Dataset Information

Dynamic decision policy reconfiguration under outcome uncertainty.

ABSTRACT: In uncertain or unstable environments, sometimes the best decision is to change your mind. To shed light on this flexibility, we evaluated how the underlying decision policy adapts when the most rewarding action changes. Human participants performed a dynamic two-armed bandit task that manipulated the certainty in relative reward (conflict) and the reliability of action-outcomes (volatility). Continuous estimates of conflict and volatility contributed to shifts in exploratory states by changing both the rate of evidence accumulation (drift rate) and the amount of evidence needed to make a decision (boundary height), respectively. At the trialwise level, following a switch in the optimal choice, the drift rate plummets and the boundary height weakly spikes, leading to a slow exploratory state. We find that the drift rate drives most of this response, with an unreliable contribution of boundary height across experiments. Surprisingly, we find no evidence that pupillary responses associated with decision policy changes. We conclude that humans show a stereotypical shift in their decision policies in response to environmental changes.

SUBMITTER: Bond K

PROVIDER: S-EPMC8806193 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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Publications

Dynamic decision policy reconfiguration under outcome uncertainty.

Bond Krista K Dunovan Kyle K Porter Alexis A Rubin Jonathan E JE Verstynen Timothy T

eLife 20211224

In uncertain or unstable environments, sometimes the best decision is to change your mind. To shed light on this flexibility, we evaluated how the underlying decision policy adapts when the most rewarding action changes. Human participants performed a dynamic two-armed bandit task that manipulated the certainty in relative reward (conflict) and the reliability of action-outcomes (volatility). Continuous estimates of conflict and volatility contributed to shifts in exploratory states by changing ...[more]

PMID: 34951589

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Project description:ObjectiveTo introduce a statistical inference framework for policy decision making on access to pediatric dental care.Data sourcesSecondary data were collected for the state of Colorado for year 2019.Study designThe access model was an optimization model, matching the demand (patients) and supply (providers) of dental care. Sampling distributions of model inputs were specified using hierarchical Bayesian models, with hyperparameters informed by prior information derived from multiple data sources. Simultaneous inference was applied to identify areas for access improvement. The model was applied to make inference on the pediatric dental care in Colorado, accounting for financial access, differentiated into public (Medicaid and CHIP), private (commercial and out-of-pocket), and without financial access.Data collection/extraction methodsMultiple data sources informed the access measurement approach including: 2017 American Community Survey, 2019 Colorado Dental Board, and 2019 National Provider Plan and Enumeration System, 2019 InsureKidsNow.gov among others.Principal findingsThe median access measure (travel distance) was greater than the Colorado access standards in 16.9% and 65.1% of census tracts for children with private financial access and publicly insured, respectively. Accounting for uncertainty (confidence level 99%), these percentages decreased to 14.6% and 25.6%, respectively, with mostly suburban and rural tracts failing to meet the standards. The median disparity for Medicaid and CHIP versus private financial access was greater than 5 miles in 84.5% and 81.6% of census tracts, respectively. Accounting for uncertainty (confidence level 99%), these percentages declined to 19.5% and 10.5%, respectively, with significant disparities around the metropolitan areas.ConclusionsWhile many communities failed to meet access standards, when accounting for uncertainty, most urban ones did not fail. Disparities in spatial access between publicly and privately insured were most acute in urban communities. Medicaid insured experienced higher disparities than CHIP insured; those differences were not identified when not accounting for uncertainty.

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Dataset Information

Dynamic decision policy reconfiguration under outcome uncertainty.

Publications

Dynamic decision policy reconfiguration under outcome uncertainty.

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