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 think the reason I enjoy Totoro so much despite the tiny scope of the conflict in the plot is in it’s careful handling of dramatic elements.
Despite the lack of epic scenarios, Totoro still strikes me as the most moving of the Miyazaki films. The problems encountered by the children seem trivial to an adult (except the final conflict of the disappearance of Mei), and so too seem the joys of resolution. Certainly, they would not have a great effect on me today. But to Satsuki and Mei, with their rich imaginations and lack of experience, the conflicts and the joys challenged their existing boundaries and suggest unfathomable possibilities joyful or dark. Living through Satsuki and Mei is to relieve a childlike fascination with the world.
The importance of relativity of experience in Totoro cannot be overstated. The representation of childlike fascination would easily be broken should the everyday life of Satsuki and Mei provide any fantastical or emotionally intense elements. The impact of the fantastic in Totoro exists solely in relativity to the mundane everyday.
Even after seeing Totoro no less than eight times, I am still awestruck by the subtle but masterful directing balancing the mundane and the fantastic.
But going back to the relativity of experience, I think this sort of relativity applies to a huge variety of settings in life. Most qualities in our lives have been stable enough to have an established baseline – qualities like the level of hardship, the requirement for patience, eventfulness, and even the sodium level of our diet to name a few. Just like our desensitization to our baseline level of sodium, other qualities become desensitized over time as well, leaving us to see only fluctuations as notable experiences.
This theory has significant implications. For example, it may seem counter-intuitive to enforce deadlines and pressure on oneself, but a lifestyle desensitized to the additional stress level may reap the benefit of added productivity. Discipline seems uncomfortable to outsiders, but perhaps this is a reason why people can stay in demanding routines. Perhaps it is possible to minimize distractions by desensitizing oneself to a low baseline of distraction quantity, and perhaps it is possible to minimize indulgence by establishing small things as indulgences.
Naturally, baselines have a limit. We cannot lower our baseline desire for water beyond our body’s minimum requirements. From my experiences however, most measures in life can be adapted for, and the range is surprisingly wide if the baseline is moved slowly.
If this is the case, most people will have lots to optimize for, and can identify simple criteria to judge their own optimization. Off the top of my head: