Identifying Essential Rule Sets in Agent-Based Models Through Systematic Ablation: A Tumor Evolution Case Study

Nhung Duong, Đoàn Minh Lượng, Anh Do, Anh Truong, Ngoc Do, Nguyen Tran Nam Tien, Tuan Do

Keywords: Agent-based simulation, Tumor modeling, Complex systems, Model reduction, Multi-scale modeling

Abstract: We present a systematic methodology for identifying essential rule sets in agent-based models, demonstrated through tumor evolution under therapeutic pressure. Our framework addresses a critical challenge: determining which interaction rules are necessary for reproducing emergent spatiotemporal patterns versus those representing auxiliary complexity. We couple autonomous cancer cell agents with reaction-diffusion fields, then systematically ablate mechanisms including paracrine signaling, metabolic competition, phenotypic plasticity, and spatial interactions. Our approach establishes empirically-calibrated significance thresholds from baseline system stochasticity, providing principled criteria for mechanism classification. A key methodological insight distinguishes ablatable biological rules from non-ablatable framework requirements: removing spatial diffusion caused model failure, revealing it encodes physical constraints rather than testable hypotheses. We demonstrate that complex patterns emerge from minimal rule sets, with several commonly-modeled mechanisms contributing negligibly to tissue-scale behavior. This methodology advances multi-agent simulation by providing objective model reduction techniques that enhance mechanistic interpretability and facilitate parameter calibration.

Area: Modelling and Simluation of Societies (SIM)

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Submission Number: 1239