ArticleProceedings of the National Academy of Sciences of the United States of America2013
Quantifying causal emergence shows that macro can beat micro.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 118 papers.
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Who cites it
118 citing papers in PubMed.
- Interaction matrices as unifying tools for navigating ecological complexity.Nature ecology & evolution · 2026Review
- Review
- A Sequential Markov Probabilistic Aggregation Algorithm for Causal Emergence in Markov Aggregation.Entropy (Basel, Switzerland) · 2026Article
- Causal graph neural networks for healthcare.Nature biomedical engineering · 2026Review
- Measuring the spatial lag effect of Tobler's First Law of Geography in Earth system predictive learning.Scientific reports · 2026Article
- Electronic medical record-based causal network modeling for acute myocardial infarction diagnosis in the emergency department.iScience · 2026Article
- Community First Theory: How Collective Organization Generates Individual Diversity.Entropy (Basel, Switzerland) · 2026Article
- Intrinsic Cause-Effect Power: The Tradeoff Between Differentiation and Specification.Entropy (Basel, Switzerland) · 2026Article
- When Wholes Resist Decomposition: A Spectral Measure of Epistemic Emergence.Entropy (Basel, Switzerland) · 2026Article
- Evolving reservoir computers reveal bidirectional coupling between predictive power and emergent dynamics.Patterns (New York, N.Y.) · 2026Article
- Mind Everywhere: A Framework for Conceptualizing Goal-Directedness in Biology and Other Domains-Part Two.Biological theory · 2026Article
- What is emergence, after all?PNAS nexus · 2026Article
- A reframed landscape of causal emergence.Patterns (New York, N.Y.) · 2026Article
- Quantifying emergent complexity.Patterns (New York, N.Y.) · 2026Article
- Intrinsic units: identifying a system's causal grain.Neuroscience of consciousness · 2026Article
- Emergent multiscale organisation of neural dynamics fragments in anaesthesia.Imaging neuroscience (Cambridge, Mass.) · 2026Article
- Identifying and investigating emergent behaviour in neural systems.Frontiers in neuroscience · 2026Article
- Whole-to-parts causation mechanism.Frontiers in psychology · 2026Article
- Directed connectomes across species reveal conserved and divergent pathways of neural signaling.bioRxiv : the preprint server for biology · 2025Article
- Topographic Variation in Human Neurotransmitter Receptor Densities Explains Differences in Intracranial EEG Spectra.Human brain mapping · 2025Article
58 more citing papers are in PubMed but not listed here.
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3 authors.
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Abstract
Causal interactions within complex systems can be analyzed at multiple spatial and temporal scales. For example, the brain can be analyzed at the level of neurons, neuronal groups, and areas, over tens, hundreds, or thousands of milliseconds. It is widely assumed that, once a micro level is fixed, macro levels are fixed too, a relation called supervenience. It is also assumed that, although macro descriptions may be convenient, only the micro level is causally complete, because it includes every detail, thus leaving no room for causation at the macro level. However, this assumption can only be evaluated under a proper measure of causation. Here, we use a measure [effective information (EI)] that depends on both the effectiveness of a system's mechanisms and the size of its state space: EI is higher the more the mechanisms constrain the system's possible past and future states. By measuring EI at micro and macro levels in simple systems whose micro mechanisms are fixed, we show that for certain causal architectures EI can peak at a macro level in space and/or time. This happens when coarse-grained macro mechanisms are more effective (more deterministic and/or less degenerate) than the underlying micro mechanisms, to an extent that overcomes the smaller state space. Thus, although the macro level supervenes upon the micro, it can supersede it causally, leading to genuine causal emergence--the gain in EI when moving from a micro to a macro level of analysis.
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