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Levin Brinkmann

@levinbrinkmann.bsky.social
91 followers 133 following 18 posts

Research Scientist | Max Planck Institute for Human Development | Understanding and shaping the dynamics of the Human-Machine Society

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Levin Brinkmann @levinbrinkmann.bsky.social · 18/08/2026
Selective social learning was the mechanism behind this shift. People reliably picked the best demonstrator, so by gen 4 every player culturally descended from a machine. Our work suggests limits: solutions must be hard to discover, easy to transmit and visibly better.
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Levin Brinkmann @levinbrinkmann.bsky.social · 18/08/2026
Human–machine populations scored higher in every generation after the machines were gone. Their moves also matched machine behaviour far more often. And when asked, participants were able to verbally express the machine-inherited strategy.
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Levin Brinkmann @levinbrinkmann.bsky.social · 18/08/2026
14 of 15 human-only populations never found the optimal strategy. Just 1 of ~600 participants discovered it. Among the 15 human–machine populations, 9 preserved it in every single generation, although the machines were only ever present in generation 0.
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Levin Brinkmann @levinbrinkmann.bsky.social · 18/08/2026
We built multi-generational cultures in the lab. 30 populations × 5 generations × 8 players (n=1,155). In hybrid populations three gen-0 players were machines; controls were human-only. Each generation picks a demonstrator from the last, watches, replays, then solves alone.
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Levin Brinkmann @levinbrinkmann.bsky.social · 18/08/2026
Humans avoid these early losses, and hence collectively failed to discover the profitable strategy (with one exception). Our AI model (DQN) on the other hand reliably identified the optimal strategy.
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Levin Brinkmann @levinbrinkmann.bsky.social · 18/08/2026
We ask participants to traverse a hidden network and collect points. To unlock the highest-paying links participants had to take early losses. Taking these early losses outperformed greedy solutions on average.
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