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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Smooth Quadratic Prediction Markets
Authors: Enrique Nueve, Bo Waggoner
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Figure 1, we demonstrate the varying convergence behavior of the Smooth Quadratic Prediction Market when using different norms. There are numerous convergence results for gradient (general steepest) descent. To avoid redundancy with the literature, we refer the reader to Appendix D for further convergence results, which may be of interest to the application of Smooth Quadratic Prediction Markets. [...] We perform simulations for the ℓ1 and ℓ∞ cases. Overall, either analytically or experimentally, we observe that in both situations, the market state converges to the belief of a sequence of traders, regardless of the used p-norm, motivating the use of the Smooth Quadratic Prediction Market in either of these realistic scenarios. [...] Figure 2 in Appendix E demonstrates the convergence behavior of the market under budget constraints. Figure 3: Let q0 = (10, 20, 10), C is softmax with smoothness of L = 1, and µ = (1/6, 1/6, 2/3). The blue square expresses µ and the orange path towards the blue square demonstrates the updating market distribution states in a buy-only market. As denote by the titles s of each plot, we vary the norm used for the Smooth Quadratic Prediction Market. |
| Researcher Affiliation | Academia | Enrique Nueve Department of Computer Science University of Colorado Boulder EMAIL Bo Waggoner Department of Computer Science University of Colorado Boulder EMAIL |
| Pseudocode | No | The paper does not contain explicitly labeled pseudocode or algorithm blocks. Protocol 1 in Appendix C describes a process but is not formatted as pseudocode or an algorithm block. |
| Open Source Code | Yes | Code can be found at: https://github.com/Enrique Nueve/Smooth-Quadratic-Prediction-Markets |
| Open Datasets | No | The paper does not mention or use any external datasets for its simulations or analyses. The parameters used for generating figures (e.g., q0 = (10, 20, 10), L = 1, µ = (1/6, 1/6, 2/3)) are model parameters, not external datasets. |
| Dataset Splits | No | The paper does not use any datasets, thus no dataset splits are mentioned. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its simulations or analyses. |
| Software Dependencies | No | The paper does not provide specific software dependencies, such as library names with version numbers, used to replicate the experiments. |
| Experiment Setup | Yes | Let q0 = (10, 20, 10), C is softmax with smoothness of L = 1, and µ = (1/6, 1/6, 2/3). The blue square expresses µ and the orange path towards the blue square demonstrates the updating market distribution states. As denote by the titles s of each plot, we vary the norm used for the Smooth Quadratic Prediction Market. (Figures 1, 2, 3 captions) |