Throughout the month of November I finally found the time to clear my list thoughts and observations about everyday life. This is a project that I have started since summer, and while some answers were not thorough, I can honestly say that the observations made and the habit of constantly thinking has served me tremendously.
This doesn’t mean I’m done thinking about life questions. I believe that there will be a new wave of questions when I start reading rationalist literature. However, the questions will probably come less frequently now, as I shift my focus toward acquiring new skills.
EDIT: To improve readability, I grouped my thoughts loosely into five groups: self-efficacy, execution, communication, metaphysics, and personal notes. This way, the thoughts are arranged in order of averageness to the reader, and there’s room for breaks. Have fun!
I have a problem for you:
Suppose we want to maximize our combined number of X and Y products over 10 days, and each day we can either:
Over 10 days, how should we structure our choice to obtain the maximum number for X + Y?
If you have a strategy bent, then this problem will be a bit of fun, and a bit more complex than it seems at first. This is a type of problem that strategy and resource management games depend on often – when multiple resources have growth patterns that are co-dependent, simple rules can give rise to unexpectedly nuanced problems. Even better, the formula is pretty modular and simple to tweak, so it’s easy to introduce variations.
I call these type of problems resource entanglement problems, since their hallmark is multiple valuable resources with co-dependent growth curves. This type of problem rely on straightforward goals and rules to attract new players and keep those players with unexpected depth. The high ratio between depth and complexity of rules is a hallmark of good game design.
But I’m not here today to talk about game design.
These problems are not always hypothetical. Resource management is a real field of study, and even outside of business there’s plenty of tradeoffs in everyday life. Consider a classic: time vs. money. With money, we can purchase services to save time. With time, we can put additional effort into making money. Since we value both time and money, we find ourselves in an intriguing cycle.
But there is a trap. Elegant optimization problems are fun to think about, but elegance does not correlate with necessity. Elegant problems grabs our attention but may distract us from more important problems. For example, resource management problems are often much more trivial when the ultimate value of one resource is removed. What if hypothetically, we were to demote our value of money? How much does money mean? What about time? These problems risk sounding senseless initially, but depending on the person may have surprising answers.
If you find yourself working on some intricate problem, perhaps the first thing to ask is what you can demote.
Richard Garfield, the designer of Magic the Gathering, defines luck in his ITU Copenhagen talk as “uncertainty in outcome”. I think by modeling human reasoning as Bayesian, we can come up with another fruitful, if not a more fruitful, definition.
Suppose I were to take out a quarter from my pocket and ask you to guess my next twenty flips. You perceive the heads/tails probability to be 50/50 with high confidence, and so your accuracy on my first three flips, which turned out to be all heads, is very heavily luck based.
Now suppose that my next fourteen flips are all heads. At this point, you will be quite certain that the game was rigged, and your heads/tails probability becomes 100/0. Indeed, your next three guesses are correct. Curiously, at this point in the game we no longer perceive luck as being involved.
In Bayesian speak, our 50/50 distribution is our prior belief, and 100/0 distribution posterior belief. Note that we consider the prior (no pun intended) to be highly random, whereas the latter not so much. Wikipedia defines random as “the lack of pattern or predictability in events”, which suits our current purpose. We will roll (pun intended) with it.
Now another example.
Suppose I leave right now to catch the next bus to work. The bus arrives just as I get to the bus stop. How lucky!
Now suppose I tell you that I have memorized the bus table and had been stealing glances from the wall clock while talking to you, cutting our conversation short when it’s time to leave. The bus arrives just as I get to the bus stop. Not very lucky, but pretty calculating.
This example is insightful in two ways. Firstly, to be perceived as lucky I don’t have to intentionally make a choice. As long as the outcome benefits me, I seem lucky. Secondly, we can make things seem less luck-based via additional information. My new prior (which is my posterior after memorizing the timetable) was good enough to reduce randomness. What appears to you as random may be fairly predictable for me. Randomness can be subjective. In fact, it often is. The stock market, weather patterns, and even the search for a good romantic partner can be predictable for one and random for another. Thus, a definition of luck necessarily takes subjectivity into account.
So I think that perhaps a better definition for luck is “when apparently unpredictable outcome offers high utility”. In Bayesian speak, when an outcome of good utility occurred despite a prior that doesn’t favor it. We should note too that utility, which is subjective too, also affects our perception of luckiness. In a bet with 90% chance to win one million and 10% to loss one million, the winner is still declared pretty lucky. Our perception of utility is biased. In the case of loss-aversion, sometimes the bias is advantageous.
Prior and utility are both extremely malleable, and a variety of cool insights arise (a.k.a. this is an insight dump where I stop being good at explaining things):
Elon Musk caught my attention when he said something to the effect of “successful
Over break I made a pretty big mistake of being enamored with an unfeasible research plan. I had an idea that my instinct told me was going to work, and assumed that it is indeed going to work for at least a month. To make matters worse, over the month many chances to question the idea has came up, but I avoided taking the difficult route each of the time because 1.) I didn’t want to face the possibility of losing the idea and 2) I assumed that things are probably going ok and everything will magically solve itself.
The reason I caught up to the mistake early was because I forced myself to work on the plan for 2 hours a day. If I didn’t do that, it’s conceivable that I would have caught the mistake close to the due date of my proposal.
When I think back to my mistake, I saw a few surprising lessons. Firstly, up until the point I started procrastinating thinking about the idea, I had done absolutely nothing wrong. It’s impossible to prevent a problematic idea from stealing your attention, so the only way to fix the problem would be thinking more or trying. On the other hand, since at the time we did not know a problem exists, the advice “fix mistakes fast” isn’t applicable here. Rather, we should try to confirm untested assumptions before depending on them. This often boils down to “testing assumptions fast”, since over time we rely on our assumptions more. Often, during this testing, we discover better answers.
My procrastination taught me that I should be extremely suspicious of untested assumptions with high stakes, and be especially alert to assumptions that my minds attempts to persuade me not to suspect.
Isn’t that natural, after all? We all want to be right, but given the complexity of the world, we are bound to make many wrong predictions.
I’ve been wondering about why I had such a great time reading Daniel Kahneman and Scott Alexander (of Star Slate Codex), and in the process I stumbled upon some important academic truths.
In many questions of more-than-trivial complexity, the closest we are going to get to actual answers is through scientific inquiry . For this reason, in informed discussions, we have a license to reference these academic findings as if they were truths. In this way, while academic work don’t directly answer some of the most complex and important questions in life, they take us much closer.
But all those discussions are based on the assumption that academic work is conducted in good faith – i.e. the numbers are correct and faithfully represented, the conclusions are reasonable and substantiated, etc. A good analogy for the value of academic work is the U.S. dollar, as both are based on good faith. As the USD would be worthless without the backing of the government, research would be useless without the assumption of academic honesty (and this honesty is somewhat reasonably connected to faith in the research institution).
The insistence of relying on primary source exist for the same reason. With each quotation, some information is inevitably lost. Worse, as most work attempt to make a point, the results may be misunderstood or deliberately misconstrued. The outcome of using information distant from primary source is largely the same as using bad research: your points will be inaccurate. While the chain of references used in academic may still cause some deviations from initial references, the decay of quality is much slower that way.
One of the reasons I like Daniel Kahneman and Scott Alexander is that they present surprisingly useful conclusions but are able to back their conclusions with research. This is worlds apart from the status quo psychology and self-help books in terms of reliability.
I remember being drilled on the importance of primary source in seventh grade, then at least two or three times more in the course of my K-12. The claims never appeared to be backed up, and I soon began to see the practice as some of formality. But now I my views have come around.
I still wonder though, did the teachers or the educators know? Four years of college education certainly taught me little on the subject. Concerning how the importance of these concepts hinge on persuasiveness, the failure of the concepts to latch on to students is ironic.
 This article deals with research, but first-hand interviews follow’s the same logic.