Continuous time limits of the Utterance Selection Model

TL;DR

This paper derives new continuous time limits for the Utterance Selection Model in language change, accounting for broader parameter ranges and network structures, revealing finite size effects and node degree influences on dynamics.

Contribution

It introduces a weak-noise stochastic differential equation as a new continuous time limit and proposes a heterogeneous mean field approximation for complex networks.

Findings

Finite size effects influence the emergence of conventions.

Node degree acts as a time scale, affecting dynamics.

Heterogeneous networks exhibit noisier behavior in highly connected nodes.

Abstract

In this paper, we derive new continuous time limits of the Utterance Selection Model (USM) for language change (Baxter et al., Phys. Rev. E {\bf 73}, 046118, 2006). This is motivated by the fact that the Fokker-Planck continuous time limit derived in the original version of the USM is only valid for a small range of parameters. We investigate the consequences of relaxing these constraints on parameters. Using the normal approximation of the multinomial approximation, we derive a new continuous time limit of the USM in the form of a weak-noise stochastic differential equation. We argue that this weak noise, not captured by the Kramers-Moyal expansion, can not be neglected. We then propose a coarse-graining procedure, which takes the form of a stochastic version of the \emph{heterogeneous mean field} approximation. This approximation groups the behaviour of nodes of same degree, reducing the complexity of the problem. With the help of this approximation, we study in detail two simple families of networks: the regular networks and the star-shaped networks. The analysis reveals and quantifies a finite size effect of the dynamics. If we increase the size of the network by keeping all the other parameters constant, we transition from a state where conventions emerge to a state when no convention emerges. Furthermore, we show that the degree of a node acts as a time scale. For heterogeneous networks such as star-shaped networks, the time scale difference can become very large leading to a noisier behaviour of highly connected nodes.