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
(2000) · Choice Reviews Online · doi:10.5860/choice.38-1494a · Cited by 2,968
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
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Items 201–283 of 283
Confounding Ghost Channels and Causality: A New Approach to Causal Information Flows
Ay, N. (2021) · Vietnam Journal of Mathematics · doi:10.1007/s10013-021-00511-w · Cited by 2
“interventional calculus of causal networks, and corresponding measures of causal information flow have been proposed [5, 6].”
BackgroundResultInfluentialSelf-citationA Wasserstein-based measure of conditional dependence
Etesami, J., Zhang, K., & Kiyavash, N. (2022) · Behaviormetrika · doi:10.1007/s41237-022-00170-2 · Cited by 2
“Another quantity that has been introduced in the literature to quantify causal influences in a network is information flow (Ay and Polani 2008).”
BackgroundA visual attention mechanism for autonomous robots controlled by sensorimotor contingencies
Maÿe, A., Trendafilov, D., Polani, D., & Engel, A. K. (2015) · University of Hertfordshire Research Archive (University of Hertfordshire) · Cited by 2
Self-citationQuantifying Morphological Computation based on an Information Decomposition of the Sensorimotor Loop
Ghazi-Zahedi, K., & Rauh, J. (2015) · doi:10.1162/978-0-262-33027-5-ch017 · Cited by 2
Structured Compressed Sensing Matrices for Causality Detection
Abhijith, M., Nambissan, A., Gopinath, A., et al. (2018) · IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology · doi:10.1109/rteict42901.2018.9012521 · Cited by 2
“These disciplines include neuroscience [5, 6], climatology [7], physics [8], econometrics [9, 10] and complex systems analysis [11, 12].”
BackgroundA Stimuli-Relevant Directed Dependency Index for Time Series
Baboukani, P. S., Theodoridis, S., & Østergaard, J. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747254 · Cited by 2
The Consensus Problem in Polities of Agents with Dissimilar Cognitive Architectures
Sowinski, D., Carroll-Nellenback, J., DeSilva, J. M., Frank, A., Ghoshal, G., & Gleiser, M. (2022) · Entropy · doi:10.3390/e24101378 · Cited by 2
On Causal Explanations in Bayesian Networks
Nielsen, U. H. (2007) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.6943 · Cited by 2
Information Driven Self-Organization of Agents and Agent Collectives
Harder, M. (2014) · University of Hertfordshire Research Archive (University of Hertfordshire) · doi:10.18745/th.13907 · Cited by 2
“This is similar to the measure of information flow (Ay and Polani, 2008), where a causal influence between random variables is measured via the intervention at variables in a CBN.”
BackgroundMethodInfluentialA general framework for characterizing optimal communication in brain networksPreprint
Fakhar, K., Hadaeghi, F., Seguin, C., Dixit, S., Messé, A., Zamora‐López, G., Mišić, B., & Hilgetag, C. C. (2024) · eLife · doi:10.7554/elife.101780 · Cited by 2
Sober, E. (2020) · European Journal for Philosophy of Science · doi:10.1007/s13194-020-00299-5 · Cited by 1
Model of Coordination Flow in Remote Collaborative Interaction
Trendafilov, D., Polani, D., & Murray‐Smith, R. (2015) · doi:10.1109/uksim.2015.71 · Cited by 1
“Building on work of Pearl [13], Ay and Polani [2] introduced a concept of information flow for discrete worlds, formalised on causal Bayesian networks.”
BackgroundInfluentialSelf-citationA Theory of Morphological Intelligence
Ghazi-Zahedi, K. (2019) · Springer eBooks · doi:10.1007/978-3-030-20621-5_3 · Cited by 1
Lurker: Backdoor attack-based explainable rumor detection on online media
Lin, Y., Xue, W., Bai, C., Li, J., Yin, X., & Wu, C. Q. (2025) · Science Progress · doi:10.1177/00368504241307816 · Cited by 1
On the Practical Estimation and Interpretation of Rényi Transfer Entropy
Tabachová, Z., Petr Jizba, B., Lavička, H., et al. (2026) · Communications in Nonlinear Science and Numerical Simulation · doi:10.1016/j.cnsns.2026.110935 · Cited by 1
“To address this limitation, a variety of nonlinear extensions of GC have been proposed in the literature, drawing on ideas from the theory of nonlinear dynamical systems [5–7], data compression efficiency [8], machine learning [9], and information theory [10–13], among others.”
Smirnov, D. A. (2026) · The European Physical Journal Special Topics · doi:10.1140/epjs/s11734-026-02552-z · Cited by 2
SELF-ORGANIZING PARTICLE SYSTEMS
Harder, M., & Polani, D. (2012) · Advances in Complex Systems · doi:10.1142/s0219525912500890 · Cited by 1
“…program (DSAE), School of Computing and Augmented Intelligence (SCAI), Arizona State University (ASU) (Tempe, Arizona) (Aug 12 – present) Graduate Faculty, Computer Engineering PhD program (CEN), School of Computing and Augmented Intelligence (SCAI), Arizona State University ASU) (Tempe, Arizona)”
BackgroundSelf-citationReview of Classical Information Theory
Ito, S. (2016) · Springer theses · doi:10.1007/978-981-10-1664-6_2 · Cited by 1
Lizier, J. T. (2012) · Springer theses · doi:10.1007/978-3-642-32952-4_4 · Cited by 1
Network comparison via encoding, decoding, and causality
Tian, Y., Hou, H., Xu, G., Zhang, Z., & Sun, P. (2023) · Physical Review Research · doi:10.1103/physrevresearch.5.033129 · Cited by 1
“, enable TG and TT approximate causal information flow [101]), one need to”
BackgroundFunctional control of network dynamical systems: An information theoretic approach
Singh, M. S., Pasumarthy, R., Vaidya, U., & Leonhardt, S. K. (2024) · Information Sciences · doi:10.1016/j.ins.2024.121781 · Cited by 1
Information Theoretical Approaches
Wibral, M., & Priesemann, V. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-397025-1.00338-9 · Cited by 1
Information Flow in Markov Chains
Niu, X., & Quinn, C. J. (2021) · Proceedings of the IEEE Conference on Decision & Control, including the Symposium on Adaptive Processes · doi:10.1109/cdc45484.2021.9683569 · Cited by 1
Why living bodies could be dead weight
McGregor, S. (2019) · Adaptive Behavior · doi:10.1177/1059712319838211 · Cited by 1
The dynamic importance of nodes is poorly predicted by static network featuresPreprint
van Elteren, C., Quax, R., & Sloot, P. M. A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.06654 · Cited by 1
“…for determining how much information flow between two processes is truly causal; examples include (but not limited to) conditional mutual information under causal intervention [2], causation entropy [52], permutation conditional mutual information [45], time-delayed Shannon mutual information [31].”
BackgroundQuantifying intrinsic causal contributions via structure preserving interventionsPreprint
Janzing, D., Blöbaum, P., Mastakouri, A. A., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.00714 · Cited by 1
“A tentative taxonomy could classify measures according to the following three aspects: 1/ What type of intervention is used: Ay and Polani (2008); Datta et al. (2016); Lundberg and Lee (2017); Frye et al. (2020); Janzing et al. (2020); Heskes et al. (2020); Jung et al. (2022) use do-interventions…”
BackgroundMethodInfluentialEncoding Causal MacrovariablesPreprint
Höltgen, B. T. A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.14724 · Cited by 1
“A variety of such measures can be found in the literature, often employing the mutual information between interventional distributions [3, 13, 15].”
BackgroundOn Solution Sets of Information InequalitiesPreprint
Ay, N., Wenzel, W., Ay, N., et al. (2011) · Kobra (Universitätsbibliothek Kassel) · Cited by 1
Self-citationA General Framework for Characterizing Optimal Communication in Brain NetworksPreprint
Fakhar, K., Hadaeghi, F., Seguin, C., Dixit, S., Messé, A., Zamora‐López, G., Mišić, B., & Hilgetag, C. C. (2024) · eLife · doi:10.7554/elife.101780.1 · Cited by 1
CauTrigger: deciphering biological system state transitions by hierarchical causal decomposition
Zhang, C., Cai, D., Aihara, K., & Chen, L. (2026) · Nature Methods · doi:10.1038/s41592-026-03274-9
Information-theoretic measures as a generic approach to human-robot interaction
Salge, C., Glackin, C., Ristić–Durrant, D., et al. (2014) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/2559636.2563711
“2 Information Flow Information flow [6] quantifies the amount of causal influence one variable has on another.”
BackgroundSelf-citationMutual information based measures on complex interdependent networks of neuro data sets
Razak, F. A. (2013) · Spiral (Imperial College London) · doi:10.25560/11579
“An example of a method that is often classed as being near the confirmatory end of the spectrum is the Direct Causal Model (DCM) introduced by Friston [41] and the graphical model [76].”
BackgroundMethodHidden information transfer in an autonomous swinging robot
Thorniley, J., & Husbands, P. (2013) · doi:10.7551/978-0-262-31709-2-ch074
“Recent work has studied the relationship between transfer entropy and causal inference in part motivated by phenomena similar to the one described here (e.g. Ay and Polani, 2008; Lizier and Prokopenko, 2010).”
BackgroundWollstadt, P., Sellers, K. K., Rudelt, L., Priesemann, V., Hutt, A., Frӧhlich, F., & Wibral, M. (2016)
Digested information, a non-semantic motivation for agent-agent interaction
Salge, C. (2010) · University of Hertfordshire Research Archive (University of Hertfordshire)
Fields of Application of Information Geometry
Ay, N., Jost, J., Lê, H. V., & Schwachhöfer, L. (2017) · Ergebnisse der Mathematik und ihrer Grenzgebiete · doi:10.1007/978-3-319-56478-4_6
Self-citationPellet, J. (2010)
Measuring Fitness Effects of Agent-Environment Interactions
McGregor, S., & Mediano, P. A. M. (2018) · Artificial Life · doi:10.1162/artl_a_00269
Towards designing artificial universes for artificial agents under interaction closure
Biehl, M., Salge, C., & Polani, D. (2015)
Self-citationAnalysis of fitness among Korean adults by the cause–effect relation in lump mean scheme
Kang, N. L. (2021) · BMC Sports Science Medicine and Rehabilitation · doi:10.1186/s13102-021-00338-5
Gençağa, D. (2018) · MDPI eBooks · doi:10.3390/books978-3-03842-920-3
Complexity, Criticality and Computation (C³)
Prokopenko, M. (2017) · MDPI eBooks · doi:10.3390/books978-3-03842-515-1
On the Nature of Information: How FAIR Digital Objects are Building-up Semantic Space
Döbereiner, H.-G. (2022) · Research Ideas and Outcomes · doi:10.3897/rio.8.e95119
Denecke, M. (2023) · Digitale Gesellschaft · doi:10.1515/9783839464960-008
Informationsströme in digitalen Kulturen
Denecke, M. (2023) · Digitale Gesellschaft · doi:10.1515/9783839464960 · Cited by 3
4. Geregelte Ströme. Kybernetik, elektrisch geschalteter Strom, Flowchart
Denecke, M. (2023) · Digitale Gesellschaft · doi:10.14361/9783839464960-004
1. Einleitung. Flow als epistemische Ressource
Denecke, M. (2023) · Digitale Gesellschaft · doi:10.14361/9783839464960-001
2. Informationswelten im Fluss
Denecke, M. (2023) · Digitale Gesellschaft · doi:10.14361/9783839464960-002
Denecke, M. (2023) · Digitale Gesellschaft · doi:10.14361/9783839464960-007
3. Ströme im medientechnisch bedingten Kapitalismus
Denecke, M. (2023) · Digitale Gesellschaft · doi:10.14361/9783839464960-003
5. Globale Ströme und Zirkulationen im Diskurs zum logistischen Kapitalismus
Denecke, M. (2023) · Digitale Gesellschaft · doi:10.14361/9783839464960-005
6. Schluss. Schreiben an der Gegenwart
Denecke, M. (2023) · Digitale Gesellschaft · doi:10.14361/9783839464960-006
Denecke, M. (2023) · Digitale Gesellschaft · doi:10.14361/9783839464960-toc
Denecke, M. (2023) · Digitale Gesellschaft · doi:10.14361/9783839464960-fm
Information-driven intrinsic motivation in reinforcement learning
Ghazi-Zahedi, K., Martius, G., & Ay, N. (2013)
Self-citationCausal modeling in high-order scenarios: unfolding mechanisms by moving across scales
Martinelli, T. (2023) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.76.2023.tde-20062023-095829
From Global to local Functional Connectivity: Application to Listening Effort
Baboukani, P. S. (2022) · doi:10.54337/aau527272197
Information Flows in Continuous-Time Stochastic System as Dynamical Causal Effects
Smirnov, D. A. (2023) · Scientific School Dynamics of Complex Networks and their Applications (DCNA) · doi:10.1109/dcna59899.2023.10290576
Steering cell-state and phenotype transitions by causal disentanglement learningPreprint
Zhang, C., Chen, Z., Miao, Y., Xue, Y., Cai, D., Guo, W., Ji, H., Aihara, K., & Chen, L. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.16.607277
An Information-Theoretic Perspective on Acting Agents
Langer, C., & Ay, N. (2024) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/1321/1/012008 · Cited by 1
Self-citationA General Framework for Characterizing Optimal Communication in Brain NetworksPreprint
Fakhar, K., Hadaeghi, F., Seguin, C., Dixit, S., Messé, A., Zamora‐López, G., Mišić, B., & Hilgetag, C. C. (2025) · eLife · doi:10.7554/elife.101780.2
Disentangling Reasoning Factors for Natural Language Inference
Zhou, X., Zeng, L., Zhao, Z., Bu, J., Liang, W., & Wang, H. (2025) · Big Data Mining and Analytics · doi:10.26599/bdma.2024.9020096
Evolutionary Computation for Causality-Driven Feature Selection: A Preliminary Study
Nardone, E., D’Alessandro, T., Stefano, C. D., et al. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-90062-4_24
CStaG: A Causality-Inspired Stable Generative Model for the Interpretation of Graph Neural Networks
Xie, X. (2025) · International Conference on Advanced Algorithms and Control Engineering (ICAACE) · doi:10.1109/icaace65325.2025.11019334
“[13] Meanwhile, to quantitatively measure the casual effect of the explanation, we leverage the concept of information flow [14] to calculate the casual information transmission from the latent features to the predictions which the pre-trained model made.”
From Function to Implementation: Exploring Degeneracy in Evolved Artificial Agents
Hu, Z., Cingiler, O., Bohm, C., & Albantakis, L. (2025) · Neural Computation · doi:10.1162/neco.a.19 · Cited by 1
Unsupervised Gaze Representation Learning by Switching Features
Sun, Y., Zeng, J., Shan, S., et al. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3600680
AMSVGAE-Based Causal Inference for Interpretable Graph Neural Networks
Wu, J., Lin, L., Huang, Y., et al. (2025) · Communications in computer and information science · doi:10.1007/978-981-95-0129-8_22
Simple physical systems as a reference for multivariate information dynamics
Liardi, A., Sas, M. I., Blackburne, G., Knottenbelt, W. J., Mediano, P. A. M., & Jensen, H. J. (2025) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0276247 · Cited by 1
Information Theoretic Models of Social Interaction
Salge, C. (2013) · University of Hertfordshire Research Archive (University of Hertfordshire) · doi:10.18745/th.13887
“It might be undesirable to classify this as social interaction, but in this case one could utilize some measure of information flow (Ay and Polani 2008) to differentiate further.”
BackgroundPlankton interaction networks under human pressure
Balkoni, A., zu Castell, W., Wiltshire, K. H., et al. (2026) · Ecological Complexity · doi:10.1016/j.ecocom.2026.101163
Mechanisms and roles of information processing in collective motion
Jiāng, L. (2017) · theses.fr (ABES) · doi:10.70675/fe9d11ecz6accz4d36zbeefzdb3b1feeea5c
Learning increasingly complex skills through deep reinforcement learning using intrinsic motivation
Aubret, A. (2021) · theses.fr (ABES) · doi:10.70675/6f863351zdd37z4da4z9fa2z5ceed27ddd07 · Cited by 1
Machine Learning-Assisted Spatiotemporal Chaos Forecasting
Murr, G. (2024) · theses.fr (ABES) · doi:10.70675/6099f172za5bdz41f3z991bzd83042480608
Machine Learning for Dynamical Systems Modelling
Noto, D. (2024) · theses.fr (ABES) · doi:10.70675/803225a0z13baz4ec1zbe08z87a890834e18
Deng, H. C., Shu, Z. R., He, X. H., et al. (2026) · International Journal of Climatology · doi:10.1002/joc.70435
A Causal Information-Flow Framework for Unbiased Learning-to-Rank
Gong, H., Ai, Q., Tao, Z., et al. (2026) · IEEE Conference on Artificial Intelligence (CAI) · doi:10.1109/cai68641.2026.11536539
“Our formulation is inspired by causal information-flow ideas that quantify information transmission along causal graphs [30], and by causal representation learning [31].”
CauFinder: Steering Cell‐State and Phenotype Transitions by Causal Disentanglement Learning
Zhang, C., Chen, Z., Miao, Y., Shen, Z., Cai, D., Tang, S., Xue, Y., Guo, W.-F., Ji, H., Liu, J., et al. (2026) · Advanced Science · doi:10.1002/advs.76177 · Cited by 1
“We then employ information flow 𝐼(𝑧 𝑐 → 𝑦 ) [ 77, 78 ] to quantify the causal influence of z c on model prediction y .”
Four faces of information in natural systems
Rosas, F. E., Mediano, P. A. M., Luppi, A. I., Boyd, A. B., Berta, M., Jensen, H. J., Gastpar, M., & Carroll, S. M. (2026) · Nature Reviews Physics · doi:10.1038/s42254-026-00971-4
“165,166 for related discussions).”
Braun, V. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7466522
Quantifying Granger causality for stationary Markov chains
Niemiro, W. (2026) · Statistics & Probability Letters · doi:10.1016/j.spl.2026.110991
Measuring Directional Couplings via Information Responses
Smirnov, D. A. (2026) · Scientific School Dynamics of Complex Networks and their Applications (DCNA) · doi:10.1109/dcna72004.2026.11733443
Integrated information theory of consciousness: an updated account.
(2012) · PubMed · doi:10.4449/aib.v149i5.1388 · Cited by 370
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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