Ahmed El Hady
@zamakany
Group leader at Center for advanced Study of Collective Behavior CEO and co-founder of Ecodylic Science Behavior, Biophysics, Quantification, Analytics
For this year Konstanz School of Collective Behavior, the students designed a shirt :D @cbehav.bsky.social
The best part of the Konstanz School of Collective Behavior is the student project presentations . This year: 14 students, 14 projects @cbehav.bsky.social
We summarized our results in the following table to generate hypothesis for experiments and field studies.
How does the performance of different types of agents when it comes to learning the statistical structure of the environment ? Variable patches favor timing over counting, and a model pays until it is wrong
What if the patches are replenishing ? Replenishment sets the reward-seeker’s orbit, the orbits attract, and replenishment splits rate learning from composition learning.
Foragers can be reward or information seekers. Reward- and information-seeking foragers diverge in observable patch-leaving behavior, and the direction of the divergence depends on what remains uncertain. We quantify this across environmental structures.
The patch-type fraction is learned slowly, and depletion does not affect it
Patch yield rates are learned faster in depleting than non-depleting environments, in homogeneous and binary environments alike
Foraging can seen as hierarchical statistical inference: Animal moves among patches, each with its own yield rate setting the rate at which it encounters resources then, Within a patch, the animal encounters resources and the posterior over the patch’s initial count sharpens from early to late.
New Study! @cbehav.bsky.social @mpi-animalbehav.bsky.social Often foraging models assume that the forager knows the environment and proceed to predict decision strategies but how does the foraging learn the structure of the environment ? A new preprint delves into this question
Then a tour de force by Iain Couzin highlighting the importance of geometry for decision making unifying concepts from physics , neuroscience and ecology .
Now a morning tutorial on dynamical systems approach to decision making by Anastasia Bizyaeva
Now @allysonsgro.bsky.social is showing us how slime molds show amazing collective behavior
The yearly tradition begins again . Welcoming all our amazing students to the Konstanz School of Collective Behavior 2026 . So happy it is happening again . All details here : www.exc.uni-konstanz.de/kscb/ @cbehav.bsky.social @uni-konstanz.de
So happy that our study on patch foraging in Drosophila larvae is out in @elife.bsky.social Check it out here: elifesciences.org/reviewed-pre...
Doing some physics with a view . Working on a paper in a good fresh mood overlooking this bright ocean ☺️❤️ also thumbs up @overleaf.com the best 🙌🏾 @kitp-ucsb.bsky.social
In my review on Integrative approach to foraging, we propose six axes on which experiments can be plotted, hoping to inspire experimenters to push further along one or more of these axes. Think about your experiments and plot it on those axes :D More here: www.annualreviews.org/content/jour...