An interesting discussion has developed over on Zenpundit. I just wanted to share some initial thoughts that I included in the comments for that post:
Applications of network theory certainly exist within the biological sciences. For instance, the network structure of food webs in a variety of ecosystems are being studied in depth. The 'network' structure of the molecules used in cellular metabolism is another interesting application of network theory.
But the reverse is true as well, where analogs to principles in evolution are being used to study how randomness affects network structure and decision-making in networks. Genetic algorithms, where small pieces of two different, valid rule-sets are interchanged to produce an 'offspring rule-set,'can be used to see how rule-sets evolve within a given network. The Franks paper involved this technique, for instance.
I tend to believe, like Curtis, that there is much to the argument that a truly resilient network needs to be able to do more than just react. If we define resiliency as the ability to respond to new challenges, the response capabilities, resources and infrastructure of real networks need to be able to anticipate problems or attacks on that network. Now, this means we have great challenges. It is difficult to even attempt to understand the behavior of a complex system in any sort of detail, but add to that the fact that a complex system typically exists in a complex, dynamic environment, and the number of possible interactions and problems that may arise is staggering. For instance, think of the political example being raised.
There is obviously a 'barrier' to what political/policy decision is made because of the polarized state of the country right now. Here is the interesting part...in a relatively noise-free environment, there is esentially no crossover between the two parties and how they view or decide to act on some issue. Crossover and consensus do not occur until there is an increase in the noise (again, research along the lines of the Franks paper studies the effect of noise in networks; there is great applicability of the concept). True changing of political minds tends to come about with increased noise; political decision making tends to be reactionary in nature (not many visionaries). 9/11 was a 'big bang' that led to unification of the parties, at least as unified as we'll see them at any given moment. I don't think it lasted very long, nor can it realistically last very long, because of the complex environment politics has to navigate. The noise (at least within the US) died down, and the decision-making boundaries have been redrawn. Curtis is right to say routine has become the norm again, because individals like routine and go back to worrying about day to day problems for survival sake. It is evolution in action..adapt to the environment and focus on individual survival. To be resilient will require societal concerns and planning, more long-term thinking, and anticipatory thinking, which politicians are not very good at (particularly long-term thinking/planning).
2 comments:
Wow,
Enjoyed that! I'm new to your blog, and don't study network theory (I studied microbiology and was fascinated by feedback loops, but for now I'm assuming I know what it is!) but I'm very relieved to find this. Fritjof Captra's 'Web of Life' is the closest thing I have to a bible; I think network theory offers the best re-assurance that we - or at least life somewhere - will evolve into something great.
Hi Adam,
Thanks. I tend to think of network theory (and I am no expert on it, but have been trying to learn as I, too, find it fascinating...my background is in high energy physics)as a primary tool and subfield that falls under the broader complex systems umbrella. I am intrigued by how the mathematics of countless systems are identical to each other. Evolution, being built around mutation and adaptation to the environment, which means randomness and dynamic change, is a prime example of resiliency of those organisms that survive. Perhaps greatness awaits something, somewhere. One question: In your mind, what does 'great' mean?
I look forward to other thoughts and discussions.
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