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13Statistics in service to the nationStephen E. FienbergDepartment of StatisticsCarnegie Mellon University, Pittsburgh, PA13.1 IntroductionLet me begin with a technical question:Who first implemented large-scale hierarchical Bayesian models, when,and why?I suspect the answer will surprise you. It was none other than John Tukey, in1962, with David Wallace and David Brillinger, as part of the NBC ElectionNight team. What I am referring to is their statistical model for predictingelection results based on early returns; see Fienberg (2007).The methods and election night forecasting model were indeed novel, andare now recognizable as hierarchical Bayesian methods with the use of empiricalBayesian techniques at the top level. The specific version of hierarchicalBayes in the election night model remains unpublished to this day, butTukey’s students and his collaborators began to use related ideas on “borrowingstrength,” and all of this happened before the methodology was describedin somewhat different form by I.J. Good in his 1965 book (Good, 1965) andchristened as “hierarchical Bayes” in the classic 1970 paper by Dennis Lindleyand Adrian Smith (Lindley and Smith, 1972); see also Good (1980).I was privileged to be part of the team in 1976 and 1978, and there wereclose to 20 PhD statisticians involved in one form or another, working inCherry Hill, New Jersey, in the RCA Lab which housed a large mainframecomputer dedicated to the evening’s activities (as well as a back-up computer),and a few in New York interacting with the NBC “decision desk.” The analystseach had a computer terminal and an assignment of states and politicalraces. A summary of each run of the model for a given race could be read easilyfrom the terminal console but full output went to a nearby line printer andwas almost immediately available for detailed examination. Analysts workedwith the model, often trying different prior distributions (different past electionschosen as “models” for the ones for which they were creating forecasts)141