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].
A Study of Proxies for Shapley Allocations of Transport Costs
Authors: Haris Aziz, Casey Cahan, Charles Gretton, Philip Kilby, Nicholas Mattei, Toby Walsh
JAIR 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform an experimental evaluation using synthetic Euclidean games as well as games derived from real-world tours calculated for scenarios involving fast-moving goods; where deliveries are made on a road network every day. We explore several computationally tractable allocation techniques that are good proxies for the Shapley value in problem instances of a size and complexity that is commercially relevant. |
| Researcher Affiliation | Collaboration | Haris Aziz EMAIL Data61/CSIRO and University of New South Wales (UNSW), Sydney, Australia Casey Cahan EMAIL University of Auckland, Auckland, New Zealand Charles Gretton EMAIL Hivery, Sydney, Australia; and Australian National University (ANU), Canberra, Australia; and Griffith University, Gold Coast, Australia |
| Pseudocode | Yes | Algorithm 1 DP-TSP-Shapley Algorithm 2 Appro Shapley Algorithm 3 Subset Shapley |
| Open Source Code | Yes | All code and data used in this project is available in a public Git repository at: https://github.com/nmattei/ Shapley TSG. |
| Open Datasets | Yes | Corpus available online at https://github.com/nmattei/Shapley TSG |
| Dataset Splits | Yes | For this tests we take the full Synthetic dataset and perform a 10-fold cross-validation (Bishop, 2006). To perform k-fold cross-validation, we take the dataset and break it into k equal sized folds F = {f1,..., fk}. |
| Hardware Specification | Yes | All timing experiments reported were performed on a computer with an Intel Xeon E5405 CPU running at 2.0 GHz with 4 GB of RAM running Debian 6.0 (build 2.6.32-5-amd64 Squeeze10). |
| Software Dependencies | No | The paper mentions using "Concorde (Applegate et al., 2007)", "Sci Kit Learn (Pedregosa et al., 2011)", and "Sci Py" but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | No. Samples = 300,000 2.812 (2 142)2 4 0.752 283,052 We choose to limit our linear model to two highest scoring elements φ DEPOTand φ MOATas these are both significantly higher scoring than the others and adding more elements may cause overfitting. |