iiinova — where your digital self keeps evolving

Sergei Shevchenko @sergeishevchenko.bsky.social · 14/10/2026

First class of my Philosophy of Science course yesterday. Meno’s paradox, first led students to axiomatics. Then 11-month-olds suggested a more pragmatist answer: inquiry can begin not from explicit premises, but from expectations that fail. doi.org/10.1126/scie...

doi.org

Observing the unexpected enhances infants’ learning and exploration

Early in life, violations of core expectations provide a special opportunity for improved learning. [Also see Perspective by Schulz]

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Sergei Shevchenko @sergeishevchenko.bsky.social · 04/10/2026

Wood’s N-ray experiment looks like a suitable example. He removed something that was supposed to be necessary for the effect, but observers still reported the effect. Here, too much agreement became suspicious. Should robustness sometimes include this kind of test?

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Sergei Shevchenko @sergeishevchenko.bsky.social · 04/10/2026

Really enjoyed this, especially the point that disagreement can be useful. It made me wonder about the opposite case: when different methods agree. Wimsatt’s third step asks us to test when this agreement holds. How often should we also try to break it?

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Sergei Shevchenko @sergeishevchenko.bsky.social · 04/10/2026

Yes, partly. But many cancer models already produce individualized risk estimates, and newer models operate at a single-cell level. My point is that their integration is statistical and selective, while the cell has materially integrated far more of the relevant history.

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Sergei Shevchenko @sergeishevchenko.bsky.social · 02/10/2026

Thanks all the current and former ROTO members for their kind support!

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Sergei Shevchenko @sergeishevchenko.bsky.social · 02/10/2026

Perhaps the funniest part is that the difference between these “experienced environments” can nevertheless be mostly captured by a simple physical quantity.

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Sergei Shevchenko @sergeishevchenko.bsky.social · 02/10/2026

My new paper in Biological Theory follows that path. Evening: why are we still so limited in predicting cancer risk? Morning: look again at the cells themselves. Models integrate data; cells materially integrate much of the history we are trying to reconstruct. link.springer.com/article/10.1...

link.springer.com

The Missing Variability Problem in Cancer Prediction: Integrating Multilevel Factors - Biological Theory

Efforts to predict individual cancer risk typically rely on genomic data together with environmental factors external to the organism. Yet even when these domains are considered jointly, a substantial...

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Sergei Shevchenko @sergeishevchenko.bsky.social · 02/10/2026

Evening science steps back from the day’s work and asks the almost childish question that still matters beyond the field. Morning science turns back toward research: what do our concepts commit us to, and what should we actually measure, follow, or do differently?

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Sergei Shevchenko @sergeishevchenko.bsky.social · 02/10/2026

François Jacob distinguished day science - ordered, testable, public - from night science, where questions, guesses, and unexpected connections emerge. For a while, I’ve been thinking that philosophy can add an evening and a morning to this cycle.

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Sergei Shevchenko @sergeishevchenko.bsky.social · 02/10/2026

We are all impressed by models that integrate tens of thousands of biological variables. But here is the awkward question: if cancer research has so much data and such sophisticated models, why are we still so limited in predicting individual risk?

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