A formal definition of a "measure of association" I've been trying to come up with a formal definition for a 'measure of association'. An intuitive definition might be something along the lines of 'a function that tells you about the existence or strength of dependence among a collection of random variables'.
I've constructed the following definition with this intuitive notion of association. Notice that I use an implication, rather than a biconditional. This is to allow for a function to tell us about specific types of association, rather than dependence in general.

Given a suitable probability space $(\Omega, \mathcal{F}, P)$ with real-valued random variables $\{X_j{(\omega}) | \omega \in \Omega \}_{j=1}^{n}$, a measure of association of order n is a function $f:\mathbb{R}^n \mapsto \mathbb{R}$ such that $\perp\!\!\!\!\perp \left( X_1, \cdots, X_n \right) \implies f \left( X_1, \cdots, X_n  \right) = 0$.

However, it comes up a bit short. This definition doesn't really involve any notion of quantifying the strength of association. I've been mulling over the idea that with sufficient smoothness that perhaps some expression in terms of derivatives would be possible. In a comment below, @whuber nicely summarizes my dissatisfaction with this definition:

It would be more accurate to characterize your definition as an indicator of association. To be a "measure," it ought to change monotonically with some property of "association." The issue revolves around what might constitute a property one would characterize as quantifying some aspect of "association." The main difficulty is that "dependence among variables" is a rich and complex thing that is inadequately characterized by any single scalar-valued function. AFAIK, there is no axiomatization of such things.

How can this definition be revised to include functions that quantify the strength of association?

Books on this topic include Correlation and Dependence by Samuel Kotz and Dominique Drouet and Multivariate Models and Multivariate Dependence Concepts by Harry Joe. The second is more practical, the first more theoretical.
And there is a paper by A Rényi: On measures of dependence in  Acta Mathematica Academiae Scientiarum Hungaricae 10, 441–451 (1959). https://doi.org/10.1007/BF02024507, proposing some criteria a measure of association $A(x,y)$ should satisfy. Let us list them:
I Standardization A should be in $[0,1]$
II Independence $A=0$ when independence holds
III Functional dependence $A=1$ if $x$ is a function of $y$, or viceversa
IV Increasing property $A$ must be increasing when dependence is increasing
V Invariance with respect to separate linear (or affine)  transformations of each of the variables.  A stronger requirement would be that $A$ is marginal free, that is, it only depends on the bivariate distribution through its copula
VI Symmetry If the variables are exchangeable, $A$ should be symmetric
VII Relationship with measures for ordinal variables If $A$ is defined for both ordinal and numerical variables, there should be a close connection between the two cases
As some of these criteria are informal, they cannot really be called axioms. Let us look at a Pearson correlation as an association measures and see how it fares:
Pearson correlation only seems to comply with VI, VII. Specifically, it is not marginal free. Let us look more in detail at that, as it has interesting consequences that should be more known, and maybe taken into account in interpretation.  If we transform $x$ and $y$ separately with increasing transformations, this will only change the marginal distributions, and the copula will remain the same. But if these transformations are nonlinear, then they will destroy straight lines in the scatter plot, and so the Pearson correlation will change. And, when the marginal distributions have different shapes, the maximal correlation value of 1 is not reachable!
Let us use some simple example data distributed with R:
data(mammals, package="MASS")
with(mammals, cor(body, brain))
with(mammals, cor(log(body), log(brain)))
[1] 0.9341638
[1] 0.9595748

Now, to calculate the maximal correlation possible with the actual marginal distribution of the data, we can just sort the values in increasing order before calculating the correlation. That will preserve the marginals, but obviously destroys the copula:
maxcor <- function(x, y, ...) {
    xx <- sort(x) ; yy <- sort(y)
    cor(xx, yy, ...)
}

 with(mammals, maxcor(body, brain))
with(mammals, maxcor(log(body), log(brain)))
[1] 0.9435413
> [1] 0.9921567

So the maximal correlation possible is actually only $0.944$, compared with  the actual value of $0.934$. And since the log transformation preserves the copula, but not the Pearson correlation, we see that it is not marginal free.
