Artificial Intelligence Development Races in Heterogeneous Settings
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Abstract (arXiv)
Regulation of advanced technologies such as Artificial Intelligence (AI) has become increasingly important, given the associated risks and apparent ethical issues. With the great benefits promised from being able to first supply such technologies, safety precautions and societal consequences might be ignored or shortchanged in exchange for speeding up the development, therefore engendering a racing narrative among the developers. Starting from a game-theoretical model describing an idealised technology race in a fully connected world of players, here we investigate how different interaction structures among race participants can alter collective choices and requirements for regulatory actions. Our findings indicate that, when participants portray a strong diversity in terms of connections and peer-influence (e.g., when scale-free networks shape interactions among parties), the conflicts that exist in homogeneous settings are significantly reduced, thereby lessening the need for regulatory actions. Furthermore, our results suggest that technology governance and regulation may profit from the world's patent heterogeneity and inequality among firms and nations, so as to enable the design and implementation of meticulous interventions on a minority of participants, which is capable of influencing an entire population towards an ethical and sustainable use of advanced technologies.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Dec 2020.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 1; 2025: 1; 2026: 4.
8 papers.
- 2024Trust AI Regulation? Discerning users are vital to build trust and effective AI regulationcited by 9
- 2026Social physics in the age of artificial intelligencecited by 4
- 2025Can Media Act as a Soft Regulator of Safe AI Development? A Game Theoretical Analysiscited by 3
- 2023Both eyes open: Vigilant Incentives help Regulatory Markets improve AI Safetycited by 2
- 2022Understanding Emergent Behaviours in Multi-Agent Systems with Evolutionary Game Theorycited by 1
- 2026Strategic commitments shape collective cybersecurity under AI inequalitycited by 1
- 2026Falling Behind Drives Unsafe Development in an Idealised AI Race Experimentcited by 0
- 2026Humans Are More Diverse: Frontier LLMs Show Extreme Policies in Idealised AI Development Racescited by 0
- 2026Humans Are More Diverse: Frontier LLMs Show Extreme Policies in Idealised AI Development Races2 Aug 2026
- 2026Falling Behind Drives Unsafe Development in an Idealised AI Race Experiment28 Jul 2026
- 2026Strategic commitments shape collective cybersecurity under AI inequality10 May 2026
- 2026Social physics in the age of artificial intelligence4 Mar 2026
- 2025Can Media Act as a Soft Regulator of Safe AI Development? A Game Theoretical Analysis2 Sep 2025
- 2024Trust AI Regulation? Discerning users are vital to build trust and effective AI regulation14 Mar 2024
- 2023Both eyes open: Vigilant Incentives help Regulatory Markets improve AI Safety6 Mar 2023
- 2022Understanding Emergent Behaviours in Multi-Agent Systems with Evolutionary Game Theory15 May 2022