ArticleEntropy (Basel, Switzerland)2018
Zipf's, Heaps' and Taylor's Laws are Determined by the Expansion into the Adjacent Possible.
Article in Entropy (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- The dynamics of higher-order novelties.Nature communications · 2025Article
- Interacting innovation processes.Scientific reports · 2023Article
- Twitter as an innovation process with damping effect.Scientific reports · 2021Article
- Robot Art, in the Eye of the Beholder?: Personalized Metaphors Facilitate Communication of Emotions and Creativity.Frontiers in robotics and AI · 2021Article
- The Compressed Vocabulary of Microbial Life.Frontiers in microbiology · 2021Article
- Taylor's Law in Innovation Processes.Entropy (Basel, Switzerland) · 2020Article
- Zipf's Law of Vasovagal Heart Rate Variability Sequences.Entropy (Basel, Switzerland) · 2020Article
- Heaps' Law and Heaps functions in tagged texts: evidences of their linguistic relevance.Royal Society open science · 2020Article
- A Standardized Project Gutenberg Corpus for Statistical Analysis of Natural Language and Quantitative Linguistics.Entropy (Basel, Switzerland) · 2020Article
- The Language of Innovation.PloS one · 2020Article
- Optimal Microbiome Networks: Macroecology and Criticality.Entropy (Basel, Switzerland) · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Zipf's, Heaps' and Taylor's laws are ubiquitous in many different systems where innovation processes are at play. Together, they represent a compelling set of stylized facts regarding the overall statistics, the innovation rate and the scaling of fluctuations for systems as diverse as written texts and cities, ecological systems and stock markets. Many modeling schemes have been proposed in literature to explain those laws, but only recently a modeling framework has been introduced that accounts for the emergence of those laws without deducing the emergence of one of the laws from the others or without ad hoc assumptions. This modeling framework is based on the concept of adjacent possible space and its key feature of being dynamically restructured while its boundaries get explored, i.e., conditional to the occurrence of novel events. Here, we illustrate this approach and show how this simple modeling framework, instantiated through a modified Pólya's urn model, is able to reproduce Zipf's, Heaps' and Taylor's laws within a unique self-consistent scheme. In addition, the same modeling scheme embraces other less common evolutionary laws (Hoppe's model and Dirichlet processes) as particular cases.
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