ABSTRACT
In this chapter, we outline major stages in an ongoing attempt to model the dynamics of polarization. The emphasis throughout is on the role of conceptual issues in computational modeling, firmly in a long tradition of philosophical analysis. In order to model opinion polarization, it turns out, one must distinguish between, and construct measures for, nine very different senses of polarization
that appear in the literature. The model we construct is used to illustrate both overlaps and distinctions between those different senses. We think this case has immediate implications for the study of polarization across various fields. We hope it may also serve as an example of the importance of conceptual analysis within computational modeling generally. 1.1 IntroductionComputational modeling and computer simulation have quickly established themselves not merely as useful add-ons, but as core tools across the range of both the physical and social sciences. We consider computational modeling to be a promising approach to a range of philosophical questions as well, and to questions that sit on the border between philosophy and other disciplines [1-5]. Questions regarding the transference of belief, social networks, and opinion polarization fall in the latter category, bridging epistemology, social philosophy, sociology, political science, network studies and complex systems. These are the focus of our current research.Our purpose here is not to sing the praises of computational modeling as a new philosophical technique. Our purpose is rather to emphasize the continuity of computational model-building with the long philosophical tradition of conceptual analysis [6-8]. With reflections from the process of building a specific model, we want to emphasize two points: (1) the work of constructing a computational model can serve the philosophical ends of conceptual understanding, in part because (2) attempts at computational modeling often require clarification of the core concepts at issue.Our long-range goal is an agent-based model adequate to the social dynamics evident in data on opinion polarization from social psychology to political science. Agents are initially connected in a random network and randomly placed on a spectrum of opinion modeled on the [0,1] continuum, updating their views by a weighted averaging of those they trust, close to them in opinion. Those to which he is connected who are closer to an agent’s current view and thus more trusted prove more influential; those farther away less so. In this regard our model extends that of Hegselmann and Krause [9], though employing a more realistically random rather
than complete network structure and using degrees of influence in place of an artificially sharp threshold. Though the final form of our model does show the emergence of certain types of polarization given certain scalings of trust, our emphasis here is on the conceptual distinctions and decisions crucial throughout the modeling process itself. 1.2 Computational Modeling and
In their final form, papers in scientific computational modeling always look perfect: They appear to be the work of a rational investigator who thought things through step by step in advance: from methods, to results, to discussion and conclusion. It is appropriate that these papers look that way-beneficial with regard to brevity, evaluation, and use in future work. That is how we want our work on belief networks and polarization to look eventually.Of course, the polished published form of a paper can give an entirely misleading impression of the research trajectory-the impression that both the conceptual work at issue and the path of design and programming were neat, tidy, and foreordained. Almost inevitably, they were not. We will use our current work in progress as an example. Here, unlike its future final form, we will lay out the research in something more like real time, complete with fits, starts, and second thoughts. A key point is that those fits, starts, and second thoughts often indicate the need for philosophical analysis in a fully traditional sense. Computational modeling calls for and enforces a full and explicit conceptual understanding of what it is one is trying to model. To employ computational techniques, one must have such an understanding of what it is one is trying to find out, within which parameters, with which background assumptions, and why.
