Update – see foot of post for Bsky exchange…
It is really hard to think “in systems”.
Our brains did not evolve for this. Our brains evolved for “(how) can I eat this?” “how can I avoid getting eaten?” and “who can I shag around here?”
Our brains did not evolve for thinking about final common pathways, nonlinearity, feedback loops, homeodynamics etc etc.
So, we flail, leap to conclusions and cherry pick “evidence” etc to provide after the fact justifications. We invent gods (Yahweh, Progress, Marx, the Market, Xoanon, the Flying Spaghetti Monster, whatever) to help us not to have to even try to think. We might as well face it, we’re addicted to heuristics.
It is really hard to think “in systems”, especially when the vocabulary is sparse, confused.
So, I am going to try, intermittently, to do some vocabulation about systems. Mostly for my own benefit (I’m selfish that way). You like it, let me know. You have terms of your own you use and misuse for thinking in systems, let me know?!
First up (drum roll) Flickering.
When systems are about to shift from one state to another, they begin to “flicker”. See this 2012 article
There is a recognized need to anticipate tipping points, or critical transitions, in social–ecological systems1,2. Studies of mathematical3–5 and experimental6–9 systems have shown that systems may ‘wobble’ before a critical transition. Such early warning signals10 may be due to the phenomenon of critical slowing down, which causes a system to recover slowly from small impacts, or to a flickering phenomenon, which causes a system to switch back and forth between alternative states in response to relatively large impacts. Such signals for transitions in social–ecological systems have rarely been observed11, not the least because high-resolution time series are normally required. Here we combine empirical data from a lake-catchment system with a mathematical model and show that flickering can be detected from sparse data. We show how rising variance coupled to decreasing autocorrelation and skewness started 10–30 years before the transition to eutrophic lake conditions in both the empirical records and the model output, a finding that is consistent with flickering rather than critical slowing down4,12. Our results suggest that if environmental regimes are sufficiently affected by large external impacts that flickering is induced, then early warning signals of transitions in modern social–ecological systems may be stronger, and hence easier to identify, than previously thought. (emphasis added
Flickering gives early warning signals of a critical transition to a eutrophic lake state
Well, you can always over-interpret from the “natural” world to the human one. I am aware of the danger of just-so stories, in both directions.
But here we are, in a flickering world (blah blah Gramsi morbid symptoms, interregnum blah blah).
One very very crude measure of a political system’s stability is the turnover of party leaders (that’s not to say a system is “stable” under one long-term leader, necessarily).
In Australia between December 1975 and June 2010 there were five prime ministers – Fraser, Hawke, Keating, Howard, Rudd. Then, in the following eight years there were 6 – Rudd, Gillard, Rudd, Abbot, Turnbull, Morrison.
In the UK in the last ten years we’ve had Cameron, May, Johnson, Truss (remember her?), Sunak and now Starmer, who is only still in place because the timing is wrong for two of the prime challengers and the third can’t get back into Parliament (yet).
Related concepts
The Glitch, I guess.
Oh, and as for the T-1000 – well, at the end of T2, after he has been frozen with the liquid nitrogen and then re-assembled, he has glitch issues. Then, when he is in the vat of molten steel, boiling away, he desperately flicks through all the shapes he took on before, trying to see if any of them will work. It has always moved me…
Another more recent article:
Multitude of theoretical works and some empirical observations have shown that systems approaching a TP show characteristic changes in their spatial and temporal patterns. Such LIs e.g., variance or return times, have been suggested as a way to identifying the so-called EWS (Carpenter et al., 2008, 2011; Dakos et al., 2012; K´efi et al., 2007a; Scheffer et al., 2012; Cline et al., 2014; K´efi et al., 2014; Berdugo et al., 2017; Butitta et al., 2017; van Belzen et al., 2017). However, most of the time, detection of these LIs in ecosystems is very difficult due to the lack of high-frequency sampling data (Carpenter et al., 2011). It is worth noting that some studies propose the possibility that ecosystems experience state transitions without showing increased variance or long return times (Schreiber and Rudolf, 2008; Hastings and Wysham, 2010). Moreover, deterministic ecological systems typically display bifurcation-induced collapses (B-tipping) (McCann and Yodzis, 1994; Duarte et al., 2012; Dhamala and Lai, 1999; Sardany´es et al., 2018). In this sense, further research is much needed on EWS for other bifurcations beyond the saddle-node. For instance, the LIs could behave differently for populations going to collapse by means of oscillating or chaotic transients. The statistics for such time series including noise deserves intensive research, both theoretical and, despite the difficulties, experimental. As mentioned, many works have proposed different methods to detect critical transitions (CTs). Broadly speaking, these LIs are related to memory, variability, and flickering phenomena. For example, autocorrelation (Carpenter et al., 2008) or spectral density (Kleinen et al., 2003) in ecological time series tend to increase as the system approaches a CT. (emphasis added)
marchudson.net 
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