The subtlety is about what numerical data can formally represent your full state of knowledge. The claim is that a mere probability of getting the $2 payout does not.

However, a single probability for each outcome given each strategy is all the information needed. The problem is not with using single probabilities to represent knowledge about the world, it's the straw math that was used to represent the technique. To me, this reasoning is equivalent to the following:

"You work at a store where management is highly disorganized. Although they precisely track the number of days you have worked since the last payday, they never remember when they last paid you, and thus every day of the work week has a 1/5 chance of being a payday. For simplicity's sake, let's assume you earn $100 a day.

You wake up on Monday and do the following calculation: If you go in to work, you have a 1/5 chance of being paid. Thus the expected payoff of working today is $20, which is too low for it to be worth it. So you skip work. On Tuesday, you make the same calculation, and decide that it's not worth it to work again, and so you continue forever.

I visit you and immediately point out that you're being irrational. After all, a salary of $100 a day clearly is worth it to you, yet you are not working. I look at your calculations, and immediately find the problem: You're using a single probability to represent your expected payoff from working! I tell you that using a meta-probability distribution fixes this problem, and so you excitedly scrap your previous calculations and set about using a meta-probability distribution instead. We decide that a Gaussian sharply peaked at 0.2 best represents our meta-probability distribution, and I send you on your way."

Of course, in this case, the meta-probability distribution doesn't change anything. You still continue skipping work, because I have devised the hypothetical situation to illustrate my point (evil laugh). The point is that in this problem the meta-probability distribution solves nothing, because the problem is not with a lack of meta-probability, but rather a lack of considering future consequences.

In both the OPs example and mine, the problem is that the math was done incorrectly, not that you need meta-probabilities. As you said, meta-probabilities are a method of screening off additional labels on your probability distributions for a particular class of problems where you are taking repeated samples that are entangled in a very particular sort of way. As I said above, I appreciate the exposition of meta-probabilities as a tool, and your comment as well has helped me better understand their instrumental nature, but I take issue with what sort of tool they are presented as.

If you do the calculations directly with the probabilities, your calculation will succeed if you do the math right, and fail if you do the math wrong. Meta-probabilities are a particular way of representing a certain calculation that succeed and fail on their own right. If you use them to represent the correct direct probabilities, you will get the right answer, but they are only an aid in the calculation, they never fix any problem with direct probability calculations. The fixing of the calculation and the use of probabilities are orthogonal issues.

To make a blunt analogy, this is like someone trying to plug an Ethernet cable into a phone jack, and then saying "when Ethernet fails, wifi works", conveniently plugging in the wifi adapter correctly.

The key of the dispute in my eyes is not whether wifi can work for certain situations, but whether there's anything actually wrong with Ethernet in the first place.