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 language | English (US) |
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Article number | 130132 |
Journal | Physica A: Statistical Mechanics and its Applications |
Volume | 654 |
DOIs | |
State | Published - Nov 15 2024 |
Externally published | Yes |
Keywords
- Cellular automata
- Innovation
- Markov chain
ASJC Scopus subject areas
- Statistical and Nonlinear Physics
- Statistics and Probability