The Puzzle of How Large-Scale Order Emerges in Complex Systems
Researchers explore emergence in complex systems, defining it as patterns from interactions. A new framework likens emergent phenomena to "software in nature," governed by macroscale rules. Computational mechanics simplifies predicting emergent behaviors.
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Researchers are delving into the puzzle of how large-scale order emerges in complex systems, aiming to explain regularities on macro scales from countless constituent parts. The concept of emergence involves patterns and organization arising from interactions between components, challenging scientists to define when a phenomenon is emergent. A new framework suggests thinking of emergent phenomena as "software in the natural world," governed by macroscale rules independent of microscale details. Using computational mechanics, researchers identified criteria for hierarchical structures in emergent systems, testing them on neural networks and cellular automata. The framework introduces three types of closure—informational, causal, and computational—to understand emergent systems. By applying computational mechanics, researchers simplify complex systems into causal states, showing how emergent behaviors can be predicted at the macro level without detailed microscale information. The study explores emergent behaviors in various model systems, demonstrating how emergent phenomena exhibit a hierarchical structure of strongly lumpable causal states. This research sheds light on understanding emergence in complex systems and offers a new perspective on how large-scale order emerges from interactions between components.
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- Readers suggest various books and authors, including Stuart Kauffman and Philip Ball, to deepen understanding of emergent phenomena.
- There is a discussion about the limitations of predicting emergent behaviors in complex systems, particularly in technology and planning.
- Several commenters share personal experiences and research related to emergent order and chaos, indicating a strong community interest in the subject.
- Questions arise about the relationship between emergent behaviors and mathematical patterns, highlighting ongoing curiosity in the field.
- Recommendations for resources, including the Santa Fe Institute, are provided for those looking to further explore complexity science.
11 commentsBe warned, it's not light reading, it's basically his PhD student's and his research, written up as a book, it is engaging though, and he leads you gently through the concepts - you do have to master one chapter before getting to the next though. Having said that, it does show how chaos and order interact with each other, and he then leads into the mechanism of evolution along the boundary of that interaction.
Spoiler: the boundary is where all the interesting stuff happens...
Not that there aren't puzzles a-plenty here. Why things like energy minimisation make water freeze in complex regular shapes which presents as "how do the limbs know to take that pattern" delight me. I kind of felt ok with "just because" but I realize that's not really an answer.
I can add more detail later, but for now, see https://slackermanz.com/understanding-multiple-neighborhood-...
Crutchfield introduced a conceptual device called the epsilon (ε) machine. This device can exist in some finite set of states and can predict its own future state on the basis of its current one. It’s a bit like an elevator, said Rosas; an input to the machine, like pressing a button, will cause the machine to transition to a different state (floor) in a deterministic way that depends on its past history—namely, its current floor, whether it’s going up or down, and which other buttons were pressed already.
The reason your carefully reasoned worldview isn't correct/doesn't match with reality is because of emergent behaviors.
That might be a related problem, or the more general version of the problem.
See also: non-linear dynamics, dynamical systems
The study of Complexity in the US Sciences is fairly recent, with the notable kickoff of the Santa Fe Institute [1]. They have all the resources there, from classes to scientific conferences.
A few book recommendations I've picked up over the years:
- Lewin, Complexity: Life at the Edge of Chaos [2] (recommended for a gentle introduction with history)
- Gleick, Chaos: Making a New Science [3]
- Holland, Emergence: From Chaos To Order [4]
- Holland, Signals and Boundaries: Building Blocks for Complex Adaptive Systems [5]
- Holland, Hidden Order: How Adaptation Builds Complexity [6] (unread but part of the series)
- Miller, Complex Adaptive Systems: An Introduction to Computational Models of Social Life [7]
- Strogatz, Nonlinear Dynamics And Chaos: With Applications To Physics, Biology, Chemistry, And Engineering [11] (edit: forgot i had this on my desk)
Also a shoutout to Stephen Wolfram, reading one of his articles [8] about underlying structures while stuck at an airport proved to be a pivotal moment in my life.
- Wolfram's New Kind of Science, as an exploration of simple rules computed to the point of complex behavior. [9]
- Wolfram's Physics Project, a later iteration on NKS ideas with some significant and thorough publication of work. [10]
Links:
[2] https://www.amazon.com/gp/product/0226476553
[3] https://www.amazon.com/gp/product/0143113453
[4] https://www.amazon.com/gp/product/0738201421
[5] https://www.amazon.com/gp/product/0262525933
[6] https://www.amazon.com/Hidden-Order-Adaptation-Builds-Comple...
[7] https://www.amazon.com/gp/product/0691127026
[8] https://writings.stephenwolfram.com/2015/12/what-is-spacetim...
[9] https://www.wolframscience.com/nks/
[10] https://wolframphysics.org/
[11] https://www.amazon.com/Nonlinear-Dynamics-Chaos-Applications...
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Structure and Interpretation of Classical Mechanics
Classical mechanics experiences a revival with a focus on complex behavior like nonlinear resonances and chaos. A book introduces general methods, mathematical notation, and computational algorithms to study system behavior effectively. It emphasizes understanding motion and nonlinear dynamics through exercises and projects.
Computational Life: How Well-Formed, Self-Replicating Programs Emerge
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