Accepted Manuscript

Title: Classifying Global Catastrophic Risks

Authors: Shahar Avin, Bonnie C. Wintle, Julius Weitzd¨orfer, Sean´ S. Oh´ Eigeartaigh,´ William J. Sutherland, Martin J. Rees

PII: S0016-3287(17)30195-7 DOI: https://doi.org/10.1016/j.futures.2018.02.001 Reference: JFTR 2277

To appear in:

Received date: 26-5-2017 Revised date: 26-1-2018 Accepted date: 12-2-2018

Please cite this article as: Shahar Avin, Bonnie C.Wintle, Julius Weitzd¨orfer, Sean´ S.O´ hEigeartaigh,´ William J.Sutherland, Martin J.Rees, Classifying Global Catastrophic Risks, Futures https://doi.org/10.1016/j.futures.2018.02.001

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Shahar Avin1, Bonnie C. Wintle1, Julius Weitzdörfer1, Seán S. Ó hÉigeartaigh1, William J. Sutherland2, Martin J. Rees1

1Centre for the Study of Existential Risk, University of Cambridge, UK. e-mail: [email protected] 2William J. Sutherland is professor of conservation biology in the Department of Zoology, University of Cambridge, UK.

We present a novel classification framework for severe global catastrophic risk scenarios. Extending beyond existing work that identifies individual risk scenarios, we propose analysing global catastrophic risks along three dimensions: the critical systems affected, global spread mechanisms, and prevention and mitigation failures. The classification highlights areas of convergence between risk scenarios, which supports prioritisation of particular research and of policy interventions. It also points to potential knowledge gaps regarding catastrophic risks, and provides an interdisciplinary structure for mapping and tracking the multitude of factors that could contribute to global catastrophic risks.

Highlights: ● Classifying global catastrophic risks according to critical system affected, global spread mechanism, and prevention and mitigation failure, provides a novel means of framing risks ● By concentrating on component factors that contribute to catastrophic risks, the classification system highlights convergent risk factors that merit prioritisation and also uncovers potential knowledge gaps. ● The classification system can structure an ongoing, dynamic process of knowledge aggregation and horizon scanning with policy implications.

1 Introduction In our uncertain times it is good to have something we can all agree on: global catastrophes are undesirable. As our science advances we gain a better understanding of a broad class of global catastrophic risk (GCR) scenarios that could, in severe cases, take the lives of a significant portion of the human population, and may leave survivors at enhanced risk by undermining global resilience systems (Bostrom, 2002; Rees, 2003; Posner, 2004; Bostrom & Ćirković, 2008; Tonn & MacGregor, 2009; Baum & Tonn, 2015). Much progress ACCEPTEDhas been made in identifying individual MANUSCRIPTGCR scenarios, and in compiling lists of the scenarios of greatest concern, but there is currently no known methodology for compiling a comprehensive, interdisciplinary view of severe global catastrophic risks. While a fully complete list of GCRs may remain beyond reach, we present here a classification framework designed specifically to draw on as 1

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