Design and analysis of a synthetic prediction market using dynamic convex sets

We present a synthetic prediction market whose agent purchase logic is defined using a sigmoid transformation of a convex semi-algebraic set defined in feature space. Asset prices are determined by a logarithmic scoring market rule. Time varying asset prices affect the structure of the semi-algebrai...

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Autores principales:	Nishanth Nakshatri, Arjun Menon, C. Lee Giles, Sarah Rajtmajer, Christopher Griffin
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Prediction market Machine learning Dynamical systems Applied mathematics. Quantitative methods T57-57.97
Acceso en línea:	https://doaj.org/article/0371b0eea7d244c1a7354a283dde5f59
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spelling	oai:doaj.org-article:0371b0eea7d244c1a7354a283dde5f592021-12-04T04:36:22ZDesign and analysis of a synthetic prediction market using dynamic convex sets2666-720710.1016/j.rico.2021.100052https://doaj.org/article/0371b0eea7d244c1a7354a283dde5f592021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2666720721000308https://doaj.org/toc/2666-7207We present a synthetic prediction market whose agent purchase logic is defined using a sigmoid transformation of a convex semi-algebraic set defined in feature space. Asset prices are determined by a logarithmic scoring market rule. Time varying asset prices affect the structure of the semi-algebraic sets leading to time-varying agent purchase rules. We show that under certain assumptions on the underlying geometry, the resulting synthetic prediction market can be used to arbitrarily closely approximate a binary function defined on a set of input data. We also provide sufficient conditions for market convergence and show that under certain instances markets can exhibit limit cycles in asset spot price. We provide an evolutionary algorithm for training agent parameters to allow a market to model the distribution of a given data set and illustrate the market approximation using three open source data sets. Results are compared to standard machine learning methods.Nishanth NakshatriArjun MenonC. Lee GilesSarah RajtmajerChristopher GriffinElsevierarticlePrediction marketMachine learningDynamical systemsApplied mathematics. Quantitative methodsT57-57.97ENResults in Control and Optimization, Vol 5, Iss , Pp 100052- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Prediction market Machine learning Dynamical systems Applied mathematics. Quantitative methods T57-57.97
spellingShingle	Prediction market Machine learning Dynamical systems Applied mathematics. Quantitative methods T57-57.97 Nishanth Nakshatri Arjun Menon C. Lee Giles Sarah Rajtmajer Christopher Griffin Design and analysis of a synthetic prediction market using dynamic convex sets
description	We present a synthetic prediction market whose agent purchase logic is defined using a sigmoid transformation of a convex semi-algebraic set defined in feature space. Asset prices are determined by a logarithmic scoring market rule. Time varying asset prices affect the structure of the semi-algebraic sets leading to time-varying agent purchase rules. We show that under certain assumptions on the underlying geometry, the resulting synthetic prediction market can be used to arbitrarily closely approximate a binary function defined on a set of input data. We also provide sufficient conditions for market convergence and show that under certain instances markets can exhibit limit cycles in asset spot price. We provide an evolutionary algorithm for training agent parameters to allow a market to model the distribution of a given data set and illustrate the market approximation using three open source data sets. Results are compared to standard machine learning methods.
format	article
author	Nishanth Nakshatri Arjun Menon C. Lee Giles Sarah Rajtmajer Christopher Griffin
author_facet	Nishanth Nakshatri Arjun Menon C. Lee Giles Sarah Rajtmajer Christopher Griffin
author_sort	Nishanth Nakshatri
title	Design and analysis of a synthetic prediction market using dynamic convex sets
title_short	Design and analysis of a synthetic prediction market using dynamic convex sets
title_full	Design and analysis of a synthetic prediction market using dynamic convex sets
title_fullStr	Design and analysis of a synthetic prediction market using dynamic convex sets
title_full_unstemmed	Design and analysis of a synthetic prediction market using dynamic convex sets
title_sort	design and analysis of a synthetic prediction market using dynamic convex sets
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/0371b0eea7d244c1a7354a283dde5f59
work_keys_str_mv	AT nishanthnakshatri designandanalysisofasyntheticpredictionmarketusingdynamicconvexsets AT arjunmenon designandanalysisofasyntheticpredictionmarketusingdynamicconvexsets AT cleegiles designandanalysisofasyntheticpredictionmarketusingdynamicconvexsets AT sarahrajtmajer designandanalysisofasyntheticpredictionmarketusingdynamicconvexsets AT christophergriffin designandanalysisofasyntheticpredictionmarketusingdynamicconvexsets
_version_	1718372891045658624

Design and analysis of a synthetic prediction market using dynamic convex sets

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