Will Carey asked about this blog and two papers in the Permaculture Climate Action! group on Facebook, so I tried to understand it:
I am not certain if it’s revolutionary but it looks consistent to me amnd pretty interesting – and possibly something which better shows the links between strands of systems thinking and strands of ‘complexity’ thinking. As always with these papers, the argument is dependent on certain framings.
As I worked it out, there are seven key points:
1- A system can be modelled as states and transition probabilities (a Markov chain) [this seems to me to be a ‘yes if you frame it in a particular way and can gather appropriate data to support that framing – but this certainly speaks to Requisite Variety]
2. You can group states in every possible way to create higher-level ‘scales’
[Perhaps the neatest bit is how clustering=hierarchy – the hierarchy emerges when you recognise that some clusters are supersets of others, e.g.
Partition A: (1)(2)(3)(4) → each state separate (micro level).
Partition B: (12)(3)(4) → merges 1 and 2 (a bit coarser grained).
Partition C: (12)(34) → merges more (coarser still)]
3. Each scale has its own causal structure. You can score it, based on determinism and degeneracy (links of groupings to effects, and to effects which are meaningfully distinct) – Variety Engineering, in cybernetics
4- Most ‘scales’ are ‘redundant’ – most groupings do not show that grouping parts that way is meaningfully exaplantory. But a few scales provide genuinely new, irreducible causal power. Those few define the system’s emergent hierarchy. So some clusters of ways of seeing demonstrate that grouped features are determinative of outcomes.
5- That hierarchy has a shape, like a rock formation – bottom-heavy, middle bulge, top-heavy, balloon, etc (this is related to the way the concept came to him in a dream! which is fun)
6- You can now deliberately design / tune systems to get the hierarchy shape you want. This is ‘engineering emergence.’ [Again, in Viable System Model terms, that’s a ‘d’oh, yeah, that’s Variety Engineering!’]
7- When causal contribution is evenly distributed across many scales, you get something like scale-freeness, which lines up with ideas from complexity science and self-organising networks.
And the big ideas he floats at the end are that it’s computable to locate and apportion causation across levels – and that if macroscales can have irreducible causal power, this might have implications e.g. for free will.
It’s pretty neat and I can see why the original concept got so many references – this new paper essentially follows very logically but adds a lot of interesting implications! [In an earlier era it would have been used as proof of the existence of God, I think]
Blog [it’s a little… odd?]: Erik Hoel Oct 22, 2025
https://www.theintrinsicperspective.com/p/i-figured-out-how-to-engineer-emergence
Original paper:
Quantifying causal emergence shows that macro can beat micro
Erik P. Hoel, Larissa Albantakis, and Giulio Tononi gtononi@wisc.eduAuthors Info & Affiliations
Edited by Michael S. Gazzaniga, University of California, Santa Barbara, CA, and approved October 22, 2013 (received for review August 6, 2013)
November 18, 2013
110 (49) 19790-19795
https://doi.org/10.1073/pnas.1314922110
Significance
Properly characterizing emergence requires a causal approach. Here, we construct causal models of simple systems at micro and macro spatiotemporal scales and measure their causal effectiveness using a general measure of causation [effective information (EI)]. EI is dependent on the size of the system’s state space and reflects key properties of causation (selectivity, determinism, and degeneracy). Although in the example systems the macro mechanisms are completely specified by their underlying micro mechanisms, EI can nevertheless peak at a macro spatiotemporal scale. This approach leads to a straightforward way of quantifying causal emergence as the supersedence of a macro causal model over a micro one.
Abstract
Causal interactions within complex systems can be analyzed at multiple spatial and temporal scales. For example, the brain can be analyzed at the level of neurons, neuronal groups, and areas, over tens, hundreds, or thousands of milliseconds. It is widely assumed that, once a micro level is fixed, macro levels are fixed too, a relation called supervenience. It is also assumed that, although macro descriptions may be convenient, only the micro level is causally complete, because it includes every detail, thus leaving no room for causation at the macro level. However, this assumption can only be evaluated under a proper measure of causation. Here, we use a measure [effective information (EI)] that depends on both the effectiveness of a system’s mechanisms and the size of its state space: EI is higher the more the mechanisms constrain the system’s possible past and future states. By measuring EI at micro and macro levels in simple systems whose micro mechanisms are fixed, we show that for certain causal architectures EI can peak at a macro level in space and/or time. This happens when coarse-grained macro mechanisms are more effective (more deterministic and/or less degenerate) than the underlying micro mechanisms, to an extent that overcomes the smaller state space. Thus, although the macro level supervenes upon the micro, it can supersede it causally, leading to genuine causal emergence—the gain in EI when moving from a micro to a macro level of analysis.
https://www.pnas.org/doi/10.1073/pnas.1314922110
New paper [preprint, I’m pretty sure?]:
Engineering Emergence
A defining property of complex systems is that they have multiscale structure. How does this multiscale structure come about? We argue that within systems there emerges a hierarchy of scales that contribute to a system’s causal workings. An intuitive example is how a computer can be described at the level of its hardware circuitry (its microscale) but also its machine code (a mesoscale) and all the way up at its operating system (its macroscale). Here we show that even simple systems possess this kind of emergent hierarchy, which usually forms over only a small subset of the super-exponentially many possible scales of description. To capture this formally, we extend the theory of causal emergence (version 2.0) so as to analyze how causal contributions span the full multiscale structure of a system. Our analysis reveals that systems can be classified along a taxonomy of emergence, such as being either top-heavy or bottom-heavy in their causal workings. From this new taxonomy of emergence, we derive a measure of complexity based on a literal notion of scale-freeness (here, when causation is spread equally across the scales of a system) and compare this to the standard network science definition of scale-freeness based on degree distribution, showing the two are closely related. Finally, we demonstrate the ability to engineer not just the degree of emergence in a system, but to control it with pinpoint precision.
Systems Community of Inquiry 