Prof. Kobi Gal

In many networked decision-making settings, information about the world is distributed across multiple agents and agents’ success depends on their ability to aggregate and reason about their local information over time. This paper presents a computational model of information aggregation in such settings in which agents’ utilities depend on an unknown event. Agents initially receive a noisy signal about the event and take actions repeatedly while observing the actions of their neighbors in the network at each round. Such settings characterize many distributed systems such as sensor networks for intrusion detection and routing systems for Internet traffic. Using the model, we show that (1) agents converge in action and in knowledge for a general class of decision-making rules and for all network structures; (2) all networks converge to playing the same action regardless of the network structure; and (3) for particular network configurations, agents can converge to the correct action when using a well-defined class of myopic decision rules. These theoretical results are also supported by a new simulation-based open-source empirical test-bed for facilitating the study of information aggregation in general networks.