Online Fair Division Redux
Authors: Martin Aleksandrov
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | To analyse mechanisms, I study axioms such as strategyproofness, envy-freeness, efficiency among many others. In addition, I validate their competitiveness against the optimal (offline or online) mechanism using generated and realworld data; see e.g. [Dubey, 1986; Koutsoupias and Papadimitriou, 2009; Mattei and Walsh, 2013]. Moreover, I investigate complexity questions around computing outcomes, optimal strategies and manipulations; see e.g. [Aziz et al., 2015; Bouveret and Lang, 2014]. I presented a strategyproof and bounded envyfree ex post mechanism for the model in [Walsh, 2015], a bounded envy-free ex post mechanism for the extended model with multiple items and a strategyproof repeated auction mechanism for the model with budget-constrained agents when the budgets are fixed. |
| Researcher Affiliation | Academia | Martin Aleksandrov UNSW Australia and Data61 (formerly NICTA) |
| Pseudocode | No | The paper describes mechanisms in prose but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper mentions 'generated and real-world data' as part of a research plan for future validation, but it does not provide concrete access information (link, DOI, specific citation with authors/year) for any publicly available or open dataset used in the current work. |
| Dataset Splits | No | The paper is theoretical and does not discuss dataset splits for training or validation. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not include details about an experimental setup, hyperparameters, or training configurations. |