Detection of phase transition in generalized P\'olya urn in information cascade experiment

TL;DR

This paper introduces a method to detect phase transitions in a generalized Pólya urn model within information cascade experiments, using correlation behavior to identify different phases based on experimental data.

Contribution

The paper presents a novel correlation-based approach to identify phase transitions in a generalized Pólya urn model through experimental voting data.

Findings

Detected phase transition at specific q values.

Correlation C(t) converges to positive or zero depending on phase.

Experimental results support the theoretical model.

Abstract

We propose a method of detecting a phase transition in a generalized P\'olya urn in an information cascade experiment. The method is based on the asymptotic behavior of the correlation $C(t)$ between the first subject's choice and the $t+1$-th subject's choice, the limit value of which, $c\equiv \lim_{t\to \infty}C(t)$, is the order parameter of the phase transition. To verify the method, we perform a voting experiment using two-choice questions. An urn X is chosen at random from two urns A and B, which contain red and blue balls in different configurations. Subjects sequentially guess whether X is A or B using information about the prior subjects' choices and the color of a ball randomly drawn from X. The color tells the subject which is X with probability $q$. We set $q\in \{5/9,6/9,7/9,8/9\}$ by controlling the configurations of red and blue balls in A and B. The (average) lengths of the sequence of the subjects are 63, 63, 54.0, and 60.5 for $q\in \{5/9,6/9,7/9,8/9\}$, respectively. We describe the sequential voting process by a nonlinear P\'olya urn model. The model suggests the possibility of a phase transition when $q$ changes. We show that $c>0\,\,\,(=0)$ for $q=5/9,6/9\,\,\,(7/9,8/9 )$ and detect the phase transition using the proposed method.