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In the current absence of medical treatment and vaccination, the unfolding COVID-19 pandemic can only be brought under control by massive and rapid behaviour change. To achieve this we need to systematically monitor and understand how different individuals perceive risk and what prompts them to act upon it, argues Cornelia Betsch.
The human tendency to impose a single interpretation in ambiguous situations carries huge dangers in addressing COVID-19. We need to search actively for multiple interpretations, and governments need to choose policies that are robust if their preferred theory turns out to be wrong, argues Nick Chater.
The global practice of monetizing ecosystems to further national economic development has laid fertile ground for the COVID-19 pandemic and others like it, writes Cobus van Staden.
Over the past decades, the availability of new methods and digitization has dramatically changed how scientific data are recorded, stored and analysed. This has enabled researchers to pull together the data underlying single research efforts into larger standardized datasets for reuse. The publication of these datasets - in the Resource format in our pages - represents a contribution of exceptional value to the scientific community.
Why is there no consensual way of conducting Bayesian analyses? We present a summary of agreements and disagreements of the authors on several discussion points regarding Bayesian inference. We also provide a thinking guideline to assist researchers in conducting Bayesian inference in the social and behavioural sciences.
The behavioral sciences underestimate the uncertainty of research findings and thus overestimate replicability. Metrologists in the physical sciences quantify all material components of uncertainty, even if some components must be quantified using non-statistical means. Behavioral science should follow suit.
