Learning Market Parameters Using Aggregate Demand Queries

Authors: Xiaohui Bei, Wei Chen, Jugal Garg, Martin Hoefer, Xiaoming Sun

AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical Our results are the first polynomial-time algorithms to learn utility and budget parameters via revealed preference queries in classic Fisher markets with multiple buyers. We present efficient algorithms to learn market parameters in Fisher and exchange markets.
Researcher Affiliation Collaboration Xiaohui Bei Nanyang Technological University Singapore Wei Chen Microsoft Research Beijing, China Jugal Garg MPI f ur Informatik Saarbr ucken, Germany Martin Hoefer MPI f ur Informatik Saarbr ucken, Germany Xiaoming Sun China Academy of Sciences Beijing, China
Pseudocode Yes Algorithm 1: LIN-LEARN-BUDGET (A, M, L) Algorithm 2: LIN-FISHER-MAIN (m, L) Algorithm 3: LIN-EXCHANGE (m, L, k) Algorithm 4: LIN-EXCHANGE-MAIN (m, L)
Open Source Code No The paper does not provide any statement or link indicating the availability of open-source code for the described methodology.
Open Datasets No The paper is theoretical and proposes algorithms without empirical evaluation on a dataset; therefore, no dataset availability information is provided.
Dataset Splits No The paper is theoretical and does not report on empirical experiments; therefore, no dataset split information (training, validation, test) is provided.
Hardware Specification No The paper is theoretical and does not describe any experimental setup or hardware used for computation.
Software Dependencies No The paper is theoretical and focuses on algorithms, not their implementation or empirical evaluation; therefore, no software dependencies with version numbers are mentioned.
Experiment Setup No The paper is theoretical and describes algorithms and their properties, not an empirical experimental setup with specific parameters or configurations.