arXiv:2606.03919v1 Announce Type: cross Abstract: Understanding and anticipating scientific change requires models that distinguish between endogenous consolidation and exogenous diffusion of scientific concepts. Using the quantum computing subtree of concepts in OpenAlex, we construct a temporally resolved concept co-occurrence network and track each concept pair through its upstream citation lineage and downstream diffusion. We train LightGBM models on distributional and diversity-aware features to predict four outcomes: endogenous reinforcement, exogenous diffusion, their ratio, and diffusi
The proliferation of AI and the increasing complexity of scientific fields necessitate better tools for understanding and predicting conceptual evolution, making this research timely.
Understanding how scientific concepts diffuse and consolidate can significantly aid strategic R&D investment, policy-making, and resource allocation in rapidly evolving domains like AI and quantum computing.
This research provides a methodology to model and forecast the trajectory of scientific concepts, potentially offering a predictive edge in technology foresight and innovation management.
- · Governments
- · R&D institutions
- · Science policy makers
- · Venture capitalists
- · Researchers operating in silos
- · Reactive innovation strategies
Improved foresight into emerging scientific fields like quantum computing is enabled by predictive models of conceptual diffusion.
Strategic national investments in specific scientific domains can be better optimized, accelerating national capabilities in critical technologies.
Nations or entities with superior conceptual diffusion forecasting could gain a significant competitive advantage in technological leadership and economic development.
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Read at arXiv cs.LG