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A decision analytic model to guide early‐stage government regulatory action: Applications for synthetic biology

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  • Benjamin Trump
  • Christopher Cummings
  • Jennifer Kuzma
  • Igor Linkov

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.

Suggested Citation

  • Benjamin Trump & Christopher Cummings & Jennifer Kuzma & Igor Linkov, 2018. "A decision analytic model to guide early‐stage government regulatory action: Applications for synthetic biology," Regulation & Governance, John Wiley & Sons, vol. 12(1), pages 88-100, March.
    • Handle: RePEc:wly:reggov:v:12:y:2018:i:1:p:88-100
    DOI: 10.1111/rego.12142
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    References listed on IDEAS

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    1. Jennifer Kuzma & Jordan Paradise & Gurumurthy Ramachandran & Jee‐Ae Kim & Adam Kokotovich & Susan M. Wolf, 2008. "An Integrated Approach to Oversight Assessment for Emerging Technologies," Risk Analysis, John Wiley & Sons, vol. 28(5), pages 1197-1220, October.
    2. Christopher A. Voigt, 2012. "Bacteria collaborate to sense arsenic," Nature, Nature, vol. 481(7379), pages 33-34, January.
    3. Belton, Valerie, 1986. "A comparison of the analytic hierarchy process and a simple multi-attribute value function," European Journal of Operational Research, Elsevier, vol. 26(1), pages 7-21, July.
    4. Theodor J Stewart, 2005. "Dealing with Uncertainties in MCDA," International Series in Operations Research & Management Science, in: Multiple Criteria Decision Analysis: State of the Art Surveys, chapter 0, pages 445-466, Springer.
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    1. Nylund, Petra A. & Ferràs-Hernández, Xavier & Pareras, Luis & Brem, Alexander, 2022. "The emergence of entrepreneurial ecosystems based on enabling technologies: Evidence from synthetic biology," Journal of Business Research, Elsevier, vol. 149(C), pages 728-735.
    2. Li Tang & Jennifer Kuzma & Xi Zhang & Xinyu Song & Yin Li & Hongxu Liu & Guangyuan Hu, 2023. "Synthetic biology and governance research in China: a 40-year evolution," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(9), pages 5293-5310, September.

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