Abstract
The ability to formalize problems in a quantitative manner is the key to predictive power. We characterize a lack of formality as unruliness, relate unruliness as a property of un(3) (undecidability, uncomputability, and unpredictability), and define a class of problems which even when well-posed remain highly informal in nature. Despite this lack of formalism, systems represented by these problems still exhibit significant structure. We call this class of problems hard-to-represent, and are characterized by the difficulties of quantification and symbolization, as well as the inherent un-physicality of a system. A significant part of this difficulty involves both finding the proper metaphor for such systems and a method for analyzing the system components. To counter these difficulties, we propose a new analytical paradigm called perceptual analysis, which brings an umbrella of diverse approaches to bear. These include neural-inspired modeling, visualization-based feature selection, and soft computation, which provide an alternate means to quantify features and discover structure in a manner that is less dependent on traditional mathematical presumptions.
Bradly John Alicea