Advanced simulation paradigm of human behaviour unveils complex financial systemic projection

ArXiv ID: 2503.20787 “View on arXiv”

Authors: Unknown

Abstract

The high-order complexity of human behaviour is likely the root cause of extreme difficulty in financial market projections. We consider that behavioural simulation can unveil systemic dynamics to support analysis. Simulating diverse human groups must account for the behavioural heterogeneity, especially in finance. To address the fidelity of simulated agents, on the basis of agent-based modeling, we propose a new paradigm of behavioural simulation where each agent is supported and driven by a hierarchical knowledge architecture. This architecture, integrating language and professional models, imitates behavioural processes in specific scenarios. Evaluated on futures markets, our simulator achieves a 13.29% deviation in simulating crisis scenarios whose price increase rate reaches 285.34%. Under normal conditions, our simulator also exhibits lower mean square error in predicting futures price of specific commodities. This technique bridges non-quantitative information with diverse market behaviour, offering a promising platform to simulate investor behaviour and its impact on market dynamics.

Keywords: Agent-Based Modeling, Behavioral Simulation, Hierarchical Knowledge Architecture, Market Dynamics, Futures Markets, Futures

Complexity vs Empirical Score

• Math Complexity: 7.5/10
• Empirical Rigor: 6.0/10
• Quadrant: Holy Grail
• Why: The paper presents a complex agent-based modeling paradigm with a hierarchical knowledge architecture (high math), backed by specific numerical results on futures markets like 13.29% deviation and MSE comparisons (moderate empirical rigor).

  flowchart TD
    A["Research Goal:<br>Simulate financial market<br>systemic projection"] --> B{"Methodology:<br>Agent-Based Modeling"}
    B --> C["Key Innovation:<br>Hierarchical Knowledge Architecture<br>Language & Professional Models"]
    C --> D["Simulation Engine:<br>Agents + Heterogeneity<br>vs. Futures Market Data"]
    D --> E{"Evaluation"}
    E -- Crisis Scenario --> F["Findings: Low Deviation<br>13.29% (Price: 285.34%)"]
    E -- Normal Conditions --> G["Findings: Lower MSE<br>Predicting Futures Prices"]