A decision analytic model to guide early‐stage government regulatory action: Applications for synthetic biology. Issue 1 (27th February 2017)

• Record Type: Journal Article
• Authors: Trump, Benjamin; Cummings, Christopher; Kuzma, Jennifer; Linkov, Igor
• Is Part Of: Regulation & governance. Volume 12:Issue 1(2018)
• Page Start: 88
• Page End: 100
• Publication Date: 2017-02-27
• Subjects: emerging technology -- risk governance -- synthetic biology -- technology governance -- uncertainty; Delegated legislation -- Periodicals; Corporate governance -- Periodicals; 320.05
• DOI: 10.1111/rego.12142
• Languages: English
• ISSNs: 1748-5983

Abstract

Abstract: Synthetic biology (SB) involves the alteration of living cells and biomolecules for specific purposes. Products developed using these approaches could have significant societal benefits, but also pose uncertain risks to human and environmental health. Policymakers currently face decisions regarding how stringently to regulate and monitor various SB applications. This is a complex task, in which policymakers must balance uncertain economic, political, social, and health‐related decision factors associated with SB use. We argue that formal decision analytical tools could serve as a method to integrate available evidence‐based information and expert judgment on the impacts associated with SB innovations, synthesize that information into quantitative indicators, and serve as the first step toward guiding governance of these emerging technologies. For this paper, we apply multi‐criteria decision analysis to a specific case of SB, a micro‐robot based on biological cells called "cyberplasm." We use data from a Delphi study to assess cyberplasm governance options and demonstrate how such decision tools may be used for assessments of SB oversight.