Knowledge underlies rational choice between options. Predictive optimization is the prioritization of options according to their predicted outcomes based on available knowledge. The epistemological justification of predictive optimization is based on positivism, which asserts that facts and laws about the world exist and are discoverable. However, knowledge of human affairs in strategic adversarial interactions is often severely limited and erroneous: residual uncertainty is often vast. This results especially from deception and innovation by the adversary which introduce deep Knightian uncertainty. Consequently, predictive optimization is unreliable: outcomes may differ substantially from predictions. An alternative strategy for prioritization of options is info-gap robust satisficing: achieve critical goals (that are adequate but perhaps suboptimal) over a wide range of deviation of reality from current knowledge. The epistemological justification of robust satisficing is based on extending positivism to acknowledge and manage the unknown. Prioritization of options by robust satisficing manages both the limitations of knowledge and the need for achieving critical goals. This critique of positivism is not constructivist. Rather, we extend positivism to account for highly deficient knowledge. We present several examples and conclude by discussing the relation between inductive, abductive and deductive inference.
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