Tag: Availability Bias

Stop Preparing For The Last Disaster

Reading Time: 5 minutes

When something goes wrong, we often strive to be better prepared if the same thing happens again. But the same disasters tend not to happen twice in a row. A more effective approach is simply to prepare to be surprised by life, instead of expecting the past to repeat itself.

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If we want to become less fragile, we need to stop preparing for the last disaster.

When disaster strikes, we learn a lot about ourselves. We learn whether we are resilient, whether we can adapt to challenges and come out stronger. We learn what has meaning for us, we discover core values, and we identify what we’re willing to fight for. Disaster, if it doesn’t kill us, can make us stronger. Maybe we discover abilities we didn’t know we had. Maybe we adapt to a new normal with more confidence. And often we make changes so we will be better prepared in the future.

But better prepared for what?

After a particularly trying event, most people prepare for a repeat of whatever challenge they just faced. From the micro level to the macro level, we succumb to the availability bias and get ready to fight a war we’ve already fought. We learn that one lesson, but we don’t generalize that knowledge or expand it to other areas. Nor do we necessarily let the fact that a disaster happened teach us that disasters do, as a rule, tend to happen. Because we focus on the particulars, we don’t extrapolate what we learn to identifying what we can better do to prepare for adversity in general.

We tend to have the same reaction to challenge, regardless of the scale of impact on our lives.

Sometimes the impact is strictly personal. For example, our partner cheats on us, so we vow never to have that happen again and make changes designed to catch the next cheater before they get a chance; in future relationships, we let jealousy cloud everything.

But other times, the consequences are far reaching and impact the social, cultural, and national narratives we are a part of. Like when a terrorist uses an airplane to attack our city, so we immediately increase security at airports so that planes can never be used again to do so much damage and kill so many people.

The changes we make may keep us safe from a repeat of those scenarios that hurt us. The problem is, we’re still fragile. We haven’t done anything to increase our resilience—which means the next disaster is likely to knock us on our ass.

Why do we keep preparing for the last disaster?

Disasters cause pain. Whether it’s emotional or physical, the hurt causes vivid and strong reactions. We remember pain, and we want to avoid it in the future through whatever means possible. The availability of memories of our recent pain informs what we think we should do to stop it from happening again.

This process, called the availability bias, has significant implications for how we react in the aftermath of disaster. Writing in The Legal Analyst: A Toolkit for Thinking about the Law about the information cascades this logical fallacy sets off, Ward Farnsworth says they “also help explain why it’s politically so hard to take strong measures against disasters before they have happened at least once. Until they occur they aren’t available enough to the public imagination to seem important; after they occur their availability cascades and there is an exaggerated rush to prevent the identical thing from happening again. Thus after the terrorist attacks on the World Trade Center, cutlery was banned from airplanes and invasive security measures were imposed at airports. There wasn’t the political will to take drastic measures against the possibility of nuclear or other terrorist attacks of a type that hadn’t yet happened and so weren’t very available.”

In the aftermath of a disaster, we want to be reassured of future safety. We lived through it, and we don’t want to do so again. By focusing on the particulars of a single event, however, we miss identifying the changes that will improve our chances of better outcomes next time. Yes, we don’t want any more planes to fly into buildings. But preparing for the last disaster leaves us just as underprepared for the next one.

What might we do instead?

We rarely take a step back and go beyond the pain to look at what made us so vulnerable to it in the first place. However, that’s exactly where we need to start if we really want to better prepare ourselves for future disaster. Because really, what most of us want is to not be taken by surprise again, caught unprepared and vulnerable.

The reality is that the same disaster is unlikely to happen twice. Your next lover is unlikely to hurt you in the same way your former one did, just as the next terrorist is unlikely to attack in the same way as their predecessor. If we want to make ourselves less fragile in the face of great challenge, the first step is to accept that you are never going to know what the next disaster will be. Then ask yourself: How can I prepare anyway? What changes can I make to better face the unknown?

As Andrew Zolli and Ann Marie Healy explain in Resilience: Why Things Bounce Back, “surprises are by definition inevitable and unforeseeable, but seeking out their potential sources is the first step toward adopting the open, ready stance on which resilient responses depend.”

Giving serious thought to the range of possible disasters immediately makes you aware that you can’t prepare for all of them. But what are the common threads? What safeguards can you put in place that will be useful in a variety of situations? A good place to start is increasing your adaptability. The easier you can adapt to change, the more flexibility you have. More flexibility means having more options to deal with, mitigate, and even capitalize on disaster.

Another important mental tool is to accept that disasters will happen. Expect them. It’s not about walking around every day with your adrenaline pumped in anticipation; it’s about making plans assuming that they will get derailed at some point. So you insert backup systems. You create a cushion, moving away from razor-thin margins. You give yourself the optionality to respond differently when the next disaster hits.

Finally, we can find ways to benefit from disaster. Author and economist Keisha Blair, in Holistic Wealth, suggests that “building our resilience muscles starts with the way we process the negative events in our lives. Mental toughness is a prerequisite for personal growth and success.” She further writes, “adversity allows us to become better rounded, richer in experience, and to strengthen our inner resources.” We can learn from the last disaster how to grow and leverage our experiences to better prepare for the next one.

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Predicting the Future with Bayes’ Theorem

Reading Time: 5 minutes

In a recent podcast, we talked with professional poker player Annie Duke about thinking in probabilities, something good poker players do all the time. At the poker table or in life, it’s useful to think in probabilities versus absolutes based on all the information you have available to you. You can improve your decisions and get better outcomes.

Probabilistic thinking leads you to ask yourself, how confident am I in this prediction? What information would impact this confidence?

Bayes’ Theorem

Bayes’ theorem is an accessible way of integrating probability thinking into our lives. Thomas Bayes was an English minister in the 18th century, whose most famous work, “An Essay toward Solving a Problem in the Doctrine of Chances,” was brought to the attention of the Royal Society in 1763—two years after his death—by his friend Richard Price. The essay did not contain the theorem as we now know it but had the seeds of the idea. It looked at how we should adjust our estimates of probabilities when we encounter new data that influence a situation. Later development by French scholar Pierre-Simon Laplace and others helped codify the theorem and develop it into a useful tool for thinking.

Knowing the exact math of probability calculations is not the key to understanding Bayesian thinking. More critical is your ability and desire to assign probabilities of truth and accuracy to anything you think you know, and then being willing to update those probabilities when new information comes in. Here is a short example, found in Investing: The Last Liberal Art, of how it works:

Let’s imagine that you and a friend have spent the afternoon playing your favorite board game, and now, at the end of the game, you are chatting about this and that. Something your friend says leads you to make a friendly wager: that with one roll of the die from the game, you will get a 6. Straight odds are one in six, a 16 percent probability. But then suppose your friend rolls the die, quickly covers it with her hand, and takes a peek. “I can tell you this much,” she says; “it’s an even number.” Now you have new information and your odds change dramatically to one in three, a 33 percent probability. While you are considering whether to change your bet, your friend teasingly adds: “And it’s not a 4.” With this additional bit of information, your odds have changed again, to one in two, a 50 percent probability. With this very simple example, you have performed a Bayesian analysis. Each new piece of information affected the original probability, and that is Bayesian [updating].

Both Nate Silver and Eliezer Yudkowsky have written about Bayes’ theorem in the context of medical testing, specifically mammograms. Imagine you live in a country with 100 million women under 40. Past trends have revealed that there is a 1.4% chance of a woman under 40 in this country getting breast cancer—so roughly 1.4 million women.

Mammograms will detect breast cancer 75% of the time. They will give out false positives—say a woman has breast cancer when she actually doesn’t—about 10% of the time. At first, you might focus just on the mammogram numbers and think that a 75% success rate means that a positive is bad news. Let’s do the math.

If all the women under 40 get mammograms, then the false positive rate will give 10 million women under 40 the news that they have breast cancer. But because you know the first statistic, that only 1.4 women under 40 actually get breast cancer, you know that 8.6 million of the women who tested positive are not actually going to have breast cancer!
That’s a lot of needless worrying, which leads to a lot of needless medical care. To remedy this poor understanding and make better decisions about using mammograms, we absolutely must consider prior knowledge when we look at the results, and try to update our beliefs with that knowledge in mind.

Weigh the Evidence

Often we ignore prior information, simply called “priors” in Bayesian-speak. We can blame this habit in part on the availability heuristic—we focus on what’s readily available. In this case, we focus on the newest information, and the bigger picture gets lost. We fail to adjust the probability of old information to reflect what we have learned.

The big idea behind Bayes’ theorem is that we must continuously update our probability estimates on an as-needed basis. In their book The Signal and the Noise, Nate Silver and Allen Lane give a contemporary example, reminding us that new information is often most useful when we put it in the larger context of what we already know:

Bayes’ theorem is an important reality check on our efforts to forecast the future. How, for instance, should we reconcile a large body of theory and evidence predicting global warming with the fact that there has been no warming trend over the last decade or so? Skeptics react with glee, while true believers dismiss the new information.

A better response is to use Bayes’ theorem: the lack of recent warming is evidence against recent global warming predictions, but it is weak evidence. This is because there is enough variability in global temperatures to make such an outcome unsurprising. The new information should reduce our confidence in our models of global warming—but only a little.

The same approach can be used in anything from an economic forecast to a hand of poker, and while Bayes’ theorem can be a formal affair, Bayesian reasoning also works as a rule of thumb. We tend to either dismiss new evidence, or embrace it as though nothing else matters. Bayesians try to weigh both the old hypothesis and the new evidence in a sensible way.

Limitations of the Bayesian

Don’t walk away thinking the Bayesian approach will enable you to predict everything! In addition to seeing the world as an ever-shifting array of probabilities, we must also remember the limitations of inductive reasoning.

A high probability of something being true is not the same as saying it is true. Consider this example from Bertrand Russell’s The Problems of Philosophy:

A horse which has been often driven along a certain road resists the attempt to drive him in a different direction. Domestic animals expect food when they see the person who usually feeds them. We know that all these rather crude expectations of uniformity are liable to be misleading. The man who has fed the chicken every day throughout its life at last wrings its neck instead, showing that more refined views as to the uniformity of nature would have been useful to the chicken.

In the final analysis, though, picking up Bayesian reasoning can change your life, as observed in this Big Think video by Julia Galef of the Center for Applied Rationality:

After you’ve been steeped in Bayes’ rule for a little while, it starts to produce some fundamental changes to your thinking. For example, you become much more aware that your beliefs are grayscale. They’re not black and white and that you have levels of confidence in your beliefs about how the world works that are less than 100 percent but greater than zero percent and even more importantly as you go through the world and encounter new ideas and new evidence, that level of confidence fluctuates, as you encounter evidence for and against your beliefs.

So be okay with uncertainty, and use it to your advantage. Instead of holding on to outdated beliefs by rejecting new information, take in what comes your way through a system of evaluating probabilities.

Bayes’ Theorem is part of the Farnam Street latticework of mental models. Still Curious? Read Bayes and Deadweight: Using Statistics to Eject the Deadweight From Your Life next. 

Learning community members can discuss this on the member forum. 

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The Art of Thinking Clearly

Reading Time: 4 minutes

Rolf Dobelli’s book, The Art of Thinking Clearly, is a compendium of systematic errors in decision making. While the list of fallacies is not complete, it’s a great launching pad into the best of what others have already figured out.

To avoid frivolous gambles with the wealth I had accumulated over the course of my literary career, I began to put together a list of … systematic cognitive errors, complete with notes and personal anecdotes — with no intention of ever publishing them. The list was originally designed to be used by me alone. Some of these thinking errors have been known for centuries; others have been discovered in the last few years. Some came with two or three names attached to them. … Soon I realized that such a compilation of pitfalls was not only useful for making investing decisions but also for business and personal matters. Once I had prepared the list, I felt calmer and more levelheaded. I began to recognize my own errors sooner and was able to change course before any lasting damage was done. And, for the first time in my life, I was able to recognize when others might be in the thrall of these very same systematic errors. Armed with my list, I could now resist their pull — and perhaps even gain an upper hand in my dealings.

Dobelli’s goal is to learn to recognize and evade the biggest errors in thinking. In so doing, he believes we might “experience a leap in prosperity. We need no extra cunning, no new ideas, no unnecessary gadgets, no frantic hyperactivity—all we need is less irrationality.”

Let’s take a look at some of the content.

Guarding Against Survivorship Bias

People systematically overestimate their chances of success. Guard against it by frequently visiting the graves of once-promising projects, investments, and careers. It is a sad walk but one that should clear your mind.

Pattern Recognition

When it comes to pattern recognition, we are oversensitive. Regain your scepticism. If you think you have discovered a pattern, first consider it pure chance. If it seems too good to be true, find a mathematician and have the data tested statistically.

Fighting Against Confirmation Bias

[T]ry writing down your beliefs — whether in terms of worldview, investments, marriage, health care, diet, career strategies — and set out to find disconfirming evidence. Axing beliefs that feel like old friends is hard work but imperative.

Dating Advice and Contrast

If you are seeking a partner, never go out in the company of your supermodel friends. People will find you less attractive than you really are. Go alone or, better yet, take two ugly friends.

Think Different

Fend it off (availability bias) by spending time with people who think different than you do—people whose experiences and expertise are different from yours.

Guard Against Chauffeur Knowledge

Be on the lookout for chauffeur knowledge. Do not confuse the company spokesperson, the ringmaster, the newscaster, the schmoozer, the verbiage vendor, or the cliche generator with those who possess true knowledge. How do you recognize the difference? There is a clear indicator: True experts recognize the limits of what they know and what they do not know.

The Swimmer’s Body Illusion

Professional swimmers don’t have perfect bodies because they train extensively. Rather, they are good swimmers because of their physiques. How their bodies are designed is a factor for selection and not the result of their activities. … Whenever we confuse selection factors with results, we fall prey to what Taleb calls the swimmer’s body illusion. Without this illusion, half of advertising campaigns would not work. But this bias has to do with more than just the pursuit of chiseled cheekbones and chests. For example, Harvard has the reputation of being a top university. Many highly successful people have studied there. Does this mean that Harvard is a good school? We don’t know. Perhaps the school is terrible, and it simply recruits the brightest students around.

Peer Pressure

A simple experiment, carried out in the 1950s by legendary psychologist Solomon Asch, shows how peer pressure can warp common sense. A subject is shown a line drawn on paper, and next to it three lines—numbered 1, 2, and 3—one shorter, one longer, and one the same length as the original one. He or she must indicate which of the three lines corresponds to the original one. If the person is alone in the room, he gives correct answers because the task is really quite simple. Now five other people enter the room; they are all actors, which the subject does not know. One after another, they give wrong answers, saying “number 1,” although it’s very clear that number 3 is the correct answer. Then it is the subject’s turn again. In one-third of cases, he will answer incorrectly to match the other people’s responses

Rational Decision Making and The Sunk Cost Fallacy

The sunk cost fallacy is most dangerous when we have invested a lot of time, money, energy, or love in something. This investment becomes a reason to carry on, even if we are dealing with a lost cause. The more we invest, the greater the sunk costs are, and the greater the urge to continue becomes. … Rational decision making requires you to forget about the costs incurred to date. No matter how much you have already invested, only your assessment of the future costs and benefits counts.

Avoid Negative Black Swans

But even if you feel compelled to continue as such, avoid surroundings where negative Black Swans thrive. This means: Stay out of debt, invest your savings as conservatively as possible, and get used to a modest standard of living—no matter whether your big breakthrough comes or not

Disconfirming Evidence

Munger Destroy ideas

The confirmation bias is alive and well in the business world. One example: An executive team decides on a new strategy. The team enthusiastically celebrates any sign that the strategy is a success. Everywhere the executives look, they see plenty of confirming evidence, while indications to the contrary remain unseen or are quickly dismissed as “exceptions” or “special cases.” They have become blind to disconfirming evidence.

Still curious?

Read The Art of Thinking Clearly.

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How You Can Instantly Improve Your Relationship

Reading Time: 2 minutes

Most of us see what we want to see.

If we’re arguing with a spouse, we’re going to start seeing all of their faults. After all, it’s not my fault it’s your fault. Once we’ve labeled someone as, say, selfish, it becomes self-reinforcing thanks to the availability and confirmation bias. Our views become so clouded that we can’t appreciate the positive attributes about our partner.

Not only do we search for information that agrees with us but we fail to notice anything to the contrary. “We see,” writes Aaron Beck in his book Love is Never Enough, “each other through the bias of negative frames.”

There is a way for couples to fight the tendency to only notice what’s wrong: keep a “marriage diary,” with a list of all the things your partner does that you like.

In his book, Beck describes a couple, Karen and Ted, who are having marriage troubles. Beck suggested they keep a marriage diary.

After I proposed to Karen and Ted that each take notes of what the other did that was pleasing during the previous week, Karen reported the following:

Ted was great. I was really upset by some of my clients. They are a real pain. … Anyhow, I told Ted about it. He was very sympathetic. He didn’t try to tell me what to do. He said that if he was in my position, he would probably feel frustrated, too. He said that my clients are tough to deal with. I felt a lot better.

…

Each of Ted’s actions pleased Karen, who remarked, “They were like presents.” Although Ted had done similar things for Karen in the past, they had been erased from her memory because of the negative view of Ted.

The same was also true for Ted.

Psychologist Mark Kane Goldstein has used this method to help husbands and wives keep “track of their partner’s pleasant actions.”

Each spouse is given several sheets of graph paper on which to record whatever his or her partner does that is pleasing. The spouse rates these acts on a ten-point scale, indicating degree of satisfaction. Dr. Goldstein found that 70 percent of the couples who tried this simple method reported an improvement in their relationship.

Simply by shifting their focus away from the negative and onto little pleasures, couples were more aware of their satisfaction.

Psychologists call this “considering the opposite.”

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The evolutionary roots of human behaviour

Reading Time: 1 minute

Anthony Gottlieb writing in the New Yorker:

Indeed, the guilty secret of psychology and of behavioral economics is that their experiments and surveys are conducted almost entirely with people from Western, industrialized countries, mostly of college age, and very often students of psychology at colleges in the United States. This is particularly unfortunate for evolutionary psychologists, who are trying to find universal features of our species. American college kids, whatever their charms, are a laughable proxy for Homo sapiens. The relatively few experiments conducted in non-Western cultures suggest that the minds of American students are highly unusual in many respects, including their spatial cognition, responses to optical illusions, styles of reasoning, coöperative behavior, ideas of fairness, and risk-taking strategies. Joseph Henrich and his colleagues at the University of British Columbia concluded recently that U.S. college kids are “one of the worst subpopulations one could study” when it comes to generalizing about human psychology. Their main appeal to evolutionary psychologists is that they’re readily available. Man’s closest relatives are all long extinct; breeding experiments on humans aren’t allowed (they would take far too long, anyway); and the mental life of our ancestors left few fossils.

He concludes:

Barash muses, at the end of his book, on the fact that our minds have a stubborn fondness for simple-sounding explanations that may be false. That’s true enough, and not only at bedtime. It complements a fondness for thinking that one has found the key to everything. Perhaps there’s an evolutionary explanation for such proclivities.

Still curious? Check out Barash’s book, Homo Mysterious: Evolutionary Puzzles of Human Nature, for yourself.
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The Noise Bottleneck: When More Information is Harmful

Reading Time: 6 minutes

When consuming information, we strive for more signal and less noise. The problem is a cognitive illusion: we feel like the more information we consume the more signal we receive.

While this is probably true on an absolute basis, Nassim Taleb argues in this excerpt from Antifragile, that it is not true on a relative basis. He calls is the noise bottleneck.

Taleb argues that as you consume more data and the ratio of noise to signal increases, the less you know about what’s going on and the more inadvertent trouble you are likely to cause.

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The Institutionalization Of Neuroticism

Imagine someone of the type we call neurotic in common parlance. He is wiry, looks contorted, and speaks with an uneven voice. His necks moves around when he tries to express himself. When he has a small pimple his first reaction is to assume that it is cancerous, that the cancer is of the lethal type, and that it has already spread. His hypochondria is not just in the medical department: he incurs a small setback in business and reacts as if bankruptcy were both near and certain. In the office, he is tuned to every single possible detail, systematically transforming every molehill into a mountain. The last thing you want in life is to be in the same car with him when stuck in traffic on your way to an important appointment. The expression overreact was designed with him in mind: he does not have reactions, just overreactions.

Compare him to someone with the opposite temperament, imperturbable, with the calm under fire that is considered necessary to become a leader, military commander or a mafia godfather. Usually unruffled and immune to small information —they can impress you with their self-control in difficult circumstances. For a sample of a composed, call and pondered voice, listen to interview of “Sammy the Bull” Salvatore Gravano who was involved in the murder of nineteen people (all competing mobsters). He speaks with minimal effort. In the rare situations when he is angry, unlike with the neurotic fellow, everyone knows it and takes it seriously.

The supply of information to which we are exposed under modernity is transforming humans from the equable second fellow to the neurotic first. For the purpose of our discussion, the second fellow only reacts to real information, the first largely to noise. The difference between the two fellows will show us the difference between noise and signal. Noise is what you are supposed to ignore; signal what you need to heed.

Indeed, we have been loosely mentioning “noise” earlier in the book; time to be precise about it. In science, noise is a generalization beyond the actual sound to describe random information that is totally useless for any purpose, and that you need to clean up to make sense of what you are listening to. Consider, for examples, elements in an encrypted message that have absolutely no meaning, just randomized letters to confuse the spies, or the hiss you hear on a telephone line and that you try to ignore in order to just focus on the voice of your interlocutor.

Noise and Signal

If you want to accelerate someone’s death, give him a personal doctor.

One can see from the tonsillectomy story that access to data increases intervention —as with neuroticism. Rory Sutherland signaled to me that those with a personal doctor on staff should be particularly vulnerable to naive interventionism, hence iatrogenics; doctors need to justify their salaries and prove to themselves that they have some work ethics, something “doing nothing” doesn’t satisfy (Editor’s note: the same forces apply to leaders, managers, etc.). Indeed at the time of writing the personal doctor or the late singer Michael Jackson is being sued for something that is equivalent to overintervention-to-stifle-antifragility (but it will take the law courts a while before they become familiar with the concept). Conceivably, the same happened to Elvis Prestley. So with overmedicated politicians and heads of state.

Likewise those in corporations or in policymaking (like Fragilista Greenspan) endowed with a sophisticated statistics department and therefore getting a lot of “timely” data are capable of overreacting and mistaking noise for information —Greenspan kept an eye on such fluctuations as the sales of vacuum cleaners in Cleveland “to get a precise idea about where the economy is going”, and, of course micromanaged us into chaos.

In business and economic decision-making, data causes severe side effects —data is now plentiful thanks to connectivity; and the share of spuriousness in the data increases as one gets more immersed into it. A not well discussed property of data: it is toxic in large quantities —even in moderate quantities.

The previous two chapters showed how you can use and take advantage of noise and randomness; but noise and randomness can also use and take advantage of you, particularly when totally unnatural —the data you get on the web or thanks to the media.

The more frequently you look at data, the more noise you are disproportionally likely to get (rather than the valuable part called the signal); hence the higher the noise to signal ratio. And there is a confusion, that is not psychological at all, but inherent in the data itself. Say you look at information on a yearly basis, for stock prices or the fertilizer sales of your father-in-law’s factory, or inflation numbers in Vladivostock. Assume further that for what you are observing, at the yearly frequency the ratio of signal to noise is about one to one (say half noise, half signal) —it means that about half of changes are real improvements or degradations, the other half comes from randomness. This ratio is what you get from yearly observations. But if you look at the very same data on a daily basis, the composition would change to 95% noise, 5% signal. And if you observe data on an hourly basis, as people immersed in the news and markets price variations do, the split becomes 99.5% noise to .5% signal. That is two hundred times more noise than signal —which is why anyone who listens to news (except when very, very significant events take place) is one step below sucker.

There is a biological story with information. I have been repeating that in a natural environment, a stressor is information. So too much information would be too much stress, exceeding the threshold of antifragility. In medicine, we are discovering the healing powers of fasting, as the avoidance of too much hormonal rushes that come with the ingestion of food. Hormones convey information to the different parts of our system and too much of it confuses our biology. Here again, as with the story of the news received at too high a frequency, too much information becomes harmful. And in Chapter x (on ethics) I will show how too much data (particularly when sterile) causes statistics to be completely meaningless.

Now let’s add the psychological to this: we are not made to understand the point, so we overreact emotionally to noise. The best solution is to only look at very large changes in data or conditions, never small ones.

Just as we are not likely to mistake a bear for a stone (but likely to mistake a stone for a bear), it is almost impossible for someone rational with a clear, uninfected mind, one who is not drowning in data, to mistake a vital signal, one that matters for his survival, for noise. Significant signals have a way to reach you. In the tonsillectomies, the best filter would have been to only consider the children who are very ill, those with periodically recurring throat inflammation.

There was even more noise coming from the media and its glorification of the anecdote. Thanks to it, we are living more and more in virtual reality, separated from the real world, a little bit more every day, while realizing it less and less. Consider that every day, 6,200 persons die in the United States, many of preventable causes. But the media only reports the most anecdotal and sensational cases (hurricanes, freak incidents, small plane crashes) giving us a more and more distorted map of real risks. In an ancestral environment, the anecdote, the “interesting” is information; no longer today. Likewise, by presenting us with explanations and theories the media induces an illusion of understanding the world.

And the understanding of events (and risks) on the part of members of the press is so retrospective that they would put the security checks after the plane ride, or what the ancients call post bellum auxilium, send troops after the battle. Owing to domain dependence, we forget the need to check our map of the world against reality. So we are living in a more and more fragile world, while thinking it is more and more understandable.

To conclude, the best way to mitigate interventionism is to ration the supply of information, as naturalistically as possible. This is hard to accept in the age of the internet. It has been very hard for me to explain that the more data you get, the less you know what’s going on, and the more iatrogenics you will cause.

***

The noise bottleneck is really a paradox. We think the more information we consume the more signal we’ll consume. Only the mind doesn’t work like that. When the volume of information increases, our ability to comprehend the relevant from the irrelevant becomes compromised. We place too much emphasis on irrelevant data and lose sight of what’s really important.

Still Curious? Read The Pot Belly of Ignorance.

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