Sequentially simulated outcomes: Kind experience versus nontransparent description.

Abstract

Recently, researchers have investigated differences in decision making based on description and experience. We address the issue of when experience-based judgments of probability are more accurate than are those based on description. If description is well understood ("transparent") and experience is misleading ("wicked"), it is preferable to experience. However, if description is not transparent, will valid ("kind") experience lead to more accurate judgments? We report 2 experiments. The first involved 7 well-known probabilistic inference tasks. Participants differed in statistical sophistication and answered with and without experience obtained through sequentially simulated outcomes. The second experiment involved interpreting the outcomes of a regression analysis when making inferences for investment decisions. In both experiments, even the statistically naïve achieved accurate probabilistic inferences after experiencing sequentially simulated outcomes, and many preferred this presentation format. We conclude by discussing theoretical and practical implications.

Communicating forecasts: The simplicity of simulated experience

“…And third, we tested the methodology on a decision problem where people had to assess the probability of being successful in a competition. Since detailed accounts of this work have already appeared in the literature, we only provide an overview (for the probability problems and investment task, see Hogarth & Soyer, 2011; for the competition scenario, see Hogarth, Mukherjee, & Soyer, 2013; for further elaboration, see Hogarth & Soyer, 2015).…”

“…Figs. 4-7 provide histograms of results for the Bayesian updating problem, the birthday problem, the hospital problem, and regression toward the mean problem (all findings are presented in Hogarth & Soyer, 2011).…”

“…4. Histograms of responses given to the Bayesian updating problem in Hogarth and Soyer (2011). From left to right, columns represent sophisticated A-E, sophisticated E-A, and naïve, with sample sizes 29, 31, and 20, respectively.…”

The golden rule of forecasting: Objections, refinements, and enhancements

“…Literature on simulated experience suggests that such tools reduce biases in judgment (Goldstein, Johnson, & Sharpe, 2008;Hayes, Newell, & Hawkins, 2013;Hogarth, Mukherjee, & Soyer, 2013). Also, as problems become more complex, decision makers prefer experience over analysis (Hogarth & Soyer, 2011;Lejarraga, 2010). That is, they feel more comfortable in their understanding of the uncertainties surrounding a forecast if they experience outcomes of a simulation model as opposed to being presented with analytical statements of levels of uncertainty.…”

The Subjective Understanding of Guideline Recommendations and of the Risks of the Side Effects of Medication

“…We already have methods available that, for example, promote thinking in probabilities (1,12). Both the academic training of physicians and continuing medical education courses offer opportunities for the targeted use of development measures.…”