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Social Networking and the Curse of Aristotle

The recent release and early rapid growth of Google+ has mostly been a direct consequence of social networking privacy concerns—with the Circles functionality being the key distinguishing feature versus Facebook.  Circles allows for a somewhat easier categorization of people with whom you would like to share (and gratefully only you see the categorizations in which you place people!).

What people rapidly find as their connections and number of Circles or Facebook Lists grow, however, is that the core issue isn’t so much privacy per se, but the ability to effectively and efficiently categorize at scale. A good perspective on this is Yoav Shoham’s recent blog on TechCrunch about the difficulties of manual categorization and his experience trying to categorize 300+ friends on Facebook. Circles is susceptible to the same problem—it just makes it easier and faster to run head-long into the inevitable categorization problem.

A root cause of the problem, as I harp on in The Learning Layer, rests with that purveyor of what-seems-to-be-common-sense-that-isn’t-quite-right, Aristotle.  Aristotle had the notion that an item must either fit in a category or not.  There was no maybe fits, sort of fits, or partly fits for Aristotle.  And Google+ (like Facebook and most other social networks) only enables you to compartmentalize people via the standard Aristotelian (i.e., “crisp”) set. A person is either fully in a circle/list/group or not—there is no capacity for partial inclusion.

But our brain more typically actually categorizes in accordance with non-Aristotelian, or “fuzzy” sets—that is, a person may be included in any given set by degree.  For example, someone may be sort of a friend and also a colleague, but not really a close friend, another person can be a soul mate, another mostly interested in a mutually shared hobby, etc. Sure, there are some social categories that are not fuzzy—either a person was your 12th grade classmate or not—but since non-fuzzy sets are just a special case of the more generalized fuzzy sets, fuzzy sets can gracefully handle all cases. So fuzzy sets have many advantages and this type of categorization naturally leads to fuzzy network-based structures, where relationships are by degree.  (The basic structure of our brain, not surprisingly, is a fuzzy network—the structure I therefore call “the architecture of learning” in The Learning Layer.)

But an issue with implementing in a system the reality of our social networks as fuzzy networks is that it can be hard to prescribe ahead of time sharing controls for fuzzy relations.  If we actually bothered to decide on an individual basis as to whether to share a specific item of content or posting, we would naturally do so on the basis of our nuanced, fuzzy relationships.  But that, or course, would take some consideration and time to do.

So the grand social networking bargain seems to be that for maximum expedience we either resign ourselves to share everything with everyone (what most people do on Facebook), or we employ coarse-grained non-fuzzy controls (e.g., Circles, Lists) that are a pain to set up, imprecise, and don’t scale.  Or there is another option—we cast Aristotle aside and establish and/or let the system establish a fuzzy categorization and then let our system learn from us to become an intelligent sharing proxy that shares as we would if we had time to consider fully each sharing action.  That will, of course, require trusting the system’s learning, which will necessarily have to be earned.  But ultimately that approach and the sharing everything with everyone are the only two alternatives that are durable and will scale.