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Decision research has revealed a variety of adaptive strategies that experts use when making decisions; however, there is no widely accepted model of how experienced decision makers choose such a strategy to solve a particular decision problem. Within most decision-making models that include a selection mechanism, decision strategies are selected according to cost-benefit trade-offs. These models assume that the selection is based on an evaluation of the subjectively expected utility of a correct decision and the effort the decision maker is willing to make in the situation at hand. In opposition, there are research findings showing that proficient decision makers mainly seem to select strategies based on recognition of the decision situation and a history of successful applications of a certain strategy. In this context I discuss findings from a field study in production planning and scheduling that are contrary to predictions from cost-benefit models. In accordance with recent scholarly work on routine decisions, I suggest that decision-strategy selection mechanisms based on recognition are a valid theoretical background for the design of future decision support systems. Accordingly, the cognitive engineering focus would shift from accuracy maximization and effort reduction to situational differentiation and strategy learning.

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