A cognitive modeling analysis of risk in sequential choice tasks}
There are many ways to measure how people manage risk when they make decisions. A standard approach is to measure risk propensity using self-report questionnaires. An alternative approach is to use decision-making tasks that involve risk and uncertainty, and apply cognitive models of task behavior t...
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Society for Judgment and Decision Making
2020-09-01
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doaj-d04fa04525e048258bd0766231119e262021-05-03T01:29:17ZengSociety for Judgment and Decision MakingJudgment and Decision Making1930-29752020-09-01155823850A cognitive modeling analysis of risk in sequential choice tasks}Maime GuanRyan StokesJoachim VandekerckhoveMichael D. LeeThere are many ways to measure how people manage risk when they make decisions. A standard approach is to measure risk propensity using self-report questionnaires. An alternative approach is to use decision-making tasks that involve risk and uncertainty, and apply cognitive models of task behavior to infer parameters that measure people's risk propensity. We report the results of a within-participants experiment that used three questionnaires and four decision-making tasks. The questionnaires are the Risk Propensity Scale, the Risk Taking Index, and the Domain Specific Risk Taking Scale. The decision-making tasks are the Balloon Analogue Risk Task, the preferential choice gambling task, the optimal stopping problem, and the bandit problem. We analyze the relationships between the risk measures and cognitive parameters using Bayesian inferences about the patterns of correlation, and using a novel cognitive latent variable modeling approach. The results show that people's risk propensity is generally consistent within different conditions for each of the decision-making tasks. There is, however, little evidence that the way people manage risk generalizes across the tasks, or that it corresponds to the questionnaire measures.http://journal.sjdm.org/19/190814b/jdm190814b.pdfrisky decision making sequential choice tasks optimal stopping problems bandit problems balloon analogue risk task cognitive latent variable modelingnakeywords |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Maime Guan Ryan Stokes Joachim Vandekerckhove Michael D. Lee |
spellingShingle |
Maime Guan Ryan Stokes Joachim Vandekerckhove Michael D. Lee A cognitive modeling analysis of risk in sequential choice tasks} Judgment and Decision Making risky decision making sequential choice tasks optimal stopping problems bandit problems balloon analogue risk task cognitive latent variable modelingnakeywords |
author_facet |
Maime Guan Ryan Stokes Joachim Vandekerckhove Michael D. Lee |
author_sort |
Maime Guan |
title |
A cognitive modeling
analysis of risk in sequential choice tasks} |
title_short |
A cognitive modeling
analysis of risk in sequential choice tasks} |
title_full |
A cognitive modeling
analysis of risk in sequential choice tasks} |
title_fullStr |
A cognitive modeling
analysis of risk in sequential choice tasks} |
title_full_unstemmed |
A cognitive modeling
analysis of risk in sequential choice tasks} |
title_sort |
cognitive modeling
analysis of risk in sequential choice tasks} |
publisher |
Society for Judgment and Decision Making |
series |
Judgment and Decision Making |
issn |
1930-2975 |
publishDate |
2020-09-01 |
description |
There are many ways to
measure how people manage risk when they make decisions. A standard approach is
to measure risk propensity using self-report questionnaires. An alternative
approach is to use decision-making tasks that involve risk and uncertainty, and
apply cognitive models of task behavior to infer parameters that measure
people's risk propensity. We report the results of a within-participants
experiment that used three questionnaires and four decision-making tasks. The
questionnaires are the Risk Propensity Scale, the Risk Taking Index, and the
Domain Specific Risk Taking Scale. The decision-making tasks are the Balloon
Analogue Risk Task, the preferential choice gambling task, the optimal stopping
problem, and the bandit problem. We analyze the relationships between the risk
measures and cognitive parameters using Bayesian inferences about the patterns
of correlation, and using a novel cognitive latent variable modeling approach.
The results show that people's risk propensity is generally consistent within
different conditions for each of the decision-making tasks. There is, however,
little evidence that the way people manage risk generalizes across the tasks,
or that it corresponds to the questionnaire measures. |
topic |
risky decision making sequential choice tasks optimal stopping problems bandit problems balloon analogue risk task cognitive latent variable modelingnakeywords |
url |
http://journal.sjdm.org/19/190814b/jdm190814b.pdf |
work_keys_str_mv |
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_version_ |
1721485997055672320 |