Computation at the edge of chaos: Phase transitions and emergent computation
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Adaptive systems require a high C to cope with changes of their environment while maintaining their integrity.
“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
The Results section (1.2; page unavailable) links complex, information-processing behavior near a phase transition to life's ability to maintain itself amid changing environmental conditions. The concluding discussion (:12; page unavailable) says precursors of life had to respond to fluctuating conditions while maintaining their dynamical state. It does not establish that adaptive systems generally require a high value of a quantity called C; it emphasizes a balance between too much order and too much chaos. (LLM, conf=0.84)
Experimental — accuracy not yet measuredComplexity can be seen as a balance between order and disorder.
“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
Computation and life require this balance to be able to compute and adapt.
“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
A randomly connected circuit model, akin to a 'reservoir' network, can reproduce the observed distribution of timescales, provided that the network operates at the critical point (or 'edge of chaos').
“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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- 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
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