  We employ simulated annealing to identify the global solution of a dynamical
model, to make a favorable impression upon colleagues at the departmental
holiday party and then exit undetected as soon as possible. The procedure,
Gradual Freeze-out of an Optimal Estimation via Optimization of Parameter
Quantification - GFOOEOPQ, is designed for the socially awkward. The socially
awkward among us possess little instinct for pulling off such a maneuver, and
may benefit from a machine to do it for us. The method rests upon Bayes'
Theorem, where the probability of a future model state depends on current
knowledge of the model. Here, model state vectors are party attendees, and the
future event of interest is their disposition toward us at times following the
party. We want these dispositions to be favorable. To this end, we first
interact so as to make favorable impressions, or at least ensure that these
people remember having seen us there. Then we identify the exit that minimizes
the chance that anyone notes how early we high-tailed it. Now, poorly-resolved
estimates will correspond to degenerate solutions. As noted, we possess no
instinct to identify a global optimum by ourselves. This can have disastrous
consequences. For this reason, GFOOEOPQ employs annealing to iteratively home
in on this optimum. The method is illustrated via a simulated event hosted by
someone in the physics department (I am not sure who), in a two-bedroom
apartment on the fifth floor of an elevator building in Manhattan, with viable
Exit parameters: front door, side door to a stairwell, fire escape, and a
bathroom window that opens onto the fire escape. Preliminary tests are reported
at two real social celebrations. The procedure is generalizable to corporate
events and family gatherings. Readers are encouraged to report novel
applications of GFOOEOPQ, to expand the algorithm.
