How are individuals’ decisions influenced by self-learning, social information and professional suggestions？

Last registered on August 24, 2023
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Pre-Trial

Trial Information

General Information

Title
How are individuals’ decisions influenced by self-learning, social information and professional suggestions？
RCT ID
AEARCTR-0011947
Initial registration date
August 18, 2023

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
August 24, 2023, 6:12 AM EDT

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

Locations

Country
Italy
Region
Rome

Primary Investigator

Name
Lihui Lin
Affiliation
Lancaster University
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Other Primary Investigator(s)

PI Name
Daniela Di Cagno
PI Affiliation
LUISS Guido Carli
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Additional Trial Information

Status
Completed
Start date
2022-10-25
End date
2022-10-28
Keywords
Behavior, Lab
Additional Keywords
JEL code(s)
Official advice; Social influence; Information mechanism; Bayesian learning
Secondary IDs
Prior work
This trial is based on or builds upon one or more prior RCTs.
Abstract
We experimentally examine how the decisions of individuals with heterogenous true states of the world are interactively influenced by self-experience, professional suggestions and non-instrumental social information, and individuals react to contradictory information between information sources. Our main research found an important factor: information congruence between information sources significantly affects the evolution of the decisions in a group. Moreover, we surprisingly found stronger irrational herding (following the social consensus) when the official suggestions were private personalized information. The possible application of advice mechanisms is discussed based on simulation.
External Link(s)

Registration Citation

Citation
Cagno , Daniela Di and Lihui Lin. 2023. "How are individuals’ decisions influenced by self-learning, social information and professional suggestions？." AEA RCT Registry. August 24. https://doi.org/10.1257/rct.11947-1.0
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Experimental Details

Interventions

Intervention(s)
Subjects were assigned into two treatments where have different information structure. In two treatments, subjects received different official suggestions generated based on common suggestion rules and private suggestion rules. Subjects were asked to make decision on insurance investment to against potential loss.
Intervention Start Date
2022-10-25
Intervention End Date
2022-10-28

Primary Outcomes

Primary Outcomes (end points)
Decision on insurance against potential loss and usage of different information sources
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The experiment is a based experiment. Subjects were given written Instructions , which were read aloud by an experimenter. In the Instructions, subjects were told about the structure of the experiment, the consequence of the bad event, the insurance scheme and the differences between subjects. However, they were not told the true probability π that the bad event will happen to them. This means that initially, they had to learn it through experience. After each round, subjects are told whether the bad event S_1 happened to them and how much they had earned. For the first 10 rounds, subjects need to take the insurance decision themselves; however, from 11 round and onwards, subjects are informed that an official suggestion and the social consensus are available. They could decide to check neither, either, or both information sources without any cost, in any round (after the 10th round).
Experimental Design Details
To test how individual's decision-makings are influenced by different information sources, we designed a decision environment and problem with the following features:
(1). We wanted to create a situation in which personal experience is costly and the optimal decision difficult to solve without a full understanding of the problem and the relevance of the information. In this situation, the demand for information is strong, but the quality of the information is ambiguous.
(2). We wanted a situation in which information from two sources is possibly contradictory. This allows us to better distinguish the effect of information sources and examine how the decision distribution evolves when facing contradictory information.
(3). We felt it was desirable to have a decision-making environment with a heterogeneous true state of the world. This structure in fundamental ways mirrors the real world, where different people in society face different situations
Randomization Method
randomization done in office by a computer
Randomization Unit
individual
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
140 subjects
Sample size: planned number of observations
4200 decisions
Sample size (or number of clusters) by treatment arms
120 in treatment 1 and 120 in treatment 2
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials