Algorithms for achieving fairness and efficiency in matching problems (Record no. 429533)

MARC details
000 -LEADER
fixed length control field 04194nam a2200253 4500
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 230626b |||||||| |||| 00| 0 eng d
041 ## - LANGUAGE CODE
Language code of text/sound track or separate title en.
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 621
Item number SHI
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Narang, Shivika
245 ## - TITLE STATEMENT
Title Algorithms for achieving fairness and efficiency in matching problems
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc Bengaluru
Name of publisher, distributor, etc IISc
Date of publication, distribution, etc 2023
300 ## - PHYSICAL DESCRIPTION
Extent xvi, 160p.
Other physical details col. ill. ;
Dimensions 29.1 cm * 20.5 cm
Accompanying material e-Thesis
Size of unit 3.105Mb
500 ## - GENERAL NOTE
General note Include bibliographical references and index.
502 ## - DISSERTATION NOTE
Dissertation note PhD; 2023; Computer science and automation
520 ## - SUMMARY, ETC.
Summary, etc Matching problems arise in numerous practical settings. Fairness and efficiency are two desirable properties in most such real world scenarios. This dissertation work presents new approaches and models for capturing and solving fairness issues in different practical settings and develops algorithms to identify fair and/or efficient matchings. The thesis is organised into two logical parts: one-sided preferences and two-sided preferences. Part 1: One-Sided Preferences Fair and Efficient Delivery Motivated by the classical delivery problem, we introduce a novel model of fair division where delivery tasks must be fairly distributed among a set of agents. The delivery tasks are placed on the vertices of a given acyclic graph. The cost incurred by the agents is determined by the distance they travel from the hub where they start to service their assigned tasks. We study the existence of fair and efficient allocations of tasks to agents. We choose the fairness notions: EF1 and MMS and efficiency notions: Pareto optimality and Social optimality. We find that while all these notions can be satisfied independently, the only combination of fairness and efficiency that can always be guaranteed is MMS and PO. For the remaining combinations, we provide characterisations of the space of instances for which they can be achieved. We find that most of the relevant problems are NP-Hard. We provide an XP-algorithm which finds the different combinations of fairness and efficiency whenever they exist. Repeated Matchings We propose a novel repeated matching model where the valuations of agents may change with how often they have received an item in the past. We study achieving fairness and efficiency separately as well as in conjunctions in this setting. We find that optimizing for social welfare is NP-Hard for general valuations and tractable when the valuations are monotone with time. We also prove that maximizing for social welfare over the space of EF1 repeated matchings is NP-Hard. Further, we provide algorithms and non-existence results for EF1 and EFX repeated matchings in different settings. Part 2: Two Sided Preferences Fairness and Stability in Many-to-One Matchings We seek to optimize a fairness measure over the space of stable many-to-one matchings, motivated by a college admissions setting. With leximin optimality as the fairness notion, we first show the intractability of this problem. We identify a minimal set of assumptions that makes this problem solvable in polynomial time. This requires that the agents on either side have the same ordinal rankings over the agents on the other side and that these must be strict. We show that on relaxing to weak rankings, the problem becomes APX-Hard. When we remove the ranking assumption but maintain strict preferences, the problem is NP-Hard. Additionally, we show that the leximin optimal stable matching can be efficiently computed in the special case of two colleges. Incentive Compatibility in Stable Fractional Matchings We investigate the existence of incentive compatible mechanisms that find stable fractional matchings. We show, for general settings, that no incentive compatible mechanism can be stable. We characterise the space of instances that have a unique stable fractional matching. We prove for this set of instances that any stable matching mechanism will be incentive compatible
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Algorithmic Game Theory
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computational Social Choice
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element fairness issues
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Repeated Matchings
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Matchings
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Narahari, Y advised
856 ## - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://etd.iisc.ac.in/handle/2005/6140
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Thesis

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