Combinatorial Auctions via Machine Learning-based Preference Elicitation
Authors: Gianluca Brero, Benjamin Lubin, Sven Seuken
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate PVM experimentally in several spectrum auction domains, and we show that it achieves high allocative efficiency even when only few values are elicited from the bidders. 6 Experimental Evaluation In this section, we present the experimental evaluation of our new approach. First, we introduce our experiment set-up. Then we evaluate the performance of our ML-based elicitation algorithm on its own, before we finally evaluate the performance of our Pseudo-VCG Mechanism (PVM). |
| Researcher Affiliation | Academia | 1 Department of Informatics, University of Zurich 2 Information Systems Department, Boston University School of Management brero@ifi.uzh.ch, blubin@bu.edu, seuken@ifi.uzh.ch |
| Pseudocode | Yes | Algorithm 1: ML-based Elicitation Algorithm. ... Algorithm 2: Pseudo-VCG Mechanism (PVM) |
| Open Source Code | No | The paper does not provide a direct link to the source code for the methodology described, nor does it explicitly state that the code is publicly released or available in supplementary materials. |
| Open Datasets | Yes | We use the Spectrum Auction Test Suite (SATS) version 0.5.2 [Weiss et al., 2017] for our experiments, which allows us to easily generate thousands of auction instances on demand. ... We tested our approach on three of the value models provided by SATS: The Global Synergy Value Model (GSVM) [Goeree and Holt, 2008], The Local Synergy Value Model (LSVM) [Scheffel et al., 2012], The Multi-Region Value Model (MRVM) [Weiss et al., 2017]. |
| Dataset Splits | Yes | In each domain, the optimal c0 under cap ce was selected using hold-out data, resulting in c 0 = 40 for GSVM and LSVM and c 0 = 30 for MRVM. As discussed in Section 5, we use SVRs with linear and quadratic kernels as the ML algorithm. The meta-parameters of the SVR were tuned using hold-out data. |
| Hardware Specification | Yes | We conducted our experiments on machines with Intel Xeon E5-2650 v4 2.20GHz processors with 40 logical cores. |
| Software Dependencies | Yes | The integer programs (IPs) used to find the allocations at were solved with CPLEX Studio (version 1261). |
| Experiment Setup | Yes | To achieve a total number of queries comparable to that found in real-world auctions, we used ce = 50 for GSVM and LSVM, and ce = 100 in MRVM. In each domain, the optimal c0 under cap ce was selected using hold-out data, resulting in c 0 = 40 for GSVM and LSVM and c 0 = 30 for MRVM. The integer programs (IPs) used to find the allocations at were solved with CPLEX Studio (version 1261). We set a time limit of 1h for solving each IP and, when the time limit was reached, adopted the best solution found so far. ... allocated 3 logical cores to CPLEX for each elicitation run, such that we never requested more than the available number of cores on a single machine. |