(Re-) Inventing the Traffic Light: Designing Recommendation Devices for Play of Strategic Games

Last registered on August 06, 2022

Pre-Trial

Trial Information

General Information

Title
(Re-) Inventing the Traffic Light: Designing Recommendation Devices for Play of Strategic Games
RCT ID
AEARCTR-0009706
Initial registration date
July 05, 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
July 08, 2022, 9:45 AM EDT

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

Last updated
August 06, 2022, 12:49 AM EDT

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

Locations

Region
Region

Primary Investigator

Affiliation
University of California, Irvine

Other Primary Investigator(s)

PI Affiliation
University of Technology Sydney
PI Affiliation
University of New South Wales
PI Affiliation
University of Technology Sydney

Additional Trial Information

Status
On going
Start date
2022-07-01
End date
2022-12-31
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
We explore whether and how individuals can design recommendations for the play of a variety of different two action, two player games. The recommendations are elicited in the form of probability distributions for each player over the possible action profiles of each game. The games are then played by programmed robot players who consider the recommendations made and decide, on a basis of best-response analysis, whether or not to follow the recommendations. The individuals providing the recommendations (the subjects) earn a baseline payment per game and they receive further rewards if their recommendations are followed by the robot players. This experimental design enables insights into how well the designers (subjects) understand the strategic incentives at play in various two-player, two action games including Prisoner's Dilemma, Matching Pennies, Battle of the Sexes and Chicken. In some of these games, there are multiple recommendation systems that the robot players will agree to follow, that is there, are multiple correlated equilibria of these games that can be selected by the designer. One aim of this research is to determine whether subjects can learn to implement the most efficient of these correlated equilibria in their choice of recommendation systems.

This experimental design differs from the traditional method of collecting data on the play of two player, two action games in that we use only a single player who designs a recommendation system for the play of the game by robot players. Nevertheless, we view this design as complementary to evidence from the traditional method and may yield evidence of greater strategic sophistication by our single, recommendation designing subjects since our experimental design reduces strategic uncertainty.
External Link(s)

Registration Citation

Citation
Anufriev, Mikhail et al. 2022. "(Re-) Inventing the Traffic Light: Designing Recommendation Devices for Play of Strategic Games." AEA RCT Registry. August 06. https://doi.org/10.1257/rct.9706
Experimental Details

Interventions

Intervention(s)
Subjects will be confronted with various two player, two action games that differ in their payoffs. In a sequence of rounds, they will first be shown the payoff matrix for each new game. Then, they will be asked to design recommendation devices for that game. A recommendation device involves the specification of probabilities with which the two players 1, and 2 will play either of their two available actions, labeled Red or Blue. Probabilities are elicited via the specification of the colors of balls in a container of 24 balls. Each ball is split in two halves, with one half labeled 1 and the other half labeled 2. Players designate the color of each half of all balls in the container. This decision comprises the recommendation device. Robot players are then presented with this recommendation device. The robot players then decide whether or not to follow the recommendations, based on a best response analysis from a simulation of playing the games a large number of periods. If one or more of the 2 robot players does not follow the recommendation device, the subject gets a baseline payoff. They also get some feedback as to why recommendations were not followed. If the recommendations are followed in all periods, then the subject earns, in addition to the baseline payoff, the minimum average payoff earned by either player 1 or 2, which ever is lower. Each game is played for several rounds and at the start of each round following the first, subjects get feedback on whether their recommendations were followed and have the opportunity to re-design the recommendation device or keep it unchanged. After all games are played, one game is chosen randomly for payoff purposes.
Intervention Start Date
2022-07-06
Intervention End Date
2022-12-31

Primary Outcomes

Primary Outcomes (end points)
Game payoff matrices.
Recommendation devices in the form of probabilities of play of each action by Player 1 and Player 2.
Payoffs earned and other feedback on outcomes, for example, whether recommendations were followed or not.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Answers to cognitive test questions and preference measures will be gathered in a questionnaire as part of the experimental design
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Subjects face a variety of different 2 player, 2 action games represented by payoff matrices. For each game they are asked to design a recommendation device specifying for each game which action each player 1 or 2 should play in each round of the 2x2 game. This recommendation device involves the specification of probabilities with which each player will be recommended to choose either of their two actions. Robot players are then presented with this recommendation device. The robot players then decide whether or not to follow the recommendations, based on a best response analysis from a simulation of playing each game a large number of periods under the given recommendation device. If one or more of the 2 robot players chooses not follow the recommendation device, the subject gets a baseline payoff. They also get some feedback as to why recommendations were not followed. If the recommendations are followed in all periods, then the subject earns, in addition to the baseline payoff, the minimum average payoff earned by either player 1 or 2, which ever is lower. Each game is played for several rounds and at the start of each round following the first, subjects get feedback on whether their recommendations were followed and have the opportunity to re-design the recommendation device or keep it unchanged. After all games are played, one game is chosen randomly for payoff purposes.
Experimental Design Details
Not available
Randomization Method
Randomization by computer program.
Randomization Unit
Order of experimental treatments, determination of game for payoff.
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
Minimum of 100 subjects
Sample size: planned number of observations
Each subject is an individual observation playing several different games. The order of the games is randomized, so each subject is an observation on the various games played. Minimum of 100 subjects total
Sample size (or number of clusters) by treatment arms
Minimum of 100 subjects total
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
UC Irvine Institutional Review Board
IRB Approval Date
2022-05-25
IRB Approval Number
UCI IRB #20118378