Aspiration-Based Learning in k-Hop Best-Shot Binary Networked Public Goods Games
In public goods games, it is common for agents to learn strategies from those who possess the highest utility.However, in reality, because of the lack of information, strategies and utilities from others cannot be obtained or predicted during learning and updating.To address Car Seat Covers this issue, we introduce a learning update mechanism based