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A.P. Dempster 277can easily happen that two men of good faith, in complex matterswhere they possess exactly the same elements of information, arriveat different conclusions on the probabilities of an event, and that inbetting together, each believes... that it is he who is the thief and theother the imbecile.” (Borel, 1924)Atypicalmodernapplicationinvolveschoicesamongdifferentinvestmentopportunities, through comparisons of DS posterior lower expectations of futuremonetary gains for buyers or losses for sellers, and for risk-taking brokerswho quote bid/ask spreads while bringing buyers and sellers together. Formathematical statisticians, the field of DS decision analysis is wide open forinvestigations of models and analyses, of interest both mathematically andfor practical use. For the latter in particular there are many potentially usefulmodels to be defined and studied, and to be implemented numerically bysoftware with acceptable speed, accuracy, and cost.A third area of potential DS topics for research statisticians concernsmodeling and inference for “parametric” models, as originally formulated byR.A. Fisher in his celebrated pair of 1920s papers on estimation. The conceptof a statistical parameter is plagued by ambiguity. I believe that the termarose in parallel with similar usage in physics. For example, the dynamics ofphysical systems are often closely approximated by classical Newtonian physicallaws, but application of the laws can depend on certain “parameters”whose actual numerical values are left “to be determined.” In contemporarybranches of probabilistic statistical sciences, stochastic models are generallydescribed in terms of parameters similarly left “to be determined” prior todirect application. The mathematics is clear, but the nature of the task ofparameter determination for practical application is murky, and in statisticsis a chief source of contention between frequentists and Bayesians.In many stochastic sampling models, including many pioneered by Fisher,parameters such as population fractions, means, and standard deviations, canactually represent specific unknown real world population quantities. Oftentimes,however, parameters are simply ad hoc quantities constructed on thefly while “fitting” mathematical forms to data. To emphasize the distinction,I like to denote parameters of the former type by Roman capital letters such asP , M, and S, while denoting analogous “parameters” fitted to data by Greekletters π, µ and σ. Thedistinctionhereisimportant,becausetheparametersof personalist statistical science draw on evidence and utilities that canonly be assessed one application at a time. “Evidence-based” assumptions, inparticular, draw upon many types of information and experience.Very little published research exists that is devoted to usable personalistmethodology and computational software along the lines of the DS standardprotocol. Even the specific DS sampling model for inference about a populationwith k exchangeable categories that was proposed in my initial 1966paper in The Annals of Mathematical Statistics has not yet been implementedand analyzed beyond the trivially simple case of k = 2. I published a briefreport in 2008 on estimates and tests for a Poisson parameter L, whichisa