On the foundations of decision making under partial information

We show how to model incompleteness in the decision maker's judgements, within a Bayesian context, providing axioms which lead us to work with families of values or probabilities and utilities. The proper solution concepts are suggested. On the whole, we provide a more robust decision theory, based on a weaker set of axioms, but embodying coherence, since it essentially implies carrying out a family of coherent decision analyses.

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