Chain reaction of ideas: Can radioactive decay predict technological innovation?

G. S.Y. Giardini, C. R. da Cunha

Research output: Contribution to journalArticlepeer-review

Abstract

This work demonstrates the application of a birth–death Markov process, inspired by radioactive decay, to capture the dynamics of innovation processes. Leveraging the Bass diffusion model, we derive a Gompertz-like function explaining long-term innovation trends. The validity of our model is confirmed using citation data, Google trends, and a recurrent neural network, which also reveals short-term fluctuations. Further analysis through an automaton model suggests these fluctuations can arise from the inherent stochastic nature of the underlying physics.

Original languageEnglish (US)
Article number130132
JournalPhysica A: Statistical Mechanics and its Applications
Volume654
DOIs
StatePublished - Nov 15 2024
Externally publishedYes

Keywords

  • Cellular automata
  • Innovation
  • Markov chain

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Statistics and Probability

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