A Conditional Expected Utility Model for Myopic Decision Makers

This article formulates and axiomatizes a conditional expected utility model that allows a decision maker to specify his actions in the form of partial rather than complete contingency plans and to simultaneously choose goals and actions in end-mean pairs. Both utility and probability are conditioned on selected goals and actions (g,a), and both are defined over the same set of possible (g,a)-conditioned events. For adaptive sequential decision problems, this symmetrical treatment of utility and probability permits agents to learn via "criterion filtering." That is, the expected utility criterion function can be directly updated in each decision period via transitional utility assessments in a manner analogous to Bayes' rule for updating probability distributions via transitional probability assessments. Annotated pointers to related work can be accessed here: http://www.econ.iastate.edu/tesfatsi/cfhome.htm