A CRISP-DM-based Methodology for Assessing Agent-based Simulation Models using Process Mining
CoRR(2024)
摘要
Agent-based simulation (ABS) models are potent tools for analyzing complex
systems. However, understanding and validating ABS models can be a significant
challenge. To address this challenge, cutting-edge data-driven techniques offer
sophisticated capabilities for analyzing the outcomes of ABS models. One such
technique is process mining, which encompasses a range of methods for
discovering, monitoring, and enhancing processes by extracting knowledge from
event logs. However, applying process mining to event logs derived from ABSs is
not trivial, and deriving meaningful insights from the resulting process models
adds an additional layer of complexity. Although process mining is invaluable
in extracting insights from ABS models, there is a lack of comprehensive
methodological guidance for its application in ABS evaluation in the research
landscape. In this paper, we propose a methodology, based on the CRoss-Industry
Standard Process for Data Mining (CRISP-DM) methodology, to assess ABS models
using process mining techniques. We incorporate process mining techniques into
the stages of the CRISP-DM methodology, facilitating the analysis of ABS model
behaviors and their underlying processes. We demonstrate our methodology using
an established agent-based model, Schelling model of segregation. Our results
show that our proposed methodology can effectively assess ABS models through
produced event logs, potentially paving the way for enhanced agent-based model
validity and more insightful decision-making.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要