Metamodeling For ABM.pdf

ABMs are complex dynamic models with high numbers of entities and parameters, interdependent relations, and emergent social phenomena, making trustworthy statistical modeling a challenge.

Key Points:

1. SEM as a solution: SEM is proposed as a promising method to analyze ABMs, allowing for the estimation and evaluation of highly networked systems, explicating interactions between agents, measuring emergent phenomena, and identifying output patterns.
2. Challenges in ABM analysis: Traditional metamodels may fail to address the increasing complexity in ABMs, and standard functional forms of metamodels can be difficult to present and understand, leading to uncertainty and a lack of understanding.
3. SEM capabilities: SEM can address complex models, interacting agents, and high-order phenomena, providing a structured functional form for simulation metamodels and allowing for the incorporation of prior conceptual model information.
4. Example application: A socio-ecological simulation model (predator-prey model) is used to demonstrate the capabilities of SEM, showing that it can reveal underlying explanatory processes and crucial drivers for emergent phenomena.

Methodology:

1. Regression-based metamodels: The paper introduces the standard regression-based process of using metamodels in ABM.
2. Structured functional metamodels: SEM is introduced as a new approach, and disparities to the benchmark metamodel are shown.
3. Application to a complex nonlinear simulation model: Two types of metamodels are generated, and the structured functional model is proposed as a new treatment of specific challenges in agent-based simulation.