Ising-like agent-based technology diffusion model: adoption patterns vs. seeding strategies

TL;DR

This paper adapts the Ising model into an agent-based framework to analyze how social influence, network topology, and early adopter distribution affect technology adoption patterns.

Contribution

It introduces an Ising-inspired agent-based model that incorporates perception and social influence to study macro-level diffusion patterns.

Findings

Higher perception gaps accelerate adoption when early adopters are dispersed.

Adding stochastic connections to a hub increases adoption speed.

Network topology influences the competition dynamics between options.

Abstract

The well-known Ising model used in statistical physics was adapted to a social dynamics context to simulate the adoption of a technological innovation. The model explicitly combines (a) an individual's perception of the advantages of an innovation and (b) social influence from members of the decision-maker's social network. The micro-level adoption dynamics are embedded into an agent-based model that allows exploration of macro-level patterns of technology diffusion throughout systems with different configurations (number and distributions of early adopters, social network topologies). In the present work we carry out many numerical simulations. We find that when the gap between the individual's perception of the options is high, the adoption speed increases if the dispersion of early adopters grows. Another test was based on changing the network topology by means of stochastic connections to a common opinion reference (hub), which resulted in an increment in the adoption speed. Finally, we performed a simulation of competition between options for both regular and small world networks.