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Thermodynamic framework for modeling social adoption in multi-agent systems.

Guilherme S Y Giardini1, Carlo R daCunha1

  • 1Northern Arizona University, Sanghi College of Engineering (SCE), Flagstaff, Arizona 86011, USA and School of Informatics, Computing, and Cyber Systems (SICCS), Flagstaff, Arizona 86011, USA.

Physical Review. E
|February 20, 2026
PubMed
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We introduce a thermodynamic framework to model innovation adoption and abandonment. This approach uses statistical mechanics to explain complex dynamics in sociotechnical systems, revealing hybrid thermodynamic-statistical signatures.

Area of Science:

  • Thermodynamics
  • Statistical Mechanics
  • Sociotechnical Systems Analysis

Background:

  • Innovation adoption and abandonment exhibit complex dynamics.
  • Existing models may not fully capture emergent behaviors in sociotechnical systems.
  • A unified framework is needed to understand these dynamics.

Purpose of the Study:

  • To develop a thermodynamic framework for modeling innovation adoption and abandonment.
  • To utilize statistical mechanics for analyzing these dynamics.
  • To capture emergent behaviors in sociotechnical systems.

Main Methods:

  • Developed a thermodynamic framework based on statistical mechanics.
  • Constructed a canonical ensemble from an empirical adoption distribution model.

Related Experiment Videos

  • Defined an effective potential and derived a dynamical Lagrangian formulation.
  • Analyzed emergent behaviors using field theory.

    • Main Results:

    • The framework yields Gompertz-like and Maxwell-Boltzmann-like equilibrium distributions.
    • The derived field theory captures key features of innovation dynamics (suppression, peak, decline).
    • Effective temperature, entropy, and equilibrium points were interpreted.

    Conclusions:

    • The thermodynamic framework successfully models innovation adoption and abandonment dynamics.
    • Sociotechnical systems exhibit hybrid thermodynamic-statistical signatures.
    • This approach offers new insights into emergent behaviors in complex systems.