Introduces DAF-AGI, a second-order conceptual artifact with ordinal criteria for AGI definition fitness and a structured governance audit, demonstrated on five measurement families and tested against a generative-systems arrival claim.
arXiv preprint arXiv:2401.02843 , year=
6 Pith papers cite this work, alongside 38 external citations. Polarity classification is still indexing.
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representative citing papers
A costly-information model provides an epistemic foundation that rationalizes linear, geometric, power, and multiplicative belief pooling and links the choice of rule to welfare losses and equilibrium prices.
AI risks arise from growth-oriented economies, and post-growth concepts such as satisficing, the Doughnut model, and resource caps can reduce those risks while prioritizing tool-like AI over agentic systems.
Delphi study of 272 experts finds 18 of 24 AI risks >10% likely to cause catastrophe by 2030 in business-as-usual, dropping to five under mitigations; users and public most vulnerable, developers and governments most responsible.
In a two-player preemption model, a catastrophe cost borne equally by both players cancels from the deployment comparison, producing a 'suicide region' where AGI is deployed despite negative expected value.
Reflections on quantum foundations research suggest new physics beyond quantum theory could emerge and transform information processing and AI futures.
citing papers explorer
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Definitional alignment before capability alignment: a Design-Science framework for adjudicating claims about AGI
Introduces DAF-AGI, a second-order conceptual artifact with ordinal criteria for AGI definition fitness and a structured governance audit, demonstrated on five measurement families and tested against a generative-systems arrival claim.
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Belief Aggregation under Costly Information
A costly-information model provides an epistemic foundation that rationalizes linear, geometric, power, and multiplicative belief pooling and links the choice of rule to welfare losses and equilibrium prices.
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The economic alignment problem of artificial intelligence
AI risks arise from growth-oriented economies, and post-growth concepts such as satisficing, the Doughnut model, and resource caps can reduce those risks while prioritizing tool-like AI over agentic systems.
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Prioritization of Risks from Artificial Intelligence: A Delphi Study of 272 International Experts
Delphi study of 272 experts finds 18 of 24 AI risks >10% likely to cause catastrophe by 2030 in business-as-usual, dropping to five under mitigations; users and public most vulnerable, developers and governments most responsible.
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Strategic Preemption Under Shared Catastrophic Risk: The Suicide Region and the Race to Artificial General Intelligence
In a two-player preemption model, a catastrophe cost borne equally by both players cancels from the deployment comparison, producing a 'suicide region' where AGI is deployed despite negative expected value.
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Fundamental Physics, Existential Risks and Human Futures
Reflections on quantum foundations research suggest new physics beyond quantum theory could emerge and transform information processing and AI futures.