PAMS: Platform for Artificial Market Simulations

Paper: arXiv 2309.10729

Abstract

This paper presents a new artificial market simulation platform, PAMS: Platform for Artificial Market Simulations. PAMS is developed as a Python-based simulator that is easily integrated with deep learning and enabling various simulation that requires easy users’ modification. In this paper, we demonstrate PAMS effectiveness through a study using agents predicting future prices by deep learning.

Complexity vs Empirical Score

• Math Complexity: 2.0/10
• Empirical Rigor: 3.0/10
• Quadrant: Philosophers — conceptual discussion, limited math and data

Why this score: The paper presents a simulation platform (PAMS) with minimal advanced mathematical derivations, focusing instead on software architecture and integration with deep learning. Empirical rigor is low as the work centers on platform introduction and a demonstration study rather than extensive backtesting with real-world data or statistical performance metrics.

Research Flowchart

  flowchart TD
  A["Research Goal: Develop & validate PAMS platform"] --> B["Methodology: Construct Python-based simulator<br>with deep learning integration"]
  B --> C["Input: Historical market data &<br>agent behavioral parameters"]
  C --> D{"Computational Process: Artificial Market Simulation"}
  D --> E["Agent training: Predict future prices<br>using deep learning models"]
  E --> F["Simulation runs &<br>market dynamics analysis"]
  F --> G["Key Findings: PAMS enables effective<br>simulations with user modifications"]
  G --> H["Outcome: Validated platform for<br>financial market research"]