MUDA: A Truthful Multi-Unit Double-Auction Mechanism
Authors: Erel Segal-Halevi, Avinatan Hassidim, Yonatan Aumann
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We complement our worst-case analysis that depends on k with simulations of both variants of MUDA on agents drawn from both synthetic and realistic distributions. The simulations show that, when valuations are random (and not worst-case), the competitive-ratio of MUDA increases with the number of traders. These are presented in Section 6. [...] 6 Simulations To complement our theoretic analysis, we simulated MUDA on traders with valuations sampled both from a synthetic distribution and an empirical distribution based on real stock-exchange data. |
| Researcher Affiliation | Academia | Erel Segal-Halevi Ariel University Ariel, Israel 40700 Avinatan Hassidim, Yonatan Aumann Bar-Ilan University Ramat-Gan, Israel 52900 |
| Pseudocode | No | The paper describes the MUDA general scheme with numbered steps but it is not formatted as pseudocode (e.g., using if/else or loops with indentation) or explicitly labeled "Pseudocode" or "Algorithm". |
| Open Source Code | Yes | Source code for reproducing the experiments is available at https://github.com/erelsgl/economics. |
| Open Datasets | Yes | In the second experiment, we used the TORQ database (Hasbrouck 1992; Lee and Radhakrishna 2000). |
| Dataset Splits | No | No specific training, validation, or test dataset splits (percentages, counts, or explicit standard splits) were mentioned. |
| Hardware Specification | No | No specific hardware details (like GPU or CPU models, memory, or cloud instance types) used for running experiments were provided. |
| Software Dependencies | No | No specific software dependencies with version numbers were mentioned. |
| Experiment Setup | Yes | In the first experiment, for each agent, we sampled M/m values from a uniform distribution with support [V A, V + A]. We considered each of these values as the marginal-value of m virtual-traders, so that each agent has M virtualtraders. We ordered the values in decreasing order to get DMR valuations. In the experiments, we took m = 100 and V = 500 and varied the noise-amplitude A between 50 and 450. Here we show the results for A = 250; varying A did not have much effect on the results. We repeated each experiment 100 times and averaged the results. |