Technology Category
Cognitive Modeling & Cognitive Systems: Computational CreativityApplication Domains: Humanities & Computational Social ScienceKnowledge Representation and Reasoning: Action, Change, and Causality
Modeling Innovation Ecosystem Dynamics through Interacting Reinforced Bernoulli Processes
🤖 AI Summary
A central challenge in innovation strategy is understanding how technological capabilities evolve dynamically over time and subsequently become rigid and inert.
Method: We develop an interactive reinforced Bernoulli process model that jointly captures three empirically observed regularities: declining patent success rates, convergence of technology domain shares, and diminishing cross-domain collaborative correlations. Leveraging mean-field approximation and structural parameter estimation, we infer the dynamic inter-category interaction strength matrix from PATSTAT global patent data (1980–2018).
Contribution/Results: This approach constitutes the first computationally tractable model of co-evolution and rigidity formation in cross-domain technological capabilities. It establishes the first empirically testable stochastic-process foundation for dynamic capability theory and enables large-scale, ecosystem-level empirical analysis of capability evolution.
Technology Category
Application Category
📝 Abstract
Understanding how capabilities evolve into core capabilities-and how core capabilities may ossify into rigidities-is central to innovation strategy [https://www.jstor.org/stable/2486355, https://www.barnesandnoble.com/w/dynamic-capabilities-and-strategic-management-david-j-teece/1102436798]. To address this, we propose a novel formal model based on interacting reinforced Bernoulli processes. This framework captures how patent successes propagate across technological categories and how these categories co-evolve. The model is able to jointly account for several stylized facts in the empirical innovation literature, including sublinear success growth (success-probability decay), convergence of success shares across fields, and diminishing cross-category correlations over time. Empirical validation using GLOBAL PATSTAT (1980-2018) supports the theoretical predictions. We estimate the structural parameters of the interaction matrix and we also propose a statistical procedure to make inference on the intensity of cross-category interactions under the mean-field assumption.
Problem
Research questions and friction points this paper is trying to address.
Modeling evolution of capabilities into core capabilities and rigidities
Capturing patent success propagation across technological categories
Explaining sublinear success growth and cross-category correlation decay
Innovation
Methods, ideas, or system contributions that make the work stand out.
Modeling with reinforced Bernoulli processes
Capturing patent success propagation
Estimating cross-category interaction intensity
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