Modeling Innovation Ecosystem Dynamics through Interacting Reinforced Bernoulli Processes

[2505.13364] Modeling Innovation Ecosystem Dynamics through Interacting Reinforced Bernoulli Processes Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Statistics > Applications arXiv:2505.13364 (stat) [Submitted on 19 May 2025 ( v1 ), last revised 9 Apr 2026 (this version, v3)] Title: Modeling Innovation Ecosystem Dynamics through Interacting Reinforced Bernoulli Processes Authors: Giacomo Aletti , Irene Crimaldi , Andrea Ghiglietti , Federico Nutarelli View a PDF of the paper titled Modeling Innovation Ecosystem Dynamics through Interacting Reinforced Bernoulli Processes, by Giacomo Aletti and 2 other authors View PDF HTML (experimental) Abstract: Innovation is cumulative and interdependent: successful inventions build on prior knowledge within technological fields and may also affect success across related ones. Yet these dimensions are often studied separately in the innovation literature. This paper asks whether patent success across technological categories can be represented within a single dynamic framework that jointly captures within-category reinforcement, cross-category spillovers, and a set of aggregate regularities observed in patent data. To address this question, we propose a model of interacting reinforced Bernoulli processes in which the probability of success in a given category depends on past successes both within that category and across other categories. The framework yields joint predictions for success probabilities, cumulative successes, relative success shares, and cross-category dependence. We implement the model using granted US patent families from GLOBAL PATSTAT (1980-2018), defining category-specific success through a cohort-normalized forward-citation index. The empirical analysis shows that successful innovations continue to accumulate, but less than proportionally to the growth in patent opportunities, while technological categories remain interdependent without becoming homogeneous. Under a mean-field restriction, the model-based inferential exercise yields an estimated interaction intensity of 0.643, pointing to positive but non-maximal interaction across technological categories. Subjects: Applications (stat.AP) ; Statistics Theory (math.ST) Cite as: arXiv:2505.13364 [stat.AP] (or arXiv:2505.13364v3 [stat.AP] for this version) https://doi.org/10.48550/arXiv.2505.13364 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Research Policy Volume 55, Issue 9, November 2026, 105587 Related DOI : https://doi.org/10.1016/j.respol.2026.105587 Focus to learn more DOI(s) linking to related resources Submission history From: Giacomo Aletti [ view email ] [v1] Mon, 19 May 2025 17:10:22 UTC (385 KB) [v2] Wed, 24 Sep 2025 17:53:11 UTC (400 KB) [v3] Thu, 9 Apr 2026 07:41:14 UTC (444 KB) Full-text links: Access Paper: View a PDF of the paper titled Modeling Innovation Ecosystem Dynamics through Interacting Reinforced Bernoulli Processes, by Giacomo Aletti and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: stat.AP < prev | next > new | recent | 2025-05 Change to browse by: math math.ST stat stat.TH References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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