Publication Type
Journal Article
Version
submittedVersion
Publication Date
6-2018
Abstract
Imagine agents having uncertain needs for a resource when the resource has to be divided before uncertainty resolves. In this situation, waste occurs when an agent's assignment turns out to exceed his realized need. How should the resource be divided in the face of possible waste? This is a question out of the scope of the existing rationing literature. Our main axiom to address the issue is no domination. It requires that no agent receive more of the resource than another while producing a larger expected waste, unless the other agent has been fully compensated. Together with conditional strict endowment monotonicity, consistency, and strong upper composition, a class of rules which we call expected-waste constrained uniform gains rules is characterized. Any such rule is associated with a function that aggregates the two types of cost generated by an agent at an allocation: the amount of the resource assigned to him and the expected waste he generates. The rule selects the allocation that equalizes as much as possible the cost generated by each agent. The subclasses of rules associated with homothetic and linear cost functions are also characterized. Lastly, to appreciate the role of no domination, we establish all the characterizations with a decomposition of no domination into two axioms: risk aversion and no reversal. They respectively capture the ideas that a rule should not be unresponsive to uncertainty, and that neither should it be overly responsive to it.
Discipline
Economic Theory | Social Welfare
Research Areas
Economic Theory
Publication
Social Choice and Welfare
Volume
51
Issue
1
First Page
105
Last Page
136
ISSN
0176-1714
Identifier
10.1007/s00355-018-1109-5
Publisher
Springer Verlag (Germany)
Citation
XUE, Jingyi.
Fair division with uncertain needs. (2018). Social Choice and Welfare. 51, (1), 105-136.
Available at: https://ink.library.smu.edu.sg/soe_research/2236
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1007/s00355-018-1109-5