Digital punishment

Last registered on May 24, 2023


Trial Information

General Information

Digital punishment
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.



Primary Investigator

Wuhan University

Other Primary Investigator(s)

PI Affiliation
Wuhan University
PI Affiliation
Wuhan University

Additional Trial Information

On going
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Digital punishment, in the context of the digital economy era, epitomizes the sophisticated practice of employing social scoring as a means of subtle control. This cutting-edge strategy harnesses the power of seamless information integration and social stigma to incentivize individuals to comply with established societal norms, especially within placid communities characterized by feeble social bonds. When juxtaposed against conventional monetary penalties, digital punishment harnesses advanced technologies like big data analytics and facial recognition, resulting in the streamlined development and implementation of social scoring systems. In the course of this undertaking, we shall undertake a comparative analysis of two enforcement mechanisms, namely social scoring and AI scoring, within a controlled laboratory setting, thereby exploring the potential of artificial intelligence to foster social cohesion.
External Link(s)

Registration Citation

Bai, Lu, Lijia Wei and Lian XUE. 2023. "Digital punishment ." AEA RCT Registry. May 24.
Experimental Details


Our experimental setup encompasses a foundational treatment wherein participants engage in a public goods game, coupled with a 2-by-3 intricate experimental design that introduces variations in the scoring method and sanction method. Specifically, we manipulate two distinct treatment arms:
(1) The first pertains to the scoring method, wherein subjects are evaluated based on either their social score or an AI score. Initially, participants assess and rate each other, subsequently allowing machine learning algorithms to harness the aggregated data for model training. The AI score is then derived directly from this trained model, providing a comprehensive evaluation.
(2) The second treatment arm focuses on the sanction method, which involves the decoupling of scores from profits, thereby rendering the score or ranking potentially consequential in terms of monetary loss. We explore two distinct modes of loss: the first entails a linear sanction approach, where a lower score corresponds to a higher magnitude of loss, while the second involves a bottom sanction method, which solely penalizes the participant ranked at the very bottom.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Upon careful examination of the average contribution values across each treatment, it becomes evident that the treatment effect manifests in a more pronounced manner. The utilization of scores/ratings and sanctions significantly influences participants' propensity towards cooperative behavior.

Primary Outcomes (explanation)
Our research incorporates the following null hypotheses for rigorous testing:

H1. The effect of social scores with sanctions: It is posited that the implementation of social score sanctions fosters pro-social behavior among participants.
H2. The effect of social scores: This hypothesis asserts that social scores alone stimulate pro-social behavior.
H3. The effect of AI scores with sanctions: It is hypothesized that the utilization of AI scores enhances the efficacy of social sanctions.
H4. The effect of AI scores: This hypothesis suggests that the adoption of AI scores diminishes the effectiveness of community enforcement.
H5. The interaction between social scores and sanctions: This hypothesis contends that the highest level of prosocial behavior is observed when both social scores and sanctions coexist.
H6. Heterogeneity within societies: This hypothesis proposes that the effectiveness of social scores varies depending on the nature of the society, with social scores proving more impactful in docile societies compared to rebellious ones.

Secondary Outcomes

Secondary Outcomes (end points)
Social welfare analysis
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Our experimental design operates within the framework of a public goods game, where participants are organized into groups of four, engaging in 20 rounds of repeated interactions within this public goods context.

Distinguishing itself from the conventional public goods game, our design incorporates a nuanced alteration in initial endowments, with participants receiving varying allocations of tokens:20tokens for two participants and 40 tokens for other two participants, effectively creating an initial endowment distribution of 20/20/40/40. Moreover, we employ a marginal per capita return (MPCR) set at 1.6. A notable divergence from standard practices lies in our approach to random matching, as we introduce a rematching process every 10 rounds, a feature intricately linked to our treatment group configurations.

Within this experimental framework, our baseline treatment adheres to the standard public goods game. However, in our 2x3 experimental setting, participants are randomly assigned to distinct treated groups, thereby experiencing variations in several key aspects: the source of scores (either derived from social scores or AI scores), the potential correspondence of scores or rates with monetary losses, as well as the diverse mappings between scores or rates and monetary losses.

Upon the conclusion of the experiment, participants are required to complete a comprehensive questionnaire pertaining to their individual characteristics and economic preferences, encompassing factors such as risk propensity and prosocial tendencies. Furthermore, two additional tests—the 12ravens test and CRT—are conducted as part of our assessment.

Regarding the payment structure, one round is randomly selected from rounds 1-10, while another round is randomly selected from rounds 11-20. The cumulative sum of these selected rounds determines the participants' profits from the public goods game, which are subsequently converted into RMB using an exchange rate of 3 tokens equating to 1 yuan. Additionally, participants may receive supplementary earnings from the aforementioned post-experiment tests. A participation fee of 20 yuan is provided, and on average, the experiment lasts approximately one hour, with an average remuneration of around 60 yuan.
Experimental Design Details
Randomization Method
The process of randomization was meticulously conducted within the confines of the laboratory using sophisticated computer algorithms.
Randomization Unit
The unit of observation can be defined at varying levels of granularity, encompassing the individual-period level, group-period level, or group level, contingent upon the specific hypotheses being tested.
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
7 treatments; 27sessions
Sample size: planned number of observations
432 subjects
Sample size (or number of clusters) by treatment arms
16 subjects per session;3-5 sessions per treatment;10 sessions totally for training
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
Center of Behavior and Economic Reasearch
IRB Approval Date
IRB Approval Number


Post Trial Information

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Is the intervention completed?
Data Collection Complete
Data Publication

Data Publication

Is public data available?

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials