Complexity and the demand and supply of narratives - Expert treatment

Last registered on October 28, 2024
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Pre-Trial

Trial Information

General Information

Title
Complexity and the demand and supply of narratives - Expert treatment
RCT ID
AEARCTR-0014618
Initial registration date
October 20, 2024

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
October 28, 2024, 12:54 PM EDT

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

Locations

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Primary Investigator

Name
Renke Schmacker
Affiliation
WZB & DIW Berlin
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Other Primary Investigator(s)

PI Name
Rustamdjan Hakimov
PI Affiliation
University of Lausanne
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PI Name
Tiziano Rotesi
PI Affiliation
Brown University
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Additional Trial Information

Status
In development
Start date
2024-10-20
End date
2024-12-31
Keywords
Behavior
Additional Keywords
Behavior, Narratives, Competition
JEL code(s)
C91, D83, D01
Secondary IDs
Prior work
This trial is based on or builds upon one or more prior RCTs.
Abstract
This project studies the effect of complexity on the selection of models in a market environment. We simulate a market involving three types of agents – buyers, non-expert sellers, and expert sellers who have access to better data statistics – interacting over multiple rounds. In this "market for models", all agents have access to the same data but must choose among different models that explain these data. Buyers are incentivized to select the most accurate model, while sellers also have an incentive to offer a model that gets chosen by the buyer. Our study will first assess how increased complexity affects the dynamics of model selection from both the sellers' and the buyers' perspectives.

This experiment builds on a previous experiment (https://www.socialscienceregistry.org/trials/13350) and adds one treatment, in which the agent are aware of the fact that one seller is an expert.
External Link(s)

Registration Citation

Citation
Hakimov, Rustamdjan , Tiziano Rotesi and Renke Schmacker. 2024. "Complexity and the demand and supply of narratives - Expert treatment." AEA RCT Registry. October 28. https://doi.org/10.1257/rct.14618-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
2x2 treatment design

Univariate&Bivariate vs. Univariate&Interacted: In the first treatment arm, we vary the complexity of the data-generating process (with univariate being the simplest and interacted being the most complex model).

Baseline vs. Expert: In the second treatment arm, we vary whether subjects know that one of the sellers is an expert. In Baseline, only experts know that they have additional information. In Expert, also buyers and non-expert sellers are informed of the existence of an expert.
Intervention Start Date
2024-10-20
Intervention End Date
2024-12-31

Primary Outcomes

Primary Outcomes (end points)
(i) Share of correct models offered (1) by experts, and (2) by non-experts
(ii) Share of buyers who select the correct model (1) before seeing the recommendations (2) after seeing the recommendations
(iii) Share of experts selected, share of non-experts selected
(iv) Share of overly simplistic models (by experts and non-experts)
(v) Share of overly complex models (by experts and non-experts)
(vi) Share of (1) non-experts and (2) experts who sell the model the buyer selected before
(vii) Payouts
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
See below
Experimental Design Details
Not available
Randomization Method
Randomisation by a computer
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
Target sample: 1137 to 1257 subjects in total

(The given sample size consists of the Baseline participants from the previous experiment and the participants from the Expert treatment, which are preregistered in this preregistration. The final sample depends on the budget and how many subjects can be matched into groups of three. Those who cannot be matched have to be paid the participation fee and it is a priori not clear how many these will be.)
Sample size: planned number of observations
Target sample size: 1137 to 1257 subjects in total
Sample size (or number of clusters) by treatment arms
Target sample size:
Baseline: 303 univ/biv + 324 univ/inter (from previous experiment)
Expert: 255-324 univ/biv + 255-324 univ/inter (85-108 groups)

(The given sample size consists of the Baseline participants from the previous experiment and the participants from the Expert treatment, which are preregistered in this preregistration. The final sample depends on the budget and how many subjects can be matched into groups of three. Those who cannot be matched have to be paid the participation fee and it is a priori not clear how many these will be.)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
H1: Assuming a share of buyers selecting one of the offered models in Baseline/Interacted of 70%, with 100 groups per treatment arm, we can detect an increase of 16 ppts with 80% power at the p=0.05 level. H2: Assuming a share of correct buyers in Baseline/Interacted of 37%, with 100 groups per treatment arm, we can detect an increase of 20 ppts with 80% power at the p=0.05 level. H3: Assuming a share of experts being chosen in Baseline of 45%, with 100 groups per treatment arm, we can detect an increase of 20 ppts with 80% power at the p=0.05 level. H4: Assuming a share of experts offering the correct model of 68%, with 100 groups per treatment arm, in the last round in Baseline/Interacted, we can detect an increase of 17 ppts with 80% power at the p=0.05 level.
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
WZB Research Ethics Review
IRB Approval Date
2023-12-04
IRB Approval Number
2023/11/224
Analysis Plan