In the same way that the high-precision measurement of the fundamental physical constants represents a rigorous test of the Standard Model, a precise determination of the Monte Carlo constant represents a fundamental test of algorithmic information theory. In fact, the Monte Carlo constant defines the exact boundary of the Monte Carlo hypothesis.

If $$X_{2N} \in \{0,1\}^{2N}$$ represents the prime-encoding of $$[1,2N] \subset \mathbb{N}$$, we may find the minimal description length of $$X_N$$ relative to a class of universal function approximators $$F_{\theta}$$:

$$\hat{f} = \arg\min_{f \in F_{\theta}} [H(X_N \lvert f) + H(f)]$$

where

$$\hat{f}(x_n) = x_{n+1} \implies \delta_{\hat{f}(x_n),x_{n+1}} = 1$$

and we note that relative to $$\hat{f}$$, if $$x_k = X_{2N}[N+k]$$ the Monte Carlo sequence $$\phi_{N}$$ is given by the true-positive rate:

$$\phi_N = \frac{1}{N_1} \sum_{n=1}^{N-1} \delta_{\hat{f}(x_n),x_{n+1}} \cdot \delta_{\hat{f}(x_n),1}$$

where

$$N_1 = \sum_{n=1}^{N-1} \delta_{\hat{f}(x_n),1}$$

Now, given that $$\phi_N$$ is decreasing and bounded it converges to the limit-computable constant:

$$\phi^* = \lim_{N \to \infty} \phi_N$$

known as the Monte Carlo constant. As prime-encodings are algorithmically random, we know that $$\phi^* \in (0,\frac{1}{2})$$.