The below text on chaos and complexity and the video on "intermediary conditions" are related to my The Gap of Intermediary Conditions, where I write in a comment on Richard Epstein:
In the end, the best answers [on many hotly debated issues, G.T.] rely on educated hunches by persons who work within the field, who may differ substantially in their conclusions. (Richard Epstein)
But theories are only approximations, ephemeral stages in the process of accumulating new insight. In the end, theories lead us to discover their dark, uncharted side, calling for their own revision. They make us see and understand intermediary conditions that we had been unaware of before. If a theory can hold its own in the face of new and more intermediary conditions, it has earned itself another lease of life. Otherwise it ought to be discarded or it can survive only in the form of an unreasonable ideology.
Freedom as Method
These are only preliminary thoughts which I hope to expand into a theory of "freedom as method" - by which I mean a method of looking into genuinely open ended issues in such a way that the presumptions of liberty help understand better and convincingly, though not exhaustively, issues contested in the public arena. There are plenty of questions that we liberals do not have conclusive answers to - but we may have an excellent method to improve on these problems asymptotically. I feel, there are many occasions where the liberal should give up his posture of rowdy opponent (in possession of the final answer) in favour of a role as intelligent contributor (adding to cumulative improvements).
The below video shows a graphic example of how intermediary conditions work.
By the "the gap of intermediary conditions", I mean:
the premises and predictions of your [political] belief system fail to link up conclusively; the consequences of adhering to your principles take a different path than predicted, owing to the influence of overlooked intermediary conditions.
Politics is a way by which to discover and (hopefully) judiciously react to the occurrence of intermediary conditions.
Apparently, a river works like politics:
Under the rubric "food for thought," consider Coilander and Kuper's
nice description of how two often confused terms, complexity and chaos, differ and interrelate:
Chaos theory is a field of applied mathematics whose roots date back to the nineteenth century, to French mathematician Henri Poincaré. Poincaré was a prolific scientist and philosopher who contributed to an extraordinary range of disciplines; among his many accomplishments is Poincaré’s conjecture that deals with a famous problem in physics first formulated by Newton in the eighteenth century: the three body problem. The goal is to calculate the trajectories of three bodies, planets for example, which interact through gravity. Although the problem is seemingly simple, it turns out that the paths of the bodies are extraordinarily difficult to calculate and highly sensitive to the initial conditions.
One of the contributions of chaos theory is demonstrating that many dynamical systems are highly sensitive to initial conditions. The behavior is sometimes referred to as the butterfly effect. This refers to the idea that a butterfly flapping its wings in Brazil might precipitate a tornado in Texas. This evocative—if unrealistic—image conveys the notion that small differences in the initial conditions can lead to a wide range of outcomes.
Sensitivity to initial conditions has a number of implications for thinking about policy in such systems. For one, such an effect makes forecasting difficult, if not impossible, as you can’t link cause and effect. For another it means that it will be very hard to backward engineer the system—understanding it precisely from its attributes because only a set of precise attributes would actually lead to the result. How much time is spent on debating the cause of a social situation, when the answer might be that it simply is, for all practical purposes, unknowable? These systems are still deterministic in the sense that they can be in principle specified by a set of equations, but one cannot rely on solving those equations to understand what the system will do. This is known as deterministic chaos, but is mostly just called chaos.
While chaos theory is not complexity theory, it is closely related. It was in chaos theory where some of the analytic tools used in complexity science were first explored. Chaos theory is concerned with the special case of complex systems, where the emergent state of the system has no order whatsoever—and is literally chaotic. Imagine birds on the power line being disrupted by a loud noise and fluttering off in all directions. You can think of a system as being in these three different kinds of states, linear, complex, or chaotic—sitting on the line, flying in formation, or scrambling in all directions.
Like chaos theory, complexity theory is about nonlinear dynamical systems, but instead of looking at nonlinear systems that become chaotic, it focuses on a subset of nonlinear systems that somehow transition spontaneously into an ordered state. So order comes out of what should be chaos. The complexity vision is that these systems represent many of the ordered states that we observe—they have no controller and are describable not by mechanical metaphors but rather by evolutionary metaphors. This vision is central to complexity science and complexity policy.