COMPARTMENTAL LIMIT OF DISCRETE BASS MODELS ON NETWORKS

Gadi Fibich, Amit Golan*, Steve Schochet

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

We introduce a new method for proving convergence and rate of convergence of discrete Bass models on various networks to their respective compartmental Bass models, as the population size M becomes infinite. In this method, the master equations are reduced to a smaller system of equations, which is closed and exact. The reduced system is embedded into an infinite system, whose convergence to the infinite limit system is proved using standard ODE estimates. Finally, an exact ansatz reduces the infinite limit system to the compartmental model. Using this method, we show that when the network is complete and homogeneous, the discrete Bass model converges to the original 1969 compartmental Bass model, at the rate of 1/M. When the network is circular, the compartmental limit is different, and the convergence rate is exponential in M. For a heterogeneous network that consists of K homogeneous groups, the limit is given by a heterogeneous compartmental Bass model, and the convergence rate is 1/M. Using this compartmental model, we show that when the heterogeneity in the external and internal influence parameters among the K groups is positively monotonically related, heterogeneity slows down the diffusion.

Original languageEnglish
Pages (from-to)3052-3078
Number of pages27
JournalDiscrete and Continuous Dynamical Systems - Series B
Volume28
Issue number5
DOIs
StatePublished - May 2023

Keywords

  • Stochastic models
  • compartmental models
  • convergence
  • diffusion in networks
  • heterogeneity
  • master equations
  • ordinary differential equations
  • rate of convergence

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