Welcome to Week 1 on tools for systems analysis

The below text gives an introduction to systems thinking. During WEEK 1 and in subsequent weeks, you will get a deeper understanding of this text. So, if you do not immediately understand everything, think of it as a teaser. As you make progress in the course, go back to this page and it will have become more meaningful to you. Maybe use it as a summary.

A good systems analysis is problem oriented. It is successful if it points to a policy that solves the problem you are interested in. Using systems analysis is motivated by complexity. However, the result of the analysis should be simplicity. This is because complexity slows down learning and diffusion of ideas. Still, systems thinking should strive to be holistic, where “everything influences everything”. However, this does not mean that every detail should be represented – important or not important. Systems thinking should focus on what is important to understand a problem, to find policies that help reduce the problem, and to address what stakeholders care about. The ultimate goal of the analysis is not to understand everything, rather it is to solve current problems and prevent future problems.

To solve complex problems, one has to consider cause and effect relationships and understand how cause and effect relationships create problem behaviors over time. With no causes and no effects, system parts cannot interact and influence each other. For instance, with no cause and effect relationships, a declining fish population would have no effect on catch, and greenhouse gases would not cause higher global temperatures. This is not a radical new idea. For instance, all decision-makers believe firmly that their own decisions and actions will cause effects. If not, it would be absurd to go on making decisions and taking actions. Therefore, decision-makers should also be open to the possibility that people and parts of nature that are affect by their own decisions, in turn will affect the decision-makers themselves. In other words, decision-makers operate in feedback loops.

According to Gary and Wood (2016), most human judgements involve reasoning about cause–effect relations. However, decision-makers often ignore and simplify complexity beyond recognition. Typically they think in short causal chains and ignore the existence of feedback loops. They also tend to assume that each effect has a single cause, typically the most salient one. Hence, we all operate with mental models of reality that range from imprecise to dangerously wrong.

It is sometimes difficult to identify and quantify cause-and-effect relationships. For instance, correlation is at times mistaken for causality. For instance, even though sales of ice cream and murder rates are correlated and show the same cyclical pattern over seasons, there is no known reason to think that consumption of ice cream causes murders, or that murders are followed by eating a lot of ice cream. Hence, governments do not try to prevent murders by banning ice cream, nor do they put people in jail for eating ice cream. Also note that correlations do not say anything about the direction of causality.

Effects can be both instantaneous and accumulating. For example, when opening a faucet, this will have an instantaneous (immediate) effect on the flow of water. However, the glass will only fill gradually as the water flows into the glass. The water accumulates in the glass and this process takes time. Therefore it seems obvious that it is important to know what type of cause and effect relationship one is dealing with. For instance, emissions of greenhouse gases accumulate in the atmosphere. Erroneously assuming that the relationship is instantaneous, one may come to believe that by stopping all emissions, all the greenhouse gases that have accumulated in the atmosphere would miraculously disappear.

Feedback loops drive the behavior of systems. There are two types of feedback loops. Imagine an island with rabbits. The rabbits produce baby rabbits, which grow up, and produce even more baby rabbits, which grow up, and so on and so forth. With no external influences, the rabbit system produces population growth by itself. Population growth is explained by a reinforcing feedback loop where mature rabbits cause babies, which in turn cause more mature rabbits.

The other type of loop is balancing. For instance, people simplify decision-making by relying on feedback. When filling a glass of water, we first observe the empty glass. This causes us to open the faucet. As the amount of water in the glass approaches our desired amount, we close the faucet. In other words, because of the decision-rule we apply, the amount of water in the glass causes the faucet opening, which in turn causes the amount of water in the glass. This is a balancing feedback loop, which works to create a balance between the desired amount and the actual amount of water in the glass.

Can you think of any decision that is not part of a feedback loop?

Nonlinearities can totally change what parts of a system are most important for the system behavior. Again consider the rabbits on the island. As long as there is plenty of food, the reinforcing feedback loop dominates and allows the population to grow fast. As the amount of food per rabbit starts to decline, lack of food takes over and becomes the most important part of the system. The effect of the food shortage becomes more and more important the less food is available, the relationship is not linear (like a straight line). The behavior of the system changes from rapid population growth to stagnation or even decline. In other words, nonlinearities can shift the dominance from one part of the system to another part. This means that appropriate systems thinking must consider nonlinearities. Ignoring that nonlinearities can change the nature of the system, leads to many of the sustainability problems we discuss in this course.

Finally, where do systems come from? Natural biological systems evolve through competition between species. At any point in time, the composition of species in a natural system reflects the outcome of previous interactions between the species (system parts) over historical time periods. Similarly, social systems evolve over time through the interactions between different people. New institutions, technologies, and cultures reflect people’s goals, beliefs, and understanding of the systems they live in. Steam engines made it easier to mine coal, which in turn made it cheaper to use steam engines to mine coal. This type of reinforcing feedback loop can explain much of the industrial and cultural evolution. We hardly ever think carefully about these processes that led to the society we live in today. When we want to change the direction in which society develops, we must consider the cause and effect relationships that brought us to where we are today. These relationships may still be in operation and counteract desired changes.

Systems and their behavior can be observed at three levels: events, behavior over time, and system structure. The demonstration of the first working steam engine was an historical event. Behind this event was a long history where ideas were developed and tested. This behavior over time did not make big headlines, however the underlying accumulation of knowledge could have been observed and described. Finally, there was a system structure that caused the behavior over time. There were perceived needs for more power than horses could provide, there was an accumulation of knowledge and tools to perform laboratory experiments, there were economic means for some people to devote all their time to carry out experiments, and there were desires to succeed in society. The relationships between these system parts (the structure) gave rise to a development over time (behavior), that one day became an historical event.

All these characteristics of systems can be dealt with by the use of System Dynamics models. Systems thinking will make you aware of the nature of systems and of opportunities and pitfalls you need to be aware of. However, models and simulations are needed when problems are complex. In this course you will not learn to build models. However, you will learn about how models work and can be used to simulate behavior over time. In other words, simulations are used to find the exact behavior over time that the hypothesized cause and effect relationships lead to. The cases that you will study are of great importance for sustainable development and for how to reach the United Nations’ Sustainable Development Goals.

Reference:

M.S. Gary and R.E. Wood: “Unpacking mental models through laboratory experiment”, System Dynamics Review, , 32(2), 101-129 (2016).