Heuristic and linear models of judgment: Matching rules and environments.

Abstract

Much research has highlighted incoherent implications of judgmental heuristics, yet other findings have demonstrated high correspondence between predictions and outcomes. At the same time, judgment has been well modeled in the form of as if linear models. Accepting the probabilistic nature of the environment, the authors use statistical tools to model how the performance of heuristic rules varies as a function of environmental characteristics. They further characterize the human use of linear models by exploring effects of different levels of cognitive ability. They illustrate with both theoretical analyses and simulations. Results are linked to the empirical literature by a meta-analysis of lens model studies. Using the same tasks, the authors estimate the performance of both heuristics and humans where the latter are assumed to use linear models. Their results emphasize that judgmental accuracy depends on matching characteristics of rules and environments and highlight the trade-off between using linear models and heuristics. Whereas the former can be cognitively demanding, the latter are simple to implement. However, heuristics require knowledge to indicate when they should be used.

Adaptive Mechanisms for Treating Missing Information: A Simulation Study

“…Thus, WADD's advantage of compensating for the mistakes of single cues by the integration of other cues has the cost of relying on less valid information. Although these findings are surprising and new for the situation with missing information, they are in line with research that has studied the performance of these strategies in situations with complete information (see, for instance, Gigerenzer & Goldstein, 1996;Hogarth & Karelaia, 2007;or Juslin & Persson, 2002). From this result, a surprising conclusion can be drawn: To achieve high accuracy, selecting a good mechanism for treating missing information is more important than selecting the best inference strategy.…”

Reconsidering “evidence” for fast-and-frugal heuristics

“…ful. It is also undeniable that simple strategies need not always yield outcomes inferior to those of more complex algorithms (Hogarth & Karelaia, 2007). Moreover, observable choices can indeed resemble noncompensatory reliance on single, highly valid cues (as demonstrated above).…”

“…First, they have provided well-argued illustrations of the reasons why simple, often noncompensatory judgment strategies can outperform allegedly more complex ones (cf. Hogarth & Karelaia, 2007). Few, I believe, would actually doubt that, in some situations, more complex strategies could actually lead us astray more severely than simple heuristics.…”

The robust beauty of ordinary information.

“…In addition, with as few as seven objects TTB U reaches a performance level close to the maximum achieved by any model with a 50% training set. Hogarth and Karelaia (2007) conjectured that heuristics such as TTB U "would be less sensitive to sampling errors" (p. 751). This is exactly what we found.…”