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Fairness and Risk in Ultimatum Bargaining
Initial registration date
May 13, 2020
June 15, 2020 2:37 PM EDT
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Naveen Jindal School of Management, The University of Texas at Dallas
Other Primary Investigator(s)
Durham University Business School
Additional Trial Information
Using online experiments on Amazon Mechanical Turk (AMT), we will investigate what constitutes a fair allocation of risk in ultimatum bargaining. There are two agents, a proposer and a responder. We are interested in how offer and acceptance decisions differ when the proposer offers a probabilistic allocation of an indivisible asset, versus the standard case in which the asset is divisible and the proposer proposes a deterministic surplus allocation. We are also interested in how the timing of the resolution of uncertainty affects behavior.
The purpose of our intervention is to understand how risk and intentions affect the fairness perceptions of agents in simple ultimatum bargaining environments. To study this, we will compare behavior of both proposers and responders in standard ultimatum games and in risky ultimatum games, where in the latter, we will vary the timing of the resolution of uncertainty.
Intervention Start Date
Intervention End Date
Primary Outcomes (end points)
Offers by proposers and accept/reject decisions by responders
Primary Outcomes (explanation)
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Subjects will participate in two ultimatum games as either a proposer or a responder: a standard ultimatum game (Standard UG) and a "lottery" ultimatum game (Lottery UG) in which there is an indivisible prize and the object of negotiation is the probability of winning the prize. We will conduct treatments with direct response by responders (Risk-Direct) and using the strategy method (Risk-Strategy). In order to also study the role of intentions, we consider a treatment variation in which the responder makes his/her accept/reject decision in the Lottery UG after the resolution of uncertainty (Intent-Direct).
Experimental Design Details
Randomization done in office by a computer.
Groups of participants (sessions) are randomized to experimental treatments. We control for the order in which the two tasks are presented by randomly assigning half of subjects within each treatment to complete the Standard UG first and the other half to complete the Lottery UG first. In the first task, roles are assigned randomly in the pair. To permit within-subjects comparisons, participants retain the same role for the second task and the new pairing.
Was the treatment clustered?
Sample size: planned number of clusters
Based on our current plan, we estimate conducting 6-7 sessions per treatment, for a total of 18 - 21 sessions.
Sample size: planned number of observations
50 complete pairs per treatment, with 3 treatments for a total of 300 subjects.
Sample size (or number of clusters) by treatment arms
50 in Risk-Direct
50 in Risk-Strategy
50 in Intent-Direct
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
This sample size gives us 75% power to detect an offer effect size of 3 percentage points and 99% power to detect an offer effect size of 5 percentage points, assuming an average offer of 40%, standard deviation of 16 percentage points and within-subjects correlation of 75%, based on prior experiments.
INSTITUTIONAL REVIEW BOARDS (IRBs)
UTD Office of Research Integrity and Outreach
IRB Approval Date
IRB Approval Number