Thermodynamics of Innovation: A Statistical Mechanics Framework of Social Adoption

Abstract

We develop a thermodynamic framework for modeling innovation adoption and abandonment dynamics using statistical mechanics. Starting from a mathematical model for an adoption distribution that fits empirically obtained date, we construct a canonical ensemble whose equilibrium distribution yields Gompertz-like and Maxwell-Boltzmann-like shapes. By reverse engineering the associated energy landscape, we define an effective potential and derive a dynamical Lagrangian formulation. The resulting field theory captures key features of emergent behaviors in socio-technical systems, from early suppression to peak dynamics and late decline. We interpret effective temperature, entropy, and equilibrium points, and show how these systems exhibit hybrid thermodynamic-statistical signatures.