Analysis of Large Urn Models with Local Mean-Field Interactions
Wen Sun, Philippe Robert

TL;DR
This paper studies large urn models with local mean-field interactions, analyzing their convergence and invariant distributions, especially under high load and power-of-d choices policies, revealing novel asymptotic properties.
Contribution
It introduces a framework for analyzing urn models with non-linear interaction ranges and unbounded jumps, extending mean-field convergence results to these complex settings.
Findings
Proves mean-field convergence under local empirical distribution analysis.
Establishes invariant distribution convergence for high load regimes.
Shows finite support of invariant measures for power of d choices policies.
Abstract
The stochastic models investigated in this paper describe the evolution of a set of identical balls scattered into urns connected by an underlying symmetrical graph with constant degree . After some random amount of time {\em all the balls} of any urn are redistributed locally, among the urns of its neighborhood. The allocation of balls is done at random according to a set of weights which depend on the state of the system. The main original features of this context is that the cardinality of the range of interaction is not necessarily linear with respect to as in a classical mean-field context and, also, that the number of simultaneous jumps of the process is not bounded due to the redistribution of all balls of an urn at the same time. The approach relies on the analysis of the evolution of the local empirical distributions associated to the state of…
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