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Decomposing coordination failure in stag hunt games
Last registered on January 28, 2019

Pre-Trial

Trial Information
General Information
Title
Decomposing coordination failure in stag hunt games
RCT ID
AEARCTR-0003435
Initial registration date
October 18, 2018
Last updated
January 28, 2019 7:04 AM EST
Location(s)
Primary Investigator
Affiliation
University College London
Other Primary Investigator(s)
Additional Trial Information
Status
In development
Start date
2018-10-25
End date
2019-06-10
Secondary IDs
Abstract
Humans are generally motivated to make choices that earn high payoffs with a low level of risk. This trade-off can be stylized as the Stag Hunt coordination game where there exists both a payoff-dominant equilibrium and a risk-dominant equilibrium. Previous models have been provided which aim to predict when humans will coordinate on either equilibrium. Here I use a new approach to show a general disconnection between these models' predictions and actual human decision making. Furthermore, I create games for a laboratory experiment which test whether or not these models predict human behaviour well in this setting.
External Link(s)
Registration Citation
Citation
Kendall, Ryan. 2019. "Decomposing coordination failure in stag hunt games." AEA RCT Registry. January 28. https://doi.org/10.1257/rct.3435-3.0
Former Citation
Kendall, Ryan. 2019. "Decomposing coordination failure in stag hunt games." AEA RCT Registry. January 28. https://www.socialscienceregistry.org/trials/3435/history/40733
Experimental Details
Interventions
Intervention(s)
There are many ways to define a 2x2 Stag Hunt game using different payoff components. This laboratory experiment studies how subjects make different choices in Stag Hunt game with different payoff components.
Intervention Start Date
2018-10-25
Intervention End Date
2019-06-10
Primary Outcomes
Primary Outcomes (end points)
First, I aim to show a disconnection between our current models and human behaviour. Second, using these findings, a model of weighted incentives better predicts behaviour in Stag Hunt games.
Primary Outcomes (explanation)
The goal of the previous literature in this direction is to understand the payoff factors that influence humans converging to either equilibria. I share this goal and hope to improve upon our current understanding of this equilibrium selection criteria.
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
The experiment design follows the protocol associated with experimental economics. More particularly, my experiment design follows as closely as possible the design of previous papers investigating this phenomena. The novelty of my approach is entirely in the payoff-construction of the games.
Experimental Design Details
Previous work has explored the payoff characteristics of Stag Hunt games that drive human behavior to either the payoff dominant or risk dominant equilibrium. These experiments use a between-subjects design where cohorts of eight subjects play one Stag Hunt game for seventy-five rounds. In each round, subjects were randomly re-matched and were aware of the outcome of their choices in previous rounds. Subjects accumulated points over the rounds which were converted to dollars at the end of the experiment. Subjects were paid for all seventy-five rounds. I plan to follow this design with 4 different experimental games. My experiment design proceeds with two phases. First, using the paper's theoretical results, two games are created which are equivalent when analyzed by a class of models (Game 1 and 2). Therefore, these concepts have the same prediction about human behavior in Games 1 and 2. The first set of hypotheses test whether this equivalence accurately describes human choices. Are human choices constant across Games 1 and 2? Second, the previous models claim empirical relationships between the direction of their concepts and human behavior. That is, the payoff-dominant choice is more likely to emerge in games with smaller or larger parameters. Games 3 and 4 are created so that all of these models predict the same behavioral change across games. Our second set of hypotheses test whether these relationships accurately describe human choices. Are human choices in agreement with these models in Games 3 and 4? I anticipate that human choices will follow the opposite prediction of these previous models.
Randomization Method
Randomization is carried out by the computer.
Randomization Unit
Individual
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
Following from the previous literature, I plan to have 8 sessions each with 8 subjects for each of my 4 treatments. This leads to a total of 256 subjects.
Sample size: planned number of observations
Each of the 256 subjects will make 75 choices. So there will be a total number of 19,200 choices observed.
Sample size (or number of clusters) by treatment arms
There will be 8 Game 1 sessions, 8 Game 2 sessions, 8 Game 3 sessions, and 8 Game 4 sessions.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
UCL Research Ethics Committee
IRB Approval Date
2017-11-28
IRB Approval Number
12439/001
Post-Trial
Post Trial Information
Study Withdrawal
Intervention
Is the intervention completed?
No
Is data collection complete?
Data Publication
Data Publication
Is public data available?
No
Program Files
Program Files
Reports and Papers
Preliminary Reports
Relevant Papers