Spatial Pattern Formation from Multi-Agent Learning in Public Goods Dilemmas
The study investigated how agents learn movement policies in a public goods setting to create spatial patterns. It found that cooperators alone can form clusters around resource peaks, while specific learning rates and agent differences can produce traveling bands. These patterns depend on training history, and charging agents for crowding significantly recovers lost welfare.