We examine how well people learn when information is noisily relayed from person to person, subject to: dropping, mutation, and deliberate manipulation. This allows us to explore how communication platforms can improve learning without censoring or even examining messages, but purely by limiting the number of times a message can be relayed or the number of people to whom someone can forward a message. In particular, we analyze learning as a function of the network depth (length of relay chains) and breadth (how many chains a person has access to). Noise builds up as depth increases and so learning requires greater breadth, which we show to be characterized via a sharp threshold above which the receiver learns fully and below which the receiver learns nothing. Moreover, we show that small uncertainty about the rates of mis- and dis-information make learning from long chains of messages impossible. Optimizing learning requires either limiting depth (by controlling how many times a message can be forwarded), or if that is not possible then limiting breadth (by capping the number of people to whom someone can forward a message). Although limiting breadth decreases the overall amount of information a learner has access to, it increases the relative fraction of messages that are coming from nearby compared to far away in the network, and thus increases the signal to noise ratio. Such policies do not require the ability to fact-check, respect privacy, increase the fraction of true to false messages, and have been implemented by communication platforms. Finally, we extend our model to study learning from dropping rates (e.g., people are more likely to pass messages with one conclusion than another). We find that as the distance to primary sources grows, all learning comes from either the total number of messages received or from the content of received messages, but the learner does not need to pay attention to both.

Whether an idea, information, disease, or innovation diffuses throughout a society depends not only on the structure of the network of interactions, but also on the timing of those interactions. Recent studies have shown that diffusion can fail on a network in which people are only active in "bursts," active for a while and then silent for a while, but diffusion could succeed on the same network if people were active in a more random Poisson manner. Those studies generally consider models in which nodes are active according to the same random timing process and then ask which timing is optimal. In reality, people differ widely in their activity patterns -- some are bursty and others are not. We model diffusion on networks in which agents differ in their activity patterns. We show that bursty behavior does not always hurt the diffusion, and in fact having some (but not all) of the population be bursty significantly helps diffusion. We prove that maximizing diffusion requires heterogeneous activity patterns across agents, and the overall maximizing pattern of agents' activity times does not involve any Poisson behavior.

How can we identify the most influential nodes in a network for initiating diffusion? Are people able to easily identify those people in their communities who are best at spreading information, and if so How? Using theory and recent data, we will examine these questions and see how the structure of social networks affects information transmission ranging from gossip to the diffusion of new products. In particular, the concept of diffusion centrality from Banerjee, Chandrasekhar, Duflo, and Jackson (2013) will be considered and shown to nest degree centrality, eigenvector centrality, and other measures of centrality as extreme special cases. Then it will be shown that by tracking gossip within a network, nodes can easily learn to rank the centrality of other nodes without knowing anything about the network itself. Finally, the theoretical predictions will be tested with data. The results are presented in Banerjee, Chandrasekhar, Duflo, and Jackson (2014).

Article Categorical cognition: a psychological model of categories and identification in decision making: extended abstract Share on Authors: Roland G. Fryer University of Chicago, Chicago, Illinois University of Chicago, Chicago, IllinoisView Profile , Matthew O. Jackson Institute of Technology, Pasadena, California Institute of Technology, Pasadena, CaliforniaView Profile Authors Info & Claims TARK '03: Proceedings of the 9th conference on Theoretical aspects of rationality and knowledgeJune 2003 Pages 29–34https://doi.org/10.1145/846241.846245Online:20 June 2003Publication History 1citation593DownloadsMetricsTotal Citations1Total Downloads593Last 12 Months37Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access