Political science viewed as a Neural Net :-)

[Scott.Fahlman@B.GP.CS.CMU.EDU]

I was thinking about the upcoming U.S. election today, and it occurred to
me that the seemingly useless electoral college mandated by the U.S.
constitution might actually be of some value.  A direct democratic election
is basically a threshold decision function with lots of inputs and with
fixed weights; add the electoral college and you've got a layered network
with fifty hidden units, each with a non-linear threshold function.

A direct election can only choose a winner based on some linearly separable
function of voter opinions.  You would expect to see complex issues
forcibly projected onto some crude 1-D scale (e.g. "liberal" vs.
"conservative" or "wimp" vs. "macho").  With a multi-layer decision network
the system should be capable of performing a more complex separation of the
feature space.  Though they lacked the sophisticated mathematical theories
available today, the designers of our constitution must have sensed the
severe computational limitations of direct democracy and opted for the more
complex decision system.  Unfortunately, we do not seem to be getting the
full benefit of this added flexibility.

What the founding fathers left out of this multi-layer network is a
mechanism for adjusting the weights in the network based on how well the
decision ultimately turned out.  Perhaps some form of back-propagation
would work here.  It might be hard to agree on a proper error measure, but
the idea seems worth exploring.  For example, everyone who voted for Nixon
in 1972 should have the weight of his his future votes reduced by epsilon;
a large momentum term would be added to the reduction for those people who
had voted for Nixon previously.  The reduction would be greater for voters
in states where the decision was close (if any such states can be found).

There is already a mechanism in place for altering the output weights of
the hidden units: those states that correlate positively with the ultimate
decision end up with more political "clout", then with more defense-related
jobs.  This leads to an influx of people and ultimately to more electoral
votes for that state.  Some sort of weight-decay term would be needed to
prevent a runaway process in which all of the people end up in California.

We might also want to add more cross-connections in the network.  At
present, each voter affects only one hidden unit, the state where he
resides.  This somewhat limits the flexibility of the learning process in
assigning arbitrary functions to the hidden units.  To fix this, we could
allow voters to register in more than one state.  George Bush has five or
six home states; why not make this option available to all voters?

More theoretical analysis of this complex system is needed.  Perhaps NSF
should fund a center for this kind of thinking.  The picture is clouded by
the observation that individual voters are not simple predicates: most of
them have a rudimentary capacity for simple inference and in some cases
they even exhibit a form of short-term learning.  However, these minor
perturbations probably cancel out on the average, and can be treated as
noise in the decision units.  Perhaps the amount of noise can be
manipulated to give a crude approximation to simulated annealing.

-- Scott