Characterising bias in regulatory risk and decision analysis: An analysis of heuristics applied in health technology appraisal, chemicals regulation, and climate change governance

• Published in: Environment international Vol. 105; pp. 20 - 33
• Main Author: MacGillivray, Brian H.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Ltd 01.08.2017; Elsevier
• Subjects: Bias; Biomedical Technology; Climate Change; Clinical trials; data collection; Decision Making; Decision Support Techniques; governance; Hazardous Substances; Heuristics; Humans; issues and policy; Methodology; Model uncertainty; public health; Risk; Risk analysis; Risk Assessment - methods; Risk regulation; Social Control, Formal; Statistical inference; Uncertainty; Clinical trials; Risk regulation; Statistical inference; Methodology; Risk analysis; Model uncertainty
• ISSN: 0160-4120; 1873-6750; 1873-6750
• DOI: 10.1016/j.envint.2017.05.002

In many environmental and public health domains, heuristic methods of risk and decision analysis must be relied upon, either because problem structures are ambiguous, reliable data is lacking, or decisions are urgent. This introduces an additional source of uncertainty beyond model and measurement error – uncertainty stemming from relying on inexact inference rules. Here we identify and analyse heuristics used to prioritise risk objects, to discriminate between signal and noise, to weight evidence, to construct models, to extrapolate beyond datasets, and to make policy. Some of these heuristics are based on causal generalisations, yet can misfire when these relationships are presumed rather than tested (e.g. surrogates in clinical trials). Others are conventions designed to confer stability to decision analysis, yet which may introduce serious error when applied ritualistically (e.g. significance testing). Some heuristics can be traced back to formal justifications, but only subject to strong assumptions that are often violated in practical applications. Heuristic decision rules (e.g. feasibility rules) in principle act as surrogates for utility maximisation or distributional concerns, yet in practice may neglect costs and benefits, be based on arbitrary thresholds, and be prone to gaming. We highlight the problem of rule-entrenchment, where analytical choices that are in principle contestable are arbitrarily fixed in practice, masking uncertainty and potentially introducing bias. Strategies for making risk and decision analysis more rigorous include: formalising the assumptions and scope conditions under which heuristics should be applied; testing rather than presuming their underlying empirical or theoretical justifications; using sensitivity analysis, simulations, multiple bias analysis, and deductive systems of inference (e.g. directed acyclic graphs) to characterise rule uncertainty and refine heuristics; adopting “recovery schemes” to correct for known biases; and basing decision rules on clearly articulated values and evidence, rather than convention. •In many environmental and public health domains, heuristic methods of risk and decision analysis are relied upon•This introduces a source of uncertainty beyond model and measurement error - uncertainty stemming from relying on inexact inference rules•We analyse heuristics used to prioritise risks, to make causal inferences, to weight evidence, to construct models, and to make policy decisions•When misapplied, heuristic methods have led to biased analyses, and inefficient or even harmful policies•Rigorizing heuristic inference is a crucial step towards evidence-based public policy