Neural Network-Based Parameter Estimation of a Labour Market Agent-Based Model
arXiv:2602.15572v1 Announce Type: new Abstract: Agent-based modelling (ABM) is a widespread approach to simulate complex systems. Advancements in computational processing and storage have facilitated the adoption of ABMs across many fields; however, ABMs face challenges that limit their use as decision-support tools. A significant issue is parameter estimation in large-scale ABMs, particularly due to computational constraints on exploring the parameter space. This study evaluates a state-of-the-art simulation-based inference (SBI) framework that uses neural networks (NN) for parameter estimation. This framework is applied to an established labour market ABM based on job transition networks. The ABM is initiated with synthetic datasets and the real U.S. labour market. Next, we compare the effectiveness of summary statistics derived from a list of statistical measures with that learned by an embedded NN. The results demonstrate that the NN-based approach recovers the original parameters
arXiv:2602.15572v1 Announce Type: new Abstract: Agent-based modelling (ABM) is a widespread approach to simulate complex systems. Advancements in computational processing and storage have facilitated the adoption of ABMs across many fields; however, ABMs face challenges that limit their use as decision-support tools. A significant issue is parameter estimation in large-scale ABMs, particularly due to computational constraints on exploring the parameter space. This study evaluates a state-of-the-art simulation-based inference (SBI) framework that uses neural networks (NN) for parameter estimation. This framework is applied to an established labour market ABM based on job transition networks. The ABM is initiated with synthetic datasets and the real U.S. labour market. Next, we compare the effectiveness of summary statistics derived from a list of statistical measures with that learned by an embedded NN. The results demonstrate that the NN-based approach recovers the original parameters when evaluating posterior distributions across various dataset scales and improves efficiency compared to traditional Bayesian methods.
Executive Summary
This study evaluates a state-of-the-art simulation-based inference framework that employs neural networks for parameter estimation in agent-based models. The framework is applied to a labour market agent-based model, demonstrating that neural networks can recover original parameters more efficiently than traditional Bayesian methods. The results suggest that neural networks can improve the accuracy and efficiency of parameter estimation in large-scale agent-based models, particularly in computationally constrained environments. By leveraging the power of neural networks, this study contributes to the development of more effective decision-support tools for complex systems.
Key Points
- ▸ The study evaluates a neural network-based framework for parameter estimation in agent-based models.
- ▸ The framework is applied to a labour market agent-based model and demonstrated to recover original parameters more efficiently than traditional Bayesian methods.
- ▸ The results suggest that neural networks can improve the accuracy and efficiency of parameter estimation in large-scale agent-based models.
Merits
Strength in Parameter Estimation
The study demonstrates the effectiveness of neural networks in recovering original parameters, which can improve the accuracy and efficiency of parameter estimation in agent-based models.
Improved Efficiency
The results show that neural networks can improve efficiency compared to traditional Bayesian methods, making them a more viable option for large-scale agent-based models.
Demerits
Limited Generalizability
The study is limited to a specific labour market agent-based model, and it is unclear whether the results can be generalized to other types of agent-based models or domains.
Dependence on Data Quality
The accuracy and efficiency of neural networks may be highly dependent on the quality of the data used to train and evaluate the models, which can be a concern in real-world applications.
Expert Commentary
This study makes a significant contribution to the field of agent-based modeling by evaluating the effectiveness of neural networks for parameter estimation. The results demonstrate the potential of neural networks to improve the accuracy and efficiency of parameter estimation, particularly in computationally constrained environments. However, the study's limitations, including its dependence on data quality and limited generalizability, must be carefully considered. Future research should aim to explore the applicability of neural networks in a broader range of agent-based models and domains. Furthermore, the study's results should be replicated and validated in real-world settings to ensure the practical relevance of neural networks in decision-support tools.
Recommendations
- ✓ Future research should explore the applicability of neural networks in a broader range of agent-based models and domains.
- ✓ The study's results should be replicated and validated in real-world settings to ensure the practical relevance of neural networks in decision-support tools.
