Monthly Archives: December 2016

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?