Life And Other Errors: Complex Are Just The Others

Life And Other Errors: Complex Are Just The Others

We are looking for monocausal, linear relationships in a dynamic, multicausal, non-linear world. If we knock the ball with a certain force from the edge of the table, it will fall to the ground at a precisely calculable speed. From A follows B – quite naturally. Our brain loves such obvious relationships. They enable us to find our way through the world and understand what is happening around us.
If, on the other hand, we knock the same ball off the edge of the table and suddenly face the problem of picking up three bouncing balls from the floor, we will involuntarily wonder where the other two balls come from. Has anyone taken the liberty of joking with us and, unnoticed, thrown two more balls in between? Or was it a rare subspecies of the standard ball type that splits into several smaller balls on contact with the ground? From our limited perspective, the answer is very hard to come by – if at all.
That is, in a nutshell, the world we face every day. The two additional balls can symbolize any situation whose causes are not immediately apparent to us[1].
Note: In order not to be misunderstood at this point – of course in the vast majority of cases only a single ball will touch the ground when we knock down a single one. The analogy presented here is not to be understood as a rejection of classical physical interactions, but rather as a sharpening of awareness for the occurrence of unexpected events[2].
In our example, we naturally expect the combination of our push and the individual ball to cause exactly one ball to fall to the ground. The whole is therefore the sum of its parts.
The idea behind the terms multicausal and nonlinear deserves a closer look.
Multicausal means that a multitude of influences can lead to a specific result. So far, so simple. However, it is not unlikely that we cannot recognize some of these influences at all. This opacity, was described, for example, in 1850 by the French philosopher Frédéric Bastiat in his parable of the broken window as “That Which Is Seen, and That Which Is Not Seen”[3]. Many of the things we do often have unintended side-effects, of which we are not always aware. Conversely, just as many variables influence our lives which we do not consciously perceive, but which are nevertheless present.
Non-linearity can also be described by the word emergence. Many will have heard of the statement “the whole is more than the sum of its parts” (and not only because I just described the antagonism). One ball suddenly turns into three and nobody really knows how that actually happened. Out of 100 billion neurons in the brain, fascinating structures such as consciousness, the recipe for pizza and depression, emerge. Nevertheless, we still don’t know exactly how consciousness is actually created – only that it exists as an emergent property of all combined neurons.
Our brain therefore does not understand itself and has to cope with a world that is just as poorly understood. Neolithic brains in a digital world. Well, if these are not ideal conditions for complete despair.
At this point, however, one should not lose heart and throw in the towel. As opaque, random and confusing as life may often seem, there are promising ideas that can help us manoeuvre through this maze. I will introduce some of them in detail on the following chapters. But it is possible that not everyone will find what they are looking for. That’s ok. This is the multivariability of life. I don’t claim to offer the solution to the most pressing questions of humanity, but I do offer some inspirational approaches to overcoming the challenges of everyday life.
Speaking of life: I would like to begin by sharpening your awareness of a trivial but at the same time enormously significant relationship. Life is complexity.
It is very easy to declare this statement obvious and without further thought, to put it aside. Of course, each of us knows that life is a string of innumerable facets and that we are never aware of all influences. But do we really know that?
To be able to grasp this fact rationally on the one hand and to act on it at the same time is not automatically identical. Imagine eating in a restaurant and being treated very unfriendly by the waitress. What thoughts go through your mind? Is this person just an unpleasant fellow? An asshole? She can certainly forget the tip. Such thoughts are intuitive, often we don’t think long about why a person behaves in a certain way, especially when we are negatively affected. The reasons for this can be exceptionally diverse. Let us remember Bastiat’s “That Which Is Not Seen”. Perhaps the work of the waitress was preceded by an argument with a colleague or a partner and she was not yet able to deal with it emotionally in an appropriate way. Maybe another guest behaved in an invasive way because he misunderstood her professional hospitality as affection and now she expects a similar treatment if she appears friendly again.
As a matter of fact: we don’t know. It is always only a small excerpt from the lives of others that we perceive and most of the causes of their behaviour are hidden.
But it is not only in relation to strangers that this happens to us, we ourselves also face the daily challenge of mastering our lives on the basis of imperfect information. This statement may also appear very trivial at first glance, but our brain is a true magician when it comes to convincing us that we have more knowledge and therefore more control than we actually possess.
This is also to a large extent the reason for the attractiveness of the utopian promises of various ideologies of salvation or political theories. They wrap a complex world in handy, beautifully labeled boxes and provide exactly the answers we so long for. Knowing what is good or bad creates security and structure.
If we were more aware of the manifold ways and abbreviations the human mind takes to have as little effort as possible and to continuously surrender ourselves to the illusion of understanding the world, things might have been different in the history of humanity. Had. Perhaps. Possibly. The thinking in conjunctives is extremely luring for those who are not completely satisfied with their current life situation. But who is?
The human mind is driven by the need to recognize meaning in one’s own and others’ actions. We can hardly bear to find ourselves in situations whose causes are not apparent to us. Our mind searches for linear relationships in a complex, non-linear world. No wonder that so many regularly despair over it.
How do we find the balance between aspiration and reality?


Footnotes

[1] Boeing, G. (2016). “Visual Analysis of Nonlinear Dynamical Systems: Chaos, Fractals, Self-Similarity and the Limits of Prediction”. Systems. 4 (4): 37.

[2] Technically speaking, this example is somewhat shortened anyway, because it assumes linear proportionality. A change in the impact force causes a proportional change in the speed at which it moves towards the ground. However, not every linear change is necessarily proportional, even if every proportionality also implies linearity. However, this should not be a hindrance for the fundamental understanding.

[3] Bastiat, Frédéric (1850). That Which Is Seen, and That Which Is Not Seen

Life And Other Errors: An Introduction

Life And Other Errors: An Introduction

We never learned how to live. Out of an infinite nothing we are thrown completely unprepared into an existence that was impossible for us to choose. If we were to be given a say beforehand, it is not unlikely that we would fiercely resist such a life. Obviously, we do not always have a choice. Which is not without irony, as the perpetual mantra of Western industrial societies is that we only have to take our lives into our own hands to achieve everything we dreamed of. Being able to choose from a plethora of options is one of the great civilisational achievements of our affluent capitalist society. I really intend that as I write it now. We live in incredibly rich and privileged societies. Today, when we argue about extending equality or the recognition of multiple genders, it is a reminder that we are far from living in a perfect but very civilized society – an immense advance over the realities of past decades or even centuries. Recognizing this status quo as good and valid is important in order to understand the background to this work. For although we are fortunate to grow up in this privileged part of the world, every one of us faces a fundamental, overshadowing question: How does life actually work?
Despite all the quality of life we have gained, too few people still seem to have any idea of the basic building blocks of human existence. Otherwise it can hardly be explained that for many children in this country school starts still take place at biologically completely nonsensical times, while sleep researchers have been struggling for years to postpone the starting time in educational institutions. Or that the WHO now counts both obesity and opponents of vaccination among the greatest challenges to global health. The immense number of people with mental illnesses, their continuing stigmatization and a significant lack of access to treatment should also give sufficient cause for thought. But even when body and mind are in the prime of health, we often lack the knowledge to use both to our advantage.
We are thrown into the world and nobody tells us exactly what we should do now. Instead, we imitate our social role models, who often don’t know better themselves, move within a self-confirming cycle, get older, make the same mistakes as everyone else, and eventually die. Of course, this pattern does not apply to everyone to the same extent, but some aspects will be recognized by all of us. I am convinced that this cycle can not only be broken, but changed for the benefit of all.

While writing this work, I was accompanied by another central question: How do you become happy in a world you don’t understand?
Among the intellectual precursors of this idea for me are the works of the statistician Nassim Nicholas Taleb, the behavior economists Cass Sunstein and Richard Thaler as well as the psychologists Daniel Kahneman and Amos Tversky. Their work will be repeatedly reflected in the respective sections, as their ideas have served as valuable sources of inspiration for my own reflections.
The world around us poses a huge problem for our brains: on the one hand, we have an immense need to recognize causal relationships everywhere – a legacy of our evolutionary prehistory – on the other hand, the world is often too complex for us to actually see these relationships. Over many millennia of human history, however, such a need has been very beneficial. Those who quickly realized that it was better not to compete alone against a mammoth or that fire ensured that it remained warm even in winter, had an undeniable survival advantage. Just because causal connections are not always apparent doesn’t automatically mean that they don’t exist and that we can’t derive helpful insights about the world from them.
But with an increasing degree of civilization, technology, networking, globalization and all the other beautiful buzzwords that go with it, the demands placed on our brains are also increasing. There are now countless variables that can influence our lives, but only a few of them are actually known to us. Our need to understand and simultaneously control the world faces an immense challenge: we have to admit that we can neither understand nor control most things in life. This brave new world therefore requires alternative strategies that allow us to live within it in the best possible way, without perishing from its challenges.
This work is therefore a combination of strategic guidance, philosophy of life, empirical research and bad humour. In other words, ideal prerequisites for walking through the maze called life with a little more certainty.

Climate, Complexity & Randomness

Climate, Complexity & Randomness

After publishing my last article on the usefulness of climate models, I received some extensive criticism, noting that several definitions and wordings were somewhat blurred. Reason enough, therefore, to devote a little more attention to the topic of climate and complex systems.

Complex systems appear in various forms. Be it within engineering sciences to model complex cycles such as traffic flow, information and communication sciences, which deal with the flow and interaction of information within networks, or political and economic sciences and their respective fields of observation. And, of course, climate sciences are also part of this.
Complex systems are characterised by the fact that they consist of innumerable components that may interact with each other. The characteristics of these interactions include non-linearity (not necessarily B from A, proportionality is not given), adaptivity (the ability to respond to change), emergence (superior, new properties not found in the individual components), feedback loops, and a few others that are not too important for basic understanding[1].

Now these are a lot of buzzwords that can cause some confusion at first reading. For a better illustration an example shall serve.
Complex systems are often characterized by a long-term, inherent robustness against minor interferences. This means that even a large number of smaller problems do not lead to the destruction of the entire system (note, of course, that there is a certain limit and at some point each system will collapse, but more about this later). Just take the Internet. Even if individual providers have network issues, which will be very unpleasant for individual users, the Internet as a global network system will not be threatened and therefore will not collapse. The infrastructure itself is secured by large nodes, so-called Internet Exchange Points, the most important of which is currently located in Frankfurt. Should the latter have technical difficulties to contend with, it is unlikely that the entire system will fail, but much more serious consequences can already be expected. However, if several of these nodes are affected at the same time, we will face a serious problem. The probability of such an event may be low, but it should not be underestimated[2].

It is therefore important to be aware of those dynamics within complex systems that are often referred to as “fat tails”. This term is basically the technical expression of Black Swan events, i.e. rare but highly influential events that often have far-reaching consequences. What most students get to know during their basic statistical education is the discussion of normal distributions and the resulting probabilities of occurrence for rare events. Such events are also referred to as 5 sigma events, i.e. those occurrences whose distance from the mean corresponds to five or more standard deviations. In a normally distributed world, it would follow that these events are highly unlikely. Statisticians such as Mandelbrot[2] and Taleb[3], on the other hand, argue that in a complex world, fat-tail distributions are particularly relevant and normal distributions often underestimate the risk of rare events.

However, not only extreme events can be relevant, but also the cumulative influence of smaller variables. The inherent non-linearity of complex systems can lead to processes that at first glance appear less intuitive. Scott E. Page illustrated this with a very nice example – the algae development of a pond:
Algae are often a consequence of increased phosphate concentrations within a water body. However, it takes a while until a clear pool becomes an algae-polluted pond. The unique characteristic of this is that it is not a gradual change, but a very rapid tilting of the entire system. For a while, the system of the pool can handle the increased phosphate concentration quite well, but at some point there comes a moment when this is no longer the case and the system undergoes a fundamental change in which the algae feel more comfortable than ever before. The gradual increase in the phosphate concentration ultimately leads to a fat tail event that completely throws the original system off course[5].
Now this example is a very simple one and should only serve as an analogy for understanding, because we can explain quite well why this change occurs. In much more complex domains this becomes far more difficult, not to say impossible. Which completes the loop and brings us back to the original topic.

The earth’s climate obviously belongs to the group of complex systems. However, there is also the problem that the behaviour of such systems cannot be predicted exactly, since no model can include all components in its calculations. At this point the climate skeptic feels completely confirmed, because he always knew that these climate scientists and their prognoses cannot be trusted. Without noticing it, however, he falls victim to one of the oldest problems of truth-finding: the induction problem, which I have already discussed here.
The failure of past forecasts is not a reliable indicator that it must always remain that way. In a complex environment, it is impossible to establish obvious causal relationships, but as more potential stressors are added, the risk of causing devastation can increase.

Climate models now face the problem that they are inherently probabilistic, operating on past data and de facto unable to provide fully accurate predictions[6]. However, this is not so much an argument against using the models as it is against the communication strategies derived from them. The media and activists often suggest a certainty of prediction that simply does not exist – but does not have to – in order to demand more reasonable environmental protection strategies.
In the past, there have repeatedly been scenarios in which warnings were given of a possible catastrophe, but which then did not occur. Take, for example, the warning of a population explosion in the 1960s, represented by Paul Ehrlich[7]. He spoke of the extinction of all relevant marine animals around 1980. Obviously, this did not happen because countless factors have developed in a direction that he and other followers of this idea did not see coming. How could they? Such predictions are doomed from the beginning.

But to deduce that there have been various climate episodes in the past, and even if the situation worsens, humanity will be able to develop a new invention that prevents the worst is dangerously naive. In a complex world, it is impossible to make predictions based on past data. It is simply impossible to calculate rare events. A high risk aversion and thus the protection of the environment is the really rational decision. People tend to forget that in complex systems one plus one does not always result in two, but often much more (keyword: non-linearity). Stressors can act as super-additive functions and cause enormous damage.

Climate research itself is very well aware of the difficulty of prognoses within complex systems and articulates them accordingly. Above all the groundwork by Edward Lorenz and the Lorenz system named after him, in popular culture also known as the butterfly effect (ironically, this is often misinterpreted in such a way that one should pay more attention to small details, since their influence could be so great – although countless of them exist and one does not know which are the relevant ones anyway)[8].
The core statement of a Lorenz system is that it is impossible to know all the initial variables within a physical system, from which it follows that the prediction of future behaviour must inevitably fail, even if the system is highly deterministic and quantum effects are ignored.
On the basis of this impossibility, Snyder et al. draw a very meaningful conclusion along the lines of the arguments of Mandelbrot and Taleb. Precisely because making exact predictions is not a realistic option, it is all the more important to develop human systems in a way that ensures their survival even when fat-tail events occur. Above all, this means reducing the number of possible stressors[9].
From this it can be deduced that one should trust the most pessimistic of all models, knowing very well that sometimes things can get even worse, since each one is probably wrong to a certain degree. One can doubt the reliability of accurate forecasts, but this is precisely the reason why one should position oneself extremely conservatively and look for better measures than before. This would have the advantage, especially in public discourse, that one could acknowledge the inadequacy of correct forecasts and still demand better environmental protection measures.
Of course, it is easy to say that not everything will be as bad as some models predict. After all, we have been doing quite well in the past. But this time it is different, because the worst-case scenario is not just a few hundred million deaths, but the complete uninhabitability of the earth for the human race. If not even existential threats force us to change our behaviour, then we do not deserve to survive.

 


Sources

[1] Boeing, Geoff. Visual Analysis of Nonlinear Dynamical Systems: Chaos, Fractals, Self-Similarity and the Limits of Prediction

[2] http://www.drpeering.net/white-papers/Art-Of-Peering-The-IX-Playbook.html

[3] Mandelbrot, B. (1997). Fractals and Scaling in Finance: Discontinuity, Concentration, Risk. Springer

[4] Taleb, N. N. (2007). The Black Swan. Random House and Penguin.

[5] Scott E. Page: Understanding Complexity

[6] https://www.climate.gov/maps-data/primer/climate-models?fbclid=IwAR1sOsZVcE2QcxmXpKGvutmMHuQ73kzcvwrHA8OK4BKzqKC1m4mvkHvxeFg

[7] Ehrlich, Paul R. (1968). The Population Bomb. Ballantine Books.

[8] Lorenz, Edward Norton (1963). “Deterministic nonperiodic flow”. Journal of the Atmospheric Sciences.

[9] Snyder, Carolyn W.; Mastrandrea, Michael D.; Schneider, Stephen H. The Complex Dyanmics of the Climate System: Constraints on our Knowledge, Policy Implications and the Necessity of Systems Thinking. Philosophy of Complex Systems. Volume 10 in Handbook of the Philosophy of Science. 2011. Pages 467-505