Instability and network effects in innovative markets

TL;DR

This paper models innovative market adoption using statistical mechanics, analyzing how interactions among innovators and followers influence market stability and outcomes through simulations on random graphs.

Contribution

It introduces a network-based model distinguishing innovators and followers, exploring the effects of feedback interactions on market stability and collective adoption phenomena.

Findings

No-feedback interactions lead to market polarization driven by innovators.

Feedback interactions cause market instability and unpredictable outcomes.

Market success depends on innovator concentration and interaction strengths.

Abstract

We consider a network of interacting agents and we model the process of choice on the adoption of a given innovative product by means of statistical-mechanics tools. The modelization allows us to focus on the effects of direct interactions among agents in establishing the success or failure of the product itself. Mimicking real systems, the whole population is divided into two sub-communities called, respectively, Innovators and Followers, where the former are assumed to display more influence power. We study in detail and via numerical simulations on a random graph two different scenarios: no-feedback interaction, where innovators are cohesive and not sensitively affected by the remaining population, and feedback interaction, where the influence of followers on innovators is non negligible. The outcomes are markedly different: in the former case, which corresponds to the creation of a niche in the market, Innovators are able to drive and polarize the whole market. In the latter case the behavior of the market cannot be definitely predicted and become unstable. In both cases we highlight the emergence of collective phenomena and we show how the final outcome, in terms of the number of buyers, is affected by the concentration of innovators and by the interaction strengths among agents.