Na\"ive Population

Research on "naive populations" spans diverse fields, focusing on understanding and modeling the behavior of initially uninformed or untrained systems, whether they be neural networks, human populations facing crises, or agents in game-theoretic settings. Current research employs various techniques, including hyper-representation learning for neural networks, agent-based modeling for social dynamics and epidemics, and machine learning algorithms for population clustering and classification. This work aims to improve model interpretability, enhance the robustness of AI systems, and provide insights into complex social and biological phenomena, ultimately leading to more effective strategies in areas such as public health, AI development, and resource management.