Title: Margin Play: A Multi-Agent System For Public Policy Analysis In The Brazilian Equatorial Margin

Abstract:

Brazil’s next offshore oil frontier, the Brazilian Equatorial Margin (BEM), is poised to commence operations in 2026 within the Foz do Amazonas basin. The region’s assets are primarily tied to the state of Maranhao, which currently holds the lowest Human Development Index (HDI) in the Federation at 0.676 (IBGE 2022). This disparity raises a critical policy inquiry: under what specific conditions does BEM exploration yield net positive externalities for Maranhao?

The issue is inherently multi-agent in nature, involving competing interests: the Federal Government aims to secure revenue and energy independence; the state government focuses on regional welfare, supported by constitutional royalty earmarking; operators seek to maximize profits while managing risk; regulatory bodies ANP and IBAMA operate with conflicting mandates; and Amazonian communities emphasize territorial and environmental concerns over monetary gains.

To address these tensions, we introduce "Margin Play," a Multi-Agent Reinforcement Learning (MARL) framework calibrated with Brazilian empirical data and grounded in classical economic theory. The system employs six distinct agents within the CTDE paradigm, utilizing the BRO-MARL training algorithm. Our analysis, spanning 60,000 episodes across six scenarios, reveals that the outcome is contingent upon the institutional regime. Under the reference baseline, welfare gains are negligible (Waval approx. 1.68). In contrast, the MA-Prospero configuration delivers a welfare increase (Delta W) of +17.5% and a revenue rise (Delta Rcom) of +21.3%, alongside a reduced environmental liability (Eamb = 0.048 compared to 0.076). Ultimately, the core challenge is not a trade-off between production and welfare, but rather the selection of a public policy regime aligned with exploration activities.

Source: arXiv Generated at: 2026-06-03 00:00:00 UTC