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.
Jan 28: Converting CSV to Netlogo Lists
Whenever I want to import data into Netlogo, I usually write a script to convert the file's formatting to be something that is Netlogo friendly before importing it. But so much data comes in CSV (comma separated value) format that it there should be an automatic file-read command for that. There isn't. In lieu of that I have the following two procedures that take care of this. Also note that this includes a Replace-All function for strings in Netlogo, which again it needs but mysteriously lacks.
Jan 22: Emergent Causal Inversion
I am keenly interested in notions of causation, and specifically how they relate to models of complex phenomena. One reason is that complexity (i.e., feedback, multiplier effects, evolution, etc.) blurs the lines of clear causal relations. I have made the point elsewhere that causation is not something in the world that we match and/or uncover, but rather only something in our models (including mental models) that structures and orders the actions and reactions. I am therefore especially focused on the methodology of mechanisms in models and how to make them clean, and conform to how we think things happen (which is why cross-level, e.g., downward or upward, causation is something to avoid). Within a level of organization a model's causal relationships follows the rules generating behavior in that model, but the higher-order (emergent) phenomena generated by that mechanism may reveal causal relationships that violate the ones generating said phenomena. Here I outline a project that can reveal such an emergent causal inversion.
Evolution by natural section describes a process by which replicators (things that make copies of themselves) tend to increase in numbers, but compete for limited resources, so that replicators with altered (by random mutation) features have (context dependent) variable rates of replication. The result is that those alterations that foster increased replication in the prevailing local context become more prevalent in that population. This view requires identification of the replicator, and genes (or some smaller fragments of DNA) are the natural candidate for life on Earth as we have ever seen it. The idea of group selection, of which earlier crude versions had been reasonably dismissed, has made a rebound. The new versions (sometimes more accurately referred to as multilevel selection) have been called upon to explain culture, morality, altruism, cooperation, and similar phenomena in a seemingly plausible way. But most proposed new versions of group selection are also flawed, and some other explanation is needed for these social phenomena.
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...
May 17: Models for Philosophy
You can use an ABM to test hypotheses for how something works, like a thought experiment. But building, running, and analyzing a model is science, in fact like an empirical science. So the relationship between the model and a philosophical point is similar to the relationship between some conceptual issue and the science of that issue. So what role does philosophy play in making and/or interpreting models? How can the results be philosophically relevant/important/implicating? Can science ever decide something in philosophy?
The structure of systems at different scales is often referred to as a hierarchy, and traditional thinking implies that this is so. Molecules make up organelles, which make up cells, which make up tissues, which make up organs, which make up organ systems, which make up bodies, and beyond. I refer to any scale at which recognizable coherent patterns in behavior are observable and describable as a level of organization. This is an epistemic notion because it relates to what we can capture in models, not to the structure of reality. Hierarchies of scale are a mixed ontological and epistemic notion: the levels are still based on what people can discern, but there is an added ontological assumption that a higher level exhaustively includes the elements of a lower level. Now I will show why levels of organization, not hierarchies, are the domains and ranges of scientifically useful reduction and emergence relationships.