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..
The Competitive Effects of Variance-based Pricing
Authors: Ludwig Dierks, Sven Seuken
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we study a variance-based pricing rule in a two-provider market setting and perform a game-theoretic analysis of the resulting competitive effects. We show that an innovative provider who employs variance-based pricing can choose a pricing strategy that guarantees himself a higher profit than using fixed per-unit prices for any individually rational response of a provider playing a fixed pricing strategy. We then characterize all equilibria for the setting where both providers use variance-based pricing strategies. We show that, in equilibrium, the providers profits may increase or decrease, depending on their cost functions. However, social welfare always weakly increases. |
| Researcher Affiliation | Academia | Ludwig Dierks and Sven Seuken Department of Informatics, University Zurich dierks@ifi.uzh.ch, seuken@ifi.uzh.ch |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement about releasing source code or a link to a code repository. |
| Open Datasets | No | The paper uses a theoretical model with a continuous random variable for user types, a probability density function f(t) and cumulative distribution function F(t), and specific cost functions for its numerical example. This is a theoretical construct, not a publicly available dataset. |
| Dataset Splits | No | The paper does not use real-world datasets or simulations that require training, validation, and test splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for its analysis or numerical examples. |
| Software Dependencies | No | The paper does not mention any specific software or programming libraries with version numbers used for its analysis. |
| Experiment Setup | No | The paper presents a theoretical game-theoretic analysis and a numerical example with defined functions, but it does not include details about an experimental setup, hyperparameters, or training configurations for a software system. |