Shifting From Parts to Patterns
“All our knowledge has its origins in our perceptions.” – Leonardo Da Vinci
I came back to this again this morning, and decided to try from a different perspective.
Leonardo only had part of the picture, and was therefore, essentially wrong.
Those of us with sufficient interest and time now have some beginnings of an understanding of how evolution works.
Evolution is about differential survival of variants in populations across all the different contexts encountered by the members of that population over deep time.
Thus variants that have very high survival value in very rare contexts, and minor costs the rest of the time, can be present in significant concentrations in populations.
This process embodies systems.
It selects patterns that survive at ever more complex levels, simultaneously across all levels.
It is now clear beyond any shadow of reasonable doubt that this process has given rise to all the living diversity that we see around us, including ourselves.
It has structured our bodies and brains.
In the biophysical contexts of genetic selection, it is responsible for all the capacities and tendencies to all the feelings we have.
In the cultural context of mimetic selection it is responsible for most of the language and wider cultural constructs that we inhabit.
In our personal journeys within both of the contexts above it is a very complex mix of choice and survival at many different levels.
So at many levels, our behaviour in reality is the result and the expression of embodied patterns of being, which in one sense encode systems that have been selected by differential survival of variants over vast times and wide sets of contexts.
So yes – one must look at the many levels of systems.
No – one cannot take a purely reductionist view of seeking certainty from lowest level systems.
And it is more complex than either of those imply.
It is more dimensional that a holistic/reductionist view implies.
To my mind the disasters are not so much caused by the use of reductionism, though being overly reductionist certainly has its problems (understanding the influences of subsystems is an essential part of understanding the operating limits of emergent systems), the biggest issue seems to be an addiction to certainty.
It seems that the hardest notion for most to give up is the idea of truth.
The idea that uncertainty, even chaos, might be a fundamental aspect of this existence within which we find ourselves seems to be the hardest thing for many to get.
It does seem to be what Heisenberg uncertainty is pointing to, a sort of fundamental yin/yang balance between order and chaos, that in pairs of fundamental quantities (like position and momentum) require that the more we order and confine one aspect, the more chaotic and dispersed becomes the other.
This balance of order and chaos seems to apply at all levels of emergent systems.
And at all levels, the limits on chaos can give very close approximations to order when aggregated over large collections. Individuals may be unpredictable in any instant, but large collections over larger times give far greater confidence to predictions in many cases (but not all – some, like weather, are essentially unpredictable at all levels).
I love David Snowden’s Cynefin Framework for the management of complexity ( https://www.youtube.com/watch?v=N7oz366X0-8 ). It shows clearly that classical engineering approaches to order are only applicable to the simplest of domain spaces.
Wolfram’s ideas of maximal computational complexity are also important. The idea that there are aspects of reality that cannot be reduced, as they are already maximally computationally complex, and the easiest way to see what they will produce is to let them do it.
A great deal about being human seems to be like that. Wolfram explains that quite beautifully in this short clip ( https://www.youtube.com/watch?v=8mQqpDNTw8M ) published in March 2017.
So there seem to be two big picture aspects here.
One aspect is that the reality of where we are going cannot be reliably predicted, as it is of an order of complexity that we must simply live to find out where it goes.
The other aspect is that we must all accept the fundamental uncertainty that comes with such complexity, and be willing to relax the boundaries of our cherished truths to allow the reality of our existence to create as close a model of itself as we can manage. And that would seem to be a potentially infinitely recursive process, as the emergent complexity of such behaviour folds back into the models of itself, ongoingly.
And there are some important lessons in this that seem to be consistent.
Complexity requires boundaries.
Without boundaries everything becomes amorphous goo.
We require boundaries at every level – physical, individual, social.
And those boundaries need to be both flexible and selectively permeable, and both of those need to be context sensitive.
So liberty in such a context cannot mean existence without boundaries, as that must, by definition, lead to non existence.
Liberty, in a context of complex systems, must mean accepting those boundaries that are necessary for survival, and no more. Morality is essential to survival.
And it seems that there cannot be any sort of absolute certainty about what those boundaries are, and there can be degrees of confidence in particular contexts.
The real problem comes with novelty.
Novelty, if real, has no historical precedent.
If something is truly novel, then by definition it has not existed previously.
That has two profound issues.
1 – most people are unlikely to see it for what it is, and are likely to classify it as something that is similar in some aspect to something from their previous experience (and in doing so miss the novelty that is present).
2 – there is no historical proven precedent for how to most effectively treat the new thing.
We already know enough to know that we face many levels of existential threat that we have no effective risk mitigation strategies for, so we have to keep on exploring new stuff, to solve the old problems, and along the way, we are bound to both encounter and create new problems. So long as we are alert for that, open to those possibilities, we are probably on the safest possible path.
And Wolfram in the short clip above points to our exponentially expanding ability to automate things.
This exponentially expanding ability to automate is now the single greatest driver of change.
Markets can only put a positive value on things that are scarce.
If you doubt that think of air. Fresh clean air is arguably the single most valuable commodity for any human being, yet of no value in any market where it is universally abundant (which is most places).
What few people have yet grasped, is that the need for scarcity to deliver market value is now directly in opposition to our technical ability to fully automate the production and delivery of a large and exponentially expanding set of goods and services.
Our social systems are currently largely driven by profit, and profit demands scarcity.
So while our existing systems can deliver radical abundance to some, they must always fall short of delivering such abundance universally, even if doing so is technically possible.
That is why something like Universal Basic Income (UBI) is required as a transition strategy, to take us from our existing market based systems (which arguably worked in times of genuine scarcity) to whatever evolves as our abundance based systems going forward.
And one of the attributes of complexity that is clear from a study of evolution from a systemic view, is that new levels of emergent complexity are always characterised by new levels of cooperation, and cooperation requires attendant strategies to prevent invasion by cheating strategies. Arguably most of our existing financial and political systems can be characterised as cheating strategies. That doesn’t mean that we need to get rid of any people necessarily, we just need to change the strategic environments within which those people exist, and their awareness and self interest will respond accordingly.
And it seems that security will always demand an element of massive redundancy and diversity in our systems, so it seems that within the broadest possible context of cooperation, we can see massive variability in instantiated sets of social and technical systems, each within their own sets of boundaries, and each interacting within wider sets of boundary conditions.
And the idea that we can use hard rules to define such conditions is not sustainable.
Those higher order boundaries must have flexibility in dimensions many will not even be able to distinguish. And that will require active communication systems and openness like never before.
And Jordan Peterson has the best development I have encountered of the levels of complexity embodied in many of our deep cultural constructs (he misses a few things, but gets far more than most – https://www.youtube.com/watch?v=YC1pvjyKYr4 is as good a place to start as any).
I am clear, beyond any shadow of reasonable doubt, that our survival demands that we go beyond markets as a dominant force in our social planning infrastructure. The context has now changed sufficiently that market values can no longer be used in a planning context, though they can retain utility in a delivery context for some time to come. Our existing debt based money creation system must change, is changing.
We have much to do.
If you look at Snowden’s Cynefin framework for managing complexity (which is a very simplified framework for getting a feel for the types of complexity present and the sorts of management responses appropriate https://www.youtube.com/watch?v=N7oz366X0-8 ), then you will understand that what most call the “scientific method” of holding all else steady and varying a single aspect to test the effect of that single aspect, is only appropriate to the simplest of Snowden’s 4 classes.
Any scientist with a reasonable understanding of complexity and statistics will understand that – and unfortunately that is a small subset of those who call themselves scientists.
So scientists come is a vast spectrum of understandings and methodologies – much like human beings generally and cultures generally. Most scientists are every bit as dogmatic as most adherents of any religious system – just a different set of dogmas.
The only thing that should be common to all scientists is a willingness to let the evidence from well formed experiment be the determinant of questions. And there is always room for argument about how well formed an experiment is, and conceptual bias from particular modes of interpretation can be a major factor (dogma).
What we “see” is very much a function of what we expect to see, modulated by what is actually there. Human beings are very adept at subconsciously pushing whatever shaped observational peg is present into the nearest shaped conceptual hole that it will fit in, and using that as evidence for “truth”.
Becoming aware of the many levels at which we have a strong subconscious tendencies to such things is a big part of the process (Yudkowski’s “Rationality from AI to Zombies” is a reasonable catalogue of many of those, and at the same time it mostly ignores the fact that rationality is only ever the tiny tip of human behaviour, most of which is and must always be, subconsciously heuristic). Reality is far too complex to ever be able to handle consciously. Everyone, at all levels, has to make vast sets of subconscious and conscious simplifying assumptions and take heuristic shortcuts to be able to make any sort of sense of anything.
Part 1 of understanding science is getting that our conscious experience is never of reality itself, but can only ever be of a subconsciously created model of reality. Once that sinks in, it demands a certain level of humility from us.
Uncertainty is fundamental.
Simplistic assumptions occur at every level, always.
Building a reasonably useful approximation to the levels of complexity present in reality takes many thousands of hours of work. One has to train and retrain neural networks over and over. There simply is no substitute for experience in doing such things.
And we each need to be able to use the tools of science to inform our models, without being blinded by the dogma of any particular set of scientific paradigms.
Building more accurate models demands that we be prepared to try out things that do not seem at all sensible from our current models (that is the definition of novelty in a very real sense).
Being willing to trust intuition and feelings enough to design tests, and then having the stamina to keep on testing modes of communication until one finds one that has a reasonable probability of success, of weakening the bounds of dogma at some level to allow new possibilities to emerge in a new mind, that is part of the process. It can be a very lonely journey.
When the core concept of economy – the market – encounters an exponentially expanding set of conditions (fully automated systems) that fundamentally undermine it, and turn it from something beneficial to something that is an existential risk to humanity, that can be very difficult for people to see.
When one has been taught that evolution is all about competition, it can be difficult to see that actually evolution is exponentially more about cooperation as successive levels of complexity emerge.
Understanding that morality is one of the necessary boundary conditions for complex systems like ourselves to exist was not clearly visible to Nietzsche or the Post Modernists, yet to those with a sufficient depth of understanding of evolution and complexity and strategy it stands out like the proverbial.
So I just caution you, yes, certainly, acknowledge that the simplistic term “scientific method” as applied to holding all but one variable constant, is appropriate to only a tiny subset of the systems in reality, and do not make that mean that the wider methods of science, statistics and complexity are not appropriate more widely.
And certainly, acknowledge that many systems are genuinely chaotic, and do not have any significant predictable attributes, and trying to predict them is a waste of time. Learn to identify them and avoid them to the greatest degree that is possible.
And many other systems are complex, and require an iterative approach of probe, sense, evaluate, amplify or dampen aspects as appropriate, and repeat. That’s life. Accept it. Enjoy it.