Finite-size scaling analysis of binary stochastic processes and universality classes of information cascade phase transition

Abstract: We propose a finite-size scaling analysis of binary stochastic processes $X(t)\in {0,1}$ based on the second moment correlation length $\xi$ for the autocorrelation function $C(t)$. The purpose is to clarify the critical properties and provide a new data analysis method for information cascades. As a simple model to represent the different behaviors of subjects in information cascade experiments, we assume that $X(t)$ is a mixture of an independent random variable that takes 1 with probability $q$ and a random variable that depends on the ratio $z$ of the variables taking 1 among recent $r$ variables. We consider two types of the probability $f(z)$ that the latter takes 1: (i) analog [$f(z)=z$] and (ii) digital [$f(z)=\theta(z-1/2)$]. We study the universal functions of scaling for $\xi$ and the integrated correlation time $\tau$. For finite $r$, $C(t)$ decays exponentially as a function of $t$, and there is only one stable renormalization group (RG) fixed point. In the limit $r\to \infty$, where $X(t)$ depends on all the previous variables, $C(t)$ in model (i) obeys a power law, and the system becomes scale invariant. In model (ii) with $q\neq 1/2$, there are two stable RG fixed points, which correspond to the ordered and disordered phases of the information cascade phase transition with critical exponents $\beta=1$ and $\nu_{||}=2$.