Abstract :
In this article, we study social networks of agents, where agents learn not only from private signals (i.e., signals only available to the agents receiving them), but from other agents too. Based on all the available information, agents modify their beliefs in events of interest and make decisions on which actions to take based on the beliefs. In doing so, they optimize functions that reflect some (cumulative) reward. This problem has been studied in various disciplines including control theory, operations research, artificial intelligence, game theory, information theory, economics, statistics, computer science, and signal processing.
Keywords :
artificial intelligence; belief networks; computer science; control theory; game theory; information theory; learning (artificial intelligence); multi-agent systems; operations research; signal processing; artificial intelligence; computer science; control theory; disturbed Bayesian learning; economics; game theory; information theory; multiagent system; operation research; signal processing; social network; statistics; Computer applications; Decision making; Social network services; Software agents;
