Alienated issue as a coordination device: An experiment in a new battle-of-the-sexes game

Last registered on November 18, 2022

Pre-Trial

Trial Information

General Information

Title
Alienated issue as a coordination device: An experiment in a new battle-of-the-sexes game
RCT ID
AEARCTR-0010436
Initial registration date
November 17, 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 18, 2022, 12:29 PM EST

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

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Primary Investigator

Affiliation
UGA, INRAE

Other Primary Investigator(s)

PI Affiliation
UGA, INRAE
PI Affiliation
UGA

Additional Trial Information

Status
In development
Start date
2022-11-23
End date
2023-03-31
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
We build a new battle-of-the-sexes game based on the seminal work of Akerlorf (1997). Contrary to the classic battle-of-the-sexes games, even if each player chooses the action that maximizes his/her own private preferences, both players get a positive payoff. We define such an issue of the game as the “alienated” issue of the game.
We consider different payoff matrixes (one type of matrix corresponding to a treatment, see Experiment design below) that vary in the number of actions each player can choose.
Our main expectations are:
i) when the “unbiased equilibrium” (see below) does not exist, the alienated issue may be a focal point;
ii) when we introduce an “unbiased equilibrium”, it weakens the salience of the alienated issue as a focal point.
External Link(s)

Registration Citation

Citation
Bonroy, Olivier, Alexis Garapin and Benjamin Ouvrard. 2022. "Alienated issue as a coordination device: An experiment in a new battle-of-the-sexes game." AEA RCT Registry. November 18. https://doi.org/10.1257/rct.10436-1.0
Experimental Details

Interventions

Intervention(s)
We consider different versions of a battle-of-the-sexes game that differ regarding the number of actions each player can (simultaneously) choose. Each version of the battle-of-the-sexes game corresponds to a treatment (i.e., intervention).

Subjects play with one type of payoff matrix only (i.e., they participate to one treatment only).
Intervention Start Date
2022-11-23
Intervention End Date
2023-03-31

Primary Outcomes

Primary Outcomes (end points)
We will mainly analyze subjects’ individual choice in each round and the coordination rates (at the group level).
Primary Outcomes (explanation)
At each repetition of the game, subjects (in groups of two) simultaneously choose an action.

Secondary Outcomes

Secondary Outcomes (end points)
We will also analyze subjects’ beliefs regarding the type of action the other player will choose during the last period of the game.
Secondary Outcomes (explanation)
Before the last period of the game, we will ask subjects about their beliefs regarding what the other player will choose.

Experimental Design

Experimental Design
1) General setting:

Subjects play a battle-of-the-sexes game.

In each session, they are in groups of 12 subjects (6 “blue” subjects and 6 “red” subjects). They stay in the same group during the entire session.
At each round, one “blue” subject is randomly matched with one “red” subject (imperfect stranger matching). Subjects will never know who they are matched with, and play 20 rounds of the game (they are randomly rematched after each round).

Subjects are informed that their role is to simultaneously choose an action. Their payoff depends on their choice as well as the choice of the other subject they are matched with. The payoffs are presented in a matrix form.
The payoffs of the matrices are determined by an extension of Akerlof (1997)’s model in which agents have opposite private preferences.
Subjects are informed that their final payoff is the sum of a fixed participation fee (€5) and their gain in one round of the game randomly selected at the end of the experiment by the computer.

Per session, we will consider two groups of 12 subjects. There will be no interaction between these two groups.

2) Treatments:

We consider 4 treatments that differ regarding the number of actions each subject can simultaneously choose, and the presence, or not, of an unbiased equilibrium. We consider that an equilibrium is “unbiased” if it corresponds to the action that coincides with the average private preferences (see Michaeli and Spiro, 2017).
In all the following matrixes, each subject can choose either the action that maximizes his/her private preferences, or the one that maximizes the private preferences of the other subject he/she is matched with. Thus, all matrixes include two equilibria such that the private preference of one of the two players is maximized, as well as the alienated issue.

The four treatments are:
a) 2x2 matrix: where the unbiased equilibrium is not included.
b) 3x3 matrix: this treatment is similar to the previous one (2x2 matrix), except that the unbiased equilibrium is included.
c) 4x4 matrix: this treatment is an extension of the 2x2 matrix, with additional choices of actions, but without the unbiased equilibrium.
d) 5x5 matrix: this treatment is similar to the previous one (4x4 matrix), except that we add the unbiased equilibrium.

Literature:

Akerlof, G. A. (1997). Social distance and social decisions. Econometrica, 1005-1027.

Michaeli, M., & Spiro, D. (2017). From peer pressure to biased norms. American Economic Journal: Microeconomics, 9(1), 152-216.
Experimental Design Details
Not available
Randomization Method
Randomization made by computer (Hroot)
Randomization Unit
Individual (student)
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
Not relevant
Sample size: planned number of observations
We will recruit a total of 288 subjects (4 treatments x 72 subjects per treatment). We will therefore consider 24 independent observations: 2 independent groups of 12 subjects per session x 3 sessions per treatment x 4 treatments.
Sample size (or number of clusters) by treatment arms
72 subjects per treatment. This corresponds to 6 independent observations per treatment (2 independent observation per session x 3 sessions per treatment).
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number