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The purpose df this paper is to examine factors that affect the calibration of judgments by systematically comparing experts' judgments to novices' when solving a complex, real-world problem that varies in its initial characteristics. Calibration in this context refers to the proportion of times decision makers provide a range about their best estimates that includes the actual outcome. We found that experts specify a narrower range and provide more accurate best estimates than novices. But their tighter ranges are not justified by their greater accuracy: they are less likely to encompass the actual outcome than are novices. However, this effect is attenuated when solving more complex problems. Novices apparently underestimate the complexity of difficult problems, hence the accuracy of their best estimates decreases as does the width of their ranges, resulting in worse calibration. The performance of experts was not significantly different across problem solving conditions. Both groups provided asymmetrical ranges about their best estimates, which suggests they account for the effect of subproblem dependencies. (C) 1996 Academic Press, Inc.
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To gain insight into how people select life insurance policies, we conducted a controlled experiment to see how consumers with varying levels of expertise make choices among life insurance policies when provided relevant information presented in an easy-to-use format. We found that those with greater product class knowledge engage in qualitatively and quantitatively different decision processes than those with less knowledge, and therefore are likely to reach different decisions. Specifically, experts are statistically more likely to engage in pairwise/multiple comparisons and to evoke a greater number of choice criteria. There is directional support that they search for more information as well. For all participants, the number of information searches far exceeded the number of elimination and choice criteria evoked. © 1998 Taylor & Francis Group, LLC.
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A growing body of literature suggests that experts are little if at all better than novices in terms of the quality of decision outputs, To explain this counter-intuitive finding, the authors propose a conceptual framework that focuses on initial problem structure as a key moderator of the effect of expertise on performance, Specifically, they argue that the expert-novice performance differential should be greatest at moderate levels of problem structure and weakest at both extremes. To examine this central hypothesis, the authors conduct a controlled experiment that compares experts with novices when solving a complex problem that had characteristics of a moderately ill-structured problem, Relative to novices, the authors find that experts select fewer, but more diagnostic, information inputs and are more consistent when evaluating nonquantified inputs, As a result, they make more accurate and tightly clustered judgments than do novices, and. also are more confident in their decisions. To examine the moderating influence of problem characteristics, certain task variables are manipulated to increase or decrease initial problem structure. As hypothesized, the benefits of expertise are less pronounced when solving a problem with increased initial structure.
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