Journal article
Long-term cost-effectiveness of interventions for loss of electricity/industry compared to artificial general intelligence safety
- Abstract:
- Abstract Extreme solar storms, high-altitude electromagnetic pulses, and coordinated cyber attacks could disrupt regional/global electricity. Since electricity basically drives industry, industrial civilization could collapse without it. This could cause anthropological civilization (cities) to collapse, from which humanity might not recover, having long-term consequences. Previous work analyzed technical solutions to save nearly everyone despite industrial loss globally, including transition to animals powering farming and transportation. The present work estimates cost-effectiveness for the long-term future with a Monte Carlo (probabilistic) model. Model 1, partly based on a poll of Effective Altruism conference participants, finds a confidence that industrial loss preparation is more cost-effective than artificial general intelligence safety of ~ 88% and ~ 99+% for the 30 millionth dollar spent on industrial loss interventions and the margin now, respectively. Model 2 populated by one of the authors produces ~ 50% and ~ 99% confidence, respectively. These confidences are likely to be reduced by model and theory uncertainty, but the conclusion of industrial loss interventions being more cost-effective was robust to changing the most important 4–7 variables simultaneously to their pessimistic ends. Both cause areas save expected lives cheaply in the present generation and funding to preparation for industrial loss is particularly urgent.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 4.2MB, Terms of use)
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- Publisher copy:
- 10.1186/s40309-021-00178-z
Authors
+ H2020 European Research Council
More from this funder
- Funder identifier:
- 10.13039/100010663
- Grant:
- 669751
- Publisher:
- SpringerOpen
- Journal:
- European Journal of Futures Research More from this journal
- Volume:
- 9
- Issue:
- 1
- Pages:
- 11
- Article number:
- 11
- Publication date:
- 2021-09-20
- DOI:
- EISSN:
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2195-2248
- ISSN:
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2195-4194
- Language:
-
English
- Keywords:
- Pubs id:
-
1200121
- Local pid:
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pubs:1200121
- Source identifiers:
-
W3199145864
- Deposit date:
-
2026-03-26
- ARK identifier:
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Terms of use
- Copyright date:
- 2021
- Licence:
- CC Attribution (CC BY)
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