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The well-known Ising model used in statistical physics was adapted to asocial dynamics context to simulate the adoption of a technological innovation.The model explicitly combines (a) an individual's perception of the advantagesof an innovation and (b) social influence from members of the decision-maker'ssocial network. The micro-level adoption dynamics are embedded into anagent-based model that allows exploration of macro-level patterns of technologydiffusion throughout systems with different configurations (number anddistributions of early adopters, social network topologies). In the presentwork we carry out many numerical simulations. We find that when the gap betweenthe individual's perception of the options is high, the adoption speedincreases if the dispersion of early adopters grows. Another test was based onchanging the network topology by means of stochastic connections to a commonopinion reference (hub), which resulted in an increment in the adoption speed.Finally, we performed a simulation of competition between options for bothregular and small world networks.

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