Abstract
Safety assessment of technologies and interventions is often underdetermined by evidence. For example, scientists have collected evidence concerning genetically modified plants for decades. This evidence was used to ground opposing safety protocols for “stacked genetically modified” plants, in which two or more genetically modified plants are combined. Evidence based policy would thus be rendered more effective by an approach that accounts for underdetermination. Douglas (2012) proposes an explanatory approach, based on the criteria of transparency, empirical competence, internal consistency of explanations, and predictive potency. However, sometimes multiple explanations can satisfy these criteria. We propose an additional criterion based on converse abduction, where explanations are selected on the basis of ontological background assumptions as well as by evidence. We then apply our proposed scheme to the case of the regulation of stacked genetically modified plants. We discuss the implications and suggest follow-up work concerning the generalizability of the approach.
Highlights
A challenge when relying on science as a key element for governance is that experts interpret scientific data differently, and disagree on how to weigh evidence, even when a reasonable amount of data is collected (Douglas, 2000; Sawyer & Loja, 2015)
Douglas’ approach is, admittedly limited, and here we suggest a development based on our view of expert disagreement, which focuses on ontological background assumptions
In Rocca & Andersen, (2017), we describe the function of back ground assumptions as follows: “Background assumptions are more general than new evidence, and play a regulatory function in relation to it
Summary
A challenge when relying on science as a key element for governance is that experts interpret scientific data differently, and disagree on how to weigh evidence, even when a reasonable amount of data is collected (Douglas, 2000; Sawyer & Loja, 2015). Algorithm-based approaches, such as Bayesian networks, can be both complete and rigorous, but they hardly ever meet the requirements for transparency or communicability This is due to the fact that basic as sumptions and general reasoning are adopted by the programmer, but remain hidden to the users. Kepler and Galilei approached underdetermination by weighing evi dence based on what they considered the most plausible ontological background assumptions This strategy has been called converse abduc tion (see Andersen, 2017; Myrstad, 2004). By transposing a strategy from the historical case to the current picture, we consider the complexities of modern evidencebased policy through a final case-study: The scientific controversy over a variety of stacked genetically modified corn We already analyzed this case in terms of diverging background assumptions of an ontological type in Rocca & Andersen, (2017). We show how converse abduction could be used to resolve the underdetermination issue
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