Personal probabilities of probabilities

By definition, the subjective probability distribution of a random event is revealed by the (‘rational’) subject's choice between bets — a view expressed by F. Ramsey, B. De Finetti, L. J. Savage and traceable to E. Borel and, it can be argued, to T. Bayes. Since hypotheses are not observable events, no bet can be made, and paid off, on a hypothesis. The subjective probability distribution of hypotheses (or of a parameter, as in the current ‘Bayesian’ statistical literature) is therefore a figure of speech, an ‘as if’, justifiable in the limit. Given a long sequence of previous observations, the subjective posterior probabilities of events still to be observed are derived by using a mathematical expression that would approximate the subjective probability distribution of hypotheses, if these could be bet on. This position was taken by most, but not all, respondents to a ‘Round Robin’ initiated by J. Marschak after M. H. De-Groot's talk on Stopping Rules presented at the UCLA Interdisciplinary Colloquium on Mathematics in Behavioral Sciences. Other participants: K. Borch, H. Chernoif, R. Dorfman, W. Edwards, T. S. Ferguson, G. Graves, K. Miyasawa, P. Randolph, L. J. Savage, R. Schlaifer, R. L. Winkler. Attention is also drawn to K. Borch's article in this issue.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/43847/1/11238_2004_Article_BF00169102.pd

Information Structures in Stochastic Programming Problems

Problems of multi-stage decision under uncertainty are usually classified into "stochastic" and "adaptive" ones, depending on whether the decision maker does or does not know the relevant probability distribution. If the Bayesian approach is taken, then, in the adaptive case, the decision maker is assumed to know the prior distribution of certain parameters. It is shown in the paper that the adaptive case is then reducible to the stochastic one. The problems can also be classified according to the kind of information (memory) available to the decision maker. In the paper, optimal policies and the expected gains they yield, are determined for some important classes of "information structures." In cases in which added information does not increase the expected gain, a sufficient information structure is specified.