Overcoming Ineffective Mental Representations in Base-rate Problems.

Author List: Roy, Marie Christine; Lerch, F. Javier;
Information Systems Research, 1996, Volume 7, Issue 2, Page 233-247.
Many biases have been observed in probabilistic reasoning, hindering the ability to follow normative rules in decision-making contexts involving uncertainty. One systematic error people make is to neglect base rates in situations where prior beliefs in a hypothesis should be taken into account when new evidence is obtained, incomplete explanations for the phenomenon have impeded the development of effective debiasing procedures or tools to support decision making in this area. In this research, we show that the main reason behind these judgment errors is the causal representation induced by the problem context. In two experiments we demonstrate that people often possess the appropriate decision rules but are unable to apply them correctly because they have an ineffective causal mental representation. We also show how this mental representation may be modified when a graph is used instead of a problem narrative. This new understanding should contribute to the design of better decision aids to overcome this bias.
Keywords: Base-rate Fallacy; Decision Support; Mental Representation; Representational Aid
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List of Topics

#57 0.225 decision support systems making design models group makers integrated article delivery representation portfolio include selection effective claims decisions rationale various
#183 0.141 explanations explanation bias use kbs biases facilities cognitive making judgment decisions likely decision important prior judgments feedback types difficult lead
#95 0.135 learning mental conceptual new learn situated development working assumptions improve ess existing investigates capture advanced proposes types context building acquisition
#31 0.090 problem problems solution solving problem-solving solutions reasoning heuristic theorizing rules solve general generating complex example formulation heuristics effective given finding
#97 0.090 set approach algorithm optimal used develop results use simulation experiments algorithms demonstrate proposed optimization present analytical distribution selection number existing
#213 0.073 assimilation beliefs belief confirmation aggregation initial investigate observed robust particular comparative circumstances aggregated tendency factors examine stages uncertainty instead confidence
#238 0.058 shared contribution groups understanding contributions group contribute work make members experience phenomenon largely central key common especially major conceptualizing study
#96 0.055 errors error construction testing spreadsheet recovery phase spreadsheets number failures inspection better studies modules rate replicated detection correction optimal discovering