Counterbalancing Learning and Strategic Incentives in Allocation Markets
Authors: Jamie Kang, Faidra Monachou, Moran Koren, Itai Ashlagi
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We quantify the improvement using simulations. |
| Researcher Affiliation | Academia | Jamie Kang Stanford University kangjh@stanford.edu Faidra Monachou Stanford University monachou@stanford.edu Moran Koren Harvard University me@mkoren.org Itai Ashlagi Stanford University iashlagi@stanford.edu |
| Pseudocode | Yes | Algorithm 1: Implementation of VGREEDY |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper describes a theoretical model with parameters (e.g., signal precision q, prior belief µ) and uses simulations based on these parameters and random variables. It does not use external, publicly available datasets for training, validation, or testing in the typical machine learning sense. |
| Dataset Splits | No | The paper does not describe dataset splits for training, validation, or testing, as it focuses on theoretical analysis and simulations rather than empirical evaluation on a specific dataset. |
| Hardware Specification | No | The paper describes simulations and theoretical analysis but provides no details about the specific hardware (e.g., GPU/CPU models) used to perform these simulations. |
| Software Dependencies | No | The paper discusses simulations and algorithms but does not specify any software dependencies with version numbers (e.g., programming languages, libraries, or solvers). |
| Experiment Setup | Yes | In our numerical analysis, we examine K(µ) for all priors µ 2 (0, 1) and signal precision q 2 {0.6, 0.7, 0.8}. ... We evaluate the correctness c(V ) of four different mechanisms V 2 {V 1 GREEDY, V 2 GREEDY, VSEQ, VALL} for q 2 {0.6, 0.7, 0.8} and µ 2 (0, 1). ... in a setting with population size I = 345. |