Different heuristics and same bias: A spectral analysis of biased judgments and individual decision rules
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Cambridge University Press
2018-09-01
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| Series: | Judgment and Decision Making |
| Subjects: | |
| Online Access: | https://www.cambridge.org/core/product/identifier/S1930297500008688/type/journal_article |
| _version_ | 1797697886848286720 |
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| author | Ola Svenson Nichel Gonzalez Gabriella Eriksson |
| author_facet | Ola Svenson Nichel Gonzalez Gabriella Eriksson |
| author_sort | Ola Svenson |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T03:46:26Z |
| format | Article |
| id | doaj.art-5e0a47ab6b7b4174a21b677818607108 |
| institution | Directory Open Access Journal |
| issn | 1930-2975 |
| language | English |
| last_indexed | 2024-03-12T03:46:26Z |
| publishDate | 2018-09-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Judgment and Decision Making |
| spelling | doaj.art-5e0a47ab6b7b4174a21b6778186071082023-09-03T12:43:29ZengCambridge University PressJudgment and Decision Making1930-29752018-09-011340141210.1017/S1930297500008688Different heuristics and same bias: A spectral analysis of biased judgments and individual decision rulesOla Svenson0Nichel Gonzalez1Gabriella Eriksson2Risk Analysis, Social and Decision Research Unit, Department of Psychology, Stockholm University, Sweden, and Decision Research, Eugene, OR, USASocial and Decision Research Unit, Department of Psychology, Stockholm University, SwedenLeeds University Business School, Leeds, UK, and Swedish Road and Transportation Research Institute, Linköping, SwedenWe 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.https://www.cambridge.org/core/product/identifier/S1930297500008688/type/journal_articlespectral analysisdrivingheuristicsbiasestimespeed |
| spellingShingle | Ola Svenson Nichel Gonzalez Gabriella Eriksson Different heuristics and same bias: A spectral analysis of biased judgments and individual decision rules Judgment and Decision Making spectral analysis driving heuristics biases time speed |
| title | Different heuristics and same bias: A spectral analysis of biased judgments and individual decision rules |
| title_full | Different heuristics and same bias: A spectral analysis of biased judgments and individual decision rules |
| title_fullStr | Different heuristics and same bias: A spectral analysis of biased judgments and individual decision rules |
| title_full_unstemmed | Different heuristics and same bias: A spectral analysis of biased judgments and individual decision rules |
| title_short | Different heuristics and same bias: A spectral analysis of biased judgments and individual decision rules |
| title_sort | different heuristics and same bias a spectral analysis of biased judgments and individual decision rules |
| topic | spectral analysis driving heuristics biases time speed |
| url | https://www.cambridge.org/core/product/identifier/S1930297500008688/type/journal_article |
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