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  • Reply to "Who could have predicted that ..." · https://www.sciencedaily.com/releases/2026/03/260306224235.htm 260306224235.htm · Yes, you get what you optimize/incentivize for. Sometimes quantitative metrics are needed and helpful though. But for these cases there is quite a simple recipe: Base these metrics on open data, use a bunch of them for different contexts, and reconsider and adjust them regularly. Like that any kind of long-term gaming of these metrics can be canceled out by future versions of the metrics, and thereby the incentive to game them in the first place is (mostly) gone. · 2026-03-10 · https://w3id.org/np/RAds2HSiCg... ^

My Papers

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2025
2024-12-05
2024-09-11
2024-05-27
2024
2023-12-05
2023-12-05
2023-08-09
2023-02-21
2022-10-12

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Algorithm for generating scale-free networks. Power law distribution. Preferential attachment.
2026-02-23T19:39:27.380Z
Studying learning and behavior based on animal experiments.
2026-02-23T19:38:05.656Z
Critical points, e.g. temperature when magnet stops working, are badly understood with microscopic models. Scaling laws (?). Universality classes of systems with the same "critical-point exponents and scaling functions". Renormalization (?).
2026-02-23T19:36:31.915Z