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.

How ( not ) to criticize

How ( not ) to criticize

Recently, I became involved in a discussion triggered by an article dealing with the alleged misconducts of Steven Pinker, Sam Harris and Michael Shermer. Since I have read most of Pinker’s and Harris books over the years, I will focus on their work.

Those who read the Salon article will immediately recognize the polemic tone of the piece. That wouldn’t be a problem in itself if Phil Torres, the author, didn’t end up in the same trap he blames the criticized people for. But first things first.
Many reactions to Torres’ statements were above all critical of the chosen language, without concentrating too much on the content.
Not surprisingly, some people felt attacked, especially by the polemics, since it is part of the nature of the human ego not liking to be told you belong to a group of annoying people. Of course, one then reflexively defends one’s own self-image.
However, this should not prevent you from naming behaviour worthy of criticism. If you don’t speak up about a fraud – you are the fraud.

The strongest criticism of Sam Harris refers to a quote he made in the course of this podcast:

“As bad luck would have it, but as you’d absolutely predict on the basis of just sheer biology, different populations of people, different racial groups, different ethnicities, different groups of people who have been historically isolated from one another geographically, test differently in terms of their average on this measure of cognitive function. So if you’re gonna give the Japanese and the Ashkenazi Jews, and African Americans, and Hawaiians … you’re gonna take populations who differ genetically – and we know they differed genetically, that’s not debatable – and you give them IQ tests, it would be a miracle if every single population had the same mean IQ. And African Americans come out about a standard deviation lower than white Americans. A standard deviation for IQ is about 15 points. So, if it’s normed to the general population, predominantly white population for an average of 100, the average in the African American community has been around 85.”

Torres now interprets this statement in a way that suggests that Harris is coquetting with racist ideas:

“In other words: black people are dumber than white people. Why? Because of genetic evolution, meaning that IQ is in the genes and the genes of white people are, well, just plain better. What a bold stance, especially amid the ongoing rise of white nationalism in the U.S. and Europe!”

Sam Harris has made valuable contributions to the debate on neuroscience, free will and mindfulness – yet this does not make him sacrosanct for all time, and if he errs on racist paths, then it is legitimate to criticize him for it. It is naive rationalism to derive fundamental statements about the distribution of intelligence from statistical fluctuations in the completion of an IQ-test and not about the ability to complete an IQ-test, and to do so believes that one can easily recognize causal relationships in a complex world. For example, for a long time ancient societies had no word for the colour “blue”, which is why some descriptions sound somewhat strange – is this an indicator of lower verbal intelligence or simply an artifact of the social context? Anyone who misinterprets statistics in this way has to put up with criticism. While the mere fact that such differences exist does not make anyone racist, it is legitimate to expect someone of Harris’ intellectual caliber to put them in context.
Yet that is exactly what he is doing. Torres shortens the quote dishonestly and thereby distorts its context in order to cast as bad a light as possible on Harris. For he also says:

“But any case this is an annoying finding. Now I happen to think there’s no reason to seek data like this, I think there’s nothing good to do with this data. There are some obviously wrong conclusions that people want to make on this – on the basis of these data and but Murray made none of those conclusions, in fact he was adamant he and his co-author who, Richard Herrnstein, who died, they said all of the cautious and ethical and politically prudent things you would want them to say in the book and it meant nothing.
So, for instance, it is in fact true that there’s so much more variation within any population for everything but in particular for intelligence than there is between populations, that you actually know nothing about a person’s intelligence by being told the color of his skin. So, to be told that someone’s white or black or Japanese tells you nothing about how good there are at anything that interests you. So, you have you have literally no information.”

That actually sounds a lot different than Torres represents it and such a distortion of meaning therefore speaks more against his methodology than against Harris. That alone is serious enough, but one of Torres’ most frequent accusations against Pinker is that he misrepresents the work of others.
You can’t blame people for pulling quotes out of context and misinterpreting them if you do exactly the same thing in the same breath. That is intellectually dishonest.
Of course, it is necessary and important to criticize the statements and work of public figures. However, this should be done in a fair, intellectually sincere manner and not by distorting these works.

A few more words about Steven Pinker.
Before I began to critically question my own statistical training, the data and graphs he presented seemed very conclusive.
By now I have a problem with the methodology used and the forecasts derived from it.
Yes, especially in the western countries we are currently living in a quasi golden age and also the standard of living in developing countries is rising higher and higher – entirely correct findings and a welcome change from a “the world is shit” narrative.
But I don’t share his optimistic interpretation that this trend will continue. Precisely because this is less about physical laws than about the behaviour of human societies, the degree of complexity is increasing exponentially.
While Pinker admits that his predictions may also be misjudgements due to events such as climate change or the strengthening of resentments and nationalisms embodied by Donald Trump or Viktor Orbán, he does not see this trend as important enough to challenge his fundamental assumption.
It seems somewhat questionable to acknowledge that trends exist that speak against his hypotheses, but to put them aside in a naive-rationalist argumentation and respond with the simple message “It will be all right”. If one knows that there are definitely contrary developments, one should be sincere enough to pay more attention to them in one’s own view instead of attempting statistical fortune-telling. This way of deriving future events on the basis of historical data is something that Karl Popper sufficiently criticized decades ago in “The Poverty of Historicism”.

In my opinion, Sam Harris is the least deserving of Phil Torres’ angry broadside. It is unfortunate that Torres ends up using the same methods that he accuses others of, which greatly weakens the legitimate criticism of Pinker in particular. It remains to be hoped that public disagreements will more often be carried out with a stronger focus on what is actually said within the right context and become less the victim of one’s own frustrated ego.
A naive wish, I know.

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