Network-Informed Optimal Control via Graph Neural Networks: A Framework with Application to Tax Enforcement
DOI:
https://doi.org/10.30871/jaic.v10i1.11909Keywords:
Optimal Control, Multiplex Networks, Machine Learning, Tax Administration, Fiscal Policy, DRCAbstract
This paper introduces a novel framework integrating multiplex network theory, machine learning, and optimal control to optimize tax revenue dynamics in the Democratic Republic of Congo (DRC). We model the Congolese economy as a multiplex network where economic sectors represent interdependent layers. Using machine learning techniques on empirical tax data (2000-2024), we reconstruct network topology and identify systemic sectors. Our network informed optimal control approach demonstrates potential revenue increases of 25-35% with 30-40% volatility reduction. The framework provides actionable insights for the upcoming transition to Corporate Income Tax (CIT) and offers a replicable methodology for developing economies.
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Copyright (c) 2026 Marcial Nguemfouo, Pierre Raymond Bossale
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