We're wired to want closure. The moment information tips us far enough in one direction, we stop gathering more and call it a decision – and that instinct, argues Ohio State decision scientist Dr. Peter Kvam, is quietly costing us better judgment, in politics, relationships, and now in AI systems too.

Dr. Kvam – head of Ohio State University’s Decision Sciences Collaborative – walks through why rushing to commit makes opinions more extreme, not more accurate: once you decide, you stop searching for information that might complicate the picture, and the "loudest" information (the most emotionally charged, most polarizing) is what gets you there fastest. We trace how that same rush-to-judgment pattern shows up when radiologists lean too hard on AI summaries, when a cancer-detection model quietly learned to read a hospital's watermark instead of the actual pathology, and when any of us fill out a spreadsheet to "decide" a job offer we'd already picked in our gut. Along the way, Kvam offers a genuinely useful reframe – rate the quality of your options instead of forcing a binary choice – and explains, in plain language, why "measuring" a belief (i.e. making someone decide) is mathematically similar to what happens when you measure a quantum particle.

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Links:

📖Substack: https://superposeddecisions.substack.com/

📺YouTube: @Superposed_Decisions

🤝LinkedIn: https://www.linkedin.com/in/aidan-m-lewis/

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What you'll take away:

• Why the fastest, most confident decisions are usually the least informed ones – and why "sitting in uncertainty" is a decision-making skill, not a weakness

• Why AI's most dangerous errors don't look like mistakes – they look like a cancer-detection model that's "right" for a reason that has nothing to do with cancer

• Why simply reframing a decision as "rate this option's quality" instead of "should I choose this" measurably reduces polarization and extremism

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Primary Sources

Kvam, P. D., Sokratous, K., Fitch, A., & Hintze, A. (2024). Using artificial intelligence to fit, compare, evaluate, and discover computational models of decision behavior. Decision, 11(4), 599–618. https://doi.org/10.1037/dec0000237

Kvam, P. D., Marley, A. A. J., & Heathcote, A. (2023). A unified theory of discrete and continuous responding. Psychological Review, 130(2), 368–400. https://doi.org/10.1037/rev0000378

Kvam, P. D., Alaukik, A., Mims, C. E., Martemyanova, A., & Baldwin, M. (2022). Rational inference strategies and the genesis of polarization and extremism. Scientific Reports, 12, 7344. https://doi.org/10.1038/s41598-022-11389-0

Konstantina Sokratous, Anderson K. Fitch, Peter D. Kvam,

How to ask twenty questions and win: Machine learning tools for assessing preferences from small samples of willingness-to-pay prices, Journal of Choice Modelling, 48, 2023, https://doi.org/10.1016/j.jocm.2023.100418

P.D. Kvam,T.J. Pleskac,S. Yu, & J.R. Busemeyer, Interference effects of choice on confidence: Quantum characteristics of evidence accumulation, Proc. Natl. Acad. Sci. U.S.A. 112 (34) 10645-10650, https://doi.org/10.1073/pnas.1500688112 (2015)

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Secondary Sources:

Cognition & Decision Modeling Laboratory, Dr. Peter Kvam (Ohio State University): https://peterkvam.com/research.html

The Ohio State University Decision Sciences Collaborative: https://decisionsciences.osu.edu/

Dr. Peter Kvam Faculty Page: https://psychology.osu.edu/people/kvam.4