✨ Remarkably, yet the long-run average number of agents on route 1 settles on the social-optimum / Nash equilibrium (bottom right) ⛳️, despite the day-to-day head-count of route 1 being provably chaotic (bottom left)! 🌪️
✨ Remarkably, yet the long-run average number of agents on route 1 settles on the social-optimum / Nash equilibrium (bottom right) ⛳️, despite the day-to-day head-count of route 1 being provably chaotic (bottom left)! 🌪️
Results: When some agents learn (adapt) very fast, their individual strategies turn chaotic 🌪️. Top panel - x axis: agent type with different learning rates, y-axis fraction of that agent selecting route 1.
Results: When some agents learn (adapt) very fast, their individual strategies turn chaotic 🌪️. Top panel - x axis: agent type with different learning rates, y-axis fraction of that agent selecting route 1.
Ever wondered how tools from statistical physics can help understand learning in diverse reinforcement-learning populations?
Check out our new PNAS paper (Special Feature: Collective Artificial Intelligence & Evolutionary Dynamics) here pnas.org/doi/10.1073/...
#PNASNews
Ever wondered how tools from statistical physics can help understand learning in diverse reinforcement-learning populations?
Check out our new PNAS paper (Special Feature: Collective Artificial Intelligence & Evolutionary Dynamics) here pnas.org/doi/10.1073/...
#PNASNews
Noise isn't just disruptive; it can enhance neural computations, especially in working memory tasks!
Biologically plausible RNNs harnessing noise also operate near the “edge of chaos,” supporting the critical brain hypothesis 🧠✨
Check out 👇
www.pnas.org/doi/10.1073/...
Noise isn't just disruptive; it can enhance neural computations, especially in working memory tasks!
Biologically plausible RNNs harnessing noise also operate near the “edge of chaos,” supporting the critical brain hypothesis 🧠✨
Check out 👇
www.pnas.org/doi/10.1073/...