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The dynamics of higher-order novelties.
Gabriele Di Bona1,2,3,4, Alessandro Bellina3,4,5, Giordano De Marzo4,5,6,7
1School of Mathematical Sciences, Queen Mary University of London, London, UK.
Nature Communications
|January 4, 2025
Summary
This study introduces higher-order novelties, defined as the first co-occurrence of elements, and higher-order Heaps' exponents to measure discovery pace. Real-world data shows these measures reveal differences in exploration processes not captured by standard methods.
Area of Science:
- Complexity science
- Network science
- Information theory
Background:
- Understanding novelty and discovery is crucial for innovation.
- Existing methods define novelty solely by first appearances.
- Novelty can also arise from combining known elements.
Purpose of the Study:
- To define and quantify higher-order novelties.
- To introduce higher-order Heaps' exponents for discovery pace.
- To model exploration processes and their evolving networks.
Main Methods:
- Defining higher-order novelties (co-occurrence).
- Introducing higher-order Heaps' exponents.
- Analyzing real-world data.
- Modeling exploration as a time-evolving random walk on a network.
Main Results:
- Standard Heaps' exponents can obscure differences in discovery at higher orders.
- Higher-order measures reveal distinct exploration process dynamics.
- The random walk network model replicates empirical higher-order novelty properties.
Conclusions:
- Higher-order novelties and their exponents provide a more nuanced understanding of discovery.
- Exploration processes and the networks they traverse co-evolve.
- The proposed model offers insights into the mechanisms of scientific and technological discovery.
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