Kristin Wilson🌀 Complexity Science Symposium 2026 in London, 15th May.
kaliwhy should you give a fuck about this niche ass, inaccessible sophistry?
because that is the way to go on about understanding phenomena in #complexsystems. it is by far not the only way to think about it, systems science is wast and diverse. but it is a good start.
and when you want criticize people like #ehrlich you should at least know your shit.
Prof. R. I. SujithOur study showing that the spatial organization of deep convective clouds in the tropics changes with the observed time frame; got published in the International Journal of Climatology.
https://doi.org/10.1002/joc.70363
#Climate #cloud #NonlinearDynamics #phd #ComplexNetworks #RMetS #complexsystems #publication #tropics #communitydetection #atmosphere
Manlio De DomenicoImportant announcement from our lab.
Since the character limit here is a bit tight, I’ve attached a screenshot with the full details from our LinkedIn post.
#ComplexSystems #AI #Emergence
nate and the others are right when they say: "no one is driving the bus". there is not a single root cause, or even a comprehensible combination of root causes, to any of this, you can simply "address" and let it RIP. sure, first we must accept the #complexsystems nature of it all. but once you have done that you will still have to get up every day and survive every day and every single one of your decisions will matter for something.
Bernd von Mallinckrodt 🦋We didn’t lose colors.
We stopped using them.
What if this isn’t taste …
but a measurable collapse of choice?
doi.org/10.5281/zeno... #CRTI #complexsystems 🖖
Structural Compression in Disc...
Structural Compression in Disc...
Structural Compression in Discrete Preference Systems: An Information-Theoretic Analysis of Automobile Color Distribution (1990–2020)
This preprint presents an information-theoretic analysis of automobile exterior color distributions from 1990 to 2020 as a proxy for structural compression in a discrete sociotechnical preference system. Using aggregated production data, we model the color space as a finite set of categorical states and quantify its temporal evolution via Shannon entropy R(t), representing effective diversity, and a normalized compression scalar \Phi(t) = 1 - R(t)/\log(N), capturing the contraction of active preference states. The results indicate a sustained shift from a relatively high-entropy distribution toward dominance by a small set of achromatic colors (black, white, gray), corresponding to a monotonic increase in \Phi(t) and a decline in diversity over time. A composite index, the Compression–Response Transition Index (CRTI) T(t) = R(t)/\Phi(t), is introduced to describe the balance between residual diversity and structural constraint. The analysis is explicitly non-causal and does not distinguish between supply-side constraints and demand-side preference shifts. Seven limitations are discussed in detail, including aggregation bias, category discretization effects, and market–preference confounding. The framework is presented as an illustrative and falsifiable application of entropy-based measures to preference-space dynamics, not as a universal theory. The approach is generalizable to other domains where distributions over discrete states evolve under potential structural constraints, including economics, ecology, and collective behavior systems. structural compression Shannon entropy preference state space CRTI sociotechnical systems complexity science information theory collective behavior early warning signals discrete systems
Early warning signals don’t always fail because of noise …
sometimes they’re simply invisible.
A structural detectability limit: alignment determines what we can see. doi.org/10.5281/zeno... #ComplexSystems #EWS 🖖
Geometry, Observability, and t...
Geometry, Observability, and t...
Geometry, Observability, and the Limits of Early Warning Signals in Complex Systems: A Dissertation Summary
This dissertation summary presents a theoretical framework addressing a persistent limitation in the detection of critical transitions in complex systems. Classical early warning signals (EWS), such as rising variance and increasing lag-1 autocorrelation, are commonly interpreted as indicators of critical slowing down. However, their empirical reliability has remained inconsistent across domains. This work argues that this inconsistency is not primarily due to noise, data limitations, or model misspecification, but reflects a more fundamental constraint: the geometry of observation. Specifically, the detectability of critical system dynamics depends on the alignment between the system’s critical mode and the observation space. When this alignment is weak or dynamically degraded, early warning signals may be suppressed or entirely absent, even as the system approaches a transition. The framework is formalized through the Compression–Response Transition Index (CRTI), defined as T = R/Φ, where R denotes adaptive capacity and Φ represents structural compression. This formulation distinguishes between amplitude-based indicators and structure-sensitive measures, and introduces boundary conditions under which early warning signals are expected to succeed or fail. In particular, the concepts of Structural–Dynamic Separability and the Relaxation–Coupling Failure Mode define regimes of detectability. The central contribution is the formulation of a detectability bound under projection: a general constraint arising from the dimensional mismatch between system dynamics and observation. This bound implies that the absence of early warning signals can be a necessary consequence of projection geometry, rather than a failure of measurement. The results suggest a shift in perspective from improving statistical indicators toward understanding and designing observation systems that preserve alignment with critical system modes. More broadly, the work positions detectability limits as an intrinsic property of the observer–system relationship in high-dimensional dynamical systems. early warning signals; critical transitions; complex systems; observability; projection; geometry; detectability; CRTI; structural compression; adaptive capacity; bifurcation; critical slowing down; system dynamics; high-dimensional systems
If early warning signals fail, is it really noise …
or the geometry of observation?
A dissertation summary shows: signals vanish when alignment is lost. doi.org/10.5281/zeno... #ComplexSystems #EWS #CriticalTransitions 🖖
Geometry, Observability, and t...
Geometry, Observability, and t...
Geometry, Observability, and the Limits of Early Warning Signals in Complex Systems: A Dissertation Summary
This dissertation summary presents a theoretical framework addressing a persistent limitation in the detection of critical transitions in complex systems. Classical early warning signals (EWS), such as rising variance and increasing lag-1 autocorrelation, are commonly interpreted as indicators of critical slowing down. However, their empirical reliability has remained inconsistent across domains. This work argues that this inconsistency is not primarily due to noise, data limitations, or model misspecification, but reflects a more fundamental constraint: the geometry of observation. Specifically, the detectability of critical system dynamics depends on the alignment between the system’s critical mode and the observation space. When this alignment is weak or dynamically degraded, early warning signals may be suppressed or entirely absent, even as the system approaches a transition. The framework is formalized through the Compression–Response Transition Index (CRTI), defined as T = R/Φ, where R denotes adaptive capacity and Φ represents structural compression. This formulation distinguishes between amplitude-based indicators and structure-sensitive measures, and introduces boundary conditions under which early warning signals are expected to succeed or fail. In particular, the concepts of Structural–Dynamic Separability and the Relaxation–Coupling Failure Mode define regimes of detectability. The central contribution is the formulation of a detectability bound under projection: a general constraint arising from the dimensional mismatch between system dynamics and observation. This bound implies that the absence of early warning signals can be a necessary consequence of projection geometry, rather than a failure of measurement. The results suggest a shift in perspective from improving statistical indicators toward understanding and designing observation systems that preserve alignment with critical system modes. More broadly, the work positions detectability limits as an intrinsic property of the observer–system relationship in high-dimensional dynamical systems. early warning signals; critical transitions; complex systems; observability; projection; geometry; detectability; CRTI; structural compression; adaptive capacity; bifurcation; critical slowing down; system dynamics; high-dimensional systems
If early warning signals fail, is it noise — or geometry?
A detectability bound shows: signals vanish when alignment is lost.
doi.org/10.5281/zenodo.19329813 #EWS #ComplexSystems 🖖
Geometry-Driven Failure of Ear...
Geometry-Driven Failure of Ear...
Geometry-Driven Failure of Early Warning Signals: A Detectability Bound Under Projection
Early warning signals (EWS) of critical transitions—such as rising variance and autocorrelation—are widely used to anticipate abrupt changes in complex systems. However, their empirical performance is inconsistent, with frequent cases of signal suppression or even sign inversion prior to transition. This paper demonstrates that such failures can arise as a necessary consequence of observation geometry. Considering a high-dimensional dynamical system observed through a dimension-reducing mapping, we derive an explicit detectability bound linking the observability of EWS to the alignment between the system’s critical mode and the observation subspace. The central result expresses observable signal strength through a dimensionless ratio combining geometric alignment and dynamical growth rates, yielding a threshold condition under which early warning signals are attenuated, suppressed, or sign-inverted. The analysis shows that EWS failure is not primarily driven by noise, sampling limitations, or estimator choice, but by a structural mismatch between the evolving critical mode and the fixed observation mapping. In particular, sign inversion becomes more likely when the rate of geometric misalignment exceeds the rate of variance growth, a condition that can arise generically as systems approach critical transitions. This provides a unified geometric explanation for the mechanism-dependent reliability of early warning signals and establishes a quantitative framework for assessing their detectability under projection. The results are model-independent, analytically derived, and directly testable in both simulated and empirical systems. early warning signals, critical transitions, complex systems, dynamical systems, covariance structure, projection geometry, detectability bound, variance scaling, critical slowing down, observability, eigenvector alignment, dimensionality reduction, signal detection, bifurcation theory, nonlinear systems
Was wie #stabileRealität erscheint,
ist oft nur eine Perspektive.
Nicht die sichtbare Dynamik täuscht …
sondern die Projektion, die ihre Struktur verbirgt.
Das ist kein Wahrnehmungsfehler.
Es ist Systemarchitektur.
#CRTI #PID #ComplexSystems 🖖
