Phase transition of social learning collectives and "Echo chamber"

Abstract

An "Echo chamber" is the state of social learning agents whose performances are deteriorated by excessive observation of others. We understand this to be the collective behavior of agents in a restless multi-armed bandit. The bandit has $M$ good levers and bad levers. A good lever changes to a bad one randomly with probability $q_C$ and a new good lever appears. $N$ agents exploit ones' lever if they know that it is a good one. Otherwise, they search for a good one by (i) random search (success probability $q_I$) and (ii) observe a good lever that is known by other agents (success probability $q_O$) with probability $1-p$ and $p$, respectively. The distribution of agents in good levers obeys the Yule distribution with power law exponent $1+\gamma$ in the limit $N,M\to \infty$ and $\gamma =1+\frac{(1-p)q_I}{p{q}_O}$. The expected value of the number of the agents with a good lever $N_1$ increases with $p$. The system shows a phase transition at $p_c=\frac{q_I}{q_I+q_o}$. For $p<p_c(>p_c)$, the variance of $N_1$ per agent $\mbox{Var}(N_{1})/N$ is finite (diverges as $\propto N^{2-\gamma }$ with $N$). There is a threshold value $N_s$ for the system size that scales as $\ln N_s\propto 1/(\gamma -1)$. For $p>p_c$ and $N<N_s$, all agents tend to share only one good lever. $\mbox{E}(N_{1})$ decreases to zero as $p\to 1$, which is referred to as the "Echo chamber".

23 pages, 6 figures

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