No abstract

Cognitive representations of decision problems are dynamic. During and after a decision, evaluations and representations of facts change to support the decision made by a decision maker her- or himself (Svenson, 2003). We investigated post-decision distortion of facts (consolidation). Participants were given vignettes with facts about two terminally ill patients, only one of whom could be given lifesaving surgery. In Study 1, contrary to the prediction, the results showed that facts were distorted after a decision both by participants who were responsible for the decisions themselves and when doctors had made the decision. In Study 2 we investigated the influence of knowledge about expert decisions on a participant's own decision and post-decisional distortion of facts. Facts were significantly more distorted when the participant's decision agreed with an expert's decision than when the participant and expert decisions disagreed. The findings imply that knowledge about experts' decisions can distort memories of facts and therefore may obstruct rational analyses of earlier decisions. This is particularly important when a decision made by a person, who is assumed to be an expert, makes a decision that is biased or wrong.

Previous research showed that accumulations of capital following stationary interest rates are underestimated by human judges. Hyperbolic discounting was suggested as a descriptive and explanatory model for this phenomenon. First, we investigated judged accumulated capital after a period of annual growth and decline. The degree of underestimation increased with accumulated growth and the results supported hyperbolic discounting as a descriptive model on the group level. However, the hyperbolic model did not apply to the data for one third of the participants. Second, we investigated how investment decisions were related to capital accumulation before the investments and to judgments of the possible outcomes of the future investments. To our surprise, the participants’ judgments of expected future accumulated capital did not add predictive power to predictions based on whether there was growth or decline before the investment decision. Unfortunately this strategy leads to suboptimal investment decisions

We used correlation and spectral analyses to investigate the cognitive structures and processes producing biased judgments. We used 5 different sets of driving problems to exemplify problems that trigger biases, specifically: (1) underestimation of the impact of occasional slow speeds on mean speed judgments, (2) overestimation of braking capacity after a speed increase, (3) the time saving bias (overestimation of the time saved by increasing a high speed further, and underestimation of time saved when increasing a low speed), (4) underestimation of increase of fatal accident risk when speed is increased, and (5) underestimation of the increase of stopping distance when speed is increased. The results verified the predicted biases. A correlation analysis found no strong links between biases; only accident risk and stopping distance biases were correlated significantly. Spectral analysis of judgments was used to identify different decision rules. Most participants were consistent in their use of a single rule within a problem set with the same bias. The participants used difference, average, weighed average and ratio rules, all producing biased judgments. Among the rules, difference rules were used most frequently across the different biases. We found no personal consistency in the rules used across problem sets. The complexity of rules varied across problem sets for most participants.

Svenson (2011) showed that choices of one of two alternative productivity increases to save production resources (e.g., man-months) were biased. Judgments of resource savings following a speed increase from a low production speed line were underestimated and following an increase of a high production speed line overestimated. The objective formula for computing savings includes differences between inverse speeds and this is intuitively very problematic for most people. The purpose of the present studies was to explore ways of ameliorating or eliminating the bias. Study 1 was a control study asking participants to increase the production speed of one production line to save the same amount of production resources (man-months) as was saved by a speed increase in a reference line. The increases judged to match the reference alternatives showed the same bias as in the earlier research on choices. In Study 2 the same task and problems were used as in Study 1, but the participants were asked first to judge the resource saving of the reference alternative in a pair of alternatives before they proceeded to the matching task. This weakened the average bias only slightly. In Study 3, the participants were asked to judge the resources saved from each of two successive increases of the same single production line (other than those of the matching task) before they continued to the matching problems. In this way a participant could realize that a second production speed increase from a higher speed (e.g., from 40 to 60 items /man-month) gives less resource savings than the same speed increase from a first lower speed (e.g., from 20 to 40 items/man-month. Following this, the judgments of the same problems as in the other studies improved and the bias decreased significantly but it did not disappear. To be able to make optimal decisions about productivity increases, people need information about the bias and/or reformulations of the problems.