Friday, June 27, 2014

A Call for More Philosophy in the Philosophy of Computer Science

Delving more deeply into the current philosophy of computer science reinforces my view that enticing subjects have escaped exploration.  New concepts and artifacts have come out of computer science-- algorithms, data structures, parallel processing protocols, axiologies of programming practices, privacy and security concerns, and networking and communication designs have all emerged.  But the research that has been done on them, in great measure, treats them within their computational context rather than as objects of interest in their own right.  Shouldn't we apply philosophy to the phenomena that have been exposed by computing, as we apply philosophy to phenomena exposed by other social and scientific movements?

I have attempted to do that with regard to algorithms, as will be discussed at HaPoC 2014.  Some of these questions will have been addressed already-- I am not conversant with all research in the philosophy of computer science-- and I hope that alert readers will note that.

In metaphysics, for instance-- Do arrays exist in nature; what are the natural phenomena that correspond most closely to arrays (or linked lists, or other abstract data types that we find useful), and in what ways?  And what about the semantic web, which represents a major effort to establish a universal, or near-universal, ontology, shared among Web users for the benefit of all?  While considering the challenges of this symbolic work, the syntax and semantics, the tension between expressive and inferential power, we could also expand the metaphysical questions pertaining.  If we were to start from scratch to build an ontology of everything on paper, for people, and we were to start from scratch to build an ontology of everything in some data model, for a database, would they turn out to be the same?  Suppose, as seems likely, each effort fails at some point; would they have carved out the same portion of the universe to represent?  To what extent does a breakdown into entities, attributes, and relationships fit the real world?  What are the alternatives?  Is there some greater abstraction, some sort of category theory (computational or not), of information capture? 

And we can follow this path into novel issues of epistemology.  How about the epistemology of Web search?  What is search, anyway?  What type of doxastic re-structuring does a search result engender; how does finding an answer compare to learning a fact in some other way?  What type of knowledge acquisition in the panoply of epistemic theories best accounts for search?

We can even follow this path into aesthetics.  What is the nature of the satisfaction that comes from solving a symbolic problem, as in a game or a software design, and how does it relate to the appreciation of other arts?  Why is programming fun?  Why are there no elegant algorithms for calendar work (determing the day of the week for a given date, for example)?  Is it because our calendar is inherently ugly, and if so, in what sense-- because, developed  incrementally and ad hoc, the calendar doesn't fit any of the clean preferred patterns that we admire?  Do "hand-made" data structures somehow escape the digital abstractions that we have developed?  Or is it because the cycles of human-scale time are arbitrary?  But we view the irregularity of nature, with its spots, wrinkles, colors, shreds, and other unorganized details, as beautiful.  What does that say about us, and what does it say about the computational paradigm?

These questions might inspire student interest, as well as fostering respectable philosophical contributions based on scrutiny and interpretation of the computing phenomena that surround us.  Many of the papers that appear, on computational models of ethics (or action or explanation), or on information theory, or on intelligent agents, instead interpret philosophical questions in terms of symbolic computing.  There seems to be an inclination to set up a formal system to capture the concept, but the formal methods strip out some of the interesting material.  The approaches are complementary, and both should be pursued.

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