We must seek a widely-applicable science of systems

That is, it may be less formal at the beginning, but then a craftsman may be found who will do everything quite strictly.

Nevertheless, now this author is developing the theory of syllogistics and there are quite practical results, for example, that the logic textbooks for lawyers can be translated into boolean algebra and made more precise.

I first encountered this as the Scientific method where there is an experimental hypothesis testing loop the defines are epistemology

That was intersectional with computers for me trying to learn path finding algorithms in early video game development that I was working on as a early teenager

I read about John Boyd and the OODA loop Which got me interested in process control systems - defined in a mechanical way While still in high school. This led me to the Air Force and studying economics and trying to apply the concept of a processing loop to solve generalized decision-making

Because at the time economics had just turned into a statistical science and cognitive science was new, my economics study really became behavioral economics and so I began to study how humans actually do the “loop” as experimental data production via actuating our effectors and sensors into a prediction network. So my research started becoming around how do we model infant learning in computing architecture and prediction for human action - Frank Guerin and Ben Goertzel became my loose mentors for this direction

It was at that point that I really started to understand the Markov decision process and I got very deep into starting to learn reinforcement learning in the tradition of Richard Sutton starting around 2008.

If you go deep enough into generalizable reinforcement learning agent structures, you wind up in cybernetics because you have an agent, environment, predictive modeling, and physical systems requirement that is best defined in the way that cybernetics allows us to define them.

Another first order problem is you must chose from all the data that is perceivable what is pertinent to model because you can't perceive and model it all (at least not in complex systems) - that 'is perceivable' and 'choosing what is pertinent' act as filters that rarely are questioned. How do you know that 'what is pertinent' hasn't changed if your only way to reason about the system is through the model itself? How do you know you aren't perceiving something more relevant? Designing models to be sensitive to what we mean and not what we've stated is a very hard problem. The state space of reality is far bigger than we can sense and store.

A second order problem lies in human propensity for low energy states, when given a model or a metric we'll champion it as the truth or the way because it is easier than facing the complexity - but complex systems are crafty they adapt continuously. For example, your boss wants 100% test coverage and a dashboard has been created to report it to them... Fine, we'll test the getters and setters, we'll modify the dashboard code to ignore certain files, we'll write pointless tests that just exercise code but make no assertions... etc. They likely won't check provided the dashboard keeps reporting what they want to see.

Another second order problem is in complex adaptive systems the act of measurement changes the behaviour of the system itself. We know this intuitively, pull out a camera and start recording some strangers who were going about their business.

As for "what should we do instead" - complexity is all about context dependence... so it depends? If you are in a complex adaptive system, get involved! You can't know it all - so have fun with it.

>While I agree that nitty gritty formalizations are important to grasp, it's been made clear that the formal sciences were often insufficient in facilitating insight on the type of complexity cyberneticians cared about -- especially that of process oriented circular causality, which often involved paradox and self-reference.

I wholeheartedly agree. Hence why I think we are dealing with an entirely new type of science, the basic principles and theorems are yet to be discovered.

Lots of interesting pointers and links throughout. Thanks again!

Maybe this is another good problem that Systems Sciences might hold a great explanation too :-O.

Thanks!