Endogenous AI-tocracy: An Experimental Investigation

Last registered on September 29, 2024
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Pre-Trial

Trial Information

General Information

Title
Endogenous AI-tocracy: An Experimental Investigation
RCT ID
AEARCTR-0011472
Initial registration date
May 24, 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
May 24, 2023, 5:06 PM EDT

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

Last updated
September 29, 2024, 8:32 PM EDT

Last updated is the most recent time when changes to the trial's registration were published.

Locations

Country
China
Region
Wuhan

Primary Investigator

Name
Lian XUE
Affiliation
Wuhan University
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Other Primary Investigator(s)

PI Name
Lijia Wei
PI Affiliation
Wuhan University
Contact Investigator
PI Name
Lu Bai
PI Affiliation
Wuhan University
Contact Investigator

Additional Trial Information

Status
Completed
Start date
2023-05-09
End date
2023-11-09
Keywords
Behavior, Lab, Welfare
Additional Keywords
public goods game; Artificial Intelligence
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
“AI-tocracy” describes a potential governance model where individual behavior is influenced and regulated by advancements in Artificial Intelligence. This study explores the nature of AI-tocracy through a series of controlled laboratory experiments. We investigate how AI-generated social scores, when coupled with punitive measures, impact individual cooperation within group settings. Furthermore, this research examines the decision-making processes of individuals when faced with the choice to adopt AI-control mechanisms. Additionally, the research aims to explore the welfare implications and potential future trajectories of AI-tocracy in various economic and social contexts.
External Link(s)

Registration Citation

Citation
Bai, Lu, Lijia Wei and Lian XUE. 2024. "Endogenous AI-tocracy: An Experimental Investigation." AEA RCT Registry. September 29. https://doi.org/10.1257/rct.11472-2.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
We conducted two experiments:
Experiment 1 compares group cooperation when a social-score system (AI-score system) is present vs. absent.
Experiment 2 examines individual preferences for incorporating the AI-score system into their group decision-making environment.

In Experiment 1, the Control Treatment (PGG) involves participants playing a standard public goods game for 20 rounds. Two participants receive an endowment of 40 tokens, while the other two receive 20 tokens. Each participant decides how to allocate their endowment between a private and a public account, with contributions to the public account benefiting all group members.

The Social-Score Treatment adds a social scoring system where participants rate each other based on their contributions. The average score for each participant is displayed as a public ranking after each round. The Social-Score-Punish Treatment further introduces punitive measures. Participants face deductions from their payoffs if they receive low scores, calculated based on the difference between their score and the maximum attainable score.

The AI-Score Treatment replaces peer evaluations with AI-generated scores, using a machine learning model trained on data from the Social-Score treatment. Scores are publicly displayed to influence group dynamics. In the AI-Score-Punish Treatment, participants face deductions based on these AI-generated scores, similar to the social punishment system.

In Experiment 2, the Endog Treatment introduces an endogenous decision-making stage. Before playing the public goods game, participants vote on whether to adopt an AI-score system in their group, with a random dictator rule deciding the outcome. The AI scores here are non-punitive, serving only as information. In contrast, the Endog-Punish Treatment includes punitive measures tied to AI-generated scores. Participants vote to decide on implementing the AI control, and if adopted, face deductions based on their scores. This setup explores the impact of punitive AI systems on cooperation and individual preferences.
Intervention (Hidden)
Intervention Start Date
2023-05-26
Intervention End Date
2023-08-26

Primary Outcomes

Primary Outcomes (end points)
Average contribution to the public good project is the primary outcome variable.
Average payoff.

Primary Outcomes (explanation)
Contribution to the public project serves as a measurement of cooperativeness in the community.
The average payoff measured the social welfare.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Experiment 1 investigates the impact of social and AI-generated scoring systems on cooperative behavior in a public goods game. Participants are randomly assigned to one of five treatments: (1) Control (standard public goods game), (2) Social-Score (peer evaluations), (3) Social-Score-Punish (peer evaluations with punitive measures), (4) AI-Score (AI-generated scores without punishment), and (5) AI-Score-Punish (AI-generated scores with punishment). Each treatment involves 20 rounds, with two participants starting with 40 tokens and two with 20 tokens. Contributions to the public account are recorded, along with scores and deductions where applicable.

Experiment 2 introduces an endogenous decision-making stage. Participants vote on whether to adopt an AI-score system for their group. Two treatments are implemented: (1) Endog (AI-score, non-punitive) and (2) Endog-Punish (AI-score with punitive measures). A random dictator rule determines the group's final decision. The main outcomes include contributions to the public good, AI adoption rates, and the impact of punitive versus non-punitive AI scoring on cooperation. Each session includes 20 rounds, with re-voting at the halfway point to observe preference changes over time.
Experimental Design Details
Randomization Method
Experiment 1 involves two-phase randomization. In phase one, participants are randomly assigned to either the Social-Score or Social-Score-Punish treatments. This initial randomization is conducted using the Weikeyan recruitment system, ensuring that participants are evenly distributed across both treatments. After data collection in phase one, the collected social score data is used to train the AI model. In phase two, participants are recruited again using the Weikeyan system and randomly assigned to one of the two additional treatments: AI-Score or AI-Score-Punish. This two-phase randomization allows for evaluating AI-generated scores based on patterns observed in the social scoring treatments.

For experiment 2, the randomization occurs at the session level. Each session is assigned to one of the two treatments, Endog (non-punitive AI score) or Endog-Punish (punitive AI score). Participants are recruited through the Weikeyan system, and random assignment to treatment occurs at the time of recruitment. This randomization at the session level ensures that all participants within a session experience the same treatment condition.
Randomization Unit
Unit of observation for this study is at the individual level.
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
6 treatments; 20 sessions
Sample size: planned number of observations
368 subjects for Experiment 1; 160 subjects for Experiment 2
Sample size (or number of clusters) by treatment arms
60-80 subjects per treatment.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Center of Behavior and Economic Reasearch
IRB Approval Date
2023-05-08
IRB Approval Number
IRB202300028
Analysis Plan

Post-Trial

Post Trial Information

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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