Jes Parent

Embodied & Diverse Intelligences: Development, Learning & Evolution across Biological, Cognitive, and Artificial Domains


Curriculum vitae


jeparent [@] ucsd.edu


1. Halıcıoğlu Data Science Institute, UCSD, San Diego CA

2. Cognition Futures, OREL, Boston MA



The illusion of structure or insufficiency of approach? the un(3) of unruly problems


Miscellaneous


Bradly Alicea, Jesse Parent, Ankit Gupta
2021

Semantic Scholar
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Cite

APA   Click to copy
Alicea, B., Parent, J., & Gupta, A. (2021). The illusion of structure or insufficiency of approach? the un(3) of unruly problems.


Chicago/Turabian   Click to copy
Alicea, Bradly, Jesse Parent, and Ankit Gupta. “The Illusion of Structure or Insufficiency of Approach? the Un(3) of Unruly Problems,” 2021.


MLA   Click to copy
Alicea, Bradly, et al. The Illusion of Structure or Insufficiency of Approach? the Un(3) of Unruly Problems. 2021.


BibTeX   Click to copy

@misc{bradly2021a,
  title = {The illusion of structure or insufficiency of approach? the un(3) of unruly problems},
  year = {2021},
  author = {Alicea, Bradly and Parent, Jesse and Gupta, Ankit}
}

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.