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We deene and exercise the expected value of computation as a fundamental component of reeection about alternative inference strategies. We present a portion of Protos research focused on the interlacing of reeection and action under scarce resources, and discuss how the techniques have been applied in a high-stakes medical domain. The work centers on endowing a computational agent with the ability to harness incomplete characterizations of problem-solving performance to control the amount of eeort applied to a problem or subproblem, before taking action in the world or turning to another problem. We explore the use of the techniques in controlling decision-theoretic inference itself, and pose the approach as a model of rationality under scarce resources. Reeection about the course of problem solving and about the interleaving of problem solving and physical activity is a hallmark of intelligent behavior. Applying a portion of available reasoning resources to consider the utility of alternative inference strategies or the value of continuing to reene a result before acting enables a computational agent to generate custom-tailored approaches to a wide variety of problems, under diierent time pressures. Such exibility can be especially useful in light of uncertain deadlines and challenges. Uncertainty about problems and problem solving plagues simple agents immersed in complex environments. Constraints on an agent's reasoning and representation resources lead to inescapable uncertainties about the problems that may be faced and about the value of future reasoning in solving those problems. The Protos project has pursued the use of decision theory for real-time control and ooine problem-solving design. The work has highlighted opportunities for the principled control of reasoning under scarce resources with problems in sorting and searching and with decision-theoretic inference itself Horvitz, 1987a]. We have particularly dwelled on the decision-theoretic control of decision-theoretic inference as a model of rational computational inference under resource constraints. In this paper, we present a component of this work centering on the use of incomplete characterizations of the progression of probabilistic inference to reason about the value of continuing to reeect about a problem versus taking action in the world. This methodology uses knowledge that partially characterizes relevant dimensions of problem-solving performance. Such knowledge can be learned and reened with experience. We shall introduce components of utility for computational or real-world actions, and deene the expected value of computation in terms of the likelihood of future probability distributions over the truth of relevant propositions about the …