Battle in the Planning Office: Biased Experts Versus Normative Statisticians

Abstract

Generally, rational decision-making is conceived as arriving at a decision by a correct application of the rules of logic and statistics. If not, the conclusions are called biased. After an impressive series of experiments and tests carried out in the last few decades, the view arose that rationality is tough for all, skilled field experts not excluded. A new type of planner’s counsellor is called for: the normative statistician, the expert in reasoning with uncertainty par excellence. To unravel this view, the paper explores a specific practice of clinical decision-making, namely Evidence-Based Medicine. This practice is chosen, because it is very explicit about how to rationalize practice. The paper shows that whether a decisionmaking process is rational cannot be assessed without taking into account the environment in which the decisions have to be taken. To be more specific, the decision to call for new evidence should be rational too. This decision and the way in which this evidence is obtained are crucial to validate the base rates. Rationality should be model-based, which means that not only the isolated decision-making process should take a Bayesian updating process as its norm, but should also model the acquisition of evidence (priors and tests results) as a rational process.