When does ignorance make us smart? Additional factors guiding heuristic inference

“Fast & frugal” heuristics represent an appealing way of implementing bounded rationality and decision-making under pressure. The recognition heuristic is the simplest and most fundamental of these heuristics. Simulation and experimental studies have shown that this ignorance-driven heuristic inference can prove superior to knowledge based inference (Borges, Goldstein, Ortman & Gigerenzer, 1999; Goldstein & Gigerenzer, 2002) and have shown how the heuristic could develop from ACT-R’s forgetting function (Schooler & Hertwig, 2005). Mathematical analyses also demonstrate that, under certain conditions, a “less-is-more effect” will always occur (Goldstein & Gigerenzer, 2002). The further analyses presented in this paper show, however, that these conditions may constitute a special case and that the less-is-more effect in decision-making is subject to the moderating influence of the number of options to be considered and the framing of the question. The Less-Is-More Effect. An interesting and counter-intuitive finding in decisionmaking research is the discovery that, under certain circumstances, individuals with less knowledge make more accurate judgments than those with greater (but still imperfect) knowledge. For example, 62% of American students tested could correctly state that San Diego has a higher population than San Antonio, but 100% of German students tested could do so (Goldstein & Gigerenzer, 1999). The argument here is that the American students were forced to consider multiple different, often unreliable, cues to size in making their judgment, whereas the German students simply needed to consider whether they had ever heard of the city: a more reliable size cue. In this case Recognition Validity (RV) exceeded Knowledge Validity (KV). A further study went on to examine the performance, over a period of time, of a recognition-based portfolio of shares with portfolios chosen on the basis of other knowledge. In this study, relative ignorance proved a better tool for playing the stock-market in a bull-market situation than did the other methods investigated (Borges, Goldstein, Ortmann & Gigerenzer, 1999, but see Boyd (2001) for a failure to replicate in a bear market). These examples show how an apparent use of recognition, via a recognition heuristic, was useful in practice. Other studies have queried the psychological status of the recognition heuristic (McCloy & Beaman, 2004; Newell & Shanks, 2004; Oppenheimer, 2003), here we are concerned with the inprinciple usefulness of recognition as a decision-making criterion. In Goldstein & Gigerenzer’s (2002) study, they made use of the “cities task” as the basis for both their theoretical speculations and a test-bed for their empirical results. The cities task has been described as a “drosophila”, or ideal research environment enabling the analysis of satisficing algorithms in a well-understood environment (Gigerenzer & Goldstein, 1996, p.651). Subsequently, Schooler & Hertwig (2005) demonstrated how the forgetting rate within the ACT-R cognitive architecture (Anderson & Lebiere, 1998) could give rise to the use of the recognition heuristic and, consequently, near-optimal performance on this task. A rational analysis of forgetting would therefore seem to encourage the formulation and use of a recognition heuristic. This conclusion is further strengthened by Goldstein and Gigerenzer’s simulation of a “less-is-more” effect on the city task. The cities task requires participants to decide, given two alternatives, which city has the higher population. The recognition heuristic states: If one of two objects is recognized and the other is not, then infer that the recognized object has the higher value (Goldstein & Gigerenzer, 1999, p. 41). The 2-alternative forced choice (2AFC) cities task is used as a test-bed because it “is an elementary case to which many problems of greater complexity (multiple choice, for instance) are reducible” (Goldstein & Gigerenzer, 1999, p. 41). The cities task is thus seen as a representative of the set