In the specific example, they could be cloned by expanding in the good locations.

More generally, if you're claiming that there's a limited supply of good locations from which to produce chickens, then that reduces to a "finite inputs" argument I discuss in the last section of the OP. (For further discussion see responses to this comment .)

In short, I agree that such effects can create a sloping long term supply curve in some cases, but I also believe that there are other effects that can lead it to slope the opposite direction, and it's not immediately obvious which wins out. My prior is that the long term supply curve for an arbitrary product is virtually flat.

Said another way, if you're going to argue that the long term cost-per-widget is higher when producing 2X widgets than X widgets, then you have to argue that the effect of finite inputs outweighs gains to scale and other factors. I haven't seen such an argument generally or in the case of chickens.

This is true in the short term, but in the long term, the dynamic changes for producers:

• The producers that know how to make chickens for $8 scale up or their production strategy is replicated by others.
• The marginal cost of production (and hence price) keeps falling until all producers are making no profit (relative to opportunity cost of capital)
• The industry can scale up/down (in the long term) to meet changing demand, but it can't drive prices any lower. If prices were any higher the industry would scale up in the short term and keep expanding until the price fell back to the Cost in the long term.

The elasticity of the demand curve changes less than the supply curve in the super long term, but if you agree with me that the supply curve is virtually flat at that point, then the elasticity of the demand curve is negligible (because as the supply curve shifts left and right, the only point on the demand curve that matters is quantity @ price = Cost/Supply price).

Basic economics that explains why the cost of chicken will drop. You are ignoring supply curves, and these exist because not all producers are identical. The drive in change of costs is competition among chicken producers.

There is a price for chicken, say 10$ per unit. To make a profit, each producer must produce chicken at less than that price. However, not all producers are making chicken at the same cost. Some are more efficient than others. Some spend 9$ making a unit, some spend 8$. Some could produce chicken for 10$ a unit and don't.. When demand for chicken drops, the business with 9$ cost lowers production or leaves the industry before the business with 8$ cost. The drop in production is concentrated in the marginal producers. Similarly if the price rose, the potential producer with 10$ costs would start producing.

There is a mirror process among consumers.

Colloquial bets are offered by skeevy con artists who probably know something you don't. Bayesian bets, on the other hand, are offered by nature.

That distinction seems a bit unclear, since con artists are a part of nature, and nature certainly knows something you don't.

Here's a toy situation where a Bayesian is willing to state their beliefs, but isn't willing to accept bets on them. Imagine that I flip a coin, look at the result, but don't tell it to you. You believe that the coin came up heads with probability 1/2, but you don't want to make a standing offer to accept either side of the bet, because then I could just take your money.

In the general case, what should a Bayesian do when they're offered a bet? I think they should either accept it, or update to a state of belief that makes the bet unprofitable ("you offered the bet because you know the coin came up heads, so I won't take it"). That covers both bets offered by nature and bets offered by con artists. Also it's useful in arguments, you can offer a bet and force your opponent to either accept it or publicly update their beliefs.

And we can’t explain away all of this low success rate as the result of illusory correlations being throw up by the standard statistical problems with findings such as small n, sampling error (A & B just happened to sync together due to randomness), selection bias, publication bias, etc. I’ve read about those problems at length, and despite knowing about all that, there still seems to be a problem: correlation too often ≠ causation.

You're missing a 4th possibility. A & B are not meaningfully linked. This is very important when dealing with large sets of variables. Your measure of correlation will have a certain percentage of false positives, and discounting the possibility of false positives is important. If the probability of false positives is 1/X you should expect one false correlation for every X comparisons.

XKCD provides an excellent example. jelly beans

On the other hand, I think the evolutionary heuristic casts doubt on the value of many other proposals for improving rationality. Many such proposals seem like things that, if they worked, humans could have evolved to do already. So why haven't we?

Most such things would have had to evolve by cultural evolution. Organic evolution makes our hardware, cultural evolution makes our software. Rationality is mostly software - evolution can't program such things in at the hardware level very easily.

Cultural evolution has only just got started. Education is still showing good progress - as manifested in the Flynn effect. Our rationality software isn't up to speed yet - partly because is hasn't had enough time to culturally evolve its adaptations yet.

Would 53 not being prime break mathematics?

LNC, not the law of identity, I think.

Your list actually doesn't go far enough. There is a fourth, and scarier category. Things which would, if possibly render probability useless as a model. "The chance that probabilities don't apply to anything." is in the fourth category. I would also place anything that violates such basic things as the consistency of physics, or the existence of the external world.

For really small probabilities, we have to take into account some sources of error that just aren't meaningful in more normal odds.

For instance, if I shuffle and draw one card from a new deck what is the chance of drawing the ace of spades? I disregard any chance of the deck being defective, any chance of my model of the universe being wrong, and any chance of laws of identity being violated. Any probabilities are eclipsed by the normal probabilities of drawing cards. (category 1)

If I shuffle and draw two cards without replacement from a new deck, what is the chance of them both being aces of spades? Now I have to consider other sources of error. There could have been a factory error or the deck may have been tampered with. (category 2)

If I shuffle and draw one card from a new deck, what is the chance of it being a live tiger? Now I have to consider my model of the universe being drastically wrong. (category 3)

If I shuffle and draw one card from a new deck, what is the chance of it being both the ace of spades and the two of clubs? Not a misprint, and not two cards, but somehow both at the same time. Now I have to consider the law of identity being violated. (category 4)