Some challenges of calibrating differentiable agent-based models

ArXiv ID: 2307.01085 “View on arXiv”

Authors: Unknown

Abstract

Agent-based models (ABMs) are a promising approach to modelling and reasoning about complex systems, yet their application in practice is impeded by their complexity, discrete nature, and the difficulty of performing parameter inference and optimisation tasks. This in turn has sparked interest in the construction of differentiable ABMs as a strategy for combatting these difficulties, yet a number of challenges remain. In this paper, we discuss and present experiments that highlight some of these challenges, along with potential solutions.

Keywords: Agent-based models (ABMs), Differentiable ABMs, Parameter inference, Complex systems modeling, General Financial Modeling

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 6.0/10
• Quadrant: Lab Rats
• Why: The paper features advanced mathematical concepts like automatic differentiation and gradient estimation strategies, but the experiments are conceptual simulations (e.g., Brock & Hommes model) and lack real-world financial data, making it more theoretical than backtest-ready.

  flowchart TD
    A["Research Goal:<br>Challenges of Calibrating<br>Differentiable ABMs"] --> B["Methodology: Experiments on<br>Gradient Estimation & Training"]
    B --> C["Inputs: Financial Systems<br>Agent Behaviors"]
    C --> D{"Computational Process:<br>Calibration & Optimization"}
    D --> E["Key Findings:<br>Gradient Instability"]
    D --> F["Key Findings:<br>Simulation-Model Mismatch"]
    E --> G["Outcome: Potential Solutions<br>for Robust ABM Calibration"]
    F --> G