  The non-identifiability issue has been frequently reported in the 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 paper theoretically analyzes that incorporating
multiple time series data features in the model calibration phase can alleviate
the non-identifiability exponentially with the increasing number of features.
To implement this theoretical finding, a maximization-based aggregation
function is applied to existing discrepancy metrics to form a new calibration
objective function. For verification, the financial market simulation, a
typical and complex social simulation task, is considered. Empirical studies on
both synthetic and real market data witness the significant improvements in
alleviating the non-identifiability with much higher simulation fidelity of the
chosen agent-based simulation model. Importantly, as a model-agnostic method,
it achieves the first successful simulation of the high-frequency market at
seconds level. Hence, this work is expected to provide not only a rigorous
understanding of non-identifiability in social simulation, but a high-fidelity
calibration objective function for financial market simulations.
