Decision Support Tools and Two Types of Uncertainty Reduction

Grand Rationality is dead, long live Reason! The 1960's saw the heydays of Grand Rationality in social decision-making. The planning ideal of that period has been termed " comprehensive rational approach " (Benveniste, 1989), " design school " (Mintzberg, 1994), or " technical rationality " (Schön, 1991). That ideal of rationality crumbled, much due to its inherent weaknesses. Is there at present any hope for decision support tools that does not strand on the same reefs that the rationality movement of the 1960's? Yes, research about human knowledge, social reasoning processes, and software development has come a long way since then. The cognitive sciences have parted ways with mathematics, logic, physics, and operational analysis that formed the basis for modelling rationality in the 1960's. Extrapolating from Herbert Simon's notion of " bounded rationality " , the emphasis of research has shifted from mathematics to cognition. One direction focuses on the limitations of unaided human decision processes. For instance, research in heuristics and biases, clinical reasoning, and dynamic decision-making indicates that human reasoning normally falls far short of ideal standards (Dawes, 1988; Plous, 1993). Another direction focuses on possible shortcuts in reasoning that take us a long way towards ideal standards with very simple tricks, so-called " fast and frugal heuristics " (Gigerenzer, Todd et al., 1999). An important conclusion on which these directions agree, is that human decision-making can improve via small, but well directed cognitive effort. We know so preciously little of many relevant aspects of the future that we do not have data to feed into the encompassing models that formed the rationalist ideals of the 1960's. But we can still have simple, robust decision procedures that will substantially improve on our decisions while feeding only on meagre or deficient data. Since the 1960's, studies of social and political decision-making have informed us of the many factors and facets of real life decision-making. Decision-making is an integral part of ongoing processes, torn between power struggle, implementation agencies, and grassroots' interference. The grand master plans of the 1960's more or less took it for granted that planning could be an enterprise for staff functions, well isolated from the turmoil of ongoing organizational processes. Not so anymore. The software revolution of the 1990's has turned the tables on the expert model of the 1960's. A minor factor is the computational power that shifts massive capacities for calculation from the Central …