Universal Uncertainty

Measuring "speed" of change is tricky - speed is relational. There does however seem to be a lot more uncertainty around: anticipating the future means grappling with very high degrees of contingency. To say "things" change, what we mean by "things" is not so much "stuff happening in the world", but rather our relation to "stuff happening in the world". It's not the stuff which is uncertain. It is the relationship between our context and perception and "stuff" which is generating more contingency in our decision-making. 

Uncertainty means disorder in relations. We can measure "maximum disorder" of relations as the entropy of the stuff in the world (particularly when new technologies increase the number of options we have, or a new virus radically restricts our capacity to adapt to the world) in relation to the entropy of our capacity to deal with it. If the equations don't balance, then there will be uncertainty.  At some point in the future, these equations will balance out again - and on it goes. This appears to be an evolutionary principle. 

COVID was a good example of this explosive relative uncertainty. A disruption at a biological level of organisation impacted on the normal institutional mechanisms for dealing with uncertainty (see here: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7518093/). As a result, it became very difficult to coordinate expectations across society with normal regulatory mechanisms. This necessitated an authoritarian doctrine of "follow the science" backed-up with the threat of force, as a way of radically changing the way people lived. The irony about this was that science is the business of exploring uncertainty, while the COVID authoritarian science (rather like "school science") excluded uncertainty in its official pronouncements, to leave doubt and inquiry in the hands of conspiracy theorists. "Following the science" is not the same as "being scientific".

 

I've been thinking about this diagram, presented by Jerry Ravetz to explain Post-Normal Science. All science displays degrees of uncertainty. In a presentation I gave the other week, I contrasted images from the Hubble telescope and images from the James Webb telescope. I said that while the technology improves, and we get more information (in fact, the maximum entropy of information increases), there is still a relation between those things which we are certain about, and those things which we are not certain about. The relation between certainty about craters on the moon, and certainty about planets in other galaxies is constant. 

In the context of COVID, this is useful, because there were things which we knew were high risk for transmission, and other things about which there was much argument. With COVID, there were also high decision stakes alongside high scientific uncertainty. The difficulty was that government not only failed to convey the systems uncertainty, but in fact attenuated it.

 

This diagram is also interesting because it reveals that there is a a gradation of causal relationships in the "systems uncertainty" direction. Attributions of causation between factors become more contingent the further one goes from left to right. It is perhaps no surprise that contingency in decision also rises, and perhaps this is related to the "stakes" of those decisions.  How might we think about this gradation of causal relationships? 

 

These must be related to the communication dynamics that are established in the light of experience. Hume argued that causes were the outcome of communication dynamics between scientists in the light of their experiments. I think he was right (although lots of people don't),  Regularity of events was the key ingredient to produce scientific consensus. The problem is that with higher systems uncertainties, the likelihood of regularity in events become less. Systems become more complex, more contingent, mechanisms harder to agree on. This lack of social agreement can impact the decision-stakes: failure to agree scientifically can render political chaos and social disorder. 

With COVID, the fundamental disruptive mechanism was a bio-techno-social dynamic, where technology took the forms of apps, masks, vaccines, etc. It's actually very similar with AI at the moment. That is also a bio-techno-social disruption, where its not a disease that represents the "bio" bit, but our cognition and emotions. The challenge for institutions is to find a way of renormalising relations. That requires finding new perspectives from which to view the dynamics we are in. 

In some ways, COVID presented an easier challenge because it (sort of) went away, and life could get "back to normal". AI is much more serious because the institutional discourse relations cannot grasp what is happening in the bio-techno-social mechanism, and are constantly blind-sided by "the next cool thing". I wonder if these are the conditions within which Copernicus and Galileo paved the way to a social gestalt-switch which restabilised European institutions.

In order to get on top of what is happening to technology, we are going to need a similar gestalt-switch.