The concepts of complexity and chaos are being invoked with increasing frequency in the health sciences literature. However, the concepts underpinning these concepts are foreign to many health scientists and there is some looseness in how they have been translated from their origins in mathematics and physics, which is leading to confusion and error in their application. Nonetheless, used carefully, “complexity science” has the potential to invigorate many areas of health science and may lead to important practical outcomes; but if it is to do so, we need the discipline that comes from a proper and responsible usage of its concepts.
the edge of chaos is the constantly shifting battle zone between stagnation and anarchy, the one place where a complex system can be spontaneous, adaptive and alive.
Evolution always seems to lead to the edge of chaos
The crucial skill is insight. The ability to see connections.
Must look at world how it is, not as some elegant theory says it ought to be.
Nearly everything and everybody caught up in non-linear web of incentives, constraints and connections.
Innovations never happen in a vacuum and often come from someone who is outside the field
Catalysis everywhere and life wouldn’t be possible without it – molecules could have catalyzed the formation of other molecules so that those in the web would have taken over. The web would keep growing and would have catalyzed its own formation, it would become an autocatalytic set – order for free. Autocatalytic set can bootstrap its own creation and evolution by growing more and more complex over time and will also experience booms and busts from small changes
Complex adaptive systems – characterized by perpetual novelty; dispersed, hierarchical, learn / adapt / evolve, anticipate the future. always unfolding, always in transition
Emergence is hierarchical – building blocks at one level combining into new blocks at a higher level. Hierarchies are one of the fundamental organizing principles of the world. Found everywhere because a well-designed hierarchy is an excellent way of getting some work done without any one person being overwhelmed or having to know everything. Also, utterly transforms a system’s ability to learn, evolve and adapt – can reshuffle building blocks and take giant leaps. Can describe a great many complicated things from relatively few building blocks
Adaptive agents always playing game with environment for fitness requires feedback and prediction. In order to learn, must be able to take advantage of what the world is trying to tell it
Competition much more essential than consistency. Competition and cooperation may seem antithetical but at some very deep level, they are two sides of the same coin
Self-reproduction requires medium to be both data and instructions. von Neumann and cellular automata
Always ask, “optimal relative to what?“
Artificial life – effort to understand life by synthesis, putting together simple pieces to generate lifelike behavior in man-made systems. Its credo is that life is not a property of matter per se, but the organization of that matter
‘Aliveness’ lies in the organization of the molecules and not the molecules themselves
Fact that simple rules leads to unpredictability is reason trial and error (Darwinian natural selection), although somewhat crude and ‘wasteful’ is the best strategy in nature and evolution
the whole is greater than the sum of the parts
Power truly lies in connections – exploitation (improving what you already have) vs. exploration (taking big risk for big reward)
Information has to flow from the bottom-up and from the top-down
Learning and evolution move agents along the edge of chaos towards ever greater complexity, sophistication and functionality
One of the greatest questions and mysteries is why life gains ‘quality’ and becomes more complex over time. It is also one of the most fascinating and profound clues as to what life is all about
Tao of complexity – there is no duality between man and nature, we are all part of this interlocking network
Optimization isn’t well defined anymore. Rather, what you’re trying to do is maximize robustness, or survivability, in the face of an ill-defined future