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Iacopo Iacopini1,2, Staša Milojević3, Vito Latora1,4

  • 1School of Mathematical Sciences, Queen Mary University of London, London E1 4NS, United Kingdom.

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Summary
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This study models innovation emergence using random walks on idea networks, where new ideas are discovered as nodes are first visited. This approach explains novelty rates and correlations, offering a framework for understanding knowledge growth.

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Area of Science:

  • Complexity Science
  • Cognitive Science
  • Network Science

Background:

  • Innovation emergence is crucial for scientific and technological advancement.
  • Understanding the cognitive processes and network dynamics underlying novelty is challenging.
  • Existing models often struggle to capture the correlations and rates of emergent innovations.

Purpose of the Study:

  • To introduce a novel computational model for innovation emergence.
  • To describe cognitive processes as random walks on concept networks.
  • To explain the generation of novelties and their intercorrelations.

Main Methods:

  • Developed a model of cognitive processes as edge-reinforced random walks on networks of ideas.
  • Network edge weights are reinforced by walker passage, influencing transition probabilities.
  • Validated the model using synthetic networks and real-world scientific data.

Main Results:

  • The model successfully reproduces empirically observed rates of novelty emergence.
  • The model captures the correlations between successive innovations.
  • Demonstrated the model's applicability across different scientific disciplines.

Conclusions:

  • Edge-reinforced random walks provide a robust framework for modeling innovation dynamics.
  • The model highlights the interplay between cognitive processes and network structure.
  • This work offers insights into the coevolution of knowledge networks and discovery processes.