Effect of different peer information on dynamic optimization decisions

Last registered on December 17, 2021

Pre-Trial

Trial Information

General Information

Title
Effect of different peer information on dynamic optimization decisions
RCT ID
AEARCTR-0008602
Initial registration date
December 05, 2021
Last updated
December 17, 2021, 12:48 AM EST

Locations

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Primary Investigator

Affiliation
The University of Queensland

Other Primary Investigator(s)

PI Affiliation
The University of Queensland
PI Affiliation
The University of Queensland

Additional Trial Information

Status
In development
Start date
2021-12-06
End date
2022-12-31
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This study investigates how different peer information affect individuals' dynamic optimization decisions over repeated lifecycles. We compare individuals who make decisions alone over the lifecycles to the others who receive either a selected peer's decisions only, decisions along with outcome, or decisions along with confidence in a selected sequence. For this purpose, this study uses a simple deterministic decision making environment with no savings or borrowings where an individual's current decisions affect the future outcomes through the utility function. The outcomes of this study would help to identify how and what peer information can be used to nudge individuals in optimal decision making paths.
External Link(s)

Registration Citation

Citation
Friesen, Lana, Sembukutti Arachchige Bhagya Niroshini Gunawardena and Kenan Kalayci. 2021. "Effect of different peer information on dynamic optimization decisions." AEA RCT Registry. December 17. https://doi.org/10.1257/rct.8602-1.1
Experimental Details

Interventions

Intervention(s)
This study provides a selected peer's information for the subjects in treatment groups. The treatments are as follows.
1. Decisions only
2. Decisions with the outcome
3. Decisions with confidence

All the subjects participate in the experiment through the online platform Prolific and the intervention will be undertaken while they are performing the main task.
Intervention Start Date
2021-12-06
Intervention End Date
2022-12-31

Primary Outcomes

Primary Outcomes (end points)
Individuals' earnings in each sequence of the main task and their confidence in earnings
Primary Outcomes (explanation)
The earnings in each sequence are the accumulated difference between the output and cost of input over the periods of each sequence and how these are calculated is explained in detail to the subjects. Confidence is self-reported by the subjects.

Secondary Outcomes

Secondary Outcomes (end points)
Individuals' decisions in each period of repeated lifecycles (sequences), the difference between the optimal decisions and individuals' decisions, and the difference between the myopic decisions and individuals'
Secondary Outcomes (explanation)
Difference between optimal decisions and individuals' decisions: This study calculates the unconditional optimal, myopic and conditional (conditioning on opening stock) optimal and myopic decisions and then the difference between those variables and individuals' actual decisions they made during the experiment.

Experimental Design

Experimental Design
Subjects start with instructions to the main task in which they need to decide how much of their endowment to allocate as input in production. Subjects are informed that this task consists of 5 sequences of decision making, where each sequence consists of 10 periods. At the end of each sequence, subjects need to elicit their confidence about their decision. After the main task of decision making, subjects face two attempts of a task which measures the ability of backward thinking, a risk elicitation task, and a demographic survey. Except for the demographic survey, all the additional tasks are incentivised. At the end of these tasks, subjects receive information on their earnings in each task and total earnings.
Experimental Design Details
Not available
Randomization Method
Randomization is done by the computer program (oTree).
Assuming subjects arrive in the experiment in a random order, the first subject register in the study will be assigned to treatment zero (control), the second subject into treatment one, etc. If a subject drops out before completing the experiment, a new place is opened by the Prolific, and a new subject can register. The treatment for this newly registered subject will be the next treatment from the last treatment. That is, if the last subject (before someone drops out) was assigned to treatment x, then this newly registered subject will be assigned to treatment x+1. The treatments are allocated iteratively (i.e. 0,1,2,3,0,1,...) to minimize treatment imbalance.
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
500 individuals
Sample size: planned number of observations
500 individuals
Sample size (or number of clusters) by treatment arms
125 per each treatment - cluster is an individual
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials

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IRB

Institutional Review Boards (IRBs)

IRB Name
The University of Queensland Human Research Ethics Committees
IRB Approval Date
2021-11-08
IRB Approval Number
2021/HE002132
Analysis Plan

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