Deterrence in the age of artificial intelligence & autonomy: a paradigm shift in nuclear deterrence theory and practice?

James Johnson

Defense & Security Analysis, 2020, vol. 36, issue 4, 422-448

Abstract: How might nuclear deterrence be affected by the proliferation of artificial intelligence (AI) and autonomous systems? How might the introduction of intelligent machines affect human-to-human (and human-to-machine) deterrence? Are existing theories of deterrence still applicable in the age of AI and autonomy? The article builds on the rich body of work on nuclear deterrence theory and practice and highlights some of the variegated and contradictory – especially human cognitive psychological – effects of AI and autonomy for nuclear deterrence. It argues that existing theories of deterrence are not applicable in the age of AI and autonomy and introducing intelligent machines into the nuclear enterprise will affect nuclear deterrence in unexpected ways with fundamentally destabilising outcomes. The article speaks to a growing consensus calling for conceptual innovation and novel approaches to nuclear deterrence, building on nascent post-classical deterrence theorising that considers the implications of introducing non-human agents into human strategic interactions.

Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/14751798.2020.1857911 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:cdanxx:v:36:y:2020:i:4:p:422-448

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CDAN20

DOI: 10.1080/14751798.2020.1857911

Access Statistics for this article

Defense & Security Analysis is currently edited by Martin Edmonds

More articles in Defense & Security Analysis from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().