Similarity and consistency in algorithm-guided exploration

Research output: Preprint/Documentation/ReportWorking paper

Contributors

  • Yongping Bao - , University of Bremen (Author)
  • Ludwig Danwitz - , University of Bremen (Author)
  • Fabian Dvorak - , University of Konstanz (Author)
  • Sebastian Fehrler - , University of Bremen (Author)
  • Lars Hornuf - , University of Bremen (Author)
  • Hsuan Yu Lin - , University of Bremen (Author)
  • Bettina von Helversen - , University of Bremen (Author)

Abstract

Algorithmic advice holds the potential to significantly enhance human decision-making, particularly in dynamic and complex tasks involving a trade-off between exploration and exploitation. We investigate the conditions under which people are willing to accept advice from algorithms in such tasks, focusing on the interplay between individuals’ and the advising algorithm’s exploration preferences. In an online experiment, we engineered reinforcement learning algorithms to favor either exploration or exploitation and observed participants’ decision-making processes, modeling them using a cognitive framework comparable to the algorithm. Interestingly, individuals showed a greater inclination to follow the advice of exploitative, consistent algorithms, possibly perceiving algorithmic consistency as a sign of competence. They did not exhibit a preference for algorithms with similar exploration tendencies to their own. Moreover, we found that participants’ exploration tendencies influenced the behavior of the algorithms, underscoring the importance of considering the mutually reinforcing co-behaviors of algorithms and humans.

Details

Original languageEnglish
Number of pages50
Publication statusPublished - 2022
Externally publishedYes

Publication series

SeriesCESifo working papers
Number10188
ISSN1617-9595
No renderer: customAssociatesEventsRenderPortal,dk.atira.pure.api.shared.model.researchoutput.WorkingPaper

External IDs

ORCID /0000-0002-0576-7759/work/142239303
Mendeley 3e3af7fc-7941-3527-b8a7-faf245ccb719