Here I seem to disagree with the author, and this disagreement has a profound impact on our respective view of the philosophical and technical foundations of DSSs. What experience provides is a knowledge of how pieces of information are related and a capacity to apply that knowledge efficiently. While empirical evidence suggests that experts indeed perform better than novices within their area of expertise, they also seem to be liable to the same judgmental biases as novices. And so, predictions of future violent behavior of psychiatric patients made by a panel of psychiatrists, who had access to patient records and interviewed the patients, were found to be inferior to a simple model that included only the past incidence of violent behavior. Similar studies have been conducted with bank loan officers, physicians, university admission committees, etc.
Experience alone seems to be too costly to rely on.
There are two philosophically distinct approaches to computer-based decision support. The danger of this approach, increasingly appreciated in the artificial intelligence community, is that along with imitating human thinking and its efficient heuristic principles we may imitate its undesirable flaws, empirically demonstrated in case of judgment and decision making under uncertainty. The second approach is based on the assumption that the soundest way of dealing with complex decisions is through a small set of normatively sound principles of how decisions should be made. The goal of a DSS, according to this view, is supporting unaided human intuition, just as the goal of using a calculator is aiding human's limited capacity for mental arithmetic. A normative theory of decision making that has gained the strongest ground is based on the subjectivist Bayesian view of probability theory and is known simply as decision theory.