The Ramsey Lab

@theramseylab.bsky.social

The Ramsey philosophy of biology lab at KU Leuven, Belgium. https://www.theramseylab.org • #HPbio #philsci #philsky #evosky #paleosky #cogsci #AI

One week (!) left to apply to this #PhD opportunity with @sylviawenmackers.be investigating uncertainty & causality in climate models: www.kuleuven.be/personeel/jo... We would be grateful if you could share this with anyone who might be interested! More info in the 🧵👇 #philsci #philsky #HPS #AI 🧪

Centre for Logic and Philosophy of Science (CLPS)@clpskuleuven.bsky.social · 2mo ago

Are you interested in climate modeling & #ML? Sylvia Wenmackers is looking for a #philsci PhD student to examine #uncertainty & #causality in climate science 👇🌍🌡️ www.kuleuven.be/personeel/jo... Apply by August 31 & please help us spread the word! #philsky #academicsky #MLsky #envhum #HPS

Three-dimensional rendering of the Earth showing the Americas and the Atlantic Ocean. A continuous blue-to-red color scale is mapped across the globe, with cooler colors concentrated at high northern latitudes and warmer colors over much of the tropics and subtropics. Land is shown as shaded gray topography with coastlines outlined in black, against a black background.

Very interesting work that reminded me of the Bonini Paradox from computer science that I often reference in the context of stem-cell-based embryo models and organoids:

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The Ramsey Lab@theramseylab.bsky.social · 4w ago

New 📄 🚨! #CellBio is teeming with simulations comprising an extraordinarily high number of components. We argue that model complexity will not automatically lead to a better understanding of biological systems & discuss how modeling practices could be repurposed: arxiv.org/abs/2608.06998 #philsci

As simulations grow in complexity, a set of non-trivial issues emerge: diminished identifiability as free parameters become too numerous relative to exp. constraints & difficulties grasping causality. In our new 📄, we point to potential avenues to move #CellBio forward 👇 arxiv.org/abs/2608.06998

Simulating is not always understanding: When model complexity obscures biology

In cell biology, computational models of biological systems range from minimal representations with a handful of parameters to whole-cell simulations tracking thousands of molecular species across a c...

arxiv.org

The Ramsey Lab@theramseylab.bsky.social · 4w ago

New 📄 🚨! #CellBio is teeming with simulations comprising an extraordinarily high number of components. We argue that model complexity will not automatically lead to a better understanding of biological systems & discuss how modeling practices could be repurposed: arxiv.org/abs/2608.06998 #philsci

New article in @nature.com by @sylviawenmackers.be & a great team of collaborators: the widely reported finding that disruptive science is in decline may be explained by dataset artefacts. Check out the 🧵 by @vincentholst.bsky.social 👇 www.nature.com/articles/s41... #philsci #academicsky #metasci

Dataset artefacts can partially drive the measured decline in disruption - Nature

Nature - Dataset artefacts can partially drive the measured decline in disruption

nature.com

vincentholst.bsky.social@vincentholst.bsky.social · 3w ago

In 2023, @nature.com published 'Papers and patents are becoming less disruptive over time', receiving world-wide media attention. Our Matters Arising, published after a 32 month delay (more on that soon), shows that the reported decline can largely be attributed to dataset artefacts. 🧵

The average CD_5 index per year for Web of Science. The original Park et al. decline (top curve) becomes essentially flat (bottom curve) when removing papers with CD_5=1. Those papers largely correspond to dataset artefacts.

New 📄 🚨! #CellBio is teeming with simulations comprising an extraordinarily high number of components. We argue that model complexity will not automatically lead to a better understanding of biological systems & discuss how modeling practices could be repurposed: arxiv.org/abs/2608.06998 #philsci

Simulating is not always understanding: When model complexity obscures biology

In cell biology, computational models of biological systems range from minimal representations with a handful of parameters to whole-cell simulations tracking thousands of molecular species across a c...

arxiv.org

New 📄 🚨! #CellBio is teeming with simulations comprising an extraordinarily high number of components. We argue that model complexity will not automatically lead to a better understanding of biological systems & discuss how modeling practices could be repurposed: arxiv.org/abs/2608.06998 #philsci

Simulating is not always understanding: When model complexity obscures biology

In cell biology, computational models of biological systems range from minimal representations with a handful of parameters to whole-cell simulations tracking thousands of molecular species across a c...

arxiv.org

"The central challenge is no longer building models that reproduce biological behavior, but building models from which causal structure can be inferred." Very sensible paper about not just throwing everything into your simulation or model. arxiv.org/abs/2608.06998

Simulating is not always understanding: When model complexity obscures biology

In cell biology, computational models of biological systems range from minimal representations with a handful of parameters to whole-cell simulations tracking thousands of molecular species across a c...

arxiv.org

Since @philsci-archive.bsky.social has been experiencing some issues in recent days, we are also sharing the link to our recent manuscript on ResearchGate (@researchgate.bsky.social) 👇📃 www.researchgate.net/publication/... #philsci #HPBio #evosky #academicsky #paleosky #HPS

The Ramsey Lab@theramseylab.bsky.social · 3mo ago

We are very excited to share our latest 📄 (@philsci.bsky.social): a revamped version of #nicheconstruction theory that captures the evolutionary consequences of organismic activities beyond changes in selection. We call it the 'Matrix Account' 👇 philsci-archive.pitt.edu/30043/ #evobio #HPS #hpbio

Promotional poster for an academic paper titled “The Matrix Account of Niche Construction” by Grant Ramsey and Alejandro Fábregas-Tejeda. The background features cascading green code on a black field, visually evoking the aesthetic of the film The Matrix. Curved white text at the top left reads, “A Ramsey & Fabregas-Tejeda Production,” while matching curved text at the top right reads, “Appearing Soon in Philosophy of Science.” Centered near the bottom, the stylized Matrix logo appears above the subtitle “Account of,” followed by the large title “Niche Construction” in white serif lettering.