Chain Reaction of Ideas: Can Radioactive Decay Predict Technological Innovation?
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866914677758361600 |
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| author | Giardini, Guilherme S. Y. da Cunha, Carlo R. |
| author_facet | Giardini, Guilherme S. Y. da Cunha, Carlo R. |
| contents | 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 the 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_08681 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Chain Reaction of Ideas: Can Radioactive Decay Predict Technological Innovation? Giardini, Guilherme S. Y. da Cunha, Carlo R. Statistical Mechanics 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 the 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. |
| title | Chain Reaction of Ideas: Can Radioactive Decay Predict Technological Innovation? |
| topic | Statistical Mechanics |
| url | https://arxiv.org/abs/2402.08681 |