Postprocessing outputs from ABMs using ML

Here's an overview of the surrogate analysis approach for calibrating ABM outputs:

1. Define Calibration Objective: Clearly define the objective of calibration, such as matching specific output patterns, optimizing a certain performance metric, or fitting the ABM outputs to observed data.

2. Select Calibration Parameters: Identify the parameters within the ABM that will be calibrated to achieve the calibration objective. These parameters could be agent attributes, system settings, interaction rules, or other components that influence the ABM outputs.

3. Design Experiments: Plan a set of experiments to generate ABM output data by varying the selected calibration parameters. The experiment design should cover a wide range of parameter values to explore the parameter space effectively.

4. Build Surrogate Model: Develop a simplified surrogate model that approximates the behavior of the original ABM using the generated output data. The surrogate model can be a regression model, a machine learning model, or any other modeling technique that captures the relationship between the calibration parameters and the ABM outputs.

5. Model Calibration: Calibrate the surrogate model using optimization techniques. This involves finding the optimal parameter values that minimize the discrepancy between the surrogate model outputs and the desired output patterns or observed data. Optimization algorithms like genetic algorithms, particle swarm optimization, or Bayesian optimization can be used for this purpose.

6. Validate Surrogate Model: Validate the calibrated surrogate model by comparing its outputs against the original ABM outputs or the observed data. Assess the accuracy and reliability of the surrogate model in replicating the behavior of the ABM.

7. Sensitivity Analysis: Conduct sensitivity analysis on the surrogate model to understand the impact of different parameters on the ABM outputs. Identify the most influential parameters that have a significant effect on the system behavior.

8. Scenario Analysis and Optimization: Utilize the calibrated surrogate model for scenario analysis and optimization. Explore different parameter combinations or scenarios to understand their effects on the ABM outputs and identify optimal parameter settings that achieve the desired outcomes.

9. Iteration and Improvement: Based on the analysis and optimization results, iterate and refine the surrogate model and the calibration process. Adjust the model structure, experiment design, or optimization techniques to enhance the accuracy and efficiency of the calibration.

It's important to note that surrogate analysis provides an approximation of the ABM behavior, and the calibrated surrogate model might not capture all the complexities and nuances of the original model. However, it can significantly reduce the computational burden and facilitate efficient exploration of the parameter space for calibration and optimization purposes.