arXiv (All)
open access
Risk-Sensitive Learning in Population Games under Extreme Events: Bifurcations and Chaotic Dynamics
Inspired by nonequilibrium phenomena in game dynamics and behavioral evidence on the impact of extreme events on decision making, we investigate the nonlinear dynamics of a discrete-time multiagent learning rule in population congestion games under extreme events affecting one of the actions. The po
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Non-Stationarity Breaks Permutation Surrogates in Multi-Agent Reinforcement Learning: Diagnosis and Remedies#986848
- Reinforcing the World's Edge: A Continual Learning Problem in the Multi-Agent-World Boundary#1013520
- Goal-Conditioned Agents that Learn Everything All at Once#222616
- Efficient Replay Memory Architectures in Multi-Agent Reinforcement Learning for Traffic Congestion Control#430510
- Promoters of cooperation in evolutionary games#215491
Record · ID 1052218
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive