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

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

Physica A: Statistical Mechanics and its Applications, 2024, vol. 654, issue C

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.

Keywords: Markov chain; Cellular automata; Innovation (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:654:y:2024:i:c:s0378437124006411

DOI: 10.1016/j.physa.2024.130132

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