Algorithm-Driven Information Similarity and Collective Action: A Laboratory Experiment

The study is a laboratory experiment.
In our experiment, participants take part in a content-moderation game. In each round, participants are placed in a group and decide whether to report a piece of online content. Reporting is costly, while successful removal of harmful content benefits all group members. Content is removed only if the number of reports reaches a threshold. Before making their decisions, participants receive an algorithmic signal about whether the content is likely to be harmful. The experiment varies both the similarity of these algorithmic signals across group members and the threshold regime that determines how many reports are needed for removal.
We run the following threshold-regime treatments between sessions:
1. Low-threshold regime: Content can be removed with a relatively small number of reports. This environment is designed to capture situations in which collective action is relatively easy. This environment is designed to capture situations in which collective action is relatively easy and free-riding incentives may be more salient.
2. Medium-threshold regime: Content removal requires an intermediate number of reports.
3. High-threshold regime: Content removal requires a relatively large number of reports. This environment is designed to capture situations in which successful collective action requires broad coordination.
Within each session, the degree of algorithmic information similarity varies across rounds. Higher similarity means that group members are more likely to receive the same algorithmic signal, while lower similarity means that signals are more likely to be independently generated. Individual signal accuracy is held fixed, so variation in similarity changes participants’ uncertainty about others’ information rather than the informativeness of their own signal.
Participants also report incentivized beliefs about others’ reporting behavior before making their reporting decision. At the end of the experiment, participants complete a short survey and incentivized preference tasks.

2026-05-24

2026-09-30

Reporting decisions

Successful removal of harmful content

The experiment employs a mixed laboratory design investigating how algorithmic information similarity affects collective action. Participants take part in a repeated content-moderation game in which they decide whether to report potentially harmful online content. Reporting is costly, and content is removed only if enough group members report it. Treatments vary the threshold regime for successful removal across sessions. Within sessions, the similarity of algorithmic signals varies across rounds while individual signal accuracy is held fixed. This design allows us to compare how information similarity affects reporting behavior across environments with different degrees of collective-action difficulty.
The experiment is designed to test the main predictions of Basak, Deb, and Kuvalekar (2026), "Similarity of Information and Collective Action" (American Economic Review): increasing information similarity should facilitate reporting when successful collective action requires broad coordination, but may reduce reporting when the threshold for success is low and free-riding incentives are more salient. The medium-threshold treatment is included to study the transition between these two cases.

Not available

Computerized randomization implemented through the oTree experimental software platform.

The threshold regime is assigned at the session level. Group composition, realized thresholds, content states, and algorithmic signals are randomized by the computer within sessions.

Yes

12 experimental sessions (4 sessions per treatment)

576 participants, generating up to 11,520 participant-round observations

Low-threshold regime: 4 sessions, 192 participants
Medium-threshold regime: 4 sessions, 192 participants
High-threshold regime: 4 sessions, 192 participants