Virtually additive learning

Itai Arieli, Yakov Babichenko and Segev Shlomov

Journal of Economic Theory, 2021, vol. 197, issue C

Abstract: We introduce the class of virtually additive non-Bayesian learning heuristics to aggregating beliefs in social networks. A virtually additive heuristic is characterized by a single function that maps a belief to a real number that represents the virtual belief. To aggregate beliefs, an agent simply sums up all the virtual beliefs of his neighbors to obtain his new virtual belief. This class of heuristics determines whether robust learning, by any naive heuristic, is possible. That is, we show that in a canonical setting with a binary state and conditionally i.i.d. signals whenever it is possible to naively learn the state robustly it is also possible to do so with a virtually additive heuristic. Moreover, we show that naive learning with virtually additive heuristics can hold without the common prior assumption.

Keywords: Learning in networks; Non-Bayesian learning; Virtually additive heuristics; Information aggregation (search for similar items in EconPapers)
JEL-codes: D83 D85 (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jetheo:v:197:y:2021:i:c:s0022053121001393

DOI: 10.1016/j.jet.2021.105322

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