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

Nishanth Nakshatri, Arjun Menon, C. Lee Giles, Sarah Rajtmajer, Christopher Griffin

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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.

Original languageEnglish (US)
Article number100052
JournalResults in Control and Optimization
StatePublished - Dec 2021

All Science Journal Classification (ASJC) codes

  • Control and Optimization
  • Artificial Intelligence
  • Applied Mathematics
  • Control and Systems Engineering
  • Modeling and Simulation


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