Permission, who needs it?

I’ve noticed, and experienced, an interesting human need lately — permission. People seek it, or at least I do. The author of Living a FI describes the problem in his process of becoming financially independent and quitting his job. The monster really rears its head when we’re trying to do something that is unusual, such as quit working for money in your thirties, or free climb the granite faces of Yosemite without protection. People question the motives of such decisions because they are contrary to typical behavior.

But is that behavior unreasonable or foolish just because it cuts against the grain? I don’t think it is. Doing something different or unusual from others’ paths is only that, unusual. A foolish or unreasonable decision could be typical or mundane. Living in perpetual credit card debt is a typical, foolish decision. It seems to me that unusual and foolish decisions aren’t necessarily related.

What seems to make folks link unusual and foolish decisions is that unusual things are, by definition, not frequently done, and consequently, we feel there must be a reason that unusual things are infrequently done. The implication is that unusual things are infrequently done because they are foolish, rash, unwise, or any other negative adjective you’d care to use in explaining them.This implication seems to drive us to seek validation for wanting to do something unusual. Enter: permission.

We feel that we need permission for many actions in life. Permission granting is built into our cultures. Parenting is all about permission. Rites of passage are, in essence, ways of granting permission to people by declaring them capable of certain roles or activities. Permission can function in different ways: it keeps us safe from activities that could be dangerous when we aren’t ready for them (e.g. using power tools or kitchen utensils before we’re appropriately trained in their use); permission can also lead us to view the world as a system of control, leading to thoughts like, “I couldn’t do that because I’m not allowed.”

If we look at permission in a negative light, it starts to cast shadows of doubt and fear, which can drive many of our decisions. Permission is one easy way to remove fear through an appeal to authority, “I can do this because my boss/teacher/parents said so.” However, wiping away fear by appealing to authority passes the buck by giving the agency of a decision to the authority who gives you permission. This takes away our own abilities to make decisions for ourselves. The parts of my life that I’m most proud of are the ones that I kept my agency in making a decision, or knowingly and willingly submitted to someone’s authority in order to learn or gain something. My proudest moments are the ones where I contributed to a project that I value, or I achieved something through learning, skill, and effort. I suspect that most folks wouldn’t say that their life highlights involve just receiving permission: that’s a bit feudal, isn’t it?

Permission can be valuable. Some rites of passage could be seen as a type of permission that would be valuable moments in a life, but rites of passage are often the beginning of a larger adventure. They aren’t the end, and this is the point: permission should enable you to do something greater and better, rather than simply serve as a checkpoint that allows you to pass the buck if something goes badly.


If Money Were No Object

“What would you do if money were no object?” It’s interesting that we typically consider just one side of this question. To simply pose the question implies that you ought to imagine you had so much money you could spend it without consequence. However, there’s another way to consider the question. There is a set of people who live as if money were no object, but they have very little money. Willfully homeless people and wandering ascetics are are a couple examples of this set.

Why do people disregard an entire subset of possible lifestyles that satisfy the question? Perhaps it’s because living without regard to money is a riskier proposition than being filthy rich, or maybe, the social stigma against homelessness makes it an unacceptable option.

However, we can learn something from considering homelessness as an answer to the question, “What would you do if money were no object?” For the majority of us, we must choose among a limited set of options. To paraphrase the tag line of a popular blog, “You can do anything, but you can’t do everything.” This maxim applies even if you were embarrassingly wealthy. Even though money isn’t a limiting factor of the wealthy, there are other details limiting their choices and lives.

Perhaps a better way to phrase the thematic question is, “What does your perfect life look like?” The Mad Fientist’s December 2016 podcast offered this reformulation of our question. This rephrasing implies that money is an object of consideration, and even if your perfect life involves having a billion-dollar net-worth, you must also account for obtaining it too.

By considering a broader spectrum of ideal lives, from extreme wealth to extreme poverty, we increase our options. For example, many people idealize the opportunity to live out of a backpack for months as they travel inexpensively through foreign countries. While there is considerable privilege in this dream, it is also a form of homelessness that carries its own risks. Why couldn’t one live out of a backpack or a car, moving from campsite to campsite every week or two? In the USA, this is a legal option, and you’d get to see many beautiful places doing it.

I want to note that I’m not attempting to glorify or idealize homelessness or poverty. Rather than ignoring it, I want to notice and consider homelessness and poverty. There are lessons to learn about personal finance by examining these issues. Moreover, we are much more likely to notice ways to help resolve these issues through these examinations than we are by turning away from them.

The Signal and the Noise — Review

7 Oct 2016

The Signal and the Noise, by Nate Silver, is a book about statistical predictions and forecasts. The primary question of the book is: how do we make predictions that capture more signal and less noise in a data set? This theme is similar to N. N. Taleb’s project in Anti-Fragile: how do we make decisions in a world we can’t fully understand? Silver’s answer to these questions involve a combination of empirical observations, statistical analysis that is appropriate to the data and question under review, and an answer that provides as precise and accurate a prediction about future events as possible. Avoiding overconfidence, over-fitting one’s model to the data set, and making too vague a prediction due to insufficient analysis are mistakes that Silver recommends we avoid.

One way to make better predictions is to use Bayes’s Theorem: (xy)/(xy+z(1-x))
f(x) = ————
xy + z(1-x)
This theorem states that the posterior probability, f(x’), which is the probability of an event, x’, occurring after we’ve considered some previous evidence, x, y, & z. x is the initial estimate of the event occurring independent of any evidence. y is the probability of an event occurring if x is true. z is the probability of an event occurring if x is false.

For example, if you find a pair of underwear in your dresser drawer that does not belong to you or your spouse, and you suspect your spouse of cheating on you, one way you could use Bayes’s Theorem to produce a probabilistic prediction about the likelihood of your spouse cheating on you is as follows:

x, the initial estimate that your spouse is cheating — 4% (which is the national average of men cheating on their wives)
y, the probability of underwear appearing if he is cheating — 50% (essentially random, even odds)
z, the probability of underwear appearing if he is not cheaing on you — 5% (is there some other, innocent explanation for the underwear’s appearance?

Using Bayes’s Theorem, our prediction that our spouse is cheating on us goes from 4% to 29%:
f(x’) = (.04 x .5) / ((.04 x .5) + .05(1-.04))
f(x’) = .02 / (.02 + .048)
f(x’) = .2941

Silver’s analysis of predictions and forecasts echoes that of Taleb in Anti-Fragile. Both claim that financial analysts are generally over confident in their abilities to predict financial markets, based on their performance. Both claim that our models for predicting most events aren’t as good as we say they are.

However, Silver notes some interesting exceptions, where our predictions are successful: weather and baseball, and chess. These fields yield to statistical analysis because they have a few common features:
– First, we understand the principles that cause the events pretty well. In other words, we can avoid the problem of mistaking correlation for causation in these situations. Weather, chess and baseball have relatively simple sets of rules that create the complex situations we observe. Consequently, we can use models to predict how these complex situations will evolve with some success.
– Second, there is a long history of recorded observations about these games and the weather, so we have a good data set to use in making our next predictions.
– Third, these phenomena occur regularly and frequently, so we get feedback on our predictions, which allows us to learn from our mistakes.

Phenomena that don’t share these three features — well-understood causes, a large and accurate data set, and frequent events — are harder to predict. The stock market is hard to predict because we don’t understand the complex set of causes that drives the two-dimensional change in a price chart, and extreme price changes don’t happen very often that allow us to test and learn from our predictions. Earthquakes and epidemics are hard to predict because they don’t happen very often and we have difficulty observing their causes: earthquakes are caused by forces hidden deep in the Earth’s crust, and epidemics have complicated generation and transmission paths.

The Signal and The Noise is a good foil to Taleb’s Anti-Fragile. It provides a useful introduction into statistical methods as well as many case studies where statistical analysis succeeds and fails. Taleb’s book focuses on the failures of statistical analysis, instead offering heuristics that allow one to navigate in an uncertain world, and while Silver’s book echoes many of the same heuristics — e.g. prefer long-surviving patterns/events over newer ones, in the absence of convincing evidence to the contrary it is useful to assume the future will be like the past, it is not useful to assume that you are special or unique without convincing evidence to the contrary (a la financial analysts) — Silver also illuminates areas where statistical analysis has succeeded, which is helpful for the beginning analyst to see.

Suggestions for predictions:
– Make probabilistic predictions, not specific ones.
– Don’t be overconfident of your skills or predictions, it’s okay to say you don’t know
– Don’t focus too much on analysis at the expense of understanding your observed events, e.g. how much data do you have to analyze? Are the data linked in a time series, or is each event independent of the others?
– Be willing to change your predictions in light of new evidence.
– Try to be less wrong, as opposed to more right. I.e. Taleb’s “via negativa” epistemic method.

Turns and Retractions

I’m re-reading Martin Heidegger’s Being and Time. It’s amazing how rigorous and incisive Heidegger’s thinking and writing are in this book. His goal is to explain the difference between Being (or the process of existing) and beings (or the things that exist in the world). It’s ironic how much writing he has to do in order to explain this very simple idea well, but simply looking at the table of contents shows how thoroughly and systematically he works through this project.

What surprises me about Heidegger’s career, is that he takes a “turn” after spending years elaborating the themes of Being and Time. In essence, he renounces his systematic elaboration and exploration of Being and Time for a more experimental and empirical style of writing and analysis. The corresponding book about ontology that he publishes after his turn is called Time and Being, and it is about 20% as long as Being and Time. What made Heidegger take this turn?

Another famous German philosopher took a similar turn late in his career. Ludwig Wittgenstein worked out an axiomatic system defining language, only to scrap it later on for a looser, less-rigorous, more adaptive model of language. Like Heidegger, Wittgenstein’s style of writing and analysis changed drastically when he began his new project.

Are these only two thinkers’ idiosyncratic careers in philosophy, or is there something to note in their rejection of a certain type of systematic thinking? If they are simply idiosyncrasies, how have their works — both young and old — garnered such attention from popular and academic readers? If there is a deeper issue in their respective ideological turns, what can we learn from their career paths?

One maxim I heard in high school was an analogy to erosion applied to human life: “Aging knocks off your sharp corners.” I understand this to say that our attitudes break down to become more general and adaptable as we age. I wonder, is this what happened to Heidegger and Wittgenstein? If so, is there a way for us to determine which version of these thinkers is more useful, powerful, effective, or somehow “better”? Are the latter Heidegger and Wittgenstein wiser and better-shaped than their younger selves? Are the younger selves sharper and more incisive? Is there a way that we can tell?

Sexist Eyebrows

Back in October, while America was still in the throes of election season, I had an interesting discussion with an acquaintance. She was telling me about the Internet’s umbrage over Tim Kaine’s unusual eyebrows. That the Internet cared about Mr. Kaine’s facial hair was news to me. It’s useful to note that I’m a bit of a Luddite. I don’t have a television or Internet subscription to my house, but I do have a smart phone, and Internet access is only a short walk. So, although I listen to the radio most mornings, I have limited access to the noise of the news, which means I don’t usually know what temporarily famous people look like or do. After finding a picture of his eyebrows and concluding that Mr. Kaine looks much like Dan Akroyd, we continued.

My friend passionately argued that Mr. Kaine’s eyebrows illustrate a double standard in society’s expectations over men’s and women’s fashion. This double standard is familiar to me: it is enough for a man to be smart and aggressive in business or politics, but it’s not enough for a woman — she must also be young and attractive. Also, aggression doesn’t commonly look as good on women as it does on men: women are perceived as “bitchy” if they are aggressive about their desires or goals, while men may be perceived as an “asshole”, but that is somehow translated into ambitious or driven, which is okay. But how do Tim Kaine’s eyebrows illustrate this double standard? That’s a good question.

According to my friend, Tim Kaine’s eyebrows show that society holds men and women to different standards because Tim Kaine doesn’t have to pluck or shape his eyebrows. In other words, because Tim Kaine can step in front of television cameras with funny looking facial features, we live in a lesser world. That is approximately how my friend’s argument goes.

And while I agree that men and women are held to different standards of beauty and success, I’m not sure that Mr. Kaine’s eyebrows are a good standard by which to measure sexism. The prima facie objection to my friend’s argument is that the Internet objects to the eyebrows. Mr. Kaine hasn’t fooled anybody here. The Internet demands that he go to a stylist and have his supra-ocular mustaches manicured.

There are more convincing cases that show a sexist double standard in society: the lack of women corporate executives in S&P 500 companies, the gender inequality in wages, and the focus on women’s appearance over their intelligence or applicable skills. All of these issues demonstrate the problem of sexism in societies around the globe better than Mr. Kaine’s eyebrows. So, let’s get Dan Akroyd back on Saturday Night Live and do a good parody while we work on fixing some real gender equity problems.

Thoughts on Happiness

Dan Gilbert has written a book and given a TED Talk about happiness. His starting point is that humans are the only animals who can imagine future scenarios and develop preferences about which future scenario we want to experience. This is a powerful skill that we have. However, Gilbert goes on to claim that we’re famously bad at choosing futures that will make us happy, even when we’re told that the choice we’re likely to make will make us less happy.

Gilbert reviews a bevy of studies that show we’re bad at choosing future experiences that make us happy. For example, we are generally less happy in situations that we know we could have chosen differently, yet we typically prefer to be in situations that offer the option to make a different choice — even when we know that this will likely make us less happy with our decision. But wait, there’s more: we’re also bad at imagining how happy we’ll be in different scenarios. If we imagine whether we’d be happier in the long run with winning the lottery or being a paraplegic, those of us who aren’t already paraplegic) will almost certainly choose the future in which we win the lottery, but research shows that lottery winners and paraplegics are equally happy several months after their respective accidents. Gilbert says we are equally happy after an unexpected tragedy and an unexpected windfall because we have a psychological immune system that works to maintain our long-tern happiness in the face of extreme events. So, we prefer situations we know will make us less satisfied with our choices and we are bad at guessing how happy we’ll be in the face of extremely events. What are we supposed to do now?

Gilbert plays the role of a consummate scientist in his writing and speaking. He doesn’t cop to being a self-help guru, and he goes even further by staying mum about practical applications of his research. He’s simply reporting what scientists have found in their research, how frustrating. Science is famously descriptive. Scientists report their findings, and if they’re feeling generous, they point towards areas of further research. But that is the role of science: to tell us how things work and how events occur as objectively as possible. The problem with this method is that it is easy for people to mistake description for prescription. The misuse of Vilfred Pareto’s discoveries of wealth distribution is a great example. Pareto discovered several situations in which twenty percent of the population owned eighty percent of the assets, and he demonstrated some interesting mathematical permutations on this observation. Subsequently, some writers have taken this observation about statistical distributions as a heuristic for leading one’s life. This is fallacious thinking at its finest: simply because we observe an interesting event over here doesn’t imply that the same event applies or occurs over there too. Gilbert is trying to avoid this kind of self-help scientism by remaining silent on what to do with this information, but simply being told by a scientist that we suck at making decisions about happiness isn’t terribly useful. We still want some help making use of his data.

Fortunately, we have a way around this epistemic void without resorting to new-agey views about science and mathematics:

First, Gilbert explains that we’re bad at making choices about extreme events, which shouldn’t be surprising because extreme events don’t happen very often, so our ability to interpret them based on past evidence will be limited because we don’t have much information available to us. This reasoning is tautological, but a good empiricist ought to know when we don’t have enough information to work with, and when we don’t have enough information, our choices are to change our method or get more data. In our case, changing our methods will get us to a better place with less work. Let me  explain a little more. Gilbert admits we are better at choosing between smaller, shorter-term options about our happiness. Moreover, Gilbert says that our happiness is more affected by more frequent, less extreme events than it is by rarer, more extreme events. This combination of greater effect and better forecasting for more common events implies that we should focus on the small stuff to have a larger impact on our happiness.

The idea that we can better address small problems resonates with ideas N.N. Taleb elaborates in his book, Anti-Fragile. We ought to look at decisions about happiness as making a series of small decisions that compound to a larger result. Because we have more and better experience with small, frequent problems, rather than extreme, rare problems, and if we can decompose big problems into smaller problems, we’ll arrive at a better, more durable solution. For example, rather than focusing on losing twenty-five pounds of fat in a year, it’s simpler and more concrete to focus on changing one’s daily diet and exercising for thirty minutes three times per week. Without even stating a goal of losing twenty-five pounds, simply focusing on improving your diet and increasing your exercise, the goal would likely be realized. And even if you didn’t meet the goal, you’d be a healthier more toned or muscular person regardless of your body weight, no yo-yo dieting required. So, the first step towards happiness is focusing on small, manageable problems.

Second, We can further address the problem of how to be happy by studying psychological experiments about the amount of pain caused by losing versus winning. Daniel Kahneman and Amos Tversky conducted these experiments and Dan Ariely popularized them in his book Predictably Irrational. In essence, we don’t like losing about twice as much as we like winning, which gives us a hint at a practical approach towards happiness: focus on losing less, rather than winning more. In other words, lose less, and lose less often, and you’ll be probably be less unhappy, which is an acceptable start towards being happy, as far as I’m concerned.

Combining these two observations gives us the following maxim:
To be happy, choose to put yourself in situations that make you less unhappy
by focusing on small, manageable problems.
This advice may seem trite and obvious, but it a second look. Browse a social media or news site for fifteen minutes, and you will likely concede that we generally choose to give our f—s to large, difficult to manage problems that we hope will make us happy: worrying about the future president, for example. This is the opposite of my recommendation. So why not give it a shot? Try being less unhappy about the little stuff you can affect: see what comes about.

We don’t study what we’re good at

(Dear Grammar Nazis, never mind the preposition hanging off the end of that title. We’re going for colloquial English today.)

I’ve spent most of my life attending and working in educational institutions, which has given me lots of time to observe academic professionals. While many of them are experts in their fields, I’ve found something ironic: academics don’t study what they are good at, or perhaps, academics aren’t good at what they study. In other words, the subjects that interest people aren’t those that they feel they have mastered. This is precisely why people study those subjects: they want to improve at something they don’t fully comprehend yet; they want to know more about something. Of course, people tend to improve, and even become accomplished, at something they spend a lot of time practicing, but that doesn’t necessarily mean they’re “good” at it.

A couple examples might help illustrate my point. First, there are many professors of communication studies where I work. One would assume that these professors are good at communicating. After all, they have been studying communication long enough to earn a Ph.D., yet many of these people are famous for terse, cryptic replies in emails, or no reply at all after they ask for assistance. In short, there are experts in communication studies who have communication problems. There are also misanthropic anthropologists, unreasonable philosophers, racist ethnic studies professors, and I’ve also worked with a Ph.D. computer scientist who couldn’t accurately diagnose a networking problem.

Second, in the movie Good Will Hunting, Robin Williams has a great monologue about Freud doing enough cocaine to kill a small horse, hinting that at least one psychiatrist has some addictive tendencies as well as some pretty colorful theories about the human mind. We’ll assume that Williams’ monologue is scientifically accurate for our purposes here, or at least anecdotally useful. Freud’s colleague, Jung also had some unusual ideas about human consciousness, as well as several extramarital affairs and possibly a mental disorder. Certainly, Freud and Jung have contributed much to the fields of Psychology and Psychiatry, but if their personal histories are any indication, they may not of have been the best examples of mental health.

What is going on here? Why are experts apparently inept at practicing what they preach, so to speak? The answer, I submit, is that we don’t study what we’re good at. Rather, we study what interests us, and along the way, we might gain some proficiency in our chosen course of study. However, there is a difference between knowing something well and doing it well. For example, having a deep understanding of music theory doesn’t let me immediately pick up a saxophone, trumpet, or guitar and play them like John Coltrane, Miles Davis, or Jimi Hendrix — even being able to write music for those instruments doesn’t ensure my ability to play them. Conversely, some musicians don’t understand music theory, yet they can play their instruments better than folks who know how to play that instrument and know music theory. In other words, performance and knowledge aren’t the same thing: knowing how to play the piano, i.e. pushing appropriate white and black keys in rhythm to create music, is different than being able to apply that knowledge. There is a similar situation going on with the communications professors and psychiatrists. These people know a great many things in the fields they study, but performing that knowledge is a different task entirely.

This is where Aristotle’s concept of wisdom might be useful. Aristotle distinguishes two types of wisdom, theoretical and practical. To paraphrase Aristotle’s point, theoretical wisdom is knowing facts about the world, and practical wisdom is knowing how to live well. We might say these two categories of wisdom are ‘knowing’ and ‘doing’. The professors and psychiatrists from earlier have theoretical wisdom and little practical wisdom: they know a great many things, but they don’t seem to apply that knowledge very well. Practical wisdom is knowing what to do at the right moment: talented musicians who play their instruments very well without knowing the theory behind their performance possess practical wisdom without much theoretical wisdom.

This two-headed wisdom monster presents a problem: how do we have both practical and theoretical wisdom? Philosophers have been bickering for millennia about wisdom, so can we even trust that they know what they’re talking about? This isn’t a question I’ll pretend I can answer, especially in a single blog post, but it’s interesting food for thought. It’s something to strive for.