1 research outputs found
Automatic Calibration of Dynamic and Heterogeneous Parameters in Agent-based Model
While simulations have been utilized in diverse domains, such as urban growth
modeling, market dynamics modeling, etc; some of these applications may require
validations based upon some real-world observations modeled in the simulation,
as well. This validation has been categorized into either qualitative
face-validation or quantitative empirical validation, but as the importance and
the accumulation of data grows, the importance of the quantitative validation
has been highlighted in the recent studies, i.e. digital twin. The key
component of quantitative validation is finding a calibrated set of parameters
to regenerate the real-world observations with simulation models. While this
parameter calibration has been fixed throughout a simulation execution, this
paper expands the static parameter calibration in two dimensions: dynamic
calibration and heterogeneous calibration. First, dynamic calibration changes
the parameter values over the simulation period by reflecting the simulation
output trend. Second, heterogeneous calibration changes the parameter values
per simulated entity clusters by considering the similarities of entity states.
We experimented the suggested calibrations on one hypothetical case and another
real-world case. As a hypothetical scenario, we use the Wealth Distribution
Model to illustrate how our calibration works. As a real-world scenario, we
selected Real Estate Market Model because of three reasons. First, the models
have heterogeneous entities as being agent-based models; second, they are
economic models with real-world trends over time; and third, they are
applicable to the real-world scenarios where we can gather validation data.Comment: 31 pages, 12 figures, Journal of Autonomous Agents and Multi-Agent
System