Human-algorithm interaction: Algorithmic pricing in hybrid laboratory markets

Normann, Hans‐Theo; Sternberg, Martin

doi:10.1016/j.euroecorev.2022.104347

European Economic Review

2023

DOI: 10.1016/j.euroecorev.2022.104347

|View full text |Cite

Human-algorithm interaction: Algorithmic pricing in hybrid laboratory markets

Hans‐Theo Normann

Martin Sternberg

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article3

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Dynamics of Reliance on Algorithmic Advice

Gill,

Gillenkirch,

Ortner

et al. 2024

Behavioral Decision Making

View full text Add to dashboard Cite

This study examines the dynamics of human reliance on algorithmic advice in a situation with strategic interaction. Participants played the strategic game of Rock–Paper–Scissors (RPS) under various conditions, receiving algorithmic decision support while facing human or algorithmic opponents. Results indicate that participants often underutilize algorithmic recommendations, particularly after early errors, but increasingly rely on the algorithm following successful early predictions. This behavior demonstrates a sensitivity to decision outcomes, with asymmetry: rejecting advice consistently reinforces rejecting advice again while accepting advice leads to varied reactions based on outcomes. We also investigate how personal characteristics, such as algorithm familiarity and domain experience, influence reliance on algorithmic advice. Both factors positively correlate with increased reliance, and algorithm familiarity significantly moderates the relationship between outcome feedback and reliance. Facing an algorithmic opponent increases advice rejection frequencies, and the determinants of trust and interaction dynamics differ from those with human opponents. Our findings enhance the understanding of algorithm aversion and reliance on AI, suggesting that increasing familiarity with algorithms can improve their integration into decision‐making processes.

show abstract

Dynamics of Reliance on Algorithmic Advice

Gill,

Gillenkirch,

Ortner

et al. 2024

Behavioral Decision Making

View full text Add to dashboard Cite

show abstract

Demand forecasting, signal precision, and collusion with hidden actions

Martin,

Rasch

2024

International Journal of Industrial Organization

View full text Add to dashboard Cite

A new sociology of humans and machines

Tsvetkova,

Yasseri,

Pescetelli

et al. 2024

Nat Hum Behav

View full text Add to dashboard Cite

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Human-algorithm interaction: Algorithmic pricing in hybrid laboratory markets

Cited by 3 publications

References 58 publications

Dynamics of Reliance on Algorithmic Advice

Dynamics of Reliance on Algorithmic Advice

Demand forecasting, signal precision, and collusion with hidden actions

A new sociology of humans and machines

Contact Info

Product

Resources

About