2012/01/18 by Jin‐Li Hu, Jinli Hu, Hu, Jinli
Business, Management and Accounting · Computer Science · Decision Sciences · Economics, Econometrics and Finance · #Advanced Bandit Algorithms Research #Artificial Intelligence (cs.AI) #Computer Science and Game Theory (cs.GT) #Consumer Market Behavior and Pricing #FOS: Computer and information sciences #FOS: Economics and business #Sports Analytics and Performance #Trading and Market Microstructure (q-fin.TR) #cs.AI #cs.GT #q-fin.TR
paper · pdf · doi:10.48550/arxiv.1201.3851
arxiv created 2012/01/18 · openalex publication_date 2012/01/18 · arxiv updated 2012/01/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Combining models in appropriate ways to achieve high performance is commonly seen in machine learning fields today. Although a large amount of combinatorial models have been created, little attention is drawn to the commons in different models and their connections. A general modelling technique is thus worth studying to understand model combination deeply and shed light on creating new models. Prediction markets show a promise of becoming such a generic, flexible combinatorial model. By reviewing on several popular combinatorial models and prediction market models, this paper aims to show how the market models can generalise different combinatorial stuctures and how they implement these popular combinatorial models in specific conditions. Besides, we will see among different market models, Storkey's Machine Learning Markets provide more fundamental, generic modelling mechanisms than the others, and it has a significant appeal for both theoretical study and application.