Influence in Social Networks

Last registered on January 23, 2023


Trial Information

General Information

Influence in Social Networks
Initial registration date
January 19, 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
January 23, 2023, 7:13 AM EST

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


Primary Investigator

Uni Essex

Other Primary Investigator(s)

PI Affiliation
PI Affiliation

Additional Trial Information

On going
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Our laboratory experiment investigates the influence, direct and indirect, that early movers have over later movers in a sequential move, binary action coordination game played on a network. Each subject is assigned to a particular node in a specified 5 or 10 node network, and chooses In or Out knowing which previous movers who are neighbors have chosen In. We measure potential influence by comparing the number of subsequent In choices following an initial In versus Out choice by a robot. We also measure the extent to which human subjects exploit their potential influence.
External Link(s)

Registration Citation

Friedman, Daniel, Jaromir Kovarik and Friederike Mengel. 2023. "Influence in Social Networks." AEA RCT Registry. January 23.
Experimental Details


Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
We will have two primary outcomes
- A measure of influence.
- A measure of the extent to which people act on their influence.

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
see abstract.
Experimental Design Details
In our experiment participants play a sequential coordination game in a group of $N$ people organized in a network. Each participant chooses (in random order) whether to ``Go In'' or ``Stay Out''. Payoffs are increasing piece-wise linearly with the number of people in the network who choose ``Go In'' with a steeper increase where payoffs are positive. Initially everyone's state is ``Out'' and participants choose sequentially whether to move ``In''. They can observe the state (``In'' or ``Out'') of their direct network neighbours. They cannot observe the state of anyone who is not a direct neighbour.

We plan to conduct different treatments that differ along two dimensions

-The presence of a robot player (yes/no).
- The network structure.

\paragraph{Presence of a robot player} In some treatments one of the nodes in the network (randomly chosen) will be played by a ``robot''. The robot will always be the first mover and will choose ``Go In'' with probability $\frac{1}{2}$.

Participants are informed about all these elements including about the fact that there might be a robot player, but they are not told which decision rule the robot follows or whether an observed move is by a human or robot.

\paragraph{Network Structure} We plan to conduct sessions with 5-player networks, 10-player networks and possibly 20-player networks. (We will not conduct 20 player networks if we already observe near complete coordination failure with 10 players).

We will start with three network structures for the 5-player and 10-player networks. One network will be the complete network (corresponding to the case of global information) and two networks will have local information, i.e. participants can only observe their direct network neighbours and they will not be linked to everyone in the network. A picture of these networks is attached.
Depending on the results we may investigate further networks, but if we do we will mention in the paper that these additional networks were added afterwards.
Randomization Method
Participants sign up for sessions and treatments are randomly assigned to sessions.

Participants will play 15 rounds of this game. Before the start of each round they are randomly assigned a network and network position. Each session will have one or multiple ``silos'' (depending on the number of people in a network) within which participants are rematched. Different silos can be considered independent observations.
Randomization Unit
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
10 silos of 10 participants each for the 5-player networks and 5 silos of 20 players each for the 10-player networks.
Sample size: planned number of observations
100 participants per treatment
Sample size (or number of clusters) by treatment arms
100 participants per treatment
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Main Tests For each of our main outcomes we will have one main empirical test. Power is based on the first outcome (``measure of influence''), but sample sizes are then adjusted to take into account our ability to test for certain dimensions of heterogeneity (see below) as well as the second main outcome (`` measure of the extent to which people act on their influence''). \textbf{Measure of Influence}: We will measure influence using only the robot treatments, where we will compare situations where the robot chose ``Go In'' in period 1 with situations where they chose ``Stay Out''. Our empirical measure of influence of a particular position in a particular network is the difference between the share of participants who choose to ``Go In'' following the robot's choice to ``Go In'' in period 1 and the share of participants who choose to ``Go In'' following the robot's choice to ``Stay Out'' in period 1. Our main specification will be a logistic regression where we regress the binary variable indicating ``Go In'' on a dummy variable indicating whether the robot chose to ``Go In''. The regression will be run separately for each network position. (Positions that are the same up to relabeling are treated as the same position). We will report odds ratios. Standard errors will be clustered at the silo level. We will report several specifications including demographic control variables, round fixed effects and controls for position in the sequence, but our main pre-registered specification will be the one without any controls. We will also conduct linear probability models with the same regressors and report those results in the Appendix. Power analysis is done based on t-tests comparing the proportion of participants choosing to ``Go In'' following the robot's choice to ``Go In'' or ``Stay Out'' in period 1. The t-tests are conducted on an initial set of sessions conducted with Network 2B (see attached graphs). We conducted these sessions to get a sense of the magnitude of possible effect sizes in order to enable us to do more meaningful power analysis. In these sessions we found the following effect sizes for network positions A/E, B, C and D: 12$\%$, 22$\%$, 37$\%$ and 4$\%$. Power analysis revealed that to detect these effect sizes with 80 percent power at the 5$\%$ level we would need 14, 5, 4 and 37 silos of 10 participants each, respectively. \textbf{Sample Size} Based on these numbers we decided to use 10 silos of 10 participants each for the 5-player networks and 5 silos of 20 players each for the 10-player networks. (Note that in the robot treatments one participant per network is played by a robot). This should allow us to reliably detect influence at least for the more influential network positions. \textbf{Awareness of Influence}: We will use the treatments without robots and regress the binary variable indicating whether individual i in network position j chose to ``Go In'' in period 1 on the measure of empirical influence of network position j obtained from the sessions with robot. We will also include a fixed effect for the rate of entry in the network as a whole. Otherwise the empirical specifications will be the same as those above. \paragraph{Other Tests} We will explore heterogeneity based on answers in a post -experimental questionnaire and based on the sequence of decisions. We will also use decision-time as a secondary measure of awareness of influence. Last we will contrast empirical influence with theoretical measures of influence of a node (e.g. centrality measures).

Institutional Review Boards (IRBs)

IRB Name
University of Essex Social Sciences Ethics Subcommittee 1
IRB Approval Date
IRB Approval Number


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