The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
INFORMATION FLOWS IN CAUSAL NETWORKS
Open accessAbstract
We use a notion of causal independence based on intervention, which is a fundamental concept of the theory of causal networks, to define a measure for the strength of a causal effect. We call this measure "information flow" and compare it with known information flow measures such as transfer entropy.
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“The assumption of conditional independence distinguishes IIT’s causal powers analysis from standard information-theoretic analyses of information flow [10, 27] and corresponds to an assumption that variables are “physical” units”
From Integrated information theory (IIT) 4.0: Formulating the properties of phenomenal existence in physical terms (2023) · cites this paper as [27]
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Citations
Cites
Measuring Information Transfer
Schreiber, T. (2000) · Physical Review Letters · doi:10.1103/physrevlett.85.461 · Cited by 4,571
Causality: models, reasoning, and inference
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Bayesian integration in sensorimotor learning
Körding, K. P., & Wolpert, D. M. (2004) · Nature · doi:10.1038/nature02169 · Cited by 2,004
Sync: The Emerging Science of Spontaneous Order
Goldenfeld, N. D. (2004) · Physics Today · doi:10.1063/1.1784276 · Cited by 1,528
Self-organization in a perceptual network
Linsker, R. (1988) · Computer · doi:10.1109/2.36 · Cited by 1,473
Complexity, Entropy And The Physics Of Information
Zurek, W. H. (2018) · CRC Press eBooks · doi:10.1201/9780429502880 · Cited by 1,451
Could information theory provide an ecological theory of sensory processing?
Atick, J. J. (1992) · Network Computation in Neural Systems · doi:10.1088/0954-898x_3_2_009 · Cited by 448
Measuring information integration
Tononi, G., & Sporns, O. (2003) · BMC Neuroscience · doi:10.1186/1471-2202-4-31 · Cited by 294
Information Transfer between Dynamical System Components
Liang, X., & Kleeman, R. D. (2005) · Physical Review Letters · doi:10.1103/physrevlett.95.244101 · Cited by 229
Information-theoretic approach to the study of control systems
Touchette, H., & Lloyd, S. (2003) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2003.09.007 · Cited by 202
Research works printed but not linked: 6
- Ay N., Theor. Biosci., 125, 93
- H. B. Barlow, Sensory Communication: Contributions to the Symposium on Principles of Sensory Communication, ed. W. A. Rosenblith (M.I.T. Press, 1959) pp. 217–234.
- R. Der, U. Steinmetz and F. Pasemann, Computational Intelligence for Modelling, Control, and Automation, Concurrent Systems Engineering Series 55, ed. M. Mohammadian (IOS Press, 1999) pp. 43–47.
- McGill W., IEEE Trans. Infor. Theor., 4, 93
- Porr B., Constr. Foundations, 1
- A. Werner, Karst Waters & Environmental Impacts, eds. Günay and Johnson (Balkema, Rotterdam, 1997) pp. 153–160.
Cited by
Sorted by citations
From the phenomenology to the mechanisms of consciousness: integrated information theory 3.0
Oizumi, M., Albantakis, L., & Tononi, G. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003588 · Cited by 1,128
“These and other features distinguish this ‘‘intrinsic’’ notion of information from the ‘‘extrinsic’’, Shannon notion (see Text S3; cf. [21–23] for related approaches to information and causation in networks).”
BackgroundIntegrated information in discrete dynamical systems: motivation and theoretical framework
Balduzzi, D., & Tononi, G. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000091 · Cited by 419
“…the a posteriori repertoire by forcibly intervening in the system and imposing each state in the a priori repertoire, thus we implement a perturbational approach [1,2,4,6]; see also [8,9] which apply perturbations to measure the average interaction between subsets for general distributions.”
Unraveling the cause-effect relation between time series
Liang, X. S. (2014) · Physical Review E · doi:10.1103/physreve.90.052150 · Cited by 410
Quantifying causal emergence shows that macro can beat micro
Hoel, E. P., Albantakis, L., & Tononi, G. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1314922110 · Cited by 378
Mapping Information Flow in Sensorimotor Networks
Lungarella, M., & Sporns, O. (2006) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0020144 · Cited by 294
“, [31,32]) will ultimately be needed to provide a truly causal description of sensorimotor and neural networks.”
Background
Metadata
- Authors
- AY, N., & POLANI, D.
- Affiliations
- Santa Fe Institute (US)
- University of Hertfordshire (GB)
- Venue
- Advances in Complex Systems
- Publisher
- World Scientific Pub Co Pte Lt
- Published
- 2008-02
- Volume
- 11
- Issue
- 01
- Pages
- 17-41
- ISSN
- 0219-5259 (ISSN-L)
- 1793-6802
- OpenAlex
- W2060841334
- Semantic Scholar
- cea9940860abc3dea72d9ebdbb699a9d885c0724
- Cited by
- 287
- References
- 18
- Citations by year
- 2026: 12 · 2025: 20 · 2024: 19 · 2023: 23 · 2022: 16 · 2021: 20 · 2020: 14 · 2019: 9 · 2018: 16 · 2017: 22 · 2016: 21 · 2015: 15 · 2014: 18 · 2013: 20 · 2012: 9
- FWCI
- 12.16
- Type
- journal article
- Language
- en
- Open access
- green
- uhra.herts.ac.uk
- Locations
- Advances in Complex Systems (publishedVersion)
- University of Hertfordshire Research Archive (University of Hertfordshire) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Max Planck Institute for Plasma Physics (submittedVersion)
- Funders
- Santa Fe Institute
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Philosophy of Biology and Scientific Explanation
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Complex Systems and Time Series Analysis
- Keywords
- information flow, causal networks, causal independence, intervention, transfer entropy, causal inference
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