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Computation at the edge of chaos: Phase transitions and emergent computation
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“As it has been proposed, adaptive systems require a high C in order to be able to cope with changes of its environment while at the same time maintaining their integrity (Langton, 1990; Kauffman, 1993).”
From Information Measures of Complexity, Emergence, Self-organization, Homeostasis, and Autopoiesis (2013) · cites this paper as Langton, 1990
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“As it has been proposed by several authors, complexity can be seen as balance between order and disorder (Langton, 1990; Kauffman, 1993; Lopez-Ruiz et al., 1995)”
From Information Measures of Complexity, Emergence, Self-organization, Homeostasis, and Autopoiesis (2013) · cites this paper as Langton, 1990
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“It has been argued that computation and life require this balance to be able to compute and adapt (Langton, 1990; Kauffman, 1993).”
From Information Measures of Complexity, Emergence, Self-organization, Homeostasis, and Autopoiesis (2013) · cites this paper as Langton, 1990
In the citing paper
“Finally, we show that a randomly connected circuit model, akin to a “reservoir” network13-15, can reproduce the observed distribution of timescales, provided that the network operates at the critical point (or “edge of chaos”)16-18.”
From A reservoir of time constants for memory traces in cortical neurons (2011) · cites this paper as [18]
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Citations
Cites
Optimization by Simulated Annealing
Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983) · Science · doi:10.1126/science.220.4598.671 · Cited by 45,134
Theory of Self-Reproducing Automata
Schwartz, J. T., von Neumann, J., & Burks, A. W. (1967) · Mathematics of Computation · doi:10.2307/2005041 · Cited by 5,468
Metabolic stability and epigenesis in randomly constructed genetic nets
Kauffman, S. A. (1969) · Journal of Theoretical Biology · doi:10.1016/0022-5193(69)90015-0 · Cited by 4,871
Bak, P., Tang, C., & Wiesenfeld, K. A. (1988) · Physical Review A · doi:10.1103/physreva.38.364 · Cited by 4,744
Statistical mechanics of cellular automata
Wolfram, S. (1983) · Reviews of Modern Physics · doi:10.1103/revmodphys.55.601 · Cited by 3,199
Universality and complexity in cellular automata
Wolfram, S. (1984) · Physica D Nonlinear Phenomena · doi:10.1016/0167-2789(84)90245-8 · Cited by 1,956
Fredkin, E., & Toffoli, T. (1982) · International Journal of Theoretical Physics · doi:10.1007/bf01857727 · Cited by 1,896
Gardner, M. R. (1970) · Scientific American · doi:10.1038/scientificamerican1070-120 · Cited by 1,764
Complexity, Entropy And The Physics Of Information
Zurek, W. H. (2018) · CRC Press eBooks · doi:10.1201/9780429502880 · Cited by 1,451
Winning Ways for Your Mathematical Plays.
Connelly, R., Berlekamp, E. R., Conway, J. H., & Guy, R. K. (1986) · American Mathematical Monthly · doi:10.2307/2323620 · Cited by 1,432
Research works printed but not linked: 1
- Kolmogorov, 1965, Prob. Inf. Transm., 1, 1
Cited by
Sorted by citations
Items 301–400 of 1,002
Goal functions for the development of natural systems
Wilhelm, T. E., & Brüggemann, R. (2000) · Ecological Modelling · doi:10.1016/s0304-3800(00)00246-5 · Cited by 35
Fundamental constraints to the logic of living systems
Solé, R., Kempes, C. P., Corominas-Murtra, B., De Domenico, M., Kolchinsky, A., Lachmann, M., Libby, E., Saavedra, S., Smith, E., & Wolpert, D. (2024) · Interface Focus · doi:10.1098/rsfs.2024.0010 · Cited by 34
Fontaine, G. (2006) · International Conference on Intelligent Systems Design and Applications (ISDA) · doi:10.1109/isda.2006.253785 · Cited by 34
Axon-like active signal transmission
Brown, T. D., Zhang, A., Nitta, F. U., Grant, E. D., Chong, J. L., Zhu, J., Radhakrishnan, S., Islam, M., Fuller, E. J., Talin, A. A., et al. (2024) · Nature · doi:10.1038/s41586-024-07921-z · Cited by 34
An analysis of different types and effects of asynchronicity in cellular automata update schemes
Bandini, S., Bonomi, A., & Vizzari, G. (2012) · Natural Computing · doi:10.1007/s11047-012-9310-4 · Cited by 34
Methodological Investigations in Agent-Based Modelling
Silverman, E. (2018) · Springer eBooks · doi:10.1007/978-3-319-72408-9 · Cited by 34
Seizure prediction and intervention
Meisel, C., & Loddenkemper, T. (2019) · Neuropharmacology · doi:10.1016/j.neuropharm.2019.107898 · Cited by 34
Radiolysis generates a complex organosynthetic chemical network
Adam, Z. R., Fahrenbach, A. C., Jacobson, S. M., Kaçar, B., & Zubarev, D. Y. (2021) · Scientific Reports · doi:10.1038/s41598-021-81293-6 · Cited by 34
Siri, B., Berry, H., Cessac, B., Delord, B., & Quoy, M. (2008) · Neural Computation · doi:10.1162/neco.2008.05-07-530 · Cited by 34
Gregorio, S. D., Serra, R., & Villani, M. (1999) · Theoretical Computer Science · doi:10.1016/s0304-3975(98)00154-6 · Cited by 34
Radiolysis Generates a Complex Organosynthetic Chemical Network
Adam, Z., Fahrenbach, A. C., Jacobson, S. M., Kacar, B., & Zubarev, D. Y. (2019) · doi:10.26434/chemrxiv.10148684 · Cited by 34
Complex Systems Theory: Some Considerations for Sociology
Condorelli, R. (2016) · Open Journal of Applied Sciences · doi:10.4236/ojapps.2016.67044 · Cited by 33
Regenerating Soft Robots Through Neural Cellular Automata
Horibe, K., Walker, K., & Risi, S. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-72812-0_3 · Cited by 36
Mean field theory of the Edge of Chaos
Gutowitz, H. A., & Langton, C. G. (1995) · Lecture notes in computer science · doi:10.1007/3-540-59496-5_288 · Cited by 33
Self-citationLiu, J. (2008) · International Conference on Natural Computation (ICNC) · doi:10.1109/icnc.2008.872 · Cited by 33
Dual Coding Theory Explains Biphasic Collective Computation in Neural Decision-Making
Daniels, B. C., Flack, J. C., & Krakauer, D. C. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00313 · Cited by 36
Suzuki, J., & Kaneko, K. (1994) · Physica D Nonlinear Phenomena · doi:10.1016/0167-2789(94)90291-7 · Cited by 33
Phenomenology of excitation in 2-D cellular automata and swarm systems
Adamatzky, A., & Holland, O. E. (1998) · Chaos Solitons & Fractals · doi:10.1016/s0960-0779(97)00123-9 · Cited by 33
A mixed analog/digital chaotic neuro-computer system for quadratic assignment problems
Horio, Y., Ikeguchi, T., & Aihara, K. (2005) · Neural Networks · doi:10.1016/j.neunet.2005.06.022 · Cited by 33
Pineda, O. K., Kim, H., & Gershenson, C. (2019) · Complexity · doi:10.1155/2019/3728621 · Cited by 32
Search optimization, funnel topography, and dynamical criticality on the string landscape
Khoury, J., & Parrikar, O. (2019) · Journal of Cosmology and Astroparticle Physics · doi:10.1088/1475-7516/2019/12/014 · Cited by 37
The self-organized critical multiverse
Kartvelishvili, G., Khoury, J., & Sharma, A. (2021) · Journal of Cosmology and Astroparticle Physics · doi:10.1088/1475-7516/2021/02/028 · Cited by 39
Assessing planetary complexity and potential agnostic biosignatures using epsilon machines
Bartlett, S., Li, J., Gu, L., Sinapayen, L., Fan, S., Natraj, V., Jiang, J. H., Crisp, D., & Yung, Y. L. (2022) · Nature Astronomy · doi:10.1038/s41550-021-01559-x · Cited by 35
The Hebbian paradigm reintegrated: Local reverberations as internal representations
Freeman, W. J. (1995) · Behavioral and Brain Sciences · doi:10.1017/s0140525x0004022x · Cited by 34
Emergence of linguistic laws in human voice
Torre, I. G., Luque, B., Lacasa, L., Luque, J., & Hernández-Fernändez, A. (2017) · Scientific Reports · doi:10.1038/srep43862 · Cited by 29
Ventra, M. D. (2022) · doi:10.1093/oso/9780192845320.001.0001 · Cited by 31
Nishioka, D., Shingaya, Y., Tsuchiya, T., Higuchi, T., & Terabe, K. (2024) · Science Advances · doi:10.1126/sciadv.adk6438 · Cited by 32
Power law scaling in synchronization of brain signals depends on cognitive load
Tinker, J., & Velázquez, J. L. P. (2014) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2014.00073 · Cited by 30
Prokopenko, M. (2007) · Advanced information and knowledge processing · doi:10.1007/978-1-84628-982-8_1 · Cited by 31
On connectivity of reconfigurable impact networks in ageless aerospace vehicles
Prokopenko, M., Wang, P., Foreman, M., Valencia, P., Price, D. C., & Poulton, G. (2005) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2005.06.003 · Cited by 31
Knowledge and Meaning: Chaos and Complexity
Crutchfield, J. P. (1992) · Woodward Conference · doi:10.1007/978-1-4613-9229-3_3 · Cited by 31
Bettinger, J. S., & Friston, K. J. (2023) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2023.105459 · Cited by 37
Chaté, H., & Muñoz, M. A. (2014) · Physics · doi:10.1103/physics.7.120 · Cited by 30
Ayoub, M. (2018) · Solar Energy · doi:10.1016/j.solener.2018.05.041 · Cited by 30
Kia, B., Mendes, A., Parnami, A., George, R., Mobley, K., & Ditto, W. L. (2020) · PLoS ONE · doi:10.1371/journal.pone.0228534 · Cited by 30
The edge of chaos: quantum field theory and deep neural networks
Grosvenor, K. T., & Jefferson, R. (2022) · SciPost Physics · doi:10.21468/scipostphys.12.3.081 · Cited by 30
Two-dimensional materials for bio-realistic neuronal computing networks
Sangwan, V. K., Liu, S. E., Trivedi, A. R., & Hersam, M. C. (2022) · Matter · doi:10.1016/j.matt.2022.10.017 · Cited by 30
From modulated Hebbian plasticity to simple behavior learning through noise and weight saturation
Soltoggio, A., & Stanley, K. O. (2012) · Neural Networks · doi:10.1016/j.neunet.2012.06.005 · Cited by 31
Critical-like self-organization and natural selection: Two facets of a single evolutionary process?
Halley, J. D., & Winkler, D. A. (2008) · Biosystems · doi:10.1016/j.biosystems.2008.01.005 · Cited by 30
Synchronization of Chaos in Neural Systems
Nobukawa, S., & Nishimura, H. (2020) · Frontiers in Applied Mathematics and Statistics · doi:10.3389/fams.2020.00019 · Cited by 29
Intelligent soft matter: towards embodied intelligence
Baulin, V. A., Giacometti, A., Fedosov, D. A., Ebbens, S. J., Varela-Rosales, N. R., Feliu, N., Chowdhury, M., Hu, M., Füchslin, R. M., Dijkstra, M., et al. (2025) · Soft Matter · doi:10.1039/d5sm00174a · Cited by 38
How a life-like system emerges from a simplistic particle motion law
Schmickl, T., Stefanec, M., & Crailsheim, K. (2016) · Scientific Reports · doi:10.1038/srep37969 · Cited by 35
Finding continuity and discontinuity in fish schools via integrated information theory
Niizato, T., Sakamoto, K., Mototake, Y.-I., Murakami, H., Tomaru, T., Hoshika, T., & Fukushima, T. (2020) · PLoS ONE · doi:10.1371/journal.pone.0229573 · Cited by 27
Engineering self-organized criticality in living cells
Vidiella, B., Guillamon, A., Josep Sardanyés, A., Maull, V., Pla, J., Pueyo, N. C., & Solé, R. V. (2021) · Nature Communications · doi:10.1038/s41467-021-24695-4 · Cited by 30
Audio security through compressive sampling and cellular automata
George, S. N., Augustine, N., & Deepthi, P. P. (2014) · Multimedia Tools and Applications · doi:10.1007/s11042-014-2172-2 · Cited by 29
Nonlinear transient computation
Crook, N. T. (2006) · Neurocomputing · doi:10.1016/j.neucom.2006.10.148 · Cited by 28
Kolmogorov complexity and cellular automata classification
Dubacq, J., Durand, B., & Formenti, E. (2001) · Theoretical Computer Science · doi:10.1016/s0304-3975(00)00012-8 · Cited by 29
Introduction to Modeling of Complex Systems Using Cellular Automata
Hoekstra, A. G., Kroc, J., & Sloot, P. M. A. (2010) · Understanding complex systems · doi:10.1007/978-3-642-12203-3_1 · Cited by 28
Searching for Life, Mindful of Lyfe’s Possibilities
Wong, M. L., Bartlett, S., Chen, S., & Tierney, L. (2022) · Life · doi:10.3390/life12060783 · Cited by 28
Takayanagi, M., Nishioka, D., Tsuchiya, T., Imura, M., Koide, Y., Higuchi, T., & Terabe, K. (2023) · Materials Today Advances · doi:10.1016/j.mtadv.2023.100393 · Cited by 28
Additive Functions in Boolean Models of Gene Regulatory Network Modules
Darabos, C., Cunto, F. D., Tomassini, M., Moore, J. H., Provero, P., & Giacobini, M. (2011) · PLoS ONE · doi:10.1371/journal.pone.0025110 · Cited by 28
Cellular Automata Music: From Sound Synthesis to Musical Forms
Miranda, E. R. (2007) · Springer eBooks · doi:10.1007/978-1-84628-600-1_8 · Cited by 28
Edge of Chaos in Stochastically Coupled Cellular Automata
Inoue, M., & Nagadome, M. (2000) · Journal of the Physical Society of Japan · doi:10.1143/jpsj.69.1 · Cited by 28
Emergent behavior in classifier systems
Forrest, S., & Miller, J. H. (1990) · Physica D Nonlinear Phenomena · doi:10.1016/0167-2789(90)90075-z · Cited by 28
Criticality supports cross-frequency cortical-thalamic information transfer during conscious states
Toker, D., Müller, E. J., Miyamoto, H., Riga, M. S., Lladó-Pelfort, L., Yamakawa, K., Artigas, F., Shine, J. M., Hudson, A. E., Pouratian, N., et al. (2023) · eLife · doi:10.7554/elife.86547 · Cited by 28
Novel plasticity rule can explain the development of sensorimotor intelligence
Der, R., & Martius, G. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1508400112 · Cited by 29
Emergence and Computation at the Edge of Classical and Quantum Systems
Licata, I. F. (2008) · PhilPapers (PhilPapers Foundation) · Cited by 28
Emergence in Agent-Based Computational Social Science
Dessalles, J.-L., Ferber, J., & Phan∥, D. (2008) · IGI Global eBooks · doi:10.4018/978-1-59904-717-1.ch009 · Cited by 27
Chaos and Relaxation Oscillations in Spin-Torque Windmill Spiking Oscillators
Matsumoto, R., Lequeux, S., Imamura, H., & Grollier, J. (2019) · Physical Review Applied · doi:10.1103/physrevapplied.11.044093 · Cited by 27
Cellular automata model for gene networks
Sales, J. A., Martins, M. L., & Stariolo, D. A. (1997) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.55.3262 · Cited by 27
Dynamic Load Balancing in Parallel Execution of Cellular Automata
Giordano, A., Rango, A. D., Rongo, R., D’Ambrosio, D., & Spataro, W. (2020) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2020.3025102 · Cited by 26
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Laredo, J. L. J., Guinand, F., Olivier, D., & Bouvry, P. (2016) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2016.2582160 · Cited by 26
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Aspelmeier, T., Blythe, R. A., Bray, A. J., & Moore, M. A. (2006) · Physical Review B · doi:10.1103/physrevb.74.184411 · Cited by 26
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Comment II on “Simple measure for complexity”
Binder, P. M., & Perry, N. (2000) · Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics · doi:10.1103/physreve.62.2998 · Cited by 26
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Gelblum, A., Pinkoviezky, I., Fonio, E., Gov, N. S., & Feinerman, O. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.09508 · Cited by 26
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Clark, K. B. (2009) · Biosystems · doi:10.1016/j.biosystems.2009.08.005 · Cited by 25
Emergence, Computation and the Freedom Degree Loss Information Principle in Complex Systems
Licata, I. F., & Minati, G. (2016) · Foundations of Science · doi:10.1007/s10699-016-9503-x · Cited by 25
Suppression of chaos via control of energy flow
Guo, S., Ma, J., & Alsaedi, A. (2018) · Pramana · doi:10.1007/s12043-018-1534-0 · Cited by 25
Dimension of reservoir computers
Carroll, T. L. (2020) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.5128898 · Cited by 25
Solving combinatorial optimization problems by nonlinear neural dynamics
Hasegawa, M., Ikeguchi, T., Matozaki, T., & Aihara, K. (2002) · IEEE International Conference on Neural Networks/IEEE ... International Conference on Neural Networks · doi:10.1109/icnn.1995.487286 · Cited by 25
GEAMAS: A Generic Architecture for Agent-Oriented Simulations of Complex Processes
Marcenac, P., & Giroux, S. (1998) · Applied Intelligence · doi:10.1023/a:1008220501261 · Cited by 26
Searching for complex CA rules with GAs
Bilotta, E., Lafusa, A., & Pantano, P. (2003) · Complexity · doi:10.1002/cplx.10063 · Cited by 25
Emergence in multi-agent systems: conceptual and methodological issues
Dessalles, J.-L., Müller, J., & Phan∥, D. (2007) · HAL · Cited by 25
Computational Universality in Symbolic Dynamical Systems
Delvenne, J., Kůrka, P., & Blondel, V. D. (2005) · Lecture notes in computer science · doi:10.1007/978-3-540-31834-7_8 · Cited by 25
Mesoscopic ultrafast nonlinear optics—the emergence of multimode quantum non-Gaussian physics
Yanagimoto, R., Ng, E., Jankowski, M., Nehra, R., McKenna, T. P., Onodera, T., Wright, L. G., Hamerly, R., Marandi, A., Fejer, M. M., et al. (2024) · Optica · doi:10.1364/optica.514075 · Cited by 27
MetaCity: Data-driven sustainable development of complex cities
Zhang, Y., Lin, Y., Zheng, G., Liu, Y., Sukiennik, N., Xu, F., Xu, Y., Lu, F., Wang, Q., Lai, Y., et al. (2025) · The Innovation · doi:10.1016/j.xinn.2024.100775 · Cited by 29
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Dennunzio, A., Lena, P. D., Formenti, E., & Margara, L. (2013) · Fundamenta Informaticae · doi:10.3233/fi-2013-877 · Cited by 24
Leadership and uncertainty: complexity and the lessons of history
Artigiani, R. (2005) · Futures · doi:10.1016/j.futures.2004.11.002 · Cited by 24
Optimal channel efficiency in a sensory network
Mosqueiro, T. S., & Maia, L. P. (2013) · Physical Review E · doi:10.1103/physreve.88.012712 · Cited by 23
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Ilgün, A., Angelov, K., Stefanec, M., Schönwetter-Fuchs, S., Stokanic, V., Vollmann, J., Hofstadler, D. N., Kärcher, M. H., Mellmann, H., Taliaronak, V., et al. (2021) · Conference on Artificial Life (ALIFE) · doi:10.1162/isal_a_00396 · Cited by 25
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Matteo Marsili, A., & Roudi, Y. (2022) · Physics Reports · doi:10.1016/j.physrep.2022.03.001 · Cited by 25
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Life as an emergent phenomenon: studies from a large-scale boid simulation and web data
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Selman, B. (1995) · International Joint Conference on Artificial Intelligence · Cited by 24
Brodski‐Guerniero, A., Naumer, M. J., Moliadze, V., Chan, J., Althen, H., Ferreira‐Santos, F., Lizier, J. T., Schlitt, S., Kitzerow, J., Schütz, M., et al. (2018) · Human Brain Mapping · doi:10.1002/hbm.24072 · Cited by 23
Conway's game of life is a near-critical metastable state in the multiverse of cellular automata
Reia, S. M., & Kinouchi, O. (2014) · Physical Review E · doi:10.1103/physreve.89.052123 · Cited by 25
The Open Computing Abstraction Layer for Parallel Complex Systems Modeling on Many-Core Systems
D’Ambrosio, D., Rango, A. D., Oliverio, M., Spataro, D., Spataro, W., Rongo, R., Mendicino, G., & Senatore, A. (2018) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2018.07.005 · Cited by 23
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Manicka, S., Marques-Pita, M., & Rocha, L. M. (2022) · Journal of The Royal Society Interface · doi:10.1098/rsif.2021.0659 · Cited by 23
Inferring attracting basins of power system with machine learning
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Ramos, V., Fernandes, C. M., & Rosa, A. C. (2005) · Cited by 23
Metadata
- Authors
- Langton, C. G.
- Affiliations
- Los Alamos National Laboratory (US)
- Corresponding author
- Langton, Chris G.
- Venue
- Physica D: Nonlinear Phenomena
- Publisher
- Elsevier BV
- Published
- 1990-06
- Volume
- 42
- Issue
- 1-3
- Pages
- 12-37
- ISSN
- 0167-2789 (ISSN-L)
- OpenAlex
- W2177218873
- Cited by
- 1,527
- References
- 26
- Citations by year
- 2026: 66 · 2025: 61 · 2024: 93 · 2023: 51 · 2022: 67 · 2021: 63 · 2020: 71 · 2019: 48 · 2018: 73 · 2017: 70 · 2016: 42 · 2015: 56 · 2014: 51 · 2013: 72 · 2012: 59
- FWCI
- 14.66
- Type
- journal article
- Language
- en
- Open access
- green
- www.osti.gov
- Locations
- Physica D Nonlinear Phenomena (publishedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Topics
- 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
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computability, Logic, AI Algorithms
- Keywords
- emergent computation, phase transitions, cellular automata, tipping points, second-order phase transition, information transmission, information storage, complexity classes, halting problem, edge of chaos
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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