Complexity (and/or "Complex Systems")
23 Oct 2024 10:19
If pressed to explain what I mean by this, I'd say that it's processes showing some combination of non-linearity and lots of strongly interacting components --- but ones where the interactions aren't so strong that there is an effective low-dimensional description. The common thread is that you need lots of information to understand what's going on. As for more precise definitions, well, I give complexity measures their own notebook for a reason. My fuller views are expounded in some of my papers, linked to below.
My own views on this subject are, naturally, hard to separate from the fact that I've been going to the Santa Fe Institute since 1997, worked there full time 1998--2002, have been on its external faculty since the '00s, etc. That place, and the scientific network around it, has not only provided me with training, resources and professional opportunities, but mentors, collaborators, close personal friends, a professional agenda, and sense of belonging to something valuable. Like any home, it has moments when the other occupants make me roll my eyes, but it would be absurd for me to pretend to be an impartial critic. I'm only too aware that someone else interested in the same topics, but not inducted into the same epistemic community, the same network-and-tradition, might well make very different judgments about the literature...
To develop, someday: An exposition of how the whole field is a "series of footnotes" to Norbert Wiener and Herbert Simon.
The linkage/references here are sparser than they could be, because today my lumper/splitter pendulum has swung towards "split", and I've moved a lot of stuff to the more specialized notebooks.
- See also:
- Adaptation
- Agent-based Modeling
- Artificial Intelligence
- Artificial Life
- W. Ross Ashby
- Biological Order and Levels of Organization
- Cellular Automata
- Chaos and Non-linear Dynamics
- Cognitive Science
- Collective Cognition
- Complexity Measures
- Complex Networks
- Computation, Automata, Languages
- Cybernetics
- Darwin Machines
- Developmental Biology
- Dissipative Structures
- Ecology
- Economics
- Edge of Chaos
- Emergent Properties
- Ergodic Theory
- Evolving Local Rules to Perform Global Computations
- Evolution
- Evolution of Complexity
- Evolutionary Computation
- Evolutionary Economics
- Evolutionary Game Theory
- Forecasting Non-Stationary Processes, and Estimating Their Parameters
- Flocking and Swarms
- Homophily and Influence in Social Networks
- Information Flow
- Information Theory
- Interacting Particle Systems
- Institutions and Organizations
- Koopman Operators for Modeling Dynamical Systems and Time Series
- Large Deviations
- Learning in Games
- Learning Theory (Formal, Computational or Statistical)
- Machine Learning, Statistical inference and Induction
- Maximum Entropy Methods (MaxEnt)
- Macroscopic Consequences of Microscopic Interactions
- Methodology for the Social Sciences
- Multi-Agent Systems
- Neural Nets, Connectionism, Perceptrons;
- Neuroscience
- Nonequilibrium Statistical Mechanics and Thermodynamics
- Parallel and Distributed Computing
- Pattern Formation
- Phase Transitions and Critical Phenomena
- Physics of Computation and Information
- Physical Principles in Biology
- Physics
- Power Law Distributions and Long-Range Correlations
- Prediction Processes; Markovian (and Conceivably Causal) Representations of Stochastic Processes
- Ilya Prigogine
- QWERTY
- Random Boolean Networks, Nk Networks
- Schelling model
- Self-organization
- Signal Transduction, Control of Metabolism, and Gene Regulation
- Herbert Simon
- Simulations
- Social Contagion
- Sociology
- Spin Glasses
- Stability and Complexity of Ecosystems
- State-Space Reconstruction
- Statistical Learning Theory with Dependent Data
- Statistical Mechanics
- Symbolic Dynamics
- Tsallis Statistics
- When Do Physical Systems Compute?
- Norbert Wiener
- Recommended, big picture, less technical:
- Robert Axelrod and Michael D. Cohen, Harnessing Complexity: Organizational Implications of a Scientific Frontier
- Jack Cohen and Ian Stewart, The Collapse of Chaos [A great book, except that, as they themselves say of Dawkins, the philosophy is completely backwards, especially on reductionism and emergent properties.]
- John Holland, Emergence
- Steven Johnson, Emergence: The Connected Lives of Ants, Brains, Cities and Software [What I buy my relatives when they ask me what all the fuss is about.]
- David C. Krakauer (ed.), Worlds Hidden in Plain Sight: The Evolving Idea of Complexity at the Santa Fe Institute, 1984--2019
- Melanie Mitchell, Complexity: A Guided Tour [Disclaimer: I used to work for Melanie.]
- Heinz Pagels, The Dreams of Reason: The Computer and the Rise of the Sciences of Complexity [What I used to buy my relatives. Deserves to be brought back into print.]
- Herbert Simon, The Sciences of the Artificial [Especially the last chapter, "The Architecture of Complexity".]
- Recommended, big picture, textbooks (or close enough):
- Remo Badii and Antonio Politi, Complexity: Hierarchical Structures and Scaling in Physics
- Nino Boccara, Modeling Complex Systems
- Gary William Flake, The Computational Beauty of Nature
- Josef Honerkamp, Stochastic Dynamical Systems: Concepts, Numerical Methods, Data Analysis
- {1988--1993} Lectures in Complex Systems [Proceedings of the SFI summer schools]
- John H. Miller and Scott E. Page, Complex Adaptive Systems: An Introduction to Computational Models of Social Life [Brief comments/disclaimer]
- Mitchel Resnick, Turtles, Termites and Traffic Jams: Explorations in Massively Parallel Microworlds
- Recommended, big picture, technical but not textbooks:
- Terry Bossomaier and David Green (eds.), Complex Systems
- David C. Krakauer (ed.), Foundational Papers in Complexity Science, 1922--2000 [Disclaimer 1: I'm a contributor, having provided an introduction and annotations to a paper in volume 1 (More on this.). Disclaimer 2: I haven't seen volume 4 yet.]
- M. E. J. Newman, "Complex Systems: A Survey", American Journal of Physics 79 (2011): 800--810, arxiv:1112.1440 [literature survey]
- Michael Strevens, "How Are the Sciences of Complex Systems Possible?", Philosophy of Science 72 (2005): 531--556
- Recommended, close-ups, more technical:
- John Bickle, "Understanding Neural Complexity: A Role for Reduction", Minds and Machines 11 (2001): 467--481
- John Tyler Bonner, The Evolution of Complexity, by Means of Natural Selection
- The work of the computational mechanics group at Santa Fe is, IMHO, the closest anyone has got yet to a general, rigorous way of tackling complexity, and it is scandalously little-known. [That sentence was there years before I became part of the group --- and is still there years after I left.]
- George Cowan, David Pines and D. Melzner (eds.), Complexity: Metaphors, Models, and Reality
- Joshua M. Epstein and Robert Axtell, Growing Artificial Societies: Social Science from the Bottom Up
- J. Doyne Farmer, Alan Lapedes, Norman Packard and Burton Wendroff (eds.), Evolution, Games, and Learning: Models for Adaptation in Machines and Nature (a.k.a. Physica D 22 (1986).)
- Stephanie Forrest (ed.), Emergent Computation: Self-Organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks (a.k.a. Physica D 42 (1990))
- Stuart Kauffman, The Origins of Order: Self-Organization and Selection in Evolution [Highly interesting and valuable, but to be read with some caution. The writing is often confused, and, frankly, in subsequent decades, many of the ideas (e.g., "the edge of chaos") did not pan out. Still extremely important, not least for its fertility of invention.]
- D. L. Stein, "Spin Glasses: Still Complex After All These Years?" cond-mat/0301104
- Daniel L. Stein and Charles M. Newman, Spin Glasses and Complexity
- Modesty forbids me to recommend:
- CRS, "Methods and Techniques of Complex Systems Science: An Overview", chapter 1 (pp. 33--114) in Deisboeck and Kresh (eds.), Complex Systems Science in Biomedicine, nlin.AO/0307015
- CRS, Chaos, Complexity, and Inference [Materials for an undergraduate statistics class I taught in 2008 and 2009]
- My papers listed under complexity measures
- Dis-recommended:
- I have a long list, available upon request. I may add it here sometime when I'm feeling more full of venom than the present.
- To read:
- Claes Andersson, "Inverse Ontomimetic Simulation: a window on complex systems", phil-sci/5304 (2009)
- Fatihcan Atay, Sarika Jalan and Jürgen Jost, "Randomness, chaos, and structure", arxiv:0711.4293
- Sunny Auyang, Foundations of Complex Systems Theories: In Economics, Evolutionary Biology, and Statistical Physics
- William Bechtel and Robert C. Richardson, Discovering Complexity: Decomposition and Localization as Strategies in Scientific Research
- Richard K. Belew and Melanie Mitchell (eds.), Adaptive Individuals in Evolving Populations: Models and Algorithms
- W. W. Burggren and M. G. Monticino, "Assessing Physiological Complexity", Journal of Experimental Biology 208 (2005): 3221--3232 [Reprint]
- Eric J. Chaisson
- Cosmic Evolution : The Rise of Complexity in Nature [Review by Dan McShea in American Scientist; proposes defining complexity operationally as power dissipated per unit mass, according to McShea. Why this should be complexity, I don't get....]
- "Complexity: An Energetics Agenda", Complexity 9 (2004): 14--21 [PDF reprint from Chaisson's website]
- David Colander and Roland Kupers, Complexity and the Art of Public Policy: Solving Society's Problems from the Bottom Up [See also the review by Kirman, link below]
- Gerald Gaus, The Open Society and Its Complexities
- Nick Gessler, Artificial Culture: Experiments in Synthetic Anthropology
- Sebastian Grauwin, Guillaume Beslon, Eric Fleury, Sara Franceschelli, Céline Robardet, Jean-Baptiste Rouquier, Pablo Jensen, "Complex Systems Science: Dreams of Universality, Reality of Interdisciplinarity", arxiv:1206.2216
- Claudius Gros, Complex and Adaptive Dynamical Systems: A Primer, arxiv:0807.4838 [Arxiv version seems to only be updated to the 2nd edition, but the paywalled one is the 4th...]
- Casey Helgeson, Vivek Srikrishnan, Klaus Keller and Nancy Tuana, "Why Simpler Computer Simulation Models Can Be Epistemically Better for Informing Decisions", Philosophy of Science 88 (2021): 213--233
- John H. Holland
- Signals and Boundaries: Building Blocks for Complex Adaptive Systems
- Complexity: A Very Short Introduction
- Cliff Hooker (ed.), Philosophy of Complex Systems
- Giorgio Israel, "The Science of Complexity: Epistemological
Problems and
Perspectives", Science in
Context 18 (2005): 479--509 [From the abstract: "The
aim of the present article is to analyze the epistemological status attributed
in the science of complexity to several fundamental ideas, such as those of
scientific law, objectivity, and prediction. The aim is to show that the hope
of superseding reductionism by means of concepts such as that of 'emergence'
is fallacious and that the science of complexity proposes forms of reductionism
that are even more restrictive than the classical ones, particularly when it
claims to unify in a single treatment problems that vary widely in nature such
as physical, biological, and social problems." Thanks to Prof. Israel for
a reprint.]
- Alan Kirman, "Complexity and Economic Policy: A Paradigm Shift or a Change in Perspective? A Review Essay on David Colander and Roland Kupers's Complexity and the Art of Public Policy", Journal of Economic Literature 54 (2016): 534--572
- Meinard Kuhlmann, "Mechanisms in Dynamically Complex Systems", phil-sci/8442
- James Ladyman and Karoline Wiesner, What Is a Complex System?
- J. Stephen Lansing
- "Complex Adaptive Systems", Annual Review of Anthropology 32 (2003): 183--204
- Perfect Order: Recognizing Complexity in Bali
- J. Stephen Lansing and Murray P. Cox, Islands of Order: A Guide to Complexity Modeling for the Social Sciences
- Simon Levin
- "Complex Adaptive Systems: Exploring the known, the unknown, and the unknowable", Bulletin of the American Mathematical Society 40 (2002): 3--19 [PDF. Survey, emphasis on evolutionary ecology]
- Fragile Dominion: Complexity and the Commons
- Christian Lindgren, "Information Theory for Complex Systems" (Online lecture notes, dated Jan. 2003)
- John H. Miller, A Crude Look at the Whole: The Science of Complex Systems in Business, Life, and Society
- Sandra Mitchell
- Harold Morowitz, The Emergence of Everything
- Harold Morowitz and Jerome L. Singer (eds.), The Mind, the Brain, and Complex Adaptive Systems
- Scott E. Page, Diversity and Complexity
- Giorgio Parisi, "Complex Systems: a Physicist's Viewpoint," cond-mat/0205297
- Max Pettersson, Complexity and Evolution [mostly of interest to me because of the introduction by Joseph Needham]
- Charles Rathkopf, "Network representation and complex systems", Synthese 195 (2018): 55--78 [Color me skeptical of at least the first of the two main theses set forth in the abstract...]
- J. Barkley Rosser, Jr., "On the Complexities of Complex Economic Dynamics", Journal of Economic Perspectives 13 (4) (1999): 169--192 [JSTOR]
- L. S. Schulman and B. Gaveau, "Complex systems under stochastic dynamics", cond-mat/0312711
- Luigi Sertorio, Thermodynamics of Complex Systems: An Introduction to Ecophysics
- Paolo Sibani and Henrik Jeldtoft Jensen, Stochastic Dynamics of Complex Systems: From Glasses to Evolution
- Didier Sornette, Critical Phenomena in Natural Sciences: Chaos, Fractals, Self-Organization and Disorder: Concepts and Tools
- Michael Strevens, Bigger than Chaos: Understanding Complexity through Probability
- Tainter, Collapse of Complex Societies
- Stefan Thurner, Peter Klimek, and Rudolf Hanel, Introduction to the Theory of Complex Systems
- V. I. Varhavsky and D. A. Pospelov, Puppets Without Strings: Reflections on the Evolution and Control of Some Man-Made Systems
- M. Norton Wise (ed.), Growing Explanations: Historical Perspectives on Recent Science
- Stanislaw Ulam, Analogies between Analogies
- Wiesner and Ladyman, "What Is a Complex System?" [PDF preprint]
- To write, someday:
- The Genealogy of Complexity
- The Statistical Analysis of Complex Systems
First version sometime in the 1990s; substantial revision of opening text 18 October 2024.
