Abstract
Agent-based modelling has been proved to be extremely useful for learning about real world societies through the analysis of simulations. Recent agent-based models usually contain a large number of parameters that capture the interactions among microheterogeneous subjects and the multistructure of the complex system. However, this can result in the “curse of dimensionality” phenomenon and decrease the robustness of the model’s output. Hence, it is still a great challenge to efficiently calibrate agent-based models to actual data. In this paper, we present a surrogate analysis method for calibration by combining supervised machine-learning and intelligent iterative sampling. Without any prior assumptions regarding the distribution of the parameter space, the proposed method can learn a surrogate model as the approximation of the original system with a relatively small number of training points, which will serve the needs of further sensitivity analysis and parameter calibration research. We take the heterogeneous asset pricing model as an example to evaluate the model’s performance using actual Chinese stock market data. The results demonstrate the good capabilities of the surrogate model at modelling the observed reality, as well as the remarkable reduction of the computational time for validating the agent-based model.
Highlights
Agent-based models (ABMs) are favoured by researchers when explaining the emergence of complex systems [1, 2]. e explanatory power of the existing ABMs mainly comes from exploring the market mechanism by describing heterogeneous agents’ behavioural activities and their interactions, which are widely used in economics, demography, and ecology [3,4,5]
As the parameter spaces geometrically expand as the number of parameters increase, it results in another challenge in the use of ABMs, which is referred to as the “dimensional disaster” [13]
We present a new approach for ABM validation and calibration based on the surrogate model
Summary
Agent-based models (ABMs) are favoured by researchers when explaining the emergence of complex systems [1, 2]. e explanatory power of the existing ABMs mainly comes from exploring the market mechanism by describing heterogeneous agents’ behavioural activities and their interactions, which are widely used in economics, demography, and ecology [3,4,5]. We should point out that our work is an improvement based on the research work of Lamperti et al [22], which has combined the xgboost algorithm with intelligent sampling method to generate a fast learning surrogate model for ABMS validation. We expect to nd the maximum number of positive labelled points in the parameter space and use them in learning to generate the surrogate model. E uncertain sampling method increases the sampling frequency of the parameter space that the surrogate model has di cultly correctly predicting based on the entropy of the existing label distribution. E proposed method can intelligently pick the meaningful parameter combination points in multiple rounds of sampling process, which continuously improves the sampling performance and the calibration accuracy at relatively low computational costs. It does not require a prior assumption regarding the approximate distribution of the model’s response. ird, the approach does not require that the points satisfy the Markov chain distribution
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