Learning from others: an experimental test of Brownian motion uncertainty models: an experimental test of Brownian motion uncertainty models

David Glick, C. Daniel Myers

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Models of decision-making with outcome uncertainty are common in political science and related fields. Recent work in flagship journals has challenged canonical work by modeling outcome uncertainty as Brownian motion. This theoretical innovation has resonated because it is highly tractable and captures intuitively important realities of many decisions in ways that earlier models cannot. As theoretically attractive as the new models are, they have not yet been evaluated empirically. This is especially important because Brownian motion models place actors in more cognitively demanding situations than previous models. We offer what we believe to be the first experimental test of actors’ ability to behave in ways consistent with the Brownian motion model by evaluating subjects’ ability to rationally learn from another actor’s experiences. We show that subjects adjust their actions in response to the Brownian motion uncertainty. However, they deviate from optimal behavior in important ways, particularly in more complex situations.
Original languageEnglish (US)
Pages (from-to)588-612
Number of pages25
JournalJournal of Theoretical Politics
Volume27
Issue number4
Early online dateDec 17 2014
DOIs
StatePublished - Oct 1 2015

Bibliographical note

Funding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This project received funding from the Princeton Laboratory for Experimental Social Science, Boston University, and the Robert Wood Johnson Foundation Scholars in Health Policy Program.

Publisher Copyright:
© 2014, © The Author(s) 2014.

Keywords

  • Brownian Motion
  • learning
  • policy diffusion
  • policy experimentation

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