B3. Complex Systems

complex systems The word ‘complex’ appears in the definition to underline that we are talking about systems from the understanding of non-linear, infinitely complex entities, almost like biological entities, and not from a predictable, finite, mechanistic understanding of systems. Let’s look at that further, and why it is important. We differentiate between ‘complex’ systems and ‘non-complex’ systems. These terms are analogous with terms used in various fields such as respectively ‘nonlinear’ and ‘linear’, and “complex” vs. “complicated”. Non-complex systems are systems of which the objects and relations can be fully indexed and under­ stood, they are finite in their composition, and in some cases, can be fully modeled using physics, mathematics, or other science tools. They’re called non-complex, rather than ‘simple’’, because non-complex systems can still be very complicated and far from simple. For example, the electrical system of a house, is a non-complex system. You can model it using mechanical system analysis and predict its behavior. But it can still be pretty complicated to figure it all out. Working with these non-complex systems has been the prevalent mode of systems analysis, design, and innovation for the last century. We know how to accurately model them, and what kind of behavior they can exhibit, which can mostly be explained from a mechanical perspective. Complex systems, however, are an entirely different ballgame. A complex system is a system which consists of a number of objects and relations so numerous that we can’t keep track of all of them. Complex systems are also called chaotic systems or non-linear systems (to further confuse it all). Complex systems exhibit behavior that cannot be predicted using normal mechanical (non-complex) systems behavior or computer simulation. Hence, the ‘non-linear’ name. An easy example is the weather. Nobody can accurately predict the weather more than a week into the future using mathematical models - there are too many factors involved, and the events that generate the governing future patterns may not yet have happened. As we try to predict further ahead, the complex­ ities multiply so fast, any attempt at doing so grinds to a halt. Some complex systems may appear like a non-complex system, or have been treated as such in the past. This is a dangerous move and continues to cause issues in society. Ever had a bad weather forecast ruin a party? This also happens to our economy, for example, by intro­ ducing policy measures that are well-intended but do not take into account the complexity of the system. One can blame the poor policy performance on ‘unexpected’ events, but really, only the expected is unexpected in complex systems. An economic policy that does not account for complex dynamics is simply not resilient, which means it was plain bad policy. Thinking you can predict a complex system is a thinking error. Just as our brains are more than a sum of molecules, so are complex systems more than the sum of their parts. They tend to behave less like machines and more like creatures displaying ‘emergent’ behavior. Acting on one aspect of a system in isolation will always have side effects on the rest of the system. Expecting complex systems to respond like machines is a one way road to disaster. In this course, we focus on complex systems over non-complex ones, because we feel they are the determinants of the future of our world. In sustainability literature, non-complex systems modeling is often used to explain certain economic or social patterns. While useful as an exercise, and to use for insight, we should exercise caution when encountering this sort of argumentation. It’s tempting to want to simplify complex systems to try to understand their behavior in mechanical terms, in order to move forward, but it’s also dangerous. It is in the quality of ‘complexity’ that systems do their special thing. Because of this importance, we find the word ‘complex’ essential in SiD’s sustainability definition. Even though complex systems can’t be predicted, they can be studied, learned from, their behaviors analyzed and intervened on in order to make them, for example, more resilient. We’ll get into more detail about this later on. To help get a feel for what a complex system is and isn’t, we’ve assembled 12 general rules that complex systems typically comply to on the next page. We go further into system dynamics and complex systems further on in week 4.