Pages from the paper titled, "Strategic Reflectivism in Intelligent Systems"

Upon Reflection, Ep. 16: Strategic Reflectivism

In late 2025, artificial intelligence companies like OpenAI popularized the idea of automating the process of selecting which model is best for a task. This allowed users to simply send their prompt or request and let the system determine whether to respond using a “fast” or a “slow” reasoning model.

In a 2022 paper, I suggested that this kind of strategic deployment of slow, more reflective reasoning could be crucial to good judgment and decision-making. Since then, I have been synthesizing the arguments and evidence for this view in a paper titled, “Strategic Reflectivism in Intelligent Systems”.

After the new paper was accepted in Lecture Notes In Computer Science, I recorded a podcast of me reading the final proofs of the paper for the podcast. I review evidence suggesting that one key to intelligence in humans and machines is pragmatic switching between intuitive and reflective thinking based on the goals of the system. The paper has a wide range of implications for applied science, computer science, decision science, and epistemology.

Page 2 of the paper titled, "Strategic Reflectivism In Intelligent Systems"
Byrd, N. (2025). Strategic Reflectivism In Intelligent Systems. Lecture Notes In Computer Science. Preprint: https://doi.org/10.48550/arXiv.2505.22987

As always, free preprints of my papers are available on my CV at byrdnick.com/cv under “Publications”.

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Nick Byrd

Nick is a cognitive scientist at Florida State University studying reasoning, wellbeing, and willpower. Check out his blog at byrdnick.com/blog