Thermodynamics of Innovation: A Statistical Mechanics Framework of Social Adoption

TL;DR

This paper introduces a thermodynamic framework based on statistical mechanics to model social innovation adoption and abandonment, capturing complex dynamics and emergent behaviors.

Contribution

It develops a novel thermodynamic approach with a dynamical Lagrangian formulation to analyze socio-technical system behaviors.

Findings

Empirically fits adoption distribution data with a thermodynamic model

Derives an energy landscape and effective potential for social dynamics

Captures key features like suppression, peak, and decline in 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.