I attended a talk by Richard Garfield, the game designer behind Magic: The Gathering. He made a point that adding randomness to a game can be a way to give it that property of "easy to learn, hard to master", and in turn make it easier for a community to grow around the game. When you add randomness, it becomes possible for novices to sometimes beat more experienced players through sheer luck. Contrast that with e.g. chess, where it doesn't take much skill difference before the most skilled player is almost certain to win. So randomness makes it easier for newcomers to get the occasional win, to keep them motivated while learning. But at the same time, randomness can make it harder to master, because the cause-effect-relationship between good decisions and good outcomes becomes blurred, like the article points out, defeating the trivial "reward function" we usually use for evaluating our performance. It takes a lot of discipline to say "I won, but it was luck, because I actually misplayed" and learn from that. And on top of that, high level play will become a game of analyzing and estimating probabilities, minimizing or maximizing variability depending on the strength of your position, basically embracing randomness as a gameplay element to be understood and sometimes even manipulated, despite being so intangible compared to most other gameplay elements. So used the right way, adding randomness to a game can both lower the barrier of entry, and raise the skill ceiling at the same time.
I'm a serious but hobbyist player with ~15k hands last month.<p>The thinking like a poker player is mostly about being upfront about your risk tolerances and then having a culture supporting people who make the best risk adjusted decision, even if it doesn't work out.<p>Expected Value is complicated because a 50% chance for $100 is the same as a 10% chance at $1000. It's the variance, not the EV that makes a lot of decisions hard.<p>You (and businesses) need to decide what risk is acceptable, communicate that clearly. Reward people who manage risk in a way that's aligned with the business even when it doesn't work out. Get rid of people who are either take too big risks, and people who don't take risks.
I've also heard this topic referred to as "results-oriented thinking," which is generally what you don't want: you shouldn't judge a decision based on its result, but on the information you had at the time.<p>The key to this idea, which I don't see covered in the blog post, for making future decisions is that you shouldn't let past bad luck affect your future decisions. If you lose a positive EV bet, it shouldn't shy you away from making the same bet again.<p>Some examples off the top of my head about decision-making traps people could make:
- Continuing to bet at the roulette wheel to "regain" what you've lost
- Not going to a well-rated restaurant again just because you had a random bad experience
- Not investing in ETFs after being burned by past downturns
- Playing MTG and not burning them out just because they had a counterspell last game
I think I first heard this quote in one of Taleb's books (paraphrasing):<p>In Ancient Greece, you were a hero for what you attempted to do and not for what you actually accomplished. This was because the Greeks considered the outcome to be primarily governed by the whim of the Fates. What you attempted to do was a function of your courage especially given that a big task could lead to a big failure outside of your control.<p>I think of this quote often anytime the "outcome vs process" discussion comes up.
I think life is way more complex than the poker. Yes the mindset could be useful, but mindset without proper data is not going to help a lot.<p>Here is what I'm thinking: How about we go up a level, and accept that life is basically random and a lot is decided by luck (your gene is luck, your childhood education is also luck, these two pretty much decide a lot of things), as indicated in the article, but maintain a (meta) mindset that can manoeuvre around or even mitigate negative emotions?<p>For example:<p>- Realize that probability is useful in poker but not that useful in life, thus it is impossible to calculate mathematical expectation.<p>- Learn to harden one against impulsive emotional acts (impulsive purchase, suicidal thoughts, etc.).<p>- Learn to control one's material requirements and save some $$ for rainy days.<p>- Keep connections active so when really bad days come maybe someone can get you out.
This article, like Duke's book, has a solid premise, but fails to provide any actionable advice aside from a simple risk/reward framework. When I read the book, I was hoping there would be more information about how to properly handicap various situations, but there just wasn't.<p>Business and life decisions aren't as simple as calculating pot odds and outs. Anyone who has estimated a complex and unfamiliar programming task knows that the unknown-unknowns are the biggest part of any equation.
Well you can play millions of poker hands online. Life isn't like that. You have only ~30 years, and you have 30 * 365 = 10950 hands if we count each day as a hand.<p>So what you need to work on is how you define "success" in life. Money is a high variance objective, whereas self-improvement is a low variance objective. I think finding a good balance of variance and tolerance to risk is a key to happiness.
I'm struggling to see how the advice applies to their own examples. Take Jane, who took a great offer but was hit by a recession:<p>- potential reward of the decision: checks out<p>- probability of getting it: 95% if you already have the offer in hand<p>- resources (time/effort/money) to bet to get the reward: not significant<p>Then there is 'imagining a bet' and 'analysing your past failures'. The first one might lead you to the <i>'what if the company goes bust?'</i> question, but would that really help in the decision making or be a reason to <i>not</i> accept the better job? The unlucky event is a recession, not this particular company doing badly (i.e. no signals to see).<p>If a recession hits, there is no reason to believe the consumer goods co. would fare any better and her old job more secure. I'd really like to see the 'poker player' mindset at play but this just feels like a complete miss.
The author sounds like a losing poker player. If my calling frequency is based entirely on pot odds, a good opponent would just start bluffing me out of every pot by increasing their bet size to a point where I can’t profitably call.
Is this site related to Knowledge Project / Farnam Street (fs.blog), a competitor, or just a ripoff???<p>I noticed because I read what seems to be the exact article there, with the same illustrations and everything on fs a couple weeks ago. Maybe it was a link in the newsletter to this article.<p>Looking around there is a lot of overlap in content.
As an amateur poker player, both at the table and in life, I can say that sometimes the biggest wins are the result of bluffing.<p>It does not matter what hand an opponent has as long as you can convince him you have a better hand.<p>Also, it is handy to recognize the situations when an opponent might bluff, so you can call.
I've found that frequently bluffing in life pays off. Generally you don't get caught very often and the cost of getting caught generally isn't very high, even if the stakes are. You have to be prepared to take your way out of sticky situations though.
Life's a game of chance where we control the odds.<p>Just this morning I was reading Scott Adams's How to Fail at Almost Everything and Still Win Big, and he was talking about making bets in Life that don't cost any money (they only cost time) and if we do this there's ~100% certainty of winning. Of course he's more eloquent and elaborate. It's also another way to look at "we miss 100% of the shots that we don't take".
The non buzzwordy way to express this is to use some basic concepts in probability to make accurate decisions in the face of randomness. Specifically when making a decision you should think about the expectation[1] of the outcomes overall and some properties of the distribution. This requires you to think through what all the outcomes are and what you think their probabilities are.<p>1)All other things being equal you should generally prefer a higher expectation decision to a lower-expectation one<p>2)But you have to avoid decisions which have outcome distribution properties you can't tolerate. Most obviously you should generally ensure that your risk of ruin is zero because even if the probability is very low, that outcome is close to impossible to recover from so must be avoided.<p>3)For two possible strategies with similar-ish expectations in real-life terms you may well want to choose the one with the lower standard deviation of payoffs. Like say you're choosing between an offer at medical school to train to be a dentist and pursuing your long-shot idea of going to Hollywood and trying to make it as an actor. Imagine that when you look at the outcomes you estimate that in acting you have an insanely low probability of making it but a huge payoff if you do and in dentistry most people do pretty well but no-one's partying on superyachts with Jay-Z and Beyonce. Well even if the expectation of acting works out slightly higher, if they are close enough you should probably pick dentistry because the variance of outcomes is just way lower. If you think about your future as a monte carlo simulation you want to end up "ok" on most of the paths in the simulation even if that means giving up on some "lights out" outcomes.<p>4)most people agonise the most about decisions where the expectation is really pretty similar so it just doesn't matter that much either way. So don't beat yourself up about whether you made the right decision - just try to learn from the decision and move forward<p>A huge mistake people make is to evaluate the quality of the decision based on the single possible outcome that crystalised into reality by actually happening rather than using the framework above. So resist that temptation and instead evaluate your decision based on whether you chose to maximise expectation while avoiding risk of ruin and excessive variance.<p>[1]In the sense of being the weighted average of all possible payoffs where the weights are the probabilities and the payoffs are the utility of each outcome (usually just in cash terms).
A great concept of Texas holdem is that of chasing the pot: calling when the odds aren't in your favor. In other words, just because you've come this far it doesn't mean you have to keep putting money towards a hand you'll likely lose.
The suggested step 2 in this article is 'calculate the expected value' . . . but isn't this going to be far more complicated in practice? See:<p><a href="https://en.wikipedia.org/wiki/Expected_value#Expected_values_of_common_distributions" rel="nofollow">https://en.wikipedia.org/wiki/Expected_value#Expected_values...</a><p>Probability distributions are <i>complicated</i>. Even determining what kind of distribution you are look at is difficult in many cases. Such distributions are also skewed in real life by things like insider information (in poker, that would be cheating). Even so the list is rather intimidating, assuming fair play:<p>Bernoulli, Binomial, Poisson, Geometric, Uniform, Exponential, Normal, Standard Normal, Pareto, Cauchy<p>If financial institutions (and crypto players) are using these kind of approaches to make their bets, then isn't the individual investor hopelessly outgunned in the vast majority of cases? Plus, not having a big pool of capital to absorb temporary losses makes that situation even worse.<p>Investment capitalism, in other words, is just a casino for the uber-wealthy. Letting it rule the economy is a serious mistake.