As the Trump inauguration approaches, Mark Zuckerberg announced that Meta is changing its content moderation policies. They are doing away with fact-checkers and will instead implement community notes similar to X/Twitter’s. Rumours say that YouTube is working on a similar feature. I think this is a bad idea, and it won’t end well, both for social media companies and us.

According to ecologist Crawford Holling, complex systems go through cycles of four phases: growth, maturity, release (or breakdown), and exploration (or reconfiguration). In a paper that just appeared, a prominent forecaster claims that human civilization is currently in the release phase, meaning we will soon have to dramatically reconfigure our social and economic order. What are we to make of this? Let’s take a look.

The idea that social media causes children mental health distress is plausible, but unfortunately it isn’t true. Trouble is, if you read what the press has written about it, you wouldn’t know. Scientists have described it as a “moral panic” that isn’t backed by data, which has been promoted most prominently by one man: Jonathan Haidt.

Last week, the US senate had a hearing on the dangers of social media in preparation of a legislation to improve child safety online. In this hearing, Meta CEO Mark Zuckerberg [[image of the guy with name]] claimed that it has not been scientifically proved that social media causes mental health problems in adolescents.

This upset a lot of people who think that the link is obvious. But I am afraid Zuckerberg is right. Let’s have a look.

Is capitalism the reason the world is going to hell in a hand basket? Or is it going to save us? What is capitalism anyway? How does money work and when do free markets fail? This video is a brief summary of a dip I did into microeconomics literature in a dark hour of my life.



Transcript, links to references, and discussion on Patreon.

It's in the headlines everywhere that the world is going through a mental health crisis. Though, when you take a closer look, all the headlines are coming from the United States. Is there a global mental health crisis? Is it localized to just the US? That's what we talk about today.



Transcript, links to references, and discussion on Patreon.

Should transgender teens transition? This rather personal question occupies a prominent place in the American culture war. One the one side you have people claiming that it’s a socially contagious fad among the brainwashed woke who want to mutilate your innocent children. On the other side there are those saying that it’s saving the lives of minorities who’ve been forced to stay in the closet for too long. And then there are normal people, like you and I, who think both sides are crazy and could someone please summarise the facts in simple words, which is what I’m here for.



Transcript, references, and discussion on Patreon.

Transcript, references, and discussion on Patreon.

The term “hard science” as opposed to “soft science” has no clear definition. But roughly speaking, the less the predictive power and the smaller the statistical significance, the softer the science. Physics, without doubt, is the hard core of the sciences, followed by the other natural sciences and the life sciences. The higher the complexity of the systems a research area is dealing with, the softer it tends to be. The social sciences are at the soft end of the spectrum.

To me the very purpose of research is making science increasingly harder. If you don’t want to improve on predictive power, what’s the point of science to begin with? The social sciences are soft mainly because data that quantifies the behavior of social, political, and economic systems is hard to come by: it’s huge amounts, difficult to obtain and even more difficult to handle. Historically, these research areas therefore worked with narratives relating plausible causal relations. Needless to say, as computing power skyrockets, increasingly larger data sets can be handled. So the social sciences are finally on the track to become useful. Or so you’d think if you’re a physicist.

But interestingly, there is a large opposition to this trend of hardening the social sciences, and this opposition is particularly pronounced towards physicists who take their knowledge to work on data about social systems. You can see this opposition in the comment section to every popular science article on the topic. “Social engineering!” they will yell accusingly.

It isn’t so surprising that social scientists themselves are unhappy because the boat of inadequate skills is sinking in the data sea and physics envy won’t keep it afloat. More interesting than the paddling social scientists is the public opposition to the idea that the behavior of social systems can be modeled, understood, and predicted. This opposition is an echo of the desperate belief in free will that ignores all evidence to the contrary. The desperation in both cases is based on unfounded fears, but unfortunately it results in a forward defense.

And so the world is full with people who argue that they must have free will because they believe they have free will, the ultimate confirmation bias. And when it comes to social systems they’ll snort at the physicists “People are not elementary particles”. That worries me, worries me more than their clinging to the belief in free will, because the only way we can solve the problems that mankind faces today – the global problems in highly connected and multi-layered political, social, economic and ecological networks – is to better understand and learn how to improve the systems that govern our lives.

That people are not elementary particles is not a particularly deep insight, but it collects several valid points of criticism:

1. People are too difficult. You can’t predict them.
   
   Humans are made of a many elementary particles and even though you don’t have to know the exact motion of every single one of these particles, a person still has an awful lot of degrees of freedom and needs to be described by a lot of parameters. That’s a complicated way of saying people can do more things than electrons, and it isn’t always clear exactly why they do what they do.
   
   That is correct of course, but this objection fails to take into account that not all possible courses of action are always relevant. If it was true that people have too many possible ways to act to gather any useful knowledge about their behavior our world would be entirely dysfunctional. Our societies work only because people are to a large degree predictable.
   
   If you go shopping you expect certain behaviors of other people. You expect them to be dressed, you expect them to walk forwards, you expect them to read labels and put things into a cart. There, I’ve made a prediction about human behavior! Yawn, you say, I could have told you that. Sure you could, because making predictions about other people’s behavior is pretty much what we do all day. Modeling social systems is just a scientific version of this.
   
   This objection that people are just too complicated is also weak because, as a matter of fact, humans can and have been modeled with quite simple systems. This is particularly effective in situations when intuitive reaction trumps conscious deliberation. Existing examples are traffic flows or the density of crowds when they have to pass through narrow passages.
   
   So, yes, people are difficult and they can do strange things, more things than any model can presently capture. But modeling a system is always an oversimplification. The only way to find out whether that simplification works is to actually test it with data.
   
   
2. People have free will. You cannot predict what they will do.
   
   To begin with it is highly questionable that people have free will. But leaving this aside for a moment, this objection confuses the predictability of individual behavior with the statistical trend of large numbers of people. Maybe you don’t feel like going to work tomorrow, but most people will go. Maybe you like to take walks in the pouring rain, but most people don’t. The existence of free will is in no conflict with discovering correlations between certain types of behavior or preferences in groups. It’s the same difference that doesn’t allow you to tell when your children will speak the first word or make the first step, but that almost certainly by the age of three they’ll have mastered it.
   
   
3. People can understand the models and this knowledge makes predictions useless.
   
   This objection always stuns me. If that was true, why then isn’t obesity cured by telling people it will remain a problem? Why are the highways still clogged at 5pm if I predict they will be clogged? Why will people drink more beer if it’s free even though they know it’s free to make them drink more? Because the fact that a prediction exists in most cases doesn’t constitute any good reason to change behavior. I can predict that you will almost certainly still be alive when you finish reading this blogpost because I know this prediction is exceedingly unlikely to make you want to prove it wrong.
   
   Yes, there are cases when people’s knowledge of a prediction changes their behavior – self-fulfilling prophecies are the best-known examples of this. But this is the exception rather than the rule. In an earlier blogpost, I referred to this as societal fixed points. These are configurations in which the backreaction of the model into the system does not change the prediction. The simplest example is a model whose predictions few people know or care about.
   
   
4. Effects don’t scale and don’t transfer.
   
   This objection is the most subtle one. It posits that the social sciences aren’t really sciences until you can do and reproduce the outcome of “experiments”, which may be designed or naturally occurring. The typical social experiment that lends itself to analysis will be in relatively small and well-controlled communities (say, testing the implementation of a new policy). But then you have to extrapolate from this how the results will be in larger and potentially very different communities. Increasing the size of the system might bring in entirely new effects that you didn’t even know of (doesn’t scale), and there are a lot of cultural variables that your experimental outcome might have depended on that you didn’t know of and thus cannot adjust for (doesn’t transfer). As a consequence, repeating the experiment elsewhere will not reproduce the outcome.
   
   Indeed, this is likely to happen and I think it is the major challenge in this type of research. For complex relations it will take a long time to identify the relevant environmental parameters and to learn how to account for their variation. The more parameters there are and the more relevant they are, the less the predictive value of a model will be. If there are too many parameters that have to be accounted for it basically means doing experiments is the only thing we can ever do. It seems plausible to me, even likely, that there are types of social behavior that fall into this category, and that will leave us with questions that we just cannot answer.
   
   However, whether or not a certain trend can or cannot be modeled we will only know by trying. We know that there are cases where it can be done. Geoffry West’s city theory I find a beautiful example where quite simple laws can be found in the midst of all these cultural and contextual differences.

In summary.

The social sciences will never be as “hard” as the natural sciences because there is much more variation among people than among particles and among cities than among molecules. But the social sciences have become harder already and there is no reason why this trend shouldn’t continue. I certainly hope it will continue because we need this knowledge to collectively solve the problems we have collectively created.

Thinking, Fast and Slow
By Daniel Kahneman
Farrar, Straus and Giroux (October 25, 2011)

I am always on the lookout for ways to improve my scientific thinking. That’s why I have an interest in the areas of sociology concerned with decision making in groups and how the individual is influenced by this. And this is also why I have an interest in cognitive biases - intuitive judgments that we make without even noticing; judgments which are just fine most of the time but can be scientifically fallacious. Daniel Kahneman’s book “Thinking, fast and slow” is an excellent introduction to the topic.

Kahneman, winner of the Nobel Price for Economics in 2002, focuses mostly on his own work, but that covers a lot of ground. He starts with distinguishing between two different modes in which we make decisions, a fast and intuitive one, and a slow, more deliberate one. Then he explains how fast intuitions lead us astray in certain circumstances.

The human brain does not make very accurate statistical computations without deliberate effort. But often we don’t make such an effort. Instead, we use shortcuts. We substitute questions, extrapolate from available memories, and try to construct plausible and coherent stories. We tend to underestimate uncertainty, are influenced by the way questions are framed, and our intuition is skewed by irrelevant details.

Kahneman quotes and summarizes a large amount of studies that have been performed, in most cases with sample questions. He offers explanations for the results when available, and also points out where the limits of present understanding are. In the later parts of the book he elaborates on the relevance of these findings about the way humans make decision for economics. While I had previously come across a big part of the studies that he summarizes in the early chapters, the relation to economics had not been very clear to me, and I found this part enlightening. I now understand my problems trying to tell economists that humans do have inconsistent preferences.

The book introduces a lot of terminology, and at the end of each chapter the reader finds a few examples for how to use them in everyday situations. “He likes the project, so he thinks its costs are low and its benefits are high. Nice example of the affect heuristic.” “We are making an additional investment because we not want to admit failure. This is an instance of the sunk-cost fallacy.” Initially, I found these examples somewhat awkward. But awkward or not, they serve very well for the purpose of putting the terminology in context.

The book is well written, reads smoothly, is well organized, and thoroughly referenced. As a bonus, the appendix contains reprints of Kahneman’s two most influential papers that contain somewhat more details than the summary in the text. He narrates along the story of his own research projects and how they came into being which I found a little tiresome after he elaborated on the third dramatic insight that he had about his own cognitive bias. Or maybe I'm just jealous because a Nobel Prize winning insight in theoretical physics isn't going to come by that way.

I have found this book very useful in my effort to understand myself and the world around me. I have only two complaints. One is that despite all the talk about the relevance of proper statistics, Kahneman does not mention the statistical significance of any of the results that he talks about. Now, this is all research which started two or three decades ago, so I have little doubt that the effects he talks about are indeed meanwhile well established, and, hey, he got a Nobel Prize after all. Yet, if it wasn’t for that I’d have to consider the possibility that some of these effects will vanish as statistical artifacts. Second, he does not at any time actually explain to the reader the basics of probability theory and Bayesian inference, though he uses it repeatedly. This, unfortunately, limits the usefulness of the book dramatically if you don’t already know how to compute probabilities. It is particularly bad when he gives a terribly vague explanation of correlation. Really, the book would have been so much better if it had at least an appendix with some of the relevant definitions and equations.

That having been said, if you know a little about statistics you will probably find, like I did, that you’ve learned to avoid at least some of the cognitive biases that deal with explicit ratios and percentages, and different ways to frame these questions. I’ve also found that when it comes to risks and losses my tolerance apparently does not agree with that of the majority of participants in the studies he quotes. Not sure why that is. Either way, whether or not you are subject to any specific bias that Kahneman writes about, the frequency by which they appear make them relevant to understand the way human society works, and they also offer a way to improve our decision making.

In summary, it’s a well-written and thoroughly useful book that is interesting for everybody with an interest in human decision-making and its shortcomings. I'd give this book four out of five stars.

Below are some passages that I marked that gave me something to think. This will give you a flavor what the book is about.

“A reliable way of making people believe in falsehoods is frequent repetition because familiarity is not easily distinguished from truth.”

“[T]he confidence that people experience is determined by the coherence of the story they manage to construct from available information. It is the consistency of the information that matters for a good story, not its completeness.”

“The world in our heads is not a precise replica of reality; our expectations about the frequency of events are distorted by the prevalence and emotional intensity of the messages to which we are exposed.”

“It is useful to remember […] that neglecting valid stereotypes inevitably results in suboptimal judgments. Resistance to stereotyping is a laudable moral position, but the simplistic idea that the resistance is cost-less is wrong.”

“A general limitation of the human mind is its imperfect ability to reconstruct past states of knowledge, or beliefs that have changed. Once you adopt a new view of the world (or any part of it), you immediately lose much of your ability to recall what you used to believe before your mind changed.”

“I have always believed that scientific research is another domain where a form of optimism is essential to success: I have yet to meet a successful scientist who lacks the ability to exaggerate the importance of what he or she is doing, and I believe that someone who lacks a delusional sense of significance will wilt in the fact of repeated experiences of multiple small failures and rare successes, the fate of most researchers.”

“The brains s of humans and other animals contain a mechanism that is designed to give priority to bad news.”

“Loss aversion is a powerful conservative force that favors minimal changes from the status quo in the lives of both institutions and individuals.”

“When it comes to rare probabilities, our mind is not designed to get things quite right. For the residents of a planet that maybe exposed to events no one has yet experienced, this is not good news.”

“We tend to make decisions as problems arise, even when we are specifically instructed to consider them jointly. We have neither the inclination not the mental resources to enforce consistency on our preferences, and our preferences are not magically set to be coherent, as they are in the rational-agent model.”

“The sunk-cost fallacy keeps people for too long in poor jobs, unhappy marriages, und unpromising research projects. I have often observed young scientists struggling to salvage a doomed project when they would be better advised to drop it and start a new one.”

“Although Humans are not irrational, they often need help to make more accurate judgments and better decisions, and in some cases policies and institutions can provide that help.”

The Edge features a half hour talk by Mark Pagel that I found very thought stimulating. Pagel is an evolutionary biologist, and his talk is provocatively titled “Infinite Stupidity.” The title primarily describes itself though and doesn’t have much to do with Pagel’s argument, which is a speculation about the origin and evolution of ideas.

Pagel starts with the analogy that ideas evolve similarly to genes, in that they reproduce and are selected for performance. Only good ideas continue to reproduce, which is however a tautology if you define a good idea by its ability to spread, and questionable otherwise. I am not particularly fond of the comparison to natural selection that he uses. The evolution of organisms and ideas are both examples for adaptive systems, and the reference to natural selection imo sets an unnecessary anchor.

In any case, this idea reproduction works well among humans because we are very good at social learning, that is learning by imitating each other. The ability of humans to copy behavior sets us apart from all other species on the planet. Sure, some other mammals are able to learn new tricks when offered rewards, but these abilities and the animals’ understanding of purpose are very limited. Humans have very little hardwired knowledge, which has the advantage that we adapt very well to new circumstances, but has the disadvantage that it takes a long time for human infants to have learned enough to be able to survive independently.

Hang on, Lara is trying to eat my post-its.

During the evolution of mankind, our ability to communicate ideas has steadily improved. Beginning with the evolution of language, over the written word, print, telegraph, phone and to the internet, we have improved on our connectivity. Since we are so good at copying others, this means, so argues Pagel, that we need fewer and fewer people to produce ideas. Innovation takes time and energy, and if we can shortcut this investment by relying on somebody else’s knowledge, we can avoid this cost.

Pagel is speaking here not primarily about innovation in the sense of technological development. He refers to things like, say, building a house. If you want to build a house, you don’t invent architecture from scratch. You ask somebody who knows, or you read a book, or, most likely, you hire somebody to do it for you. Either way, you’re copying other people’s innovations rather than innovating yourself, and in terms of time- and energy-investment that’s arguably the smart thing to do.

A consequence of our high skills in social learning combined with increasing connectivity is thus that we have become good copiers and less good inventors, which is unfortunate since we need innovators to come up with smart solutions to our problems. Or so are Pagels concerns. He says

“And so, we might see that there has been this tendency for our psychology and our humanity to be less and less innovative, at a time when, in fact, we may need to be more and more innovative, if we're going to be able to survive the vast numbers of people on this earth.”

I don’t know what he means with “tendency for our psychology.” It could mean two things. Either the (conjectured) decline in innovation is hardwired, ie it’s a genetic change. Or, it’s a reversible adaption to changing circumstances. Given the short time that has passed since the invention of print, it is most likely a social or cultural change he is referring to. But if that is so, then I have to conclude that Pagel’s perception of “a need to be more and more innovative” is apparently not reflected in our environment, at which point we’re left with opinions about societal investments in research and development, rather than facts. This is not to say that I disagree with Pagel, just that I don’t really know what insight to gain here.

So let me get to the next point he’s making, which I actually found more interesting, that is the question where ideas originate. Pagel says that ideas are probably randomly produced in our brains, like genetic mutations are randomly produced. This hypothesis doesn’t seem to be based on any actual study for all I can tell, it’s mostly an argument from plausibility.

That ideas might be random produced in our brains doesn’t mean though that we try them all. No, luckily our brains are large enough to virtually explore consequences of an idea before actually acting on it. And in that process, we discard most of the nonsensical random ideas, possibly already unconsciously. I am not sure how well this idea of Pagel fits with current research, but it makes a certain sense to me.

However, I think Pagel omitted to point out that a random generation of ideas cannot mean random from scratch, but random over a pool of already existing material. That is to say, you can only generate ideas on the information that your brain contains. Which brings me back to the need of education, and investment into research and development.

Pagel's argument is interesting, but it lacks substance. Maybe it is worth checking out his book, Wired for Culture, which might offer more support for his idea.

Infotopia: How Many Minds Produce Knowledge
By Cass R. Sunstein
Oxford University Press, USA (2006)

It's taken me a while to get through Sunstein's book though I am very interested in the topic. "Infotopia" addresses the question under which circumstances, and with which aggregation mechanisms, groups can make good decisions - and under which circumstances groups fail. With that, Sunstein's book offers the details that I found missing in Surowiecky's "Wisdom of Crowds."

Sunstein summarizes a lot of research that has been done on how groups deal with information, and how they aggregate it, and how good or bad they make decisions. He has classified modern aggregation tools into markets and prediction markets, wikis, open source, and blogs. This order seems to be a declining one for Sunstein's judgement of usefulness; he is clearly enthusiastic about prediction markets and critical about the blogosphere.

The book has grown out of his review article for the New York University Law Review, and that is, unfortunately, very noticeable. "Infotopia" contains a lot of information and many references, but it is not very engagingly written. It is essentially a long list of who did what study when and where. It is in several places repetitive, as if the author himself had forgotten what he had already covered. It is repetitive also in the choice of words, eg the word "blunder" seems to appears like every other page.

I learned a lot from this book, most notably what difficulties befall groups that want to come to good decisions.

The major problems are that group members might not disclose information that they have, and that information which is held by few or single group members has less influence on the decision than the information shared by many, irrespective of actual relevance. Sunstein discusses many studies that have shown that deliberation in groups, under very general circumstances, makes decisions worse and polarizes opinions. The reason is that people tend to focus on what they have in common and reinforce their views rather than to diversify. So, after talking it out, people often edge towards more extreme views, and are more certain about them too because they then know others share their opinion. An additional problem is that people might have a conflict of motivation, ie their personal motivation to not look stupid might not agree with the goal of coming to a good decision in the group.

Most of the examples that Sunstein draws upon are 6 years later already outdated, but the general lessons for good decision making are pretty much timeless. In the final chapter Sunstein makes suggestions for how to alleviate these problems in different situations: online communities, group meetings and so on. I'll try to learn from that book, and hope to realize some of the suggestions in the future.

In summary, this book is very useful, but it's not very inspiring and not very well written. I'd give three out of five stars.