An experiment on algorithmic mediation

Last registered on February 19, 2026
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Pre-Trial

Trial Information

General Information

Title
An experiment on algorithmic mediation
RCT ID
AEARCTR-0017830
Initial registration date
February 13, 2026

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
February 19, 2026, 7:17 AM EST

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
Sukran Dinc
Affiliation
York University
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Other Primary Investigator(s)

PI Name
Selcuk Ozyurt
PI Affiliation
York University
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PI Name
Onur Kesten
PI Affiliation
The University of Sydney
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Additional Trial Information

Status
In development
Start date
2026-02-15
End date
2026-03-31
Keywords
Behavior, Gender, Lab, Other
Additional Keywords
Algorithmic mediation, Gender, Bargaining, Online dispute resolution, Strategy-proof mechanisms
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Mediation is widely used because it is faster, cheaper, and more flexible than courts. Yet a growing body of evidence suggests that the informality of mediation can disproportionately disadvantage less powerful parties, particularly those marginalized by gender, race, or ethnicity. In response, demand for more structured and rigorous mediation practices, combined with recent technological advances, has spurred the growth of Online Dispute Resolution (ODR) systems. In a standard ODR system, mediator is an algorithm defined by predetermined rules. Although these protocols are designed to deliver impartial outcomes, we know little about how gender visibility shapes behavior and outcomes in algorithmic mediation. We study this question in a controlled experiment using the algorithmic mediation mechanism proposed by \citet{kesten2025strategy}. We measure negotiation behavior, including participants’ demands, acceptance decisions, and beliefs about others’ behavior, and we collect detailed perceptions of fairness and the salience of gender in decision-making. We manipulate gender visibility across two experimental conditions to examine its effects on evaluation outcomes.

External Link(s)

Registration Citation

Citation
Dinc, Sukran , Onur Kesten and Selcuk Ozyurt . 2026. "An experiment on algorithmic mediation ." AEA RCT Registry. February 19. https://doi.org/10.1257/rct.17830-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
The study investigates (i) individuals' negotiation behaviors and beliefs under the algorithmic mediation protocol, and (ii) whether these behaviors, beliefs, and preferences differ across gender visibility conditions. In the gender treatment, negotiators observe gender-revealing icons; in the baseline treatment, only neutral icons are used. Full details are provided in the pre-analysis plan.



Intervention Start Date
2026-02-15
Intervention End Date
2026-03-31

Primary Outcomes

Primary Outcomes (end points)
Participants' demand above their backup option (sincerity).
Successful resolution of negotiation (compatible demands and mutual acceptance).
Fairness of outcomes across matched pairs with swapped backup options.
Total welfare (sum of realized payoffs).
See the pre-analysis plan for operationalizations and measurement procedures.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
This is a three-part incentivized online study on decision-making and negotiation with computer assistance. Participants complete a series of short bargaining tasks for points that affect payment, followed by additional tasks measuring social preferences and beliefs about others’ decisions. In some sessions, participants see simple profile cues about their counterpart, while in others these cues are not shown. Full procedures and analysis plans are documented in the pre analysis plan.
Experimental Design Details
Not available
Randomization Method
Randomization is conducted by computer using Qualtrics' built-in randomization logic, based on pre-assigned quotas and random number generators.
Randomization Unit
Group-level randomization for gender visibility treatment; individual-level randomization for backup options and matching order.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
240 participants
Sample size: planned number of observations
240 participants. Each completes 10 rounds (2,400 participant-round records). Primary outcomes are analyzed at the participant level (averaged across rounds).
Sample size (or number of clusters) by treatment arms
120 participants in gender-visible treatment (balanced: 60 women, 60 men).
120 participants in gender-blind control (balanced: 60 women, 60 men).
(Total: 240 participants.)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Power is calculated for participant-level outcomes (n=240; 120 per arm) at α=0.05. We target medium effect sizes (0.40–0.50 SD). With 120 participants per arm, this gives approximately 87% power for a 0.40 SD effect and 97% power for a 0.50 SD effect.
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
York University Office of Research Ethics
IRB Approval Date
2025-03-25
IRB Approval Number
2025-065
Analysis Plan

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