On informational nudging and control of payoff-based learning

Robin Guers, Cedric Langbort, Dan Work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We investigate a model of informational nudging in a context inspired by repeated games in traffic. Starting from a simple payoff-based learning model for an individual decision-maker (DM) choosing among multiple alternatives, we introduce a recommender who provides possibly misleading payoff information for unchosen options, so as to drive the DM's preferences to a desired equilibrium. This kind of white lie on the part of the recommender can be seen as an informational nudge in the sense of Thaler & Sunstein, and may thus arguably present some benefits over monetary incentive- based strategies for the purposes of planning. Considering the fluid limit of our simplified model, we show that the recommender can create (but not necessarily globally stabilize) any outcome he desires using constant lying strategies. We also identify a framing effect, in the sense that lies about the least favorable option has a different effect compared to lies on most favorable option.

Original languageEnglish (US)
Title of host publication4th IFAC Workshop on Distributed Estimation and Control in Networked Systems, NecSys 2013 - Proceedings
Pages69-74
Number of pages6
EditionPART 1
DOIs
StatePublished - 2013
Event4th IFAC Workshop on Distributed Estimation and Control in Networked Systems, NecSys 2013 - Koblenz, Germany
Duration: Sep 25 2013Sep 26 2013

Publication series

NameIFAC Proceedings Volumes (IFAC-PapersOnline)
NumberPART 1
Volume4
ISSN (Print)1474-6670

Other

Other4th IFAC Workshop on Distributed Estimation and Control in Networked Systems, NecSys 2013
CountryGermany
CityKoblenz
Period9/25/139/26/13

Keywords

  • Learning
  • Stochastic approximation
  • Traffic control

ASJC Scopus subject areas

  • Control and Systems Engineering

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