult to find suitable policies, since the action taken offers apparent results in the short run, but once the process is rolling it‟s difficult to stop. Obviously, the best approach is to be aware of these types of processes, in other words, to be wary of using measures that attack the symptoms but make the system worse when they are relaxed. Once the addictive process has been started up you have to expect at least short-term difficulties if you plan to stop this process, be it physical pain for somebody who takes an addictive drug, rising petrol prices on inclusion of the associated environmental costs, or more pests and lower-quality food until such time as natural predators return. Sometimes it‟s advisable to wean yourself off an addiction gradually. But it‟s always less costly to avoid the addictive process in the first place than to stop it later. Shifting the Burden to the External Factor As they get older and spend more and more time reading, some people gradually get poorer eyesight. In the end they can‟t read what‟s written on a blackboard, and can‟t renew their driving licence. So they get glasses or contact lenses. Then in one year their eyesight worsens as much as it did in the previous 30 years. So their glasses become a necessity not only to see at a distance but also to read a document of any sort. Apparently this happens because for years the muscles around their eyes have been straining to compensate their poor vision, and when this effort is no longer necessary they cease to act and end up losing this ability totally. And before long they need stronger lenses. This is a classic example of shifting the burden to the external factor. In this sort of system an external force keeps the system in the desired situation. A well-intentioned, benevolent and very effective force decides to help us to get the system where we want it. This new mechanism works very well. But with this process, through the active destruction of the impediments that redirected the system towards the desired position, or simply through atrophy, the original forces that worked to correct the position of the system are weakened. When the system moves away from the desired position the external factor makes an extra effort, which weakens the original forces still further. In the end the original system adopts a position of total dependence on the external factor, as its original corrective forces have disappeared completely and in most cases irreversibly. It‟s easy and fun to find other examples of shifting the burden to the external factor. Here‟s the start of a possible list. Seeking the aid of an external factor to get the system where we want it to be isn‟t in itself a bad thing. Usually it‟s beneficial, and enables the system to tackle better objectives. Yet the dynamics of the system can be problematical, for two reasons. Firstly, the external factor that intervenes doesn‟t tend to perceive the consequences of its help on the elements of the system, particularly on those that performed the same task as itself. Secondly, the community that‟s helped today doesn‟t stop to think that this help is temporary; they lose their long-term perspective and so become more vulnerable and dependent on the external factor. The withdrawal of aid from a system that‟s being helped, whether it‟s the human body, a particular area of ecological value or a human community, doesn‟t tend to be easy and is often simply impossible. This process of withdrawing help without harming the system must be based on identifying the internal elements of the system that in their original state took care of correcting the problem, strengthening these mechanisms and, as they begin to do their job, gradually withdrawing the help. Short and Long-Term eEfects A rational analysis of the problem at hand based on our capacity for synthesis and our ability to imagine things seems to be a bad guide to find the key factors. We generally pay attention to the components of the system and their behaviour in the short term, all on the basis of incomplete information. Consequently, firms reduce their stocks of finished products when sales are seen to slump, the government extends its tax reductions for small farmers, and policies are introduced to encourage firms to replace their machinery instead of maintaining it properly. They‟re all very reasonable policies. But there‟s still something inside us that just might make us realise that our customers‟ dissatisfaction with our long delivery schedules, or farmers‟ permanent concern with increasing their output, or the idea of replacing a machine that‟s productive… all means something, but we haven‟t given it the right interpretation. Finally, I‟d like to say that in my opinion we have the capacity to understand not only simple systems but complex ones too, and to find the key factors. What we don‟t appear to have is the capacity to articulate the arguments to convince others or even ourselves that what we‟re perceiving is right. We expect the solution to be closely related to the symptom; we expect long-term profit to start with short-term profit, or a strategy that‟s satisfactory for all the agents involved. Yet we know complex systems don‟t behave that way. So something inside us still insists somehow that maybe that simple, effective solution isn‟t the best. And then we carry on proposing policies that can‟t work, denying ourselves other simpler and more effective ones that could. We try to compete instead of cooperating, and we try to reach the limits of the environment‟s capacity instead of admitting that we‟ve already gone too far. The results are famine, war, pollution and depression. And right in front of us, within reach of our capacity for understanding, stand balance between countries, peace, equality and sustainable development. Control Questionnaire After reading this paragraph, it is advisable that the reader answer the following questionnaire to look at what is understood and what must be reviewed before continuing. a. Give some examples of SYSTEM. Remember the definition of system as a set of interrelated elements such that one element affects the behaviour of the whole set. For example: a city. a.1. Name some ELEMENTS OF THE SYSTEM. For example: persons, cars, pollution, streets, etc. Incorrect elements would be: the government, the city, Barcelona, colour, asphalt, etc. They are valid as elements of the system if we can notice when the element increases or decreases, improves or worsens, etc. a.2. Name the UNITS OF THE ELEMENTS. For example: Persons: number of persons, Pollution: nº of particles in suspension/m3, Streets: m2. b. Give a system example that has JUST ONE GOAL, indicating the goal. For example: a mower; goal: to cut grass. c. Give a system example that has SEVERAL GOALS. For example: a company, where the businessperson has the following objectives: the most profit, increase the number of clients and increase product quality. d. Give a system example that has GOAL EROSION. Indicate some ways to avoid contamination (in other words, erosion) of the goal by the real situation by securing it to an external element. For example: students usually have an erosion of their initial goal of getting excellent grades. In this case, an external element that can prevent this erosion is the grades of a „rival‟ student. e. Give a system example that shows RESISTENCE TO CHANGE. For example: We prefer to wear our old shoes because they are more comfortable than new ones. f. Give a system example that shows ADDICTION to an external aid. Indicate how you believe you should adjust your activity, so that you are not completely dependent on this addiction. For example: a smoker. g. Give a system example with a goal, a corrective action that brings that goal nearer and some LONG-TERM EFFECTS that are the result of the action that have the opposite effect of those observed in the short-term. For example: a person wants to have an imposing physical appearance (goal) and takes steroids to increase muscle mass (action). The long-term effects are coronary disease that makes it necessary to be bedridden for a long time resulting in the disappearance of the muscle mass. h. Give a system example indicating the LIMITING ELEMENT that prevents an action. For example: fire does not spread because there is no more wood left; the youngster does not study because there is no more paper; the car will stop when it runs out of petrol. i. Give system examples and some of its KEY ELEMENTS. For example: the amount of salt in food is a key factor for it to be edible since, if we put too much salt, no one will be able to eat the food.