Highly connected - a recipe for success

TL;DR

This paper presents an agent-based model using majority dynamics to analyze how innovations spread within a competitive network, highlighting the importance of self-support in smaller groups for successful adoption.

Contribution

It introduces a simple binary-agent model based on Ising-like majority dynamics to study innovation diffusion and demonstrates its applicability to real-world scenarios.

Findings

Smaller innovation groups can influence larger ones if they have high self-support.

Dominant existing practices tend to persist unless disrupted by strong minority influence.

The model's conclusions are adaptable to various decision-making and innovation spreading problems.

Abstract

In this paper, we tackle the problem of innovation spreading from a modeling point of view. We consider a networked system of individuals, with a competition between two groups. We show its relation to the innovation spreading issues. We introduce an abstract model and show how it can be interpreted in this framework, as well as what conclusions we can draw form it. We further explain how model-derived conclusions can help to investigate the original problem, as well as other, similar problems. The model is an agent-based model assuming simple binary attributes of those agents. It uses a majority dynamics (Ising model to be exact), meaning that individuals attempt to be similar to the majority of their peers, barring the occasional purely individual decisions that are modeled as random. We show that this simplistic model can be related to the decision-making during innovation adoption processes. The majority dynamics for the model mean that when a dominant attribute, representing an existing practice or solution, is already established, it will persists in the system. We show however, that in a two group competition, a smaller group that represents innovation users can still convince the larger group, if it has high self-support. We argue that this conclusion, while drawn from a simple model, can be applied to real cases of innovation spreading. We also show that the model could be interpreted in different ways, allowing different problems to profit from our conclusions.