Competition Between Human and Artificial Intelligence in Digital Markets: An Experimental Analysis

Last registered on November 30, 2022

Pre-Trial

Trial Information

General Information

Title
Competition Between Human and Artificial Intelligence in Digital Markets: An Experimental Analysis
RCT ID
AEARCTR-0010439
Initial registration date
November 23, 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, 2:27 PM 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

Affiliation
University of Regensburg

Other Primary Investigator(s)

PI Affiliation
University of Regensburg

Additional Trial Information

Status
In development
Start date
2022-11-23
End date
2023-05-31
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
In digital markets business decisions are increasingly taken by artificial intelligence (AI). Especially in e-commerce, a growing share of retailers uses AI-driven algorithmic pricing, whereas remaining vendors rely on manual price setting. However, policymakers have raised concerns about anti-competitive tacit collusion between humans and AI that could allow firms to soften competition. Therefore, we empirically investigate outcomes that arise when humans and AI repeatedly interact in digital markets. Based on an economic laboratory experiment in near real-time, we compare the degree of tacit collusion in duopoly markets across settings with different decision makers and settings with different degrees of algorithmic decision support for human decision makers. In a between-subjects treatment design we systematically vary (i) the decision makers in a market between humans only, algorithms only and mixed market settings where humans and algorithms compete; and (ii) whether human participants receive decision support from an AI-driven pricing algorithm. Altogether, our study sheds light on competition in digital markets where AI plays an increasingly important role and thus bears timely policy and managerial implications.
External Link(s)

Registration Citation

Citation
Schauer, Andreas and Daniel Schnurr. 2022. "Competition Between Human and Artificial Intelligence in Digital Markets: An Experimental Analysis." AEA RCT Registry. November 30. https://doi.org/10.1257/rct.10439-1.0
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
We run five between-subjects treatments that can be derived from our two main treatment dimensions. Along the first treatment dimension, we vary the decision makers that represent firms in a price competition duopoly market. Along the second treatment dimension, we vary whether human participants receive decision support from an AI-driven pricing algorithm.
Intervention Start Date
2022-11-24
Intervention End Date
2023-03-31

Primary Outcomes

Primary Outcomes (end points)
The average degree of tacit collusion in a market.
Primary Outcomes (explanation)
The degree of tacit collusion can be measured as the relative deviation of the average price of all firms in the market from the fully competitive price level (Nash Equilibrium) towards the joint profit maximizing price level (Collusive Equilibrium).

Secondary Outcomes

Secondary Outcomes (end points)
Average market price and profits of firms in a market.
Secondary Outcomes (explanation)
Average market prices and firms’ profits indicate how economically successful firms operate in a market.

Experimental Design

Experimental Design
Experiments are run in an experimental laboratory at the School of Business, Economics and Information Systems at the University of Passau. Treatments are randomized at the session level. Participants will be recruited from the student subject pool of the University of Passau. Each subject participates in only one treatment (between-subject design). In all treatments, subjects are fully informed about the timeline of the experiment.
Experimental Design Details
Not available
Randomization Method
Randomization by computer in office
Randomization Unit
Experimental sessions
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
In all treatments the independent observation is at the market level, as competitors’ actions depend on each other and are correlated over periods. We schedule data collection aiming at 56 independent observations per treatment.

In the human-human competition treatments an independent observation requires participation of two human subjects, whereas in the treatments with a mixed market setting an independent observation only requires participation of one human subject. Market outcomes for algorithm-algorithm competition treatments are derived from simulation outcomes and require no participation of human subjects.
Sample size: planned number of observations
We schedule data collection aiming at 56 observations per treatment (with 14 human participants per session). Thus, we aim for a total of 336 individual participants across the four treatments.
Sample size (or number of clusters) by treatment arms
In each of the two treatments with a mixed market setting (HA) we plan with 56 participants, which totals 112 subjects. In each of the two treatments with a purely human market setting (HH) we plan with 112 participants, which totals 224 subjects.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
For our primary outcome we aim to detect an effect size of at least d = 0.5 with a power level of 80% based on a pairwise test of treatment groups.
Supporting Documents and Materials

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IRB

Institutional Review Boards (IRBs)

IRB Name
German Association for Experimental Economic Research e.V.
IRB Approval Date
2022-11-22
IRB Approval Number
ZxT6aGbW