Time-Dependent Urn Models reproduce the full spectrum of novelties discovery
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866917216119685120 |
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| author | Bellina, Alessandro De Marzo, Giordano Loreto, Vittorio |
| author_facet | Bellina, Alessandro De Marzo, Giordano Loreto, Vittorio |
| contents | Systems driven by innovation, a pivotal force in human society, present various intriguing statistical regularities, from the Heaps' law to logarithmic scaling or somewhat different patterns for the innovation rates. The Urn Model with Triggering (UMT) has been instrumental in modelling these innovation dynamics. Yet, a generalisation is needed to capture the richer empirical phenomenology. Here, we introduce a Time-dependent Urn Model with Triggering (TUMT), a generalisation of the UMT that crucially integrates time-dependent parameters for reinforcement and triggering to offer a broader framework for modelling innovation in non-stationary systems. Through analytical computation and numerical simulations, we show that the TUMT reconciles various behaviours observed in a broad spectrum of systems, from patenting activity to the analysis of gene mutations. We highlight how the TUMT features a "critical" region where both Heaps' and Zipf's laws coexist, for which we compute the exponents. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_10114 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Time-Dependent Urn Models reproduce the full spectrum of novelties discovery Bellina, Alessandro De Marzo, Giordano Loreto, Vittorio Statistical Mechanics Probability Systems driven by innovation, a pivotal force in human society, present various intriguing statistical regularities, from the Heaps' law to logarithmic scaling or somewhat different patterns for the innovation rates. The Urn Model with Triggering (UMT) has been instrumental in modelling these innovation dynamics. Yet, a generalisation is needed to capture the richer empirical phenomenology. Here, we introduce a Time-dependent Urn Model with Triggering (TUMT), a generalisation of the UMT that crucially integrates time-dependent parameters for reinforcement and triggering to offer a broader framework for modelling innovation in non-stationary systems. Through analytical computation and numerical simulations, we show that the TUMT reconciles various behaviours observed in a broad spectrum of systems, from patenting activity to the analysis of gene mutations. We highlight how the TUMT features a "critical" region where both Heaps' and Zipf's laws coexist, for which we compute the exponents. |
| title | Time-Dependent Urn Models reproduce the full spectrum of novelties discovery |
| topic | Statistical Mechanics Probability |
| url | https://arxiv.org/abs/2401.10114 |