The Effect of Commitment Types in Repeated Games with Long-lived and Short-lived Players

Last registered on August 04, 2025
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Pre-Trial

Trial Information

General Information

Title
The Effect of Commitment Types in Repeated Games with Long-lived and Short-lived Players
RCT ID
AEARCTR-0015903
Initial registration date
April 28, 2025

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 05, 2025, 2:48 PM EDT

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

Last updated
August 04, 2025, 7:30 AM EDT

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

Locations

Country
Norway
Region

Primary Investigator

Name
Magnus Våge Knutsen
Affiliation
BI Norwegian Business School
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Other Primary Investigator(s)

PI Name
Tom-Reiel Heggedal
PI Affiliation
BI Norwegian Business School | BI
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Additional Trial Information

Status
Completed
Start date
2025-05-04
End date
2025-07-31
Keywords
Behavior
Additional Keywords
reputation, repeated games, moral hazard
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Repeated games with a long-lived player facing a sequence of short-lived players has, sins these
games were first studied in Fudenberg and Levine (1989), been the focus of much theoretical work. In more recent years these models have received much attention due to their application to trade on online platforms such as Ebay and Amazon (which is becoming increasingly important). Like the more studied case of two long-lived players, the case with one long-lived player also suffers from
multiplicity. Thus, while it is clear from a theoretical perspective that repeated interactions can
help long-lived and short-lived players coordinate on mutually beneficial outcomes, it is unclear
how this will come about.

We set out to experimentally study strategy choices in repeated games with long-lived and short
lived players by running a series of lab experiments. Our perspective is on the role of commitment
types in this framework. That is, to what extent the presence of commitment types help coordi-
nation towards beneficial outcomes. From a theoretical perspective, commitment types will help
long-lived and short-lived players to coordinate on beneficial outcomes by excluding equilibria in
which cooperation never takes pace.

We implement the model of reputational concerns introduced by Fudenberg and Levine (1989,
1992), which has become a workhorse model in the theoretical work on situations with reputational
concerns. In the model, a long-lived player interacts with an infinite sequence of short-lived players.
The long-lived player has a commitment types and short-lived players observe the complete history
of play. Despite the vast theoretical literature, the experimental evidence on infinitely repeated
games building on the paradigm of Fudenberg and Levine (1989, 1992) is scarce. The environments
with a long-lived player facing a sequence of short lived players has been studied in experiments
before, this has been in setting with finite horizons. Thus, to our knowledge we are the first to take
the model of Fudenberg and Levine (1989) to the lab.
External Link(s)

Registration Citation

Citation
Heggedal, Tom-Reiel and Magnus Våge Knutsen. 2025. "The Effect of Commitment Types in Repeated Games with Long-lived and Short-lived Players." AEA RCT Registry. August 04. https://doi.org/10.1257/rct.15903-2.1
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Experimental Details

Interventions

Intervention(s)
The aim of the experiment is to empirically investigate the effect of commitment types in a repeated game where the long-lived player faces a trade-off between short-run and long run gains, and where the efficient outcome is an equilibrium. However, as it is only one of many, it requires that players coordinate. We hypothesis that the presence of a commit types will facilitate this coordination.

Our plan is to run two different treatments: One without commitment types, and one with commitment types. Commitment types are induced by way of computerized long-lived players who always chooses a specific action.

In addition we run a control treatment where both players are short lived. This treatment will save as a empirical benchmark.
Intervention (Hidden)
Intervention Start Date
2025-05-04
Intervention End Date
2025-07-31

Primary Outcomes

Primary Outcomes (end points)
Our focus is on four main outcome measures: The payoffs of the long-lived player, the frequency at which the long-lived player chooses the actions that most benefits the short-lived players, the frequency at which the short-lived players chose the action the most benefits the long-lived player, and the frequency at which the long and short-lived player are able to coordinate on the efficient outcome.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
See attached document.
Experimental Design Details
Randomization Method
Randomization done in office by a computer. Sessions are randomised into one of two treatments. The control treatment is without commitment types, and our treatment is with commitment types. Commitment types are induced by way of computerized long-lived players who always chooses a specific action.
Randomization Unit
The unit of randomization is session.
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
Six matching block per treatment (two treatments). There are eight participants per matching block in the treatment without induced commitment types, and seven in the treatment with induced commitment types (one participant is replaced by a computerized long-lived player.
Sample size: planned number of observations
90 (6*8 + 6*7)
Sample size (or number of clusters) by treatment arms
There are 48 participants in the treatment without induced commitment types, and 42 in the treatment with induced commitment types.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials

Documents

Document Name
Pre-study plan
Document Type
proposal
Document Description
Description of planned experiment.
File
Pre-study plan

MD5: c5b8d1d790e5ff918d3d68f7fb3d7e78

SHA1: c09f6d10434b1fc8e70130f24e494d3187276f04

Uploaded At: April 28, 2025

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IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

Post-Trial

Post Trial Information

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Intervention

Is the intervention completed?
No
Data Collection Complete
No
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials