Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Fair Allocation of Indivisible Goods to Asymmetric Agents
Authors: Alireza Farhadi, Mohammad Ghodsi, Mohammad Taghi Hajiaghayi, Sébastien Lahaie, David Pennock, Masoud Seddighin, Saeed Seddighin, Hadi Yami
JAIR 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In contrast to our theoretical results, we show in practice a fair allocation is likely to exist by providing experimental results on real-world data. The source of our experiments is a publicly available collection of bids for eBay goods and services (Medina, 2014). More details about the experiments can be found in Section 4. We also support our claim by presenting theoretical analysis for the stochastic variants of the problem in which the valuation of every agent for a good is drawn from a given distribution. |
| Researcher Affiliation | Collaboration | Alireza Farhadi EMAIL University of Maryland, College Park Maryland, USA Mohammad Ghodsi EMAIL Sharif University of Technology IPM Institute for Research in Fundamental Sciences Tehran, Iran Mohammad Taghi Hajiaghayi EMAIL University of Maryland, College Park Maryland, USA Sébastien Lahaie EMAIL Google Research New York City, USA David Pennock EMAIL Microsoft Research New York City, USA |
| Pseudocode | Yes | Algorithm 1: 1/n-WMMS allocation Algorithm 2: 1/2-WMMS allocation for the restricted case |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. It mentions algorithms and experiments but no repository link or explicit statement of code release. |
| Open Datasets | Yes | We draw the valuation of the agents for the goods based on a collection of publicly available bids for eBay items (Medina, 2014). Medina, M. (2014). Ebay Data Set. http://cims.nyu.edu/~munoz/data/. |
| Dataset Splits | No | The paper describes generating random vectors for entitlements for experimental runs (e.g., "1000 different vector of entitlements drawn from the uniform distribution"). However, it does not provide specific training/test/validation dataset splits, as the experimental methodology focuses on simulations rather than model training and evaluation with fixed data partitions. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU models, CPU types, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers, or solvers with version numbers. |
| Experiment Setup | Yes | For an instance of the problem with n agents and m items, we run the experiments with 1000 different vector of entitlements drawn from the uniform distribution (and scaled up to satisfy P ei = 1). For every n and m, we take the minimum WMMS guarantee obtained by all 1000 runs, and show it in Figures 1 and 2. We used heuristic methods to compute the maxmin guarantees and estimate maximinshare values. These heuristic methods include greedy algorithms such as local search and Algorithm 2 as well as random allocations. In our local search approach, we start by an arbitrary allocation and iteratively improve the guarantee by local exchanges of items. Another greedy algorithm we use is as follows: let γi = Vi(Si)/WMMSi, where Si is the set of currently allocated items to agent ai. At each step, we select an agent ai which minimizes γi, and allocate him the most valuable remaining good. |