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In this paper the entropy functional is derived in the context of decision making under uncertainty. It is shown that the mathematical description of a decision problem can be partitioned into two parts: a probability distribution p and a convex set LN(A) that describes the remaining structure of the problem. The question of selecting a general representative form for this convex set is explored, and a set of assumptions is proposed that specifies a form for such a representative set LN. Corresponding to this set LN, the entropy of the probability p can be interpreted as an information measure with respect to decision problems. In addition, it is shown that if this set LN is substituted for the set LN(A) in a given decision problem, the use of the maximum entropy criterion for probability selection corresponds to a minimax solution to the representative form of the problem. To the extent that the set LN resembles the set LN(A) in a given decision problem, the use of the maximum entropy criterion for probability selection corresponds to a minimax criterion for decision making.