We have been oversold on the base rate fallacy in probabilistic judgment from an empirical, normative, and methodological standpoint. At the empirical level, a thorough examination of the base rate literature (including the famous lawyer–engineer problem) does not support the conventional wisdom that people routinely ignore base rates. Quite the contrary, the literature shows that base rates are almost always used and that their degree of use depends on task structure and representation. Specifically, base rates play a relatively larger role (...) in tasks where base rates are implicitly learned or can be represented in frequentist terms. Base rates are also used more when they are reliable and relatively more diagnostic than available individuating information. At the normative level, the base rate fallacy should be rejected because few tasks map unambiguously into the narrow framework that is held up as the standard of good decision making. Mechanical applications of Bayes's theorem to identify performance errors are inappropriate when (1) key assumptions of the model are either unchecked or grossly violated, and (2) no attempt is made to identify the decision maker's goals, values, and task assumptions. Methodologically, the current approach is criticized for its failure to consider how the ambiguous, unreliable, and unstable base rates of the real world are and should be used. Where decision makers' assumptions and goals vary, and where performance criteria are complex, the traditional Bayesian standard is insufficient. Even where predictive accuracy is the goal in commonly defined problems, there may be situations (e.g., informationally redundant environments) in which base rates can be ignored with impunity. A more ecologically valid research program is called for. This program should emphasize the development of prescriptive theory in rich, realistic decision environments. (shrink)

I agree with Gibbs that the message of the base rate literature reads differently depending on which null hypothesis is used to frame the issue. But I argue that the normative null hypothesis, H0: “People use base rates in a Bayesian manner,” is no longer appropriate. I also challenge Adler's distinction between unused and ignored base rates, and criticize Goodie's reluctance to shift research attention to the field. Macchi's arguments about textual ambiguities in traditional base rate problems suggest that empirical (...) testing is needed to tease apart the effects of problem clarification and problem framing. Macdonald's, Fletcher's and Snow's skepticism about the value of Bayesian methods in real world judgment tasks is treated as a challenge for the next generation of empirical base rate studies. (shrink)

We accept Sunstein's claim that people often use moral heuristics to make judgments and decisions. However, in situations that include a risk of betrayal, we disagree with Sunstein about when the relevant moral heuristic may be said to “misfire.” We suggest that the moral heuristic people apply to avoid the possibility of safety-product betrayal may be reasonable.

Commentators agree that simple conclusions about a general base rate fallacy are not appropriate. It is more constructive to identify conditions under which base rates are differentially weighted. Commentators also agree that improving the ecological validity of the research is desirable, although this is less important to those interested exclusively in psychological processes. The philosophers and ecologists among the commentators offer a kinder perspective on base rate reasoning than the psychologists. My own perspective is that the interesting questions (both psychological (...) and normative/prescriptive) are best answered through empirical studies that use real-world performance standards. (shrink)