Search arXivSearch

arXiv · 2001.03246

The Logic of Strategic Assets: From Oil to Artificial Intelligence

Abstract

What resources and technologies are strategic? This question is often the focus of policy and theoretical debates, where the label "strategic" designates those assets that warrant the attention of the highest levels of the state. But these conversations are plagued by analytical confusion, flawed heuristics, and the rhetorical use of "strategic" to advance particular agendas. We aim to improve these conversations through conceptual clarification, introducing a theory based on important rivalrous externalities for which socially optimal behavior will not be produced alone by markets or individual national security entities. We distill and theorize the most important three forms of these externalities, which involve cumulative-, infrastructure-, and dependency-strategic logics. We then employ these logics to clarify three important cases: the Avon 2 engine in the 1950s, the U.S.-Japan technology rivalry in the late 1980s, and contemporary conversations about artificial intelligence.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Jeffrey Ding, Allan Dafoe. 2021-05-31. The Logic of Strategic Assets: From Oil to Artificial Intelligence. https://doi.org/10.1080/09636412.2021.1915583

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Access to Live AI Advice and Behavior Under Risk: An Incentivized Experiment

Generative AI has become an everyday advisor, and the systems people consult are live and interactive, not pre-scripted. We ask whether access to such a system changes behavior under risk. In an incentivized experiment (N = 158), participants made lottery choices with an optional decision aid presented as a conventional pre-written tool, a live one-shot AI, or a live interactive AI they could query, with information format held equivalent across conditions. Risk preferences are elicited via DOSE. We find no evidence that access to a live AI advisor changes risk aversion.

econ.GN

Bricks or Cash? Externalities of Housing Upgrading in High-density Cities

We estimate housing externalities in a high-density city, exploiting the staggered rollout of Singapore's nationwide Main Upgrading Programme for public housing. Controlling for nonrandom neighborhood exposure, we find that upgrading raises treated buildings' prices by 11.5% upon completion and neighboring buildings' resale prices by about 2% within 500 meters, decaying to zero beyond. A model with distance-decaying externalities shows that in dense settings spillovers justify the distortions of in-kind provision; this advantage diminishes and reverses at lower densities. Administrative data on over 2 million residents show that upgrading disproportionately retains older incumbents, suggesting age-specific amenities as an underexplored externality channel.

econ.GN

The Joneses Visit an Economics Lab

Existing literature offers persuasive evidence that individuals care about how their consumption compares to that of peers, and proposes a large variety of explanatory models. The present paper proposes a common framework for many of those models, and compares their ability to predict behavior in a laboratory experiment. We find evidence of Keeping up with the Joneses motivations but also find that conspicuous consumption is enhanced by Veblen motivations arising from peers' ability to observe one's own choice. Among the seven quasi-linear preference models we compare, our data are best explained by a model that contrasts envy and pride (upward vs downward comparisons) using a value function borrowed from Prospect Theory.

econ.GN