Quantifying causal emergence shows that macro can beat micro
By Erik P. Hoel, Larissa Albantakis, Giulio Tononi. 2013
347 citations recorded by the source corpus.
Subfields: Astronomy and Astrophysics, Atomic and Molecular Physics, and Optics, Cognitive Neuroscience
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Astronomy and Astrophysics
In Atomic and Molecular Physics, and Optics
In Cognitive Neuroscience
- Michael Breakspear
- Larissa Albantakis
- Masafumi Oizumi
- Tom Everitt
- Giulio Tononi
- Pietro Perona
- Frederick Eberhardt
- Victoria Krakovna
Citations in the corpus
- Multi-Level Cause-Effect Systems (2015, background): “One can directly apply the reasoning above to show that the cell assignment in such a partition is also a sufficient statistic.”
- Visual Causal Feature Learning (2014, background): “Identifying the visual cause from the image thus requires the ability to distinguish among the correlates of the target variables those that are actually causal, even if the non-causal correlates are (possibly more strongly) correlated with the target.”
- Modeling AGI Safety Frameworks with Causal Influence Diagrams (2019, background): “In general, there is a trade-off between representing many details and highlighting the big picture [Hoel et al., 2013].”
- From the Phenomenology to the Mechanisms of Consciousness: Integrated Information Theory 3.0 (2014, background): “…Variables recorded in most neurophysiological experiments may not correspond to the spatial and temporal grain at which integrated information reaches a maximum, which is the appropriate level of analysis [20]. iv ) The present analysis is unfeasible for systems of more than a dozen elements or so.”
- Dynamic models of large-scale brain activity (2017, background): “While research into the computational properties of spikes has flourished, movement and perception do not typically arise from the spikes of single neurons but by the collective behavior of many cortical, thalamic and spinal neurons in large-scale systems of the brain 2,3 .”
- UvA-DARE ( Digital Academic Repository ) Causal Consistency of Structural Equation Models (2017, neutral): “(b) Micro-level models versus macro-level models in which the macro-variables are aggregate features of the micro-variables [4, 5, 9, 11, 17]; e.”