The Asynchronous DeGroot Dynamics

TL;DR

This paper studies the asynchronous DeGroot opinion dynamics, showing polynomial expected convergence times in certain graphs and polylogarithmic times under specific conditions, with implications for information aggregation.

Contribution

It provides new bounds on convergence times for asynchronous DeGroot dynamics, including polylogarithmic expectations in bounded-degree graphs with i.i.d. initial opinions.

Findings

Expected convergence time is polynomial in general graphs.

Expected convergence time is polylogarithmic in bounded-degree graphs with i.i.d. initial opinions.

Variance estimates relate to the accuracy of collective opinion aggregation.

Abstract

We analyze the asynchronous version of the DeGroot dynamics: In a connected graph $G$ with $n$ nodes, each node has an initial opinion in $[0,1]$ and an independent Poisson clock. When a clock at a node $v$ rings, the opinion at $v$ is replaced by the average opinion of its neighbors. It is well known that the opinions converge to a consensus. We show that the expected time $\mathbb E(\tau_\varepsilon)$ to reach $\varepsilon$-consensus is poly$(n)$ in undirected graphs and in Eulerian digraphs, but for some digraphs of bounded degree it is exponential. Our main result is that in undirected graphs and Eulerian digraphs, if the degrees are uniformly bounded and the initial opinions are i.i.d., then $\mathbb E(\tau_\varepsilon)=\text{polylog}(n)$ for every fixed $\varepsilon>0$. We give sharp estimates for the variance of the limiting consensus opinion, which measures the ability to aggregate information (``wisdom of the crowd''). We also prove generalizations to non-reversible Markov chains and infinite graphs. New results of independent interest on fragmentation processes and coupled random walks are crucial to our analysis.