Gradual learning from incremental actions

Research output: Contribution to journalArticleScientificpeer-review

Abstract

We introduce a collective experimentation problem where a continuum of agents
choose the timing of irreversible actions under uncertainty and where public feedback from the actions arrives gradually over time. The leading application is the adoption of new technologies. The socially optimal expansion path entails an informational trade-off where acting today speeds up learning but postponing capitalizes on the option value of waiting. We contrast the social optimum to the decentralized equilibrium where agents ignore the social value of information they
generate. We show that the equilibrium can be obtained by assuming that agents
ignore the future actions of other agents, which lets us recast the complicated two-dimensional problem as a series of one-dimensional problems
Original languageEnglish
Peer-reviewed scientific journalTheoretical Economics
Volume20
Issue number1
ISSN1933-6837
Publication statusPublished - 01.2025
MoE publication typeA1 Journal article - refereed

Keywords

  • 511 Economics
  • Social learning, experimentation, optimal stopping, technology adoption

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