Naive Learning with Uninformed Agents

Abhijit Banerjee, Emily Breza, Arun Chandrasekhar and Markus Mobius ()

No 25497, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: The DeGroot model has emerged as a credible alternative to the standard Bayesian model for studying learning on networks, offering a natural way to model naive learning in a complex setting. One unattractive aspect of this model is the assumption that the process starts with every node in the network having a signal. We study a natural extension of the DeGroot model that can deal with sparse initial signals. We show that an agent's social influence in this generalized DeGroot model is essentially proportional to the number of uninformed nodes who will hear about an event for the first time via this agent. This characterization result then allows us to relate network geometry to information aggregation. We identify an example of a network structure where essentially only the signal of a single agent is aggregated, which helps us pinpoint a condition on the network structure necessary for almost full aggregation. We then simulate the modeled learning process on a set of real world networks; for these networks there is on average 21.6% information loss. We also explore how correlation in the location of seeds can exacerbate aggregation failure. Simulations with real world network data show that with clustered seeding, information loss climbs to 35%.

JEL-codes: D8 D83 D85 O1 O12 Z13 (search for similar items in EconPapers)
Date: 2019-01
New Economics Papers: this item is included in nep-gth, nep-ict, nep-mic and nep-net
Note: DEV ED IO LS PE POL PR
References: Add references at CitEc
Citations: View citations in EconPapers (7)

Published as Abhijit Banerjee & Emily Breza & Arun G. Chandrasekhar & Markus Mobius, 2021. "Naïve Learning with Uninformed Agents," American Economic Review, vol 111(11), pages 3540-3574.

Downloads: (external link)
http://www.nber.org/papers/w25497.pdf (application/pdf)

Related works:
Journal Article: Naïve Learning with Uninformed Agents (2021)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nbr:nberwo:25497

Ordering information: This working paper can be ordered from
http://www.nber.org/papers/w25497

Access Statistics for this paper

More papers in NBER Working Papers from National Bureau of Economic Research, Inc National Bureau of Economic Research, 1050 Massachusetts Avenue Cambridge, MA 02138, U.S.A.. Contact information at EDIRC.
Bibliographic data for series maintained by ().