Boundedly rational information demand

Last registered on June 14, 2023

View Trial History

Pre-Trial

Trial Information

General Information

Title

Boundedly rational information demand

RCT ID

AEARCTR-0010543

Initial registration date

November 29, 2022

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published

November 30, 2022, 4:59 PM EST

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Last updated

June 14, 2023, 9:31 PM EDT

Last updated is the most recent time when changes to the trial's registration were published.

Locations

Country

United States of America

Region

Primary Investigator

Name

Yucheng Liang

Affiliation

Carnegie Mellon University

Contact Primary Investigator

Other Primary Investigator(s)

Additional Trial Information

Status

On going

Start date

2022-11-30

End date

2023-06-30

Keywords

Behavior

Additional Keywords

JEL code(s)

Secondary IDs

Prior work

This trial does not extend or rely on any prior RCTs.

Abstract

This experiment will study how information demand is affected by the decision an individual is about to make.

External Link(s)

Registration Citation

Citation

Liang, Yucheng. 2023. "Boundedly rational information demand." AEA RCT Registry. June 14. https://doi.org/10.1257/rct.10543-4.0

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

Intervention Start Date

2022-11-30

Intervention End Date

2023-06-30

Primary Outcomes

Primary Outcomes (end points)

Demand for information

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Secondary Outcomes (explanation)

Experimental Design

The experiment varies the decision participants will make and measures their demand for information before the decision.

Experimental Design Details

Participants choose between two independent lotteries, D and O. They get a $3 bonus if their chosen lottery wins, $0 if it doesn't win. D's winning chance is higher than O's, and both are known. Participants face several scenarios with different winning chances for D and O.
D-Information treatment: Participants are asked "How much does knowing D's outcome before choosing the lottery increase your chance of getting a bonus?"
O-Information treatment: Participants are asked "How much does knowing O's outcome before choosing the lottery increase your chance of getting a bonus?"
Inconclusive good news treatment: D and O are not independent; one and only one of them wins. Information is biased toward D and against O. Participants are asked "How much does knowing the lotteries' outcome before choosing the lottery increase your chance of getting a bonus?"
Willingness-to-pay treatment: Same as D-Information treatment, except that the question is incentivized as WTP: participants pay for information by giving up winning chances in a different game.
Strategy method treatment: Same as D-Information treatment, except that participants first make a contingent plan for their lottery choices conditional on the realized information/no information, then answer the information demand question.
O-backs-up-D treatment: Participants are ask "How much does the following option increase your chance of getting a bonus: having O as a back-up for D so that if D is chosen and it doesn't win, you can still get the bonus so long as O wins."
D-backs-up-O treatment: Participants are ask "How much does the following option increase your chance of getting a bonus: having D as a back-up for O so that if O is chosen and it doesn't win, you can still get the bonus so long as D wins."
D-Information treatment with dominated O: Same as D-Information treatment, except that participants first answer the information demand question in a task where O's winning chance is zero.
O-Information treatment with dominated O: Same as O-Information treatment, except that participants first answer the information demand question in a task where O's winning chance is zero.
Correlated lotteries treatment: D and O are not independent; at most one of them wins. Participants are asked "How much does knowing D's outcome before choosing the lottery increase your chance of getting a bonus?"
Advisee treatment: Same as D-Information treatment, except that participants first read two pieces of advice written by participants from some previous treatments. One piece of advice argues that demand for information should increase with O's winning chance, the other argue the opposite. Advisees select the more convincing advice, then proceed to report their information demand.

Randomization Method

randomization done through Qualtrics

Randomization Unit

individual

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

1100 individuals

Sample size: planned number of observations

1100 individuals

Sample size (or number of clusters) by treatment arms

D-Information treatment: 150
O-Information treatment: 150
Inconclusive good news treatment: 75
Willingness-to-pay treatment: 75
Strategy method treatment: 75
O-backs-up-D treatment: 150
D-backs-up-O treatment: 150
D-Information treatment with dominated O: 75
O-Information treatment with dominated O: 75
Correlated lotteries treatment: 75
Advisee treatment: 50

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Supporting Documents and Materials

IRB