Essay 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.
Author Bio
Bradly Alicea, Jesse Parent, and Ankit Gupta are all collaborators at the Orthogonal Research and Education Lab. As head of the group, Bradly has a PhD from Michigan State University and an interdisciplinary background with a diverse set of interests. Jesse and Ankit are post-bac and undergraduate scholars, respectively, with interests spanning Cognitive Science, Neuroscience, Complex Systems, and Artificial Intelligence. Check out Orthogonal Lab on the web: http://orthogonal-research.weebly.com