Indeed, one can define the notion of the "probability of an event" without appealing to some underlying assumption randomness. A probability can instead be considered in terms of

*gambling*, where $P(X)$ is the price one would be willing to buy OR sell some hypothetical contract that paid out exactly 1 dollar if the outcome $X$ occurs. From Wikipedia:This brings me back to my previous post on forecasting. Without an underlying assumption on the nature of the world, for example the i.i.d assumption, it becomes difficult to judge "how good is a forecaster?" In the calibration setting, on each round $t$ a forecaster guesses probability values $p_t$ and nature reveals outcomes $z_t \in \{0,1\}$. Of course, for a single pair $(p_t,z_t)$ we have no way to answer the question "was the forecaster right?" The question becomes even more remote we imagine that $z_t$ is also chosen by a potential adversary.Youmust set the price of a promise to pay $1 if there was life on Mars 1 billion years ago, and $0 if there was not, and tomorrow the answer will be revealed. You know thatyour opponentwill be able to choose either to buy such a promise from you at the price you have set, or require you to buy such a promise from your opponent, still at the same price. In other words: you set the odds, but your opponent decides which side of the bet will be yours. The price you set is the "operational subjective probability" that you assign to the proposition on which you are betting. This price has to obey the probability axioms if you are not to face certain loss, as you would if you set a price above $1 (or a negative price). By considering bets on more than one event de Finetti could justify additivity. Prices, or equivalently odds, that do not expose you to certain loss through aDutch bookare calledcoherent.

So let us now return to the notion of "calibration", which is a measure of the performance of a forecaster, from the previous post. The concept of calibration can be posed something like "the probability predictions roughly match the data frequencies". But while this might seem nice, it doesn't give us a way to judge how "good" the forecaster is, so it's somewhat hard to interpret. On the other hand, if we view this through the lens of de Finetti, using the idea of betting rates, we arrive at what I view is a much more natural interpretation. I'll now give a rough sketch of this idea.

Let's say you're a forecaster and a gambler, and on each round $t$ you predict a probability $p_t$ and also promise to buy or sell a contract, at the price of $p_t$, that pays off 1USD if the outcome $z_t = 1$. But you're worried that someone might come along and realize that your predictions have some inherent bias. (As mentioned in the last post, it has apparently been observed that when weather forecasters said a 50% chance of rain, it only rained 27% of the time!) Let's call an opponent a

*threshold bettor*if he plans to buy (or sell) your contracts whenever the price is above (or below) a fixed value $\alpha$.So if we pose the prediction problem in this way, we can say that a forecaster is calibrated if and only if she loses no money, on average and in the long run, to a threshold bettor. So the forecaster's predictions may not be good, but they are at least robust to gamblers seeking to exploit fine-grained biases.

**Older confusing explanation:**

(This idea came out of a discussion I had today with my advisor Peter Bartlett, who I'd like to thank)