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http://dr.iiserpune.ac.in:8080/xmlui/handle/123456789/11125Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | Bhaskar, Umang | - |
| dc.contributor.author | GUPTA, AASTHA | - |
| dc.date.accessioned | 2026-05-21T10:46:23Z | - |
| dc.date.available | 2026-05-21T10:46:23Z | - |
| dc.date.issued | 2026-05 | - |
| dc.identifier.citation | 63 | en_US |
| dc.identifier.uri | http://dr.iiserpune.ac.in:8080/xmlui/handle/123456789/11125 | - |
| dc.description.abstract | This 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.language.iso | en | en_US |
| dc.subject | Fair Division | en_US |
| dc.subject | Efficiency | en_US |
| dc.subject | Pareto Optimal | en_US |
| dc.title | Fairness and Efficiency in Fair Division: An Empirical Analysis of Mechanisms for Fair Allocation | en_US |
| dc.type | Thesis | en_US |
| dc.description.embargo | One Year | en_US |
| dc.type.degree | BS-MS | en_US |
| dc.contributor.department | Dept. of Data Science | en_US |
| dc.contributor.registration | 20211211 | en_US |
| Appears in Collections: | MS THESES | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 20211211_AASTHA_GUPTA_MS_Thesis.pdf | MS Thesis | 758.42 kB | Adobe PDF | View/Open Request a copy |
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