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We employ simulated annealing to identify the global solution of a dynamicalmodel, to make a favorable impression upon colleagues at the departmentalholiday party and then exit undetected as soon as possible. The procedure,Gradual Freeze-out of an Optimal Estimation via Optimization of ParameterQuantification - GFOOEOPQ, is designed for the socially awkward. The sociallyawkward among us possess little instinct for pulling off such a maneuver, andmay 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 currentknowledge of the model. Here, model state vectors are party attendees, and thefuture event of interest is their disposition toward us at times following theparty. We want these dispositions to be favorable. To this end, we firstinteract so as to make favorable impressions, or at least ensure that thesepeople remember having seen us there. Then we identify the exit that minimizesthe chance that anyone notes how early we high-tailed it. Now, poorly-resolvedestimates will correspond to degenerate solutions. As noted, we possess noinstinct to identify a global optimum by ourselves. This can have disastrousconsequences. For this reason, GFOOEOPQ employs annealing to iteratively homein on this optimum. The method is illustrated via a simulated event hosted bysomeone in the physics department (I am not sure who), in a two-bedroomapartment on the fifth floor of an elevator building in Manhattan, with viableExit parameters: front door, side door to a stairwell, fire escape, and abathroom window that opens onto the fire escape. Preliminary tests are reportedat two real social celebrations. The procedure is generalizable to corporateevents and family gatherings. Readers are encouraged to report novelapplications of GFOOEOPQ, to expand the algorithm.

An Artificially-intelligent Means to Escape Discreetly from the Departmental Holiday Party; guide for the socially awkward