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Summary Most decision analyses include continuous uncertainties (e.g., oil in place, oil price, or porosity). Analysts are frequently concerned with how to best structure, compute, and communicate decision models under these circumstances. While decision trees are well suited for discrete random variables with a few possibilities, they become unmanageable for a large number of outcomes. To address this concern, analysts frequently use discrete approximations such as Swanson's Mean. Previous work has quantified how well differing discretization methods match the moments (e.g., the mean and variance) of the underlying continuous distribution. More specifically, previous work has not included the decision context in which the discretizations are used. In this paper, we begin to address this gap by comparing different discretizations within the context of an information-gathering decision problem. We find that the best discretization is highly dependent on the decision context, which is difficult to specify in advance. In addition, we contrast the use of discrete approximations to Monte Carlo simulation.
Summary The combination of computer technology and decision theory has failed to produce the dramatic improvement in the quality of corporate decisions that some predicted. In this paper, I suggest that this failure comes, at least in part, because too little attention has been paid to the environment in which corporate decisions are made. Specifically, this article proposes that corporate decision processes are not constructivist rationality, but more closely resemble the ecological rationality of biological systems shaped by evolution and natural selection. That is, decision processes in corporations evolve over time to satisfy, although not necessarily to optimize, both stated and unstated objectives subject to complex information cost structures and constraints. To illustrate, examples of persistent information asymmetry and economic signaling in nature and in decision processes are shown. Finally, this article suggests that improved understanding of the forces that impact the evolution of corporate decision processes may allow decision theory to come closer to achieving its potential.