While not surprising, many of the folks reading this blog will be too young to have seen the debut of this motion picture on the big screen as Matthew Broderick’s portrayal of an archetypical hacker and acoustic modem rake it in at the box-office. However this “game” is a little different in that the likes of Seal Team Six needs your help, and it’s not with Bin Laden as that’s old news, as our friends the seals are now hanging around off the African Coast messing with those nasty Somali Pirates who seem to lack the charisma and big screen appeal of Johnny Depp.
Whereas this game is actually sponsored by the US Military as a way to uncover potential solutions to the real world dilemma of the pirating taking place off the Ivory Coast by employing crowd sourcing as a 21st tool-set for complex problem solving. Working much like a “wet wired” version of SETI@Home, where a gaming interface connects many players allowing for the collaborative solutioning of various scenarios taken from real life. The results of this “game play” is evaluated by the computer based upon a series of undisclosed criteria to rate the players success and thus their applied methods to achieve it.
One of the aspects of this “play” that grabbed my interest was the potential for the scenarios to “refine” themselves as in most current crowd sourcing endeavors to date; the “interaction” has been mono-directional as once end game is reached then, its game over. As I’ve blogged in the past about a start up crowd sourcing company where the designs of it’s custom cars are created by the “crowd” as many may contribute to the work product. In the end, the “work product” from this example doesn’t add back into the creation scenario itself.
In “game theory” problems such as these are referred to as “landscapes” and typically are defined with a modifier which describes it’s rigidity or elasticity. For a problem such as our pirating dilemma, we would say this is a “dancing” landscape as it’s almost indefinable unlike say a chess game where there are a limited number of pieces, all of which have predefined roles and a finite space to operate within. Therefore this landscape is static as it can be wholly defined by calculating all possible combinations, which isn’t possible with our desperate pirate scenario.
However this form of collaborative interaction may in turn solve this issue as the old adage goes “to catch a crook, you have to think like a crook” and this gaming model allows for this in a “sandbox” environment. Thus as the “landscape” dances, so does the game play and without glazing your eyes over, there tends to be a coalescence of order though harmonics and good example of this was performed about a decade or so back at the MIT-AI lab where they took six independent servo powered legs which would fire independently and connected them together forming a crude mechanical centipede if you will.
After activating all six units, the make shift bug flopped around as one would expect, for several minutes then to the researchers amazement, the bug developed a “rough order” and began walking. In short order emerged out of chaos and in the preceding decade there has been quite a bit of work to understand the relationship between order and chaos, however for this post, the point is this very game play may usher in a new way to seek order out of deep chaos helping us solve for some very complex social issues placing us even closer to Ray Kurzweil’s famed “singularity“…