Who here would take a bet where there's a 95% chance of losing their home and their well paying job, for a 5% chance of becoming a billionaire? I sure wouldn't.
My take on this: don't stop at averages, look at the whole distribution.
Who here would take a bet where there's a 95% chance of losing their home and their well paying job, for a 5% chance of becoming a billionaire? I sure wouldn't.
My take on this: don't stop at averages, look at the whole distribution.
I think it's uncharitable to say the article would be easier to understand if it didn't use the language of ergodicity. Its explicit goal is to show how non-ergodicity leads to an example like yours.
So of course your comment seems easier to understand. But that's because you're just saying different distributions can be parameterized by the same mean. Ergodicity is about a lot more than that, and the language of ergodicity was the entire exercise here.
But their application to non-standard-mechanical things is very confusing.
Of course wealth is not ergodic. Ergodicity would mean that the distribution is always the same. Every point in time would be identical to every other point in time and growth would be impossible.
Someone new to ergodic theory may understand from that article that if wealth was ergodic the average for every trajectory would increase like the average for the entire system. But that doesn’t make sense.
More specifically:
- The distribution of outcomes at certain points of interest in time (like the valuation of my company when I intend to sell it).
- The probability that we cross a catastrophic threshold at some point (like bankruptcy).
Time average is a terrible metric to estimate those things. Heck, I'm not sure it can measure anything of interest, besides our own mistaken intuitions. It should probably be called something like "time average fallacy".
It seems like you think the problem here is too unsophisticated for ergodic theory or something. Which, fine sure. But this isn't an article intended to teach you about betting. It's an article intended to teach you about ergodicity, using betting as a toy example. The author isn't trying to introduce the best way to analyze betting strategies, they're trying to show what non-ergodicity is. And I think they basically succeed.
Just meet the article where it is, for its intended usage.
That's a very special case. For everything else (that is, non-ergotic processes), your time average is crap, and you must look at the distribution of outcomes directly. Even the ensemble average is not enough. Averages are crap at visualising skewed distributions. For those you want the median, the quartiles, sometimes even the percentiles.
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To be honest, this "ergotic theory" shows signs of snake oil. The definition of ergodicity itself is dead simple, so it's pretty easy to evaluate. What seems pretty clear is that ergodic processes are the exception. And a pretty uninteresting one at that, since it's a class of processes that people will have good intuitions about.
It would then seem that ergodic theory is more interested in the non ergodic processes (the very point of this blog post is to warn us about them). That is, processes that lack some property —the general case. And surprise, since the time average and ensemble averages are different, and you only care about the ensemble average (well, the ensemble distribution really), the time average won't help you. Be afraid, or lose your assets.
That's why I see snake oil: what works on non-ergodic processes will also work on the ergodic ones. Unless you need to make a split second decision using your intuition (which while inadvisable is safer with ergodic processes), there's no need to make the distinction at all. Just analyse your process without without assuming it will be ergodic, the results will be applicable even if it is.
> To be honest, this "ergotic theory" shows signs of snake oil.
lol. Alright, I’m checking out of the discussion when a major subfield of mathematics is described as snake oil.
I did not mention those stupid coin tosses, where did you get the impression I was talking about those specifically?
> a major subfield of mathematics is described as snake oil.
I did not say it was snake oil, just that it shows signs of being such. Then I described those signs. If you have counter arguments or pointers to such, I'd be happy to read them. I'd rather lose an argument and learn something than stay ignorant.
This intro doesn't get to the depths of the issue. https://www.nature.com/articles/s41567-019-0732-0, by one of the pioneers of the "egondocity economics" is very nice for both going over the math and the academic history of the error.
Given the illustrious history of statistical mechanics into Modern probability theory, information theory, theoretical computer science, etc., it's a real shame Econonomics is still stuck with this bad math.
https://aeon.co/ideas/how-ergodicity-reimagines-economics-fo... the pop-sci narrative here really doesn't seem that much an exaggeration. The way non-ergonomics fixes the math and confirms some real-world intuitions is quite profound. And certainly there is a lot to critique with orthodox economics' math. (See https://themountaingoateconomics.com/ for another example.)
Are you calling “bad math” the expected utility theory developed by von Neumann (et al.)? He knew one thing or two about ergodicity, information theory, computer science, etc.
I read https://en.wikipedia.org/wiki/Von_Neumann%E2%80%93Morgenster..., And there's no notion of time let alone non-ergoticity in the formula. I am not familiar of with the rest of its book, but I wouldn't be surprise if it's similarly fine, building a theory similarly of rich theorems about very simple models.
If so, the problem isn't Von Neumann's math then, even if the general aim of the endever was misinspired by Bernoulli's primitive notions. The problem would be all the math cargo culters in economics who constantly try to the premise premises of math theorems as if they were broad social laws.
I mean don't get me wrong, I am no fan of Von Neumannn's politics, but obviously I am not going to fight his pure math.
If you're a reasonable person instead, you recognise that probabilities instead describe a state of partial information (that is, probability is in the mind), and the "ensemble average" really comes from a probability distribution we can compute with bog standard probabilistic counterfactual reasoning, not by actually hopping universes.
My, the abstract didn't prepare me for this.
For a coin toss example like this, the distribution of heads and tails in each trial is ergodic. The distribution of earnings is not. This isn't because of any difference between time average versus ensemble average. It's because the probability of winning each toss is time invariant but the amount you stand to win or lose isn't because it's a function of both the probability of winning and your current bankroll, and current bankroll is not time invariant.
Although, ironically, because of the numbers he picked, all bankrolls tend to zero eventually, so over a large enough number of trials, wealth eventually becomes an ergodic process as well. Graphing out his scenario over more trials gives a sort of heat death of the universe plot, where some players stay alive longer than others, but in the long run, the enemy always wins.
It's funny how you say "the author made it a bit more complicated than it needs to be" and then proceed to explain it with even more jargon ("Ergodicity for a stochastic process just means the joint distribution of random variables that make up the sample space is time invariant").
As for what a realistic bet would look like (you're founding a startup or something), I believe the expectation is often not much greater than 1, so one does not simply found 100 startups and distribute the income of the 5 successful ones to everyone else. (And even if it is, the people capable of founding startups often have steadier, though less impressive, means of increasing their wealth. Startups are often founded for reasons other than wealth, after all.)