Fairness and Efficiency in Fair Division: An Empirical Analysis of Mechanisms for Fair Allocation

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dc.contributor.advisorBhaskar, Umangen_US
dc.contributor.authorGUPTA, AASTHAen_US
dc.contributor.departmentDept. of Data Scienceen_US
dc.contributor.registration20211211en_US
dc.date.accessioned2026-05-21T10:46:23Z
dc.date.available2026-05-21T10:46:23Z
dc.date.issued2026-05en_US
dc.description.abstractThis thesis presents a comprehensive empirical analysis of two prominent fair division algorithms deployed in real-world platforms: the Maximum Nash Welfare (MNW) algorithm for indivisible goods allocation (Spliddit platform) and the Adjusted Winner (AW) algorithm for household chore division (Kajibuntan platform). We evaluate both algorithms across eight fairness and efficiency metrics including envy-freeness (EF, EF1, EFX), proportionality (PROP), maximin share (MMS), equitability (EQ, EQ1), and Pareto optimality (PO).en_US
dc.description.embargoOne Yearen_US
dc.identifier.citation63en_US
dc.identifier.urihttp://dr.iiserpune.ac.in:8080/xmlui/handle/123456789/11125
dc.language.isoenen_US
dc.subjectFair Divisionen_US
dc.subjectEfficiencyen_US
dc.subjectPareto Optimalen_US
dc.titleFairness and Efficiency in Fair Division: An Empirical Analysis of Mechanisms for Fair Allocationen_US
dc.typeThesisen_US
dc.type.degreeBS-MSen_US

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