Avoiding global congestion using decentralized adaptive agents

Everyone wants to go to a bar called El Farol if it is not crowded but would rather stay home if it is. Unfortunately, the only way to know whether or not the bar is crowded is to go. While this scenario appears far removed from the typical communications literature, it provides a simple paradigm for analyzing public goods like the Internet, which may simultaneously suffer from congestion and coordination problems, e.g., multiple users trying to connect to the same server or to use the same resource simultaneously. This paper reviews previous solutions to the El Farol Santa Fe bar problem, which typically involve complex learning algorithms. A simple adaptive strategy similar to many signal processing algorithms such as LMS and its signed variants is proposed. The strategy is investigated via simulation, and the algorithm is analyzed in a few simple cases. Unlike most signal processing applications, the objective of the adaptation is not fast and accurate parameter estimation but rather the achievement of a degree of global coordination among users