title: Intuitive theories of information: beliefs about the value of redundancy.
abstract: In many situations, quantity estimates from multiple experts or diagnostic instruments must be collected and combined. Normatively, and all else equal, one should value information sources that are nonredundant, in the sense that correlation in forecast errors should be minimized. Past research on the preference for redundancy has been inconclusive. While some studies have suggested that people correctly place higher value on uncorrelated inputs when collecting estimates, others have shown that people either ignore correlation or, in some cases, even prefer it. The present experiments show that the preference for redundancy depends on one's intuitive theory of information. The most common intuitive theory identified is the Error Tradeoff Model (ETM), which explicitly distinguishes between measurement error and bias. According to ETM, measurement error can only be averaged out by consulting the same source multiple times (normatively false), and bias can only be averaged out by consulting different sources (normatively true). As a result, ETM leads people to prefer redundant estimates when the ratio of measurement error to bias is relatively high. Other participants favored different theories. Some adopted the normative model, while others were reluctant to mathematically average estimates from different sources in any circumstance. In a post hoc analysis, science majors were more likely than others to subscribe to the normative model. While tentative, this result lends insight into how intuitive theories might develop and also has potential ramifications for how statistical concepts such as correlation might best be learned and internalized.
authors: INSEAD, Fontainebleau, France. jack.soll@insead.fr
authors: J B
authors: JB
authors: Soll
mesh_terms: D000328:Adult; D015982:Bias; D003469:Culture; D003657:Decision Making; D003661:Decision Support Techniques; D005544:Forecasting / Q000379:methods; D006801:Humans; D019545:Intuition; D008962:Models, Theoretical; D012044:Regression Analysis; D013334:Students / Q000523:psychology