From the article: I’m not sure there is much economics here, but I thought that was a fun example. You can also read this article for a more vivid example of non-ergodicity using Russian roulette....
From the article:
Our taking off point is the Pólya urn model reviewed in Pemantle (2007). In this model, an urn contains M red balls and (N−M) black balls. A ball is chosen at random. If it is red (black) then 2 red balls (black balls) are re-introduced in the urn, which now contains N+1 balls. The probability of drawing a ball with a specific colour therefore increases with the number of times this colour was selected in the past.
The long-term fate of the Pólya urn is surprising. As the number of draws tends to infinity, the probability to draw a red ball converges to a limiting value; but the value of this asymptotic probability is itself random. Starting from the same urn with N red balls and M black balls, two different runs of the dynamics will lead to two limiting probabilities. To completely characterise the behaviour of this process, one must introduce probabilities over probabilities. The dynamics of the Pólya urn are non-ergodic.
I’m not sure there is much economics here, but I thought that was a fun example. You can also read this article for a more vivid example of non-ergodicity using Russian roulette.
Also, apparently there is a physicist who leverages the concept of ergodicity to claim that much of economics is wrong, but it seems it’s a crankish argument.
Before I dive into the article, I want to share something I absorbed somewhereabout this point. Probably Paul Cockshott (Marxist economist) to wager a guess. Much of mainstream economics today is...
Also, apparently there is a physicist who leverages the concept of ergodicity to claim that much of economics is wrong
Before I dive into the article, I want to share something I absorbed somewhereabout this point. Probably Paul Cockshott (Marxist economist) to wager a guess.
Much of mainstream economics today is derived by stripping away tons of details to make models, and trying to apply assumptions based on that. Moreso than many other fields. Like making a flattened out map of the world then another person declaring the world is flat based on seeing that map.
The best example is of supply and demand having a predictive power. While supply and demand do influnce the price, there is little mathemaical predictive power in it beyond knowing that a huge supply shock will affect price. There's no way of knowing what the price could/would be based on a supply change. We knew price of electricity would go up when Texas was struck by a disaster, We had no idea how high it would go.
By contrast, Labor costs are an example of something with substantial mathematical predictive power. If a machine is invented that cuts hours needed to craft a good by 10%, you have information that can be extrapolated into productive capability and product cost (and thus lowering the price floor). You can write equations to calculate economic changes as a result.
I agree that pretty much all economic models are simplistic. It doesn’t make sense to build more complicated models (like meteorologists do for weather) unless they can be shown to have predictive...
I agree that pretty much all economic models are simplistic. It doesn’t make sense to build more complicated models (like meteorologists do for weather) unless they can be shown to have predictive power, and predicting what will happen in an economy is extremely difficult, about as hard as predicting the future in general.
It’s often difficult to make quantitative predictions about prices because markets are a complicated game with a lot of players making their own decisions based on their opinions of what will happen in the future, and these opinions are often wrong. Markets can be wrong for quite a while before people change their minds.
You seem to have quite a bit of faith in your preferred experts, but, not having studied their work and taking an outside view, I’m wondering if they would be actually be better than other economists at predicting things? Are their models any less simplistic?
I’m skeptical that it’s that easy to figure out the results of an increase (or decrease) in labor costs because there is controversy over the results of an increase in the minimum wage. At least for the moderate increases for which we have data from comparisons between states, the effects aren’t necessarily what you would expect from simple models.
Some subtle effects are that pay affects what kind of workers will be interested in the job, how picky employers are about who they hire, and changing who you hire affects productivity. Employers may be more interested in keeping workers more productive when they’re paid more and change how the work is done.
(Despite this general skepticism, I don’t want to make the mistake of not being curious, so if you have interesting articles to share please go ahead.)
From the article:
I’m not sure there is much economics here, but I thought that was a fun example. You can also read this article for a more vivid example of non-ergodicity using Russian roulette.
Also, apparently there is a physicist who leverages the concept of ergodicity to claim that much of economics is wrong, but it seems it’s a crankish argument.
Before I dive into the article, I want to share something I absorbed somewhereabout this point. Probably Paul Cockshott (Marxist economist) to wager a guess.
Much of mainstream economics today is derived by stripping away tons of details to make models, and trying to apply assumptions based on that. Moreso than many other fields. Like making a flattened out map of the world then another person declaring the world is flat based on seeing that map.
The best example is of supply and demand having a predictive power. While supply and demand do influnce the price, there is little mathemaical predictive power in it beyond knowing that a huge supply shock will affect price. There's no way of knowing what the price could/would be based on a supply change. We knew price of electricity would go up when Texas was struck by a disaster, We had no idea how high it would go.
By contrast, Labor costs are an example of something with substantial mathematical predictive power. If a machine is invented that cuts hours needed to craft a good by 10%, you have information that can be extrapolated into productive capability and product cost (and thus lowering the price floor). You can write equations to calculate economic changes as a result.
I agree that pretty much all economic models are simplistic. It doesn’t make sense to build more complicated models (like meteorologists do for weather) unless they can be shown to have predictive power, and predicting what will happen in an economy is extremely difficult, about as hard as predicting the future in general.
It’s often difficult to make quantitative predictions about prices because markets are a complicated game with a lot of players making their own decisions based on their opinions of what will happen in the future, and these opinions are often wrong. Markets can be wrong for quite a while before people change their minds.
You seem to have quite a bit of faith in your preferred experts, but, not having studied their work and taking an outside view, I’m wondering if they would be actually be better than other economists at predicting things? Are their models any less simplistic?
I’m skeptical that it’s that easy to figure out the results of an increase (or decrease) in labor costs because there is controversy over the results of an increase in the minimum wage. At least for the moderate increases for which we have data from comparisons between states, the effects aren’t necessarily what you would expect from simple models.
Some subtle effects are that pay affects what kind of workers will be interested in the job, how picky employers are about who they hire, and changing who you hire affects productivity. Employers may be more interested in keeping workers more productive when they’re paid more and change how the work is done.
(Despite this general skepticism, I don’t want to make the mistake of not being curious, so if you have interesting articles to share please go ahead.)