Statistical Inference for Fisher Market Equilibrium
Authors: Luofeng Liao, Yuan Gao, Christian Kroer
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments to validate the theoretical findings, namely, the convergence of NSWγ to NSW (Theorem 9) and CLT (Eq. (2)). All figures can be found in Appendix O. |
| Researcher Affiliation | Academia | Luofeng Liao, Yuan Gao, Christian Kroer Department of Industrial Engineering and Operations Research Columbia University |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | First, we generate an infinite-dimensional market M1 of n = 50 buyers each having a linear valuation vi(θ) = aiθ + ci on Θ = [0, 1], with randomly generated ai and ci such that vi(θ) 0 on [0, 1]. Their budgets bi are also randomly generated. ... Then, following Section 2.2, for the j-th (j [k]) sampled market of size t, we randomly sample {θt,τ j }τ [t] uniformly and independently from [0, 1] and obtain markets with n buyers and t items. |
| Dataset Splits | No | The paper describes how synthetic data is generated and sampled for experiments (e.g., 't = 100, 200, . . . , 5000 and k = 10'), but does not specify traditional train/validation/test dataset splits as it's a simulation rather than an ML-style dataset evaluation. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | First, we generate an infinite-dimensional market M1 of n = 50 buyers each having a linear valuation vi(θ) = aiθ + ci on Θ = [0, 1], with randomly generated ai and ci such that vi(θ) 0 on [0, 1]. Their budgets bi are also randomly generated. ... We take t = 100, 200, . . . , 5000 and k = 10. |