The DARC Toolbox: automated, flexible, and efficient delayed and risky choice experiments using Bayesian adaptive design Article Swipe
Benjamin T. Vincent
,
Tom Rainforth
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.31234/osf.io/yehjb
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.31234/osf.io/yehjb
Delayed and risky choice (DARC) experiments are a cornerstone of research in psychology, behavioural economics and neuroeconomics. By collecting an agent's preferences between pairs of prospects we can characterise their preferences, investigate what affects them, and probe the underlying decision making mechanisms. We present a state-of-the-art approach and software toolbox allowing such DARC experiments to be run in a highly efficient way. Data collection is costly, so our toolbox automatically and adaptively generates pairs of prospects in real time to maximise the information gathered about the participant's behaviours. We demonstrate that this leads to improvements over alternative experimental paradigms. The key to releasing our real time and automatic performance is a number of advances over current Bayesian adaptive design methodology. In particular, we derive an improved estimator for discrete output problems and design a novel algorithm for automating sequential adaptive design. We provide a number of pre-prepared DARC tools for researchers to use, but a key contribution is an adaptive experiment toolbox that can be extended to virtually any 2-alternative-choice tasks. In particular, to carry out custom adaptive experiments using our toolbox, the user need only encode their behavioural model and design space - both the subsequent inference and sequential design optimisation are automated for arbitrary models the user might write.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.31234/osf.io/yehjb
- OA Status
- gold
- Cited By
- 16
- References
- 74
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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- DOI
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https://doi.org/10.31234/osf.io/yehjbDigital Object Identifier
- Title
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The DARC Toolbox: automated, flexible, and efficient delayed and risky choice experiments using Bayesian adaptive designWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2017Year of publication
- Publication date
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2017-10-20Full publication date if available
- Authors
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Benjamin T. Vincent, Tom RainforthList of authors in order
- Landing page
-
https://doi.org/10.31234/osf.io/yehjbPublisher landing page
- Open access
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.31234/osf.io/yehjbDirect OA link when available
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Toolbox, Computer science, Key (lock), Bayesian probability, Inference, Machine learning, Artificial intelligence, ENCODE, Bayesian inference, Estimator, Gene, Statistics, Computer security, Chemistry, Biochemistry, Mathematics, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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16Total citation count in OpenAlex
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2025: 2, 2024: 3, 2023: 6, 2020: 1, 2019: 3Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.toolbox, | 181 |
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| abstract_inverted_index.automated | 203 |
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| abstract_inverted_index.participant's | 86 |
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| abstract_inverted_index.2-alternative-choice | 169 |
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| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.7400000095367432 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.96235637 |
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| citation_normalized_percentile.is_in_top_10_percent | True |