It is common practice in complex systems circles to use the term ‘reductionist’ and its variants as a pejorative for models they don’t like. However the term is misleading because it is not the reductive aspect of those models that is disliked, but rather the lack of an integrative and generative approach that distinguishes one category from the other. Complexity research is every bit as dependent on reduction as other approaches, perhaps more so, and thus using the term ‘reductionist’ as a contrast to complexity research is disingenuous. What these complexity scientists actually mean is that those models are non-generative (deductive) and/or non-integrative (disjoint), and labeling them ‘reductionist’ is bad marketing at best and hiding deep misunderstandings at worst. Complexity scientists (and enthusiasts) keep using that word, but it doesn’t mean what they think it means.
Springing from my recent post distinguishing types of inter-disciplinary research, I now will go into more detail on a related topic: the difference between studying particular systems that happen to be complex, and studying complexity itself. The main point is that complexity theory includes several commitments related to levels of organization and to there being shared principles/mechanisms underpinning the dynamics of disparate systems. Studying complexity is the overt researching of these commitments and underpinnings. However, most scientists that describe themselves as doing complexity research are not doing that. Instead they are studying particular complex systems and typically ignore the commitments and underpinnings that define complexity science.
It is often said (including by me) that complex systems is inherently inter-disciplinary. It wasn't until recently that I realized people mean different things by that.; or, more accurately, that people typically don't mean anything specific by that claim. It's just another slogan of complexity. So, in preparation for a presentation on the conceptual foundations of complexity science I drew out these distinctions as an example of what kinds of results conceptual analysis can produce. Thinking more deeply about these distinct practices will likely clarify each of our role in the scientific enterprise.
In my most recent lament over the lack of support and interest in basic scientific research, I've developed an analogy that not only helps explain the role of methodology, but also highlights the import of fostering more exploratory research. The analogy is to think of normal science (in the Kuhnian sense) as akin to solving puzzles (e.g. jigsaw puzzles, mazes, and crosswords). A puzzle has a clear goal, a known solution, constrained moves, and a collection of strategies/heuristics for reaching that solution. My claim is that people, and especially scientists, unfortunately see doing science as a similar activity.
Swarmfest 2012 will take place in Downtown Charlotte, N.C. July 29-31, 2012, hosted by the Complex Systems Institute at the University of North Carolina, Charlotte. The deadline for abstract submissions has been extended (as usual) to July 11th and takes the form of a one-page .pdf file. Just email submissions to email@example.com For more info keep reading...
Jan 30: Conference: Alife 13
Michigan State University will host the 13th International Artificial Life Conference (Alife13) July 19 to 22, 2012. This year’s major conference theme is "Evolution in Action". They encourage submissions by biologists, computer scientists, and especially interdisciplinary groups projects that explore the many ways that evolution and artificial life research intersect. The current paper submission is Feb. 26, 2012 and the conference page is here
The University of North Carolina at Charlotte is sponsoring their 1st annual conference on Complexity and Human Experience from May 30th to June 1st, 2012. This conference is geared specifically toward complexity research in the humanities and social sciences. Submissions of 5000-word papers are due February 5th. More information can be found on their website.
Nov 17: Blog Back Up and Running
As some people may notice, the ComplexityBlog was down since February because GoDaddy made a mistake in migrating my files to a new server. I was able to resubmit most of my posts and Ken may upload his soon as well. There hasn't been a new post for over a year now, but that will soon change. I'm currently working frantically to complete my dissertation, but once that's done I've got a backlog of items to finish and post. Furthermore, I'll be making significant improvements to the other parts of page as well. Please notify me if you notice anything missing or broken (esp links) and please check back in a few months to see the new, and improved ComplexityBlog. Thanks.
This post is a response to an email I received through this blog enquiring as to whether I could help him explain the difference between agent-based models (aka multi-agent simulations) and computer games. Also, he noted that it would be helpful to explain the relationship of these two to mathematical modeling such as with system dynamics. There are some obvious similarities and differences among the three, and different explanations may focus on different components and features, but what follows is how I conceptualize the relevant aspects of these models and locate them on a spectrum with mathematical models and computer games on the ends and agent-based models as a happy medium. This post will not be exhaustive of the differences, benefits, and limitations of each modeling type, but rather a comparison of those aspects which are most relevant to the use of ABMs for scientific uses.
I've been away for a while. Not on vacation or sabbatical, but just diverted into research too far from complexity science to make it here. But now I've completed the required coursework for my PhD at the University of Michigan (which sadly has little to do with complexity) and can get back to doing real work. In the near term a large part of that is completing my work on tipping points, path sensitivity, and robustness; this is a colossal methodology project combining techniques from Markov Theory, Network Theory, Graph Theory, Statistics, Quantum Physics, Dynamical Systems, and others. Fun Stuff.