Modeling of Complex Systems

Description When we talk about modeling we're talking about quantitative modeling. The only way to really get a grasp on complex systems (i.e., with many moving parts) is to model the system (i.e., to conceptualize and quantitatively model the interactions across the many moving parts). We're looking for contributions in a causal analysis: for example diabetes may be caused by obesity, genetic factors, personal habits and perhaps type of diet. If we're looking to reduce diabetes, then on which of these factors do we concentrate our limited resources? We need a numeric evaluation of each of the potential causal factors in order to reach the best plan of action.
A … vision of [an] underlying unity illuminating nature and humankind is … [the] foundation of complexity studies. This claim for unity results from an [observation] that there are simple sets of mathematical rules that when followed by a computer give rise to extremely complicated, or rather, complex, patterns (here). The world also contains many extremely complicated patterns. Thus, in consequence it can be concluded that simple rules underlie many extremely complicated phenomena in the world. With the help of powerful computers scientists can root those rules out. Subsequently, at least some rules of complex systems could be unveiled.

Although such an approach has been criticized as being based on a seductive syllogism it appears that it still exists explicitly or implicitly in numerous works in … complexity research. Mesjasz, Czeslaw. Cracow University of Economics (Draft Version, 2009). Images of Organisation and Development of Information Society: Going into Metaphors

Modeling is a false seduction aka a Cheap Heuristic+. It seduces both the provider and the recipient of the model, but in fact there is no underlying validity to conclusions coming out of these models. They merely take credit for interocular+ differences, and exploit this credit in order to claim authority for the more marginal calls. The very act of mathematical modeling is filled with false generalizations and the confirmation bias+ of the modeler.  Horgan is quite eloquent in his demolition of this kind of thinking.

The very intricacy of the model precludes external inspection, and gives the modeler a sense of superiority that comes from having 'insider information.' Mathematical modeling of complex phenomena is rightly deemed a tyranny.

Following on the advice of Mosteller, let the mathematical modelers do what they do best: they might stumble across something useful. But their models are forcibly ejected from the decision making process. They corrupt the decision making process and are deemed inadmissible evidence+, with all the judicial protections needed to avoid corruption of the opinion of the jury (i.e., decision makers) from this evidence.

Appropriate Uses Anything Goes+! Let the modelers do their work within their own dark recesses, and if they come up with something interocular then we give credit where credit is due. This means we develop protections that keep modelers in their place: their output is inadmissible as evidence. Instead it is merely a pointer for further empirical research.


Further Reading