TY - GEN
T1 - (Almost) Envy-Free, Proportional and Efficient Allocations of an Indivisible Mixed Manna
AU - Livanos, Vasilis
AU - Mehta, Ruta
AU - Murhekar, Aniket
N1 - Publisher Copyright:
© 2022 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.
PY - 2022
Y1 - 2022
N2 - We study the problem of finding fair and efficient allocations of a set of indivisible items to a set of agents, where each item may be a good (positively valued) for some agents and a bad (negatively valued) for others, i.e., a mixed manna. As fairness notions, we consider arguably the strongest possible relaxations of envy-freeness and proportionality, namely envy-free up to any item (EFX and EFX0), and proportional up to the maximin good or any bad (PropMX and PropMX0). Our efficiency notion is Pareto-optimality (PO). We study two types of instances: (i) Separable, where the item set can be partitioned into goods and bads, and (ii) Restricted mixed goods (RMG), where for each item j, every agent has either a non-positive value for j, or values j at the same vj > 0. We obtain polynomial-time algorithms for the following: • Separable instances: PropMX0 allocation. • RMG instances: Let pure bads be the set of items that everyone values negatively. - PropMX allocation for general pure bads. - EFX+PropMX allocation for identically-ordered pure bads. - EFX+PropMX+PO allocation for identical pure bads. Finally, if the RMG instances are further restricted to binary mixed goods where all the vj's are the same, we strengthen the results to guarantee EFX0 and PropMX0 respectively.
AB - We study the problem of finding fair and efficient allocations of a set of indivisible items to a set of agents, where each item may be a good (positively valued) for some agents and a bad (negatively valued) for others, i.e., a mixed manna. As fairness notions, we consider arguably the strongest possible relaxations of envy-freeness and proportionality, namely envy-free up to any item (EFX and EFX0), and proportional up to the maximin good or any bad (PropMX and PropMX0). Our efficiency notion is Pareto-optimality (PO). We study two types of instances: (i) Separable, where the item set can be partitioned into goods and bads, and (ii) Restricted mixed goods (RMG), where for each item j, every agent has either a non-positive value for j, or values j at the same vj > 0. We obtain polynomial-time algorithms for the following: • Separable instances: PropMX0 allocation. • RMG instances: Let pure bads be the set of items that everyone values negatively. - PropMX allocation for general pure bads. - EFX+PropMX allocation for identically-ordered pure bads. - EFX+PropMX+PO allocation for identical pure bads. Finally, if the RMG instances are further restricted to binary mixed goods where all the vj's are the same, we strengthen the results to guarantee EFX0 and PropMX0 respectively.
KW - Envy-freeness
KW - Fair division
KW - Mixed Manna
KW - Pareto-optimality
KW - Proportionality
UR - http://www.scopus.com/inward/record.url?scp=85134310008&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85134310008&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85134310008
T3 - Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
SP - 1678
EP - 1680
BT - International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2022
PB - International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
T2 - 21st International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2022
Y2 - 9 May 2022 through 13 May 2022
ER -