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Editorial (16.4):

Understanding 'understanding'

I have been reading about the history of philosophical thought, in the hope of finding a better understanding of ‘understanding’–particularly in view of our recent recognition of complexity and the major role it plays in the world. Although many of us may make our names, reputations and livings by studies of complexity, in reality the word has the ‘negative’ implication that many things in this world are difficult, if not impossible, to understand. By ‘understand’, it is implied that we know how and why they came to be as they are, and what they may become in the future. Complexity admits that this may not be possible for many situations. Of course, a dynamical system with unchanging external circumstances and unchanging interacting elements would proceed along a deterministic trajectory into the future, which gave rise to the illusion that ‘science’ and ‘computers’ had ‘cracked’ the problem societies face and would be able to predict and plan a wonderful future. However, for most situations the very elements present in ecological, social or economic systems, change their beliefs, knowledge, interactions and their external circumstances over time.

Already in the early 19th Century Hegel understood the fact that it is only what changes at a given moment that may be comprehensible in that moment as contexts and motivations change over time. And so societies, cities and nations result from the historical accumulation of moments. This was a remarkable anticipation of System Dynamics, and shared its shortcomings, only finally resolved by the modern understanding of Complexity. Hegel, perhaps understandably, thought that the changes driven by the Dialectic (thesis, antithesis and synthesis) led to a predictable movement towards the perfect society and state. He thought that humans would, through struggle take their political system from its primitive stage to a final, perfect State in which humans would find fulfillment. Unfortunately for the world, he believed that in this perfect State, the individual should be submerged by the State. These ideas laid the foundations for Fascism on the one hand and for communism and Marxism on the other - a truly remarkable, but dreadful, harvest. Hegel and philosophy were not really to blame for the millions of dead in the misguided wars of the 20th Century, really. But as usual, some people could use the ideas to pursue their own struggle for power.

Moving back to a more precise understanding of how social systems change, when we look at a city, for example, then we find that, over time, urban development has led to a complex geographical picture in which the different and changing values and motivations of individuals and groups have driven the processes and events involved. For example, in earlier times religious buildings may have had a predominant place in the center of any growing community, but over time the different values and powers of a changing society has used prime locations for more commercial and business growth. Particular buildings and roads result from a long history of local events, particular people and their ideas, as well as from the effects of larger scale factors and international circumstances. The whole system has grown and changed as the result of the meshing of multiple scale processes and events resulting in the idea that the ‘explanation’ of a city really emerges from many things that happened, some of which may have been inevitable and many which were not. In other words we do not really understand why London, Paris, Amsterdam, New York etc. are what they are. Nor do we really understand what makes particular organizations, institutions and businesses what they are either.

The results of evolution itself are not ‘understandable’ except as the results of a particular, historical evolution. All around us are emergent structures based on both underlying processes and also events. The events are either from outside and not predictable within the system, or from inside as an instability of the internal makeup and structure of the system. So although we cannot say that we understand any particular product of evolution (why it is exactly like it is) we can say that we understand how evolution ‘works’ to build complex systems.

Furthermore we can justify our own interest and research, as well as the value of our interventions, by saying that our complex systems studies, models and thoughts help keep us aware that things could change. Success in some activities will inevitably lead to new limitations and problems, which is not to say, don’t do anything, but simply to say that systems, cities, organizations and people, if they are still capable of adaptation and learning, will always have new successes and also face new problems. The alternative is either slow or rapid decay. Complexity thinking shows us that there is no end to change, no complete ‘solution’ to living and so constant vigilance is required. If most systems and organizations got to be as they are as the result of processes (understandable) and events (not so understandable), and that processes are changed as a result of events (which can include internal instabilities such as evolutionary change) then we see that by modelling or at least inventorying current situations we can see better when reality diverges and new things occur, or indeed even be prepared for various possible events that could occur.

The recognition of ‘complexity’ is therefore an admission of the real limits to our knowledge and understanding of things. The old dream of science as being a method that could reveal all the secrets of the world has been shown to be incorrect. We are part of an on-going, multi-scale evolutionary process that contains both creative and learning possibilities amongst its interacting elements. This doesn’t mean that success is assured and that terrible mistakes cannot occur, but it does mean that things will probably turn out surprisingly, with some good and some bad outcomes. So, studying complex systems and complexity is a great leap forward–If it helps us to keep looking out for new problems and opportunities. It should also help reduce ‘fossilization’ of human systems through the self-reinforcing mechanisms of power, wealth or physical force and their ability to try to maintain the system that has brought them power, wealth and force. Evolution, adaptation and learning arise from experimentation at different scales and the retention of what works. But in human systems there is always a tendency for those who are part of the currently successful regime to try to suppress further experimentation that might lead to a change in this dominance.

So we find the paradoxical result that elements, organisms and systems that evolve successful structures and strategies are always in danger of ceasing to be complex–by freezing their successful behaviors. In the longer term this will be disastrous, but is difficult to avoid. So although it seems negative for social and cultural systems that exhibit complexity and opaqueness in that they cannot be clearly understood, and defeat our best inquiries, in reality it is precisely this ambiguity and impenetrability that gives them the power to evolve.

In this issue there is a paper that discusses whether the human mind is or is not algorithmic. Clearly, as scientists or mathematicians we may ask what is the ‘optimal’ solution to a problem, but in most of real life what matters is whether or not our answer is ‘good enough’. Clearly in an evolving, impenetrable world the question is more about what problems should I be trying to solve, and what should be seen as essential to the problem and what is simply context? Another paper discusses the mechanisms bees have evolved to organize their food collectors according to their ‘dance’ which signals ‘how good, how far and in which direction’ is a food source to their fellow gatherers. We have no idea whether bees ‘know’ what they are doing when they dance back at the hive, but the point is that it works well enough to have been retained in their behavioral patterns. One important point though is that it is probably important that there is imperfect signalling for bees at the hive, ants returning to the nest or indeed for fishermen coming into port. This is because although there is a need to mobilize individuals in some numbers to go to the latest source of nectar, food or fish it is also necessary to leave some individuals un-informed or ill-informed so that they will continue to scout out new territories and will be the source of future finds. Evolving complexity always works in this seemingly messy way, with internal diversities and ambiguities that make it both inefficient in the short term, and resilient in the long.

It seems important to realize that in a complex world, successful strategies must never be so convincing that there is no internal disagreement and diversity, and that although it seems obvious that if a particular strategy works better than others then it should be adopted across the system, this internal homogeneity will eventually lead to decay and failure for lack of any range of adaptive responses, and hence no resilience.

Summarizing the discussion above then, we may say that ‘limits to knowledge’ are in fact not a negative reality, but are what makes life worth living (when it is). You see that there can be no magic equilibrium where all problems are resolved and things can continue as they are. Knowledge of how things work, or pure accident, would lead some to try other behaviors which, if successful, would be adopted by others, changing the behaviors that underpin the ‘equilibrium’. So building a model of a system by representing the behavior of the different elements will automatically make itself invalid if people know something of the expected outcomes the model predicts for them and others. By changing their behavior they invalidate the model that has been based on their previous behavior. So, understanding the system leads to beliefs in future outcomes that change both the model and the future outcomes! This seems frustrating, but in reality this is the very essence of living, because it allows exploration, innovation, change, survival and learning. As a result our cognitive evolution and diversity will continue to take us forward into the unknowable future.

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There's another side of this question that I've explored for some years, methods for directly studying the phenomena of emergence, a subject that also recently came up as a proposed new direction for complexity science. David Pines, one of the founders of the Santa Fe research Institute stated that as a primary goal to achieve in "Emergence: A unifying theme for 21st century science"(a). I replied with a proposed approach in "But how can physics study behaviors, not the theory?"(b). a - - I've succeeded in doing to some degree successfully is to treat the predictions of mathematical theory, always defined as general predictions for classes of phenomena, as "envelopes of boundary conditions" within which to find individually developing natural system phenomena. That has helped me find and study individually occurring processes that theory may predict a general range of outcomes for, but not the specific means of. What looking for non-statistical processes by which individual events develop tends to expose are the emerging systems of energy use and organizational change needed for them to behave as predicted.My work is decidedly limited, and mostly for cases selected to be easier to study than others. Still, it does seem to offer an alternative way to study emergence, and "move the limits" of complex system knowledge by alternating study of theory and the behaviors of logic with studying individually developing energy systems, to learn from their differences.