Search

Abstract Risk assessments are common in multiple domains, from finance to medicine. They require evaluating an event’s potential severity and likelihood. We investigate the possible dependence of likelihood and severity within the domain of impact-based weather forecasting (IBF), following predictions derived from considering asymmetric loss functions. In a collaboration between UK psychologists and partners from four meteorological organisations in Southeast Asia, we conducted two studies (N = 363) eliciting weather warnings from forecasters. Forecasters provided warnings denoting higher likelihoods for high severity impacts than low severity impacts, despite these impacts being described as having the same explicit numerical likelihood of occurrence. This ‘Severity effect’ is pervasive, and we find it can have a continued influence even for an updated forecast. It is additionally observed when translating warnings made on a risk matrix to numerical probabilities.

In their book ‘Nudge: Improving Decisions About Health, Wealth and Happiness’, Thaler & Sunstein (2009) argue that choice architectures are promising public policy interventions. This research programme motivated the creation of ‘nudge units’, government agencies which aim to apply insights from behavioural science to improve public policy. We closely examine a meta-analysis of the evidence gathered by two of the largest and most influential nudge units (DellaVigna & Linos (2022 Econometrica 90, 81–116 (doi:10.3982/ECTA18709))) and use statistical techniques to detect reporting biases. Our analysis shows evidence suggestive of selective reporting. We additionally evaluate the public pre-analysis plans from one of the two nudge units (Office of Evaluation Sciences). We identify several instances of excellent practice; however, we also find that the analysis plans and reporting often lack sufficient detail to evaluate (unintentional) reporting biases. We highlight several improvements that would enhance the effectiveness of the pre-analysis plans and reports as a means to combat reporting biases. Our findings and suggestions can further improve the evidence base for policy decisions.

Abstract Background A second opinion or a prognostic algorithm may increase prognostic accuracy. This study assessed the level to which clinicians integrate advice perceived to be coming from another clinician or a prognostic algorithm into their prognostic estimates, and how participant characteristics and nature of advice received affect this. Methods An online double‐blind randomised controlled trial was conducted. Palliative doctors, nurses and other types of healthcare professionals were randomised into study arms differing by perceived source of advice (algorithm or another clinician). In fact, the advice was the same in both arms (emanating from the PiPS‐B14 prognostic model). Each participant reviewed five patient summaries. For each summary, participants: (1) provided an initial probability estimate of two‐week survival (0% ‘certain death’—100% ‘certain survival’); (2) received advice (another estimate); (3) provided a final estimate. Weight of Advice (WOA) was calculated for each summary (0 ‘100% advice discounting’ – 1 ‘0% discounting’) and multilevel linear regression analyses were conducted. Clinical trial registration number: NCT04568629. Results A total of 283 clinicians were included in the analysis. Clinicians integrated advice from the algorithm more than advice from another clinician (WOA difference = −0.12 [95% CI ‐0.18, −0.07], p

Anchoring effects are robust, varied and can be consequential. Researchers have provided a variety of alternative explanations for these effects. More recently, it has become apparent that anchoring effects might be produced by a variety of different processes, either acting simultaneously, or else individually in distinct situations. An unresolved issue is whether anchoring, aside from simple numeric priming, can transcend scales. That is, is it necessary that the anchor value and the target judgment are expressed in the same units? Despite some theoretical predictions to the contrary, this paper demonstrates semantic cross-scale anchoring in four experiments. Such effects are important for the direction of future theorising on the causes of anchoring effects and understanding the scope of their consequences in applied domains.

Using capture-recapture analysis we estimate the effective size of the active Amazon Mechanical Turk (MTurk) population that a typical laboratory can access to be about 7,300 workers. We also estimate that the time taken for half of the workers to leave the MTurk pool and be replaced is about 7 months. Each laboratory has its own population pool which overlaps, often extensively, with the hundreds of other laboratories using MTurk. Our estimate is based on a sample of 114,460 completed sessions from 33,408 unique participants and 689 sessions across seven laboratories in the US, Europe, and Australia from January 2012 to March 2015.

Objectives The aims of this study were (1) to document the clinical condition of patients considered to be in the last 2 weeks of life and (2) to compare patients who did or did not survive for 72 hours.Design A prospective observational study.Setting Two sites in London, UK (a hospice and a hospital palliative care team).Participants Any inpatient, over 18 years old, English speaking, who was identified by the palliative care team as at risk of dying within the next 2 weeks was eligible.Outcome measures Prognostic signs and symptoms were documented at a one off assessment and patients were followed up 7 days later to determine whether or not they had died.Results Fifty participants were recruited and 24/50 (48%) died within 72 hours of assessment. The most prevalent prognostic features observed were a decrease in oral food intake (60%) and a rapid decline of the participant’s global health status (56%). Participants who died within 72 hours had a lower level of consciousness and had more care needs than those who lived longer. A large portion of data was unavailable, particularly that relating to the psychological and spiritual well-being of the patient, due to the decreased consciousness of the patient.Conclusions The prevalence of prognostic signs and symptoms in the final days of life has been documented between those predicted to die and those who did not. How doctors make decisions with missing information is an area for future research, in addition to understanding the best way to use the available information to make more accurate predictions.

One of the most accepted findings across psychology is that people are unrealistically optimistic in their judgments of comparative risk concerning future life events-they judge negative events as less likely to happen to themselves than to the average person. Harris and Hahn (2011), however, demonstrated how unbiased (non-optimistic) responses can result in data patterns commonly interpreted as indicative of optimism due to statistical artifacts. In the current paper, we report the results of 5 studies that control for these statistical confounds and observe no evidence for residual unrealistic optimism, even observing a 'severity effect' whereby severe outcomes were overestimated relative to neutral ones (Studies 3 & 4). We conclude that there is no evidence supporting an optimism interpretation of previous results using the prevalent comparison method.