Urns with Multiple Drawings and Graph-Based Interaction

TL;DR

This paper studies a class of network-based urn models with multiple drawing and reinforcement mechanisms, demonstrating that most models lead to synchronization of urns' compositions, with unique behaviors on bipartite and directed graphs.

Contribution

It introduces and analyzes new urn models with multiple drawings and graph-based interactions, establishing convergence and synchronization results across various graph structures.

Findings

Urns tend to synchronize their composition over time.

Different reinforcement types influence convergence behavior.

Bipartite graphs exhibit distinct asymptotic patterns.

Abstract

Consider a finite undirected graph and place an urn with balls of two colours at each vertex. At every discrete time step, for each urn, a fixed number of balls are drawn from that same urn with probability $p$, and from a randomly chosen neighbour of that urn with probability $1-p$. Based on what is drawn, the urns then reinforce themselves or their neighbours. For every ball of a given colour in the sample, in case of P\'olya-type reinforcement, a constant multiple of balls of that colour is added while in case of Friedman-type reinforcement, balls of the other colour are reinforced. These different choices for reinforcement give rise to multiple models. In this paper, we study the convergence of the fraction of balls of either colour across urns for all of these models. We show that in most cases the urns synchronize, that is, the fraction of balls of either colour in each urn converges to the same limit almost surely. A different kind of asymptotic behaviour is observed on bipartite graphs. We also prove similar results for the case of finite directed graphs.