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Computational Mechanics: Pattern and Prediction, Structure and Simplicity
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“These can be abstract (e.g. Turing machines (Delahaye and Zenil, 2007, 2012), ϵ -machines (Shalizi and Crutchfield, 2001; G¨ ornerup and Crutchfield, 2008)), biological (ecosystems, organisms), economic, social or technological (Helbing, 2011).”
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Cites
A Mathematical Theory of Communication
Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb01338.x · Cited by 88,486
A Mathematical Theory of Communication
Shannon, C. E. (1948) · The MIT Press eBooks · doi:10.1002/j.1538-7305.1948.tb00917.x · Cited by 50,201
The Nature of Statistical Learning Theory
Vapnik, V. (2000) · Statistics for Engineering and Information Science · doi:10.1007/978-1-4757-3264-1 · Cited by 46,387
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
Elements of Information Theory
Cover, T. M., & Thomas, J. A. (2001) · Wiley eBooks · doi:10.1002/0471200611 · Cited by 37,139
Causality: Models, Reasoning, and Inference
Pearl, J. (2009) · doi:10.1017/cbo9780511803161 · Cited by 11,653
Introduction to automata theory, languages and computation
(1981) · Mathematics and Computers in Simulation · doi:10.1016/0378-4754(81)90068-9 · Cited by 10,781
Detecting strange attractors in turbulence
Takens, F. (1981) · Lecture Notes in Mathematics · doi:10.1007/bfb0091924 · Cited by 10,459
The Mathematical Theory of Communication
Shannon, C. E., Weaver, W., & Wiener, N. (1950) · Physics Today · doi:10.1063/1.3067010 · Cited by 9,882
Doob, J. L., & Doob, J. L. (1953) · CERN Document Server (European Organization for Nuclear Research) · Cited by 9,053
Chomsky, N. (1957) · doi:10.1515/9783112316009 · Cited by 7,885
Pattern formation outside of equilibrium
Cross, M. C., & Hohenberg, P. C. (1993) · Reviews of Modern Physics · doi:10.1103/revmodphys.65.851 · Cited by 7,859
An introduction to cybernetics
Ashby, W. R. (1956) · doi:10.5962/bhl.title.5851 · Cited by 7,578
Information theory and statistics
(1959) · Journal of the Franklin Institute · doi:10.1016/0016-0032(59)90368-0 · Cited by 7,318
PE, & Billingsley, P. (1996) · Journal of the American Statistical Association · doi:10.2307/2291440 · Cited by 6,681
Loève, M. (1963) · Andalas University Repository (Andalas University) · Cited by 5,938
A universal algorithm for sequential data compression
Ziv, J., & Lempel, A. (1977) · IEEE Transactions on Information Theory · doi:10.1109/tit.1977.1055714 · Cited by 5,456
Cybernetics or Control and Communication in the Animal and the Machine
Wiener, N. (2019) · The MIT Press eBooks · doi:10.7551/mitpress/11810.001.0001 · Cited by 5,085
Extrapolation, Interpolation, and Smoothing of Stationary Time Series
Wiener, N. (1949) · The MIT Press eBooks · doi:10.7551/mitpress/2946.001.0001 · Cited by 4,829
CAUSALITY: MODELS, REASONING AND INFERENCE
Hitchcock, C., & Pearl, J. (2001) · The Philosophical Review · doi:10.2307/3182612 · Cited by 4,827
Causation, Prediction, and Search
Spirtes, P., Glymour, C., & Scheines, R. (1993) · Lecture Notes in Statistics · doi:10.1007/978-1-4612-2748-9 · Cited by 4,617
Valiant, L. G. (1984) · ACM eBooks · doi:10.1145/3749740.3749746 · Cited by 4,211
Mind and Nature: A Necessary Unity
Fairlamb, H. L., & Bateson, G. (1979) · MLN · doi:10.2307/2906578 · Cited by 3,980
Packard, N. H., Crutchfield, J. P., Farmer, J. D., & Shaw, R. S. (1980) · Physical Review Letters · doi:10.1103/physrevlett.45.712 · Cited by 3,909
Self-citationAn Introduction to Kolmogorov Complexity and Its Applications
Li, M., & Vitanyi, P. M. B. (2019) · Texts in computer science · doi:10.1007/978-3-030-11298-1 · Cited by 3,769
Principles of Condensed Matter Physics
Chaikin, P. M., & Lubensky, T. C. (1995) · Cambridge University Press eBooks · doi:10.1017/cbo9780511813467 · Cited by 3,571
Valiant, L. G. (1984) · Communications of the ACM · doi:10.1145/1968.1972 · Cited by 3,279
Pattern Recognition Principles
(2009) · CRC Press eBooks · doi:10.1201/9781420090741.ch2 · Cited by 3,187
Three models for the description of language
Chomsky, N. (1956) · IEEE Transactions on Information Theory · doi:10.1109/tit.1956.1056813 · Cited by 2,984
Kemeny, J. G., & Snell, J. L. (1976) · Medical Entomology and Zoology · Cited by 2,813
On the Complexity of Finite Sequences
Lempel, A., & Ziv, J. (1976) · IEEE Transactions on Information Theory · doi:10.1109/tit.1976.1055501 · Cited by 2,713
Computation: Finite and Infinite Machines.
Clarke, D. A., & Minsky, M. L. (1968) · American Mathematical Monthly · doi:10.2307/2313471 · Cited by 2,618
Two-Dimensional Signal and Image Processing
Lim, J. S. (1989) · Medical Entomology and Zoology · Cited by 2,561
Holland, J. H. (1998) · doi:10.1093/oso/9780198504092.001.0001 · Cited by 2,497
Jordan, M. I. (1998) · Springer eBooks · doi:10.1007/978-94-011-5014-9 · Cited by 2,449
The Volterra and Wiener Theories of Nonlinear Systems
Schetzen, M. (1980) · Medical Entomology and Zoology · Cited by 2,384
Principles of Condensed Matter Physics
Chaikin, P. M., Lubensky, T. C., & Witten, T. A. (1995) · Physics Today · doi:10.1063/1.2808258 · Cited by 2,378
Wiley Series in Probability and Mathematics Statistics
Huber, P. J. (1981) · Wiley series in probability and statistics · doi:10.1002/0471725250.scard · Cited by 2,257
Three approaches to the quantitative definition of information *
Kolmogorov, A. N. (1968) · International Journal of Computer Mathematics · doi:10.1080/00207166808803030 · Cited by 2,107
Scientific Explanation and the Causal Structure of the World
Salmon, W. C. (2020) · Princeton University Press eBooks · doi:10.2307/j.ctv173f2gh · Cited by 1,993
Games and Decisions: Introduction and Critical Survey.
Simon, H. A., Luce, R. D., & Raiffa, H. (1958) · American Sociological Review · doi:10.2307/2089260 · Cited by 1,943
Introduction to Mathematical Philosophy
Russell, B., & Potter, M. (2022) · Routledge eBooks · doi:10.4324/9781003308881 · Cited by 1,926
Stochastic Complexity in Statistical Inquiry
Rissanen, J. (1989) · World Scientific Series in Computer Science · doi:10.1142/0822 · Cited by 1,840
Entropy and Information Theory
Gray, R. M. (2011) · Springer eBooks · doi:10.1007/978-1-4419-7970-4 · Cited by 1,828
A formal theory of inductive inference. Part II
Solomonoff, R. J. (1964) · Information and Control · doi:10.1016/s0019-9958(64)90131-7 · Cited by 1,758
An Introduction to Computational Learning Theory
Kearns, M. J., & Vazirani, U. (1994) · doi:10.7551/mitpress/3897.001.0001 · Cited by 1,697
The working brain : an introduction to neuropsychology
Lurii︠a︡, A. R. (1973) · Medical Entomology and Zoology · Cited by 1,492
Cognition, evolution, and behavior
Shettleworth, S. J. (2010) · Choice Reviews Online · doi:10.5860/choice.47-5652 · Cited by 1,455
Complexity, Entropy And The Physics Of Information
Zurek, W. H. (2018) · CRC Press eBooks · doi:10.1201/9780429502880 · Cited by 1,451
Error and the Growth of Experimental Knowledge
Mayo, D. G. (1996) · doi:10.7208/chicago/9780226511993.001.0001 · Cited by 1,325
Nonlinear Problems in Random Theory
Wiener, N., & Teichmann, T. (1959) · Physics Today · doi:10.1063/1.3060939 · Cited by 1,258
The definition of random sequences
Martin-Löf, P. (1966) · Information and Control · doi:10.1016/s0019-9958(66)80018-9 · Cited by 1,208
A formal theory of inductive inference. Part I
Solomonoff, R. J. (1964) · Information and Control · doi:10.1016/s0019-9958(64)90223-2 · Cited by 1,192
Human Knowledge, Its Scope and Limits
Russell, B., & Bergmann, G. (1949) · Physics Today · doi:10.1063/1.3066466 · Cited by 1,172
Mind and nature : a necessary unity
Bateson, G. (1988) · DigitalGeorgetown (Georgetown University Library) · Cited by 1,136
Blum, L., Shub, M., & Smale, S. (1989) · Bulletin of the American Mathematical Society · doi:10.1090/s0273-0979-1989-15750-9 · Cited by 1,090
Kemeny, J. G., Snell, J. L., & Knapp, A. W. (1976) · Graduate texts in mathematics · doi:10.1007/978-1-4684-9455-6 · Cited by 1,079
Cybernetics, or control and communication in the animal and the machine (2nd ed.).
Wiener, N. (1961) · doi:10.1037/13140-000 · Cited by 1,068
Ergodic Theory and Information
S., D., & Billingsley, P. (1966) · Mathematics of Computation · doi:10.2307/2003618 · Cited by 1,068
Dennett, D. C. (1991) · The Journal of Philosophy · doi:10.2307/2027085 · Cited by 1,032
Theory of Games and Statistical Decisions.
Vajda, S., Blackwell, D., & Girshick, M. A. (1955) · Journal of the Royal Statistical Society Series A (General) · doi:10.2307/2342525 · Cited by 1,027
Inferring statistical complexity
Crutchfield, J. P., & Young, K. (1989) · Physical Review Letters · doi:10.1103/physrevlett.63.105 · Cited by 987
Self-citationGraham, N. (1989) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195051544.001.0001 · Cited by 953
Turner, B., & Bateson, G. (1979) · Cited by 916
On the Length of Programs for Computing Finite Binary Sequences
Chaitin, G. J. (1966) · Journal of the ACM · doi:10.1145/321356.321363 · Cited by 913
Universal coding, information, prediction, and estimation
Rissanen, J. (1984) · IEEE Transactions on Information Theory · doi:10.1109/tit.1984.1056936 · Cited by 898
Boltzmann, L., & Seeger, R. J. (1964) · American Journal of Physics · doi:10.1119/1.1971107 · Cited by 880
Boltzmann, L. (1964) · doi:10.1525/9780520327474 · Cited by 872
Ergodic Theory and Information
Révész, P., Billingsley, P., & Revesz, P. (1966) · Revue de l Institut International de Statistique / Review of the International Statistical Institute · doi:10.2307/1401263 · Cited by 870
Eugene Charniak, D. (1994) · Medical Entomology and Zoology · Cited by 811
Statistical Mechanics of Phase Transitions
Yeomans, J. M. (1992) · doi:10.1093/oso/9780198517290.001.0001 · Cited by 809
Tukey, J. W., & Wiener, N. (1952) · Journal of the American Statistical Association · doi:10.2307/2280758 · Cited by 778
The calculi of emergence: computation, dynamics and induction
Crutchfield, J. P. (1994) · Physica D Nonlinear Phenomena · doi:10.1016/0167-2789(94)90273-9 · Cited by 748
Self-citationStatistical Mechanics of Phase Transitions
Yeomans, J. M., & Rudnick, J. (1993) · Physics Today · doi:10.1063/1.2808979 · Cited by 704
Toward a quantitative theory of self-generated complexity
Grassberger, P. (1986) · International Journal of Theoretical Physics · doi:10.1007/bf00668821 · Cited by 688
(2005) · Springer eBooks · doi:10.1007/bfb0005213 · Cited by 674
Geometrical Methods of Mathematical Physics
Schutz, B. F. (1980) · Cambridge University Press eBooks · doi:10.1017/cbo9781139171540 · Cited by 672
Entropy and Information Theory
Gray, R. M. (1990) · Springer eBooks · doi:10.1007/978-1-4757-3982-4 · Cited by 661
An Introduction to Kolmogorov Complexity and Its Applications
Li, M., & Vitanyi, P. M. B. (1993) · Springer eBooks · doi:10.1007/978-1-4757-3860-5 · Cited by 658
A universal data compression system
Rissanen, J. (1983) · IEEE Transactions on Information Theory · doi:10.1109/tit.1983.1056741 · Cited by 634
Hume, D. (2011) · University of Michigan Library Repository · Cited by 625
Introduction a? l'e?tude de la me?decine expe?rimentale
Bernard, C. (2003) · Flammarion eBooks · doi:10.14375/np.9782081307582 · Cited by 622
Gasarch, W. I. (1997) · ACM SIGACT News · doi:10.1145/262301.1040343 · Cited by 602
1. Bayesian Statistics a Review
Lindley, D. V. (1972) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611970654.ch1 · Cited by 544
Geometrical Methods of Mathematical Physics
Schutz, B. F., & Peterson, M. (1982) · American Journal of Physics · doi:10.1119/1.12961 · Cited by 491
Dominique Perrin, A. (1991) · Cited by 475
The dripping faucet as a model chaotic system
Shaw, R. (1984) · Medical Entomology and Zoology · Cited by 472
Unpredictability and undecidability in dynamical systems
Moore, C. (1990) · Physical Review Letters · doi:10.1103/physrevlett.64.2354 · Cited by 422
Bühlmann, P., & Wyner, A. J. (1999) · The Annals of Statistics · doi:10.1214/aos/1018031204 · Cited by 386
Dissipative structures and weak turbulence
Manneville, P. (2008) · Springer eBooks · doi:10.1007/3-540-60188-0_59 · Cited by 383
Hands: A Pattern Theoretic Study of Biological Shapes
Grenander, U., Chow, Y. K., & Keenan, D. M. (1990) · Medical Entomology and Zoology · Cited by 334
Subshifts of finite type and sofic systems
Weiss, B. (1973) · Monatshefte für Mathematik · doi:10.1007/bf01295322 · Cited by 314
Badii, R., & Politi, A. (1997) · Cambridge University Press eBooks · doi:10.1017/cbo9780511524691 · Cited by 314
Combinatorial foundations of information theory and the calculus of probabilities
Kolmogorov, A. N. (1983) · Russian Mathematical Surveys · doi:10.1070/rm1983v038n04abeh004203 · Cited by 307
Equations of Motion from a Data Series.
Crutchfield, J. P., & McNamara, B. (1987) · Cited by 290
Self-citationThe evolution of emergent computation.
Crutchfield, J. P., & Mitchell, M. (1995) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.92.23.10742 · Cited by 280
Self-citationThe Computational Beauty of Nature
Flake, G. W. (1998) · Cited by 267
Lindley, D. V. (1972) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611970654 · Cited by 253
Minimum description length induction, Bayesianism, and Kolmogorov complexity
Vitanyi, P. M. B., & Li, M. (2000) · IEEE Transactions on Information Theory · doi:10.1109/18.825807 · Cited by 241
Frame, M., & Flake, G. W. (2000) · American Mathematical Monthly · doi:10.2307/2589369 · Cited by 226
Symbolic dynamics of noisy chaos
Crutchfield, J. P., & Packard, N. H. (1983) · Physica D Nonlinear Phenomena · doi:10.1016/0167-2789(83)90127-6 · Cited by 214
Self-citationFirst Course in Probability and Statistics
Peterson, W. H., & Neyman, J. (1951) · Southern Economic Journal · doi:10.2307/1053927 · Cited by 209
Sinha, S., & Ditto, W. L. (1998) · Physical Review Letters · doi:10.1103/physrevlett.81.2156 · Cited by 209
Compression of two-dimensional data
Lempel, A., & Ziv, J. (1986) · IEEE Transactions on Information Theory · doi:10.1109/tit.1986.1057132 · Cited by 198
Information-Theoretic Limits of Control
Touchette, H., & Lloyd, S. (2000) · Physical Review Letters · doi:10.1103/physrevlett.84.1156 · Cited by 179
Observable Operator Models for Discrete Stochastic Time Series
Jaeger, H. (2000) · Neural Computation · doi:10.1162/089976600300015411 · Cited by 178
Computational mechanics of cellular automata: An example
Hanson, J., & Crutchfield, J. P. (1997) · Physica D Nonlinear Phenomena · doi:10.1016/s0167-2789(96)00259-x · Cited by 172
Self-citationBrainchildren: essays on designing minds
(1998) · Choice Reviews Online · doi:10.5860/choice.35-6203 · Cited by 165
Statistical language learning . By Eugene Charniak. Cambridge, MA: MIT Press, 1993. Pp. xiii, 170.
Baayen, R. H. (1997) · Language · doi:10.2307/415888 · Cited by 156
Recursion theory on the reals and continuous-time computation
Moore, C. (1996) · Theoretical Computer Science · doi:10.1016/0304-3975(95)00248-0 · Cited by 151
Thermodynamic depth of causal states: Objective complexity via minimal representations
Crutchfield, J. P., & Shalizi, C. R. (1999) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.59.275 · Cited by 144
Self-citationIdentifiability of hidden Markov information sources and their minimum degrees of freedom
Ito, H., Амари, Ш.-И., & Kobayashi, K. (1992) · IEEE Transactions on Information Theory · doi:10.1109/18.119690 · Cited by 141
(1984) · Mathematics and Computers in Simulation · doi:10.1016/0378-4754(84)90073-9 · Cited by 124
(1997) · Choice Reviews Online · doi:10.5860/choice.34-4526 · Cited by 120
On the Identifiability Problem for Functions of Finite Markov Chains
Blackwell, D., & Koopmans, L. H. (1957) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177706802 · Cited by 114
Universal Schemes for Prediction, Gambling and Portfolio Selection
Algoet, P. (1992) · The Annals of Probability · doi:10.1214/aop/1176989811 · Cited by 114
Statistical complexity of simple one-dimensional spin systems
Crutchfield, J. P., & Feldman, D. P. (1997) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.55.r1239 · Cited by 112
Self-citationGrenander, U. (1996) · Johns Hopkins University Press eBooks · doi:10.56021/9780801851872 · Cited by 107
Signal processing, image processing, and pattern recognition
(1992) · Choice Reviews Online · doi:10.5860/choice.29-5161 · Cited by 102
Grenander, U., Chow, Y., & Keenan, D. M. (1991) · Research notes in neural computing · doi:10.1007/978-1-4612-3046-5 · Cited by 99
On the Relationship between Complexity and Entropy for Markov Chains and Regular Languages.
Li, W. (1991) · Cited by 94
Précis ofThe creative mind: Myths and mechanisms
Boden, M. A. (1994) · Behavioral and Brain Sciences · doi:10.1017/s0140525x0003569x · Cited by 94
Philosophical writings : a selection
Philotheus Boehner, O. F. M. (1964) · Bobbs-Merrill eBooks · Cited by 88
Complexity measures and cellular automata
Lindgren, K. A., & Nordahl, M. G. (1988) · Chalmers Publication Library (Chalmers University of Technology) · doi:10.5555/56123.56125 · Cited by 78
Crutchfield, J. P. (1990) · Woodward Conference · doi:10.1007/978-1-4612-3440-1_12 · Cited by 78
Self-citation(2018) · Northwestern University Press eBooks · doi:10.2307/j.ctv47w6pc.9 · Cited by 77
Mathematical Hierarchies and Biology
Mirkin, B., McMorris, F. R., Roberts, F., & Rzhetsky, A. (1997) · DIMACS series in discrete mathematics and theoretical computer science · doi:10.1090/dimacs/037 · Cited by 74
Ljapin, E. (1963) · Translations of mathematical monographs · doi:10.1090/mmono/003 · Cited by 74
Lidl, R., & Pilz, G. (1984) · Undergraduate texts in mathematics · doi:10.1007/978-1-4615-6465-2 · Cited by 71
Conditional Measures and Applications.
Rao, M. M. (1995) · Biometrics · doi:10.2307/2533033 · Cited by 70
Dynamical recognizers: real-time language recognition by analog computers
Moore, C. (1998) · Theoretical Computer Science · doi:10.1016/s0304-3975(97)00028-5 · Cited by 65
A Survey of Continuous-Time Computation Theory
Orponen, P. (1997) · Springer eBooks · doi:10.1007/978-1-4613-3394-4_11 · Cited by 61
Theory and algorithms for hidden Markov models and generalized hidden Markov models
Upper, D. R. (1998) · Cited by 61
Feldman, D. P., & Crutchfield, J. P. (1998) · RePEc: Research Papers in Economics · Cited by 55
Self-citationTruth from Trash: How Learning Makes Sense
Thornton, C. (2000) · Cited by 54
Predictive InformationPreprint
Bialek, W., & Tishby, N. (1999) · arXiv (Cornell University) · doi:10.48550/arxiv.cond-mat/9902341 · Cited by 48
Computational Mechanics of Cellular Automata
Hanson, J. (1993) · PhDT · Cited by 44
Palmer, A. J., Fairall, C. W., & Brewer, W. A. (2000) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/36.851786 · Cited by 33
An almost machine-independent theory of program-length complexity, sophistication, and induction
Koppel, M., & Atlan, H. (1991) · Information Sciences · doi:10.1016/0020-0255(91)90021-l · Cited by 32
A Stochastic Shape and Color Model for Defect Detection in Potatoes
Grenander, U., & Manbeck, K. (1993) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.1993.10474604 · Cited by 31
Information-theoretic Analysis of Phase Transitions.
Arnold, D. V. (1996) · Complex Systems · Cited by 29
Inferring statistical complexity in the dripping faucet experiment
Gonçalves, W. M., Pinto, R. D., Sartorelli, J. C., & de Oliveira, M. J. (1998) · Physica A Statistical Mechanics and its Applications · doi:10.1016/s0378-4371(98)00164-2 · Cited by 29
Computational mechanics of classical spin systems
Feldman, D. P. (1998) · PhDT · Cited by 29
Finite statistical complexity for sofic systems
Perry, N., & Binder, P. M. (1999) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.60.459 · Cited by 27
Characterizing the dynamics of stochastic bistable systems by measures of complexity
Witt, A., NEIMAN, A. B., & Kurths, J. (1997) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.55.5050 · Cited by 27
Collective-induced computation
Delgado, J., & Solé, R. V. (1997) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.55.2338 · Cited by 23
Kolmogorov complexity as a descriptor of cosmic microwave background maps
Gurzadyan, V. G. (1999) · Europhysics Letters (EPL) · doi:10.1209/epl/i1999-00544-3 · Cited by 16
Is anything ever new?: considering emergence
Crutchfield, J. P. (1999) · Cited by 10
Self-citationThe Grammar and Statistical Mechanics of Complex Physical Systems
Young, K. A. (1991) · PhDT · Cited by 7
Truth from Trash: How Learning Makes Sense (Complex Adaptive Systems)
Thornton, C. (2002) · Figshare · Cited by 2
Research works printed but not linked: 11
- J. P. Crutchfield and K. Young, Computation at the onset of chaos, In Zurek et al.,(137) pages 223–269.
- J. P. Crutchfield, Semantics and thermodynamics, In Nonlinear Modeling and Forecasting, M. Casdagli and S. Eubank, eds., volume 12 of Santa Fe Institute Studies in the Sciences of Complexity, pages 317–359 (Addison-Wesley, Reading, Massachusetts, 1992).
- A. N. Whitehead and B. Russell, Principia Mathematica, 2nd ed. (Cambridge University Press, Cambridge, England), pp. 1925–27.
- J. Rhodes, Applications of Automata Theory and Algebra via the Mathematical Theory of Complexity to Biology, Physics, Psychology, Philosophy, Games, and Codes (University of California, Berkeley, California, 1971).
- C. H. Bennett, How to define complexity in physics, and why, In Zurek et al.,(137) pages 137–148.
- M. Koppel, Complexity, depth, and sophistication, Complex Systems 1:1087–1091 (1987).
- M. Bunge, Causality: The Place of the Causal Princple in Modern Science (Harvard University Press, Cambridge, Massachusetts, 1959). Reprinted as Causality and Modern Science, NY: Dover Books, 1979.
- Anonymous, Kuan Yin Tzu, T'ang Dynasty, Written in China during the T'ang dynasty. Partial translation in Joseph Needham, Science and Civilisation in China, vol. II (Cambridge University Press, 1956), p. 73.
- H. R. Lewis and C. H. Papadimitriou, Elements of the Theory of Computation, 2nd ed. (Prentice-Hall, Upper Saddle River, New Jersey, 1998).
- I. M. Gel'fand and A. M. Yaglom, Calculation of the amount of information about a random function contained in another such function, Uspekhi Matematicheski Nauk 12:3–52 (1956). Trans. in American Mathematical Society Translations 12(2):199û246 (1959).
- A. N. Kolmogorov, Interpolation und extrapolation von stationären zufälligen folgen, Bull. Acad. Sci. U.S.S.R., Math. 3:3–14 (1941). In Russian with German summary.
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An information integration theory of consciousness
Tononi, G. (2004) · BMC Neuroscience · doi:10.1186/1471-2202-5-42 · Cited by 1,958
“A related measure could be constructed using the formalism of ε-machines [63].”
MethodLadyman, J., Lambert, J., & Wiesner, K. (2013) · European Journal for Philosophy of Science · doi:10.1007/s13194-012-0056-8 · Cited by 846
Supply-chain networks: a complex adaptive systems perspective
Surana, A., Kumara, S. R. T., Greaves, M., & Raghavan, U. N. (2005) · International Journal of Production Research · doi:10.1080/00207540500142274 · Cited by 649
Predictability, Complexity, and Learning
Bialek, W., Nemenman, I. M., & Tishby, N. (2001) · Neural Computation · doi:10.1162/089976601753195969 · Cited by 524
How Efficiency Shapes Human Language
Gibson, E., Futrell, R., Piantadosi, S. P., Dautriche, I., Mahowald, K., Bergen, L., & Levy, R. (2019) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2019.02.003 · Cited by 485
Metadata
- Authors
- Shalizi, C. R., & Crutchfield, J. P.
- Affiliations
- Santa Fe Institute (US)
- Venue
- Journal of Statistical Physics
- Published
- 2001-08
- Volume
- 104
- Issue
- 3-4
- Pages
- 817-879
- ISSN
- 0022-4715 (ISSN-L)
- 1572-9613
- OpenAlex
- W1551871633
- Cited by
- 471
- References
- 152
- Citations by year
- 2026: 31 · 2025: 22 · 2024: 28 · 2023: 15 · 2022: 20 · 2021: 16 · 2020: 23 · 2019: 23 · 2018: 25 · 2017: 29 · 2016: 24 · 2015: 24 · 2014: 20 · 2013: 26 · 2012: 20
- FWCI
- 11.27
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Journal of Statistical Physics (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (9970158)
- Santa Fe Institute
- Science Foundation Ireland
- Defense Advanced Research Projects Agency
- Office of Naval Research (N00014-95-1-0975)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computability, Logic, AI Algorithms
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Cellular Automata and Applications
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Systems and Dynamics
- Keywords
- computational mechanics, causal states, structural complexity, prediction, minimal representation, randomness measures, ergodic theory, information theory
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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