Modeling Innovation Ecosystem Dynamics through Interacting Reinforced Bernoulli Processes

TL;DR

This paper introduces a dynamic model using interacting reinforced Bernoulli processes to jointly analyze patent success within and across technological categories, capturing interdependence and regularities in innovation data.

Contribution

It develops a unified framework that models both within-category reinforcement and cross-category spillovers in patent success, validated with empirical patent data.

Findings

Successful innovations accumulate less than proportionally to patent opportunities.

Technological categories remain interdependent without becoming homogeneous.

Estimated interaction intensity among categories is 0.643, indicating positive but not maximal spillovers.

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.