Search

As coronavirus disease 2019 (COVID-19) spread through the United States in 2020, states began to set up alert systems to inform policy decisions and serve as risk communication tools for the general public. Many of these systems included indicators based on an assessment of trends in numbers of reported cases. However, when cases are indexed by date of disease onset, reporting delays complicate the interpretation of trends. Despite a foundation of statistical literature with which to address this problem, these methods have not been widely applied in practice. In this paper, we develop a Bayesian spatiotemporal nowcasting model for assessing trends in county-level COVID-19 cases in Ohio. We compare the performance of our model with the approach used in Ohio and the approach included in decision support materials from the Centers for Disease Control and Prevention. We demonstrate gains in performance while still retaining interpretability using our model. In addition, we are able to fully account for uncertainty in both the time series of cases and the reporting process. While we cannot eliminate all of the uncertainty in public health surveillance and subsequent decision-making, we must use approaches that embrace these challenges and deliver more accurate and honest assessments to policy-makers. [ABSTRACT FROM AUTHOR]

In weighing the question of whether AJE should accept preprints that have received press coverage, we need to keep in mind 3 sets of interests: the public interest, the publisher's interest, and the author's interest. During public health emergencies, such as a pandemic, the author's interests (rapid communication of scientific findings to the public) are aligned with the public interest (learning about life-saving information as early as possible). However, the interests of different parties are not always aligned. In most cases, preprinted articles do not concern matters of life or death. Widespread dissemination of studies via preprint services conflicts with the journal editor's interest in delivering fresh, original content. Dissemination of study results prior to peer review can occasionally backfire and cause unintended harm if the findings turn out to be false. [ABSTRACT FROM AUTHOR]

Despite an increasing reliance on fully-automated algorithmic decision-making in our day-to-day lives, humans still make consequential decisions. While the existing literature focuses on the bias and fairness of algorithmic recommendations, an overlooked question is whether they improve human decisions. We develop a general statistical methodology for experimentally evaluating the causal impacts of algorithmic recommendations on human decisions. We also examine whether algorithmic recommendations improve the fairness of human decisions and derive the optimal decision rules under various settings. We apply the proposed methodology to the first-ever randomized controlled trial that evaluates the pretrial Public Safety Assessment in the United States criminal justice system. Our analysis of the preliminary data shows that providing the PSA to the judge has little overall impact on the judge's decisions and subsequent arrestee behaviour. [ABSTRACT FROM AUTHOR]

Objectives This research addresses how younger and older adults' decisions and evaluations of gains and losses are affected by the way in which monetary incentives are provided. We compared 2 common incentive schemes in decision making: pay one (only a single decision is incentivized) and pay all (incentives across all decisions are accumulated). Method Younger adults (18–36 years; n = 147) and older adults (60–89 years; n = 139) participated in either a pay-one or pay-all condition and made binary choices between two-outcome monetary lotteries in gain, loss, and mixed domains. We analyzed participants' decision quality, risk taking, and psychometric test scores. Computational modeling of cumulative prospect theory served to measure sensitivity to outcomes and probabilities, loss aversion, and choice sensitivity. Results Decision quality and risk aversion were higher in the gain than mixed or loss domain, but unaffected by age. Loss aversion was higher, and choice sensitivity was lower in older than younger adults. In the pay-one condition, the value functions were more strongly curved, and choice sensitivity was higher than in the pay-all condition. Discussion An opportunity of accumulating incentives has similar portfolio effects on younger and older adults' decisions. In general, people appear to decide less cautiously in pay-all than pay-one scenarios. The impact of different incentive schemes should be carefully considered in aging and decision research. [ABSTRACT FROM AUTHOR]