Forget Match.com.  Already on Facebook, arguably the most popular social networking site (SNS), there is an option called "suggest a friend," which uses statistic-based formulas to try and figure out who you know or should know in real life, and should therefore befriend on Facebook.  What's this model based off of? Mostly just numbers of common friends and basic information such as what university in what city you each attend.

    The potential this holds for the augmentation of love and (encodable definition: love - sex =) friendship continues to build as digitalization progresses. Using something familiar: The Netflix model (NM) of movie recommendation and predictive enjoyment is an amazing creature. First, you watch movies, then you give them a rating, which is logged into your personal profile. As this database begins to form, Netflix starts to get an idea of what type of movies you like, based off of your ratings and what "viewers like you" enjoy.  This allows the service to suggest selections, and even predict what rating you will give a prospective movie before the fact. It's all statistics that improve as the scope of your ratings increases. Therefore it is beneficial for both the service (like the WModel) as well as the individual for him/her to spend time honestly giving his/her opinions about movies. Users grow, service improves, and the system works. Often, out of stubbornness and distrust, I will select something that Netflix thinks is a bad idea, and more often than not it is this abstract formula that laughs last (as I give a low rating, as was predicted). 
    Lets imagine the NM were to be integrated into the "suggest a friend" as well as the newest Facebook installment "suggest a date". What type of ratings would be used? ratings of what? This presents a small problem, but one that is becoming increasingly insignificant as new web browsers and services (Chrome, del.icio.us, Evernote) collect meta-data as users surf the web. Many media based sites like youtube already implement their own rating systems ("viewers like you would probably enjoy the following videos...") but these new browsing services allow bookmarks with rating systems on almost any page on the internet. Intelligent filters could match similar articles and video/song locations (multiple locations) to understand that they correspond with each other or are similar.  What I mean is that an individual profile could be developed as a user rated each piece of media, literature, art, music, etc., he/she encountered online. If integrated into something like Facebook, this profile could be used to algorithmically suggest friends.  The formula would look something like this:

Friendship compatibility factor (FCF) = geographic proximity +/- age difference +/- political stance +/- interests (profile) +/- hobbies (profile) +/- opinions of "friends like you" +/- race +/- language [maybe not so much anymore...........] +/- other   

What about love? Your meta-data tracker (macker) could also record what type of faces/bodies you find physically attractive in both sexes, like a version of the joke hotornot.com gone horribly serious.  This might sound far fetched, but already "experiments suggest that a computer can use geometry to predict whether or not a face is attractive" (Highfield). Like everything here, the statistical NM makes everything more simplistic, as "voyeurs like you" will give ratings to the same pretty faces, and thus will establish a statistical base. With that in mind:

Date compatibility factor (DCF) = already established friendship compatibility factor +/- predicted sexual attraction (shallowness profile)  +/- opinions of "lovers like you"

We might laugh, claiming that love and friendship are too haphazard and unpredictable for any formula. I predict however, that like Netflix, resistance may be futile, contrived, and forced. It is nothing special that this type of service gives you a prediction of how well you will like something, but what is scary, is that it is almost always correct. 
    However this is perhaps too simplistic, as the test is far from double-blind.  The second I am told how well I will like something, there is a type of challenge that I am aware of while consuming whatever it is that is in question. It is a feedback loop that might positively or negatively effect my opinion of said item.  Far from calming however, what does this mean when the subjects are interpersonal relationships instead of songs and DVDs?  I'm not even close to prepared to answer that, but with Netflix I've already become weary enough of items poorly ranked in my profile that I avoid them like the plague.....that is unless someone who's opinion I greatly respect made the suggestion: DVD selectability factor = Netflix prediction +/- [(opinion) x (respect factor of the person who gave it)].  Sadly, this could also be placed into the equation. Greater statistical weight could be placed on the opinions of "viewers you trust".   If there is a formula to something, it can and will be cracked, digitalized, and placed into the larger equation- no matter how "organic" it once was.
    What each of these models shows, is an extension of BC and the combination of analog with digital.  Enjoyment would generally be considered something analog, yet by artificially quantifying it and the factors involved, we are able to (pre)determine an outcome. Analog enjoyment is given a digital number which is used to select something else which delivers analog enjoyment.  How much more significant is this process as it is applied to more important aspects of life. Perhaps this will prove to benefit the greater good and efficiency of the race: "By 'augmenting human intellect' we mean increasing the capability of a man to approach a complex problem situation, to gain comprehension to suit his particular needs, and to derive solutions to problems...and the possibility of finding solutions to problems that before seemed insoluble (see digression 5)" (Engelbart, 95). Or perhaps the next generation is doomed to a statistical madhouse where people constantly allocate different amounts of stars to their friends and loved ones like a Lear-ish figure raising and lowering inheri