Virtually additive learning

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number105322
JournalJournal of Economic Theory
Volume197
DOIs
StatePublished - Oct 2021

Keywords

  • Information aggregation
  • Learning in networks
  • Non-Bayesian learning
  • Virtually additive heuristics

All Science Journal Classification (ASJC) codes

  • Economics and Econometrics

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