We present PROSENTIR Phase 2, extending the Phase 1 framework (Santoyo Ortiz, 2026; DOI: 10.5281/zenodo.19912924) in two directions. First, we introduce lateral recurrent feedback between Level-2 association modules within each agent's hierarchical network, addressing the structural limitation identified in Phase 1 that prevented robust computation of the integrated information measure Φ. Second, we embed two PROSENTIR agents in an Iterated Prisoner's Dilemma (IPD) where decisions emerge from each agent's Level-3 global integrator state rather than from programmed strategies. We replace the Kraskov k-nearest-neighbor MI estimator with MINE (Mutual Information Neural Estimation; Belghazi et al., 2018), a distribution-free neural estimator validated on controlled signals including bimodal distributions produced by tanh nonlinearities. Simulation results confirm that IPD dynamics emerge without explicit strategy programming, reproducing the classical tension between individual and collective optimality. The structural analysis of Φ establishes that functionally specialized modular connectivity is required for Φ > 0 — the central hypothesis of PROSENTIR Phase 3.
Armando Santoyo Ortiz (Mon,) studied this question.