Alleviating Nonidentifiability: A High-Fidelity Calibration Objective for Financial Market Simulation With Multivariate Time Series Data

The nonidentifiability (NI) issue has been frequently reported in social simulation works, where different parameters of an agent-based simulation model yield indistinguishable simulated time series data under certain discrepancy metrics. This issue largely undermines the simulation fidelity yet lacks dedicated investigations. This article theoretically demonstrates that incorporating multiple time series data features during the model calibration phase can exponentially alleviate NI as the number of features increases. To implement this theoretical finding, a maximization-based aggregation function is proposed based on existing discrepancy metrics to form a new calibration objective function. For verification, the task of calibrating the financial market simulation (FMS), a typical yet complex social simulation, is considered. Empirical studies confirm the significant improvements in alleviating the NI of calibration tasks. Furthermore, as a model-agnostic method, it achieves much higher simulation fidelity of the chosen FMS model on both synthetic and real market data. Moreover, it is both theoretically and empirically analyzed that as long as the features are selected and not linearly correlated, they can contribute to alleviation, which demonstrates the robustness of the proposed objective. Hence, this article is expected to provide not only a rigorous understanding of NI in social simulation but also an off-the-shelf high-fidelity calibration objective function for FMS.