ArticlePhilosophical transactions. Series A, Mathematical, physical, and engineering sciences2022
Emergence as the conversion of information: a unifying theory.
Article in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed.
- What is emergence, after all?PNAS nexus · 2026Article
- Consilience in Causation: Causal Emergence Is Found Across Measures of Causation.Entropy (Basel, Switzerland) · 2025Article
- Two Levels of Integrated Information Theory: From Autonomous Systems to Conscious Life.Entropy (Basel, Switzerland) · 2024Article
- A synergistic workspace for human consciousness revealed by Integrated Information Decomposition.eLife · 2024Article
- Synergistic information supports modality integration and flexible learning in neural networks solving multiple tasks.PLoS computational biology · 2024Article
- Collective intelligence: A unifying concept for integrating biology across scales and substrates.Communications biology · 2024Review
- Information decomposition in complex systems via machine learning.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Article
- Emergence and Causality in Complex Systems: A Survey of Causal Emergence and Related Quantitative Studies.Entropy (Basel, Switzerland) · 2024Review
- Generalized decomposition of multivariate information.PloS one · 2024Article
- Biology's transformation: from observation through experiment to computation.Bioinformatics advances · 2024Article
- Functional hierarchies in brain dynamics characterized by signal reversibility in ferret cortex.PLoS computational biology · 2024Article
- The serotonergic psychedelic N,N-dipropyltryptamine alters information-processing dynamics in in vitro cortical neural circuits.Network neuroscience (Cambridge, Mass.) · 2024Article
- Neurosurgery for mental conditions and pain: An historical perspective on the limits of biological determinism.Surgical neurology international · 2024Review
- Partial entropy decomposition reveals higher-order information structures in human brain activity.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Multivariate information theory uncovers synergistic subsystems of the human cerebral cortex.Communications biology · 2023Article
- Reduced emergent character of neural dynamics in patients with a disrupted connectome.NeuroImage · 2023Article
- Decomposing past and future: Integrated information decomposition based on shared probability mass exclusions.PloS one · 2023Article
- Flickering Emergences: The Question of Locality in Information-Theoretic Approaches to Emergence.Entropy (Basel, Switzerland) · 2022Article
- Neural Information Squeezer for Causal Emergence.Entropy (Basel, Switzerland) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Is reduction always a good scientific strategy? The existence of the special sciences above physics suggests not. Previous research has shown that dimensionality reduction (macroscales) can increase the dependency between elements of a system (a phenomenon called 'causal emergence'). Here, we provide an umbrella mathematical framework for emergence based on information conversion. We show evidence that coarse-graining can convert information from one 'type' to another. We demonstrate this using the well-understood mutual information measure applied to Boolean networks. Using partial information decomposition, the mutual information can be decomposed into redundant, unique and synergistic information atoms. Then by introducing a novel measure of the synergy bias of a given decomposition, we are able to show that the synergy component of a Boolean network's mutual information can increase at macroscales. This can occur even when there is no difference in the total mutual information between a macroscale and its underlying microscale, proving information conversion. We relate this broad framework to previous work, compare it to other theories, and argue it complexifies any notion of universal reduction in the sciences, since such reduction would likely lead to a loss of synergistic information in scientific models. This article is part of the theme issue 'Emergent phenomena in complex physical and socio-technical systems: from cells to societies'.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.