title: Research implications of science-informed, value-based decision making.
abstract: In 'Hard' science, scientists correctly operate as the 'guardians of certainty', using hypothesis testing formulations and value judgements about error rates and time discounting that make classical inferential methods appropriate. But these methods can neither generate most of the inputs needed by decision makers in their time frame, nor generate them in a form that allows them to be integrated into the decision in an analytically coherent and transparent way. The need for transparent accountability in public decision making under uncertainty and value conflict means the analytical coherence provided by the stochastic Bayesian decision analytic approach, drawing on the outputs of Bayesian science, is needed. If scientific researchers are to play the role they should be playing in informing value-based decision making, they need to see themselves also as 'guardians of uncertainty', ensuring that the best possible current posterior distributions on relevant parameters are made available for decision making, irrespective of the state of the certainty-seeking research. The paper distinguishes the actors employing different technologies in terms of the focus of the technology (knowledge, values, choice); the 'home base' mode of their activity on the cognitive continuum of varying analysis-to-intuition ratios; and the underlying value judgements of the activity (especially error loss functions and time discount rates). Those who propose any principle of decision making other than the banal 'Best Principle', including the 'Precautionary Principle', are properly interpreted as advocates seeking to have their own value judgements and preferences regarding mode location apply. The task for accountable decision makers, and their supporting technologists, is to determine the best course of action under the universal conditions of uncertainty and value difference/conflict.
authors.affiliation: Public Health and Policy Department, London School of Hygiene and Tropical Medicine, London, United Kingdom. Jack.Dowie@lshtm.ac.uk
authors.forename: Jack
authors.initials: J
authors.lastname: Dowie
mesh_terms: D001499:Bayes Theorem; D003627:Data Interpretation, Statistical; D003657:Decision Making; D003661:Decision Support Techniques; D004782:Environmental Health; D015340:Epidemiologic Research Design; D019317:Evidence-Based Medicine; D006801:Humans; D012680:Sensitivity and Specificity; D006113:United Kingdom