Network Optimization Hits a Wall: Why Coalitions Alone Bridge the Gap from Stagnant Nash to Global Efficiency

The arXiv preprint 'Symbiosis as a systemic catalyst and the impossibility of coalitions in optimal networks' (Mocenni et al., 2026) formally proves that globally optimal network configurations under anti-coordination games are Strong Nash Equilibria, erecting topological barriers to beneficial collective deviations. Yet this leaves sub-optimal regimes locked in individualistic traps—agents cannot escape without external catalysts. Computational simulations validate the result across varied topologies, while an empirical pollination network demonstrates symbiosis-driven niche partitioning toward resilience maxima. Notably absent from the coverage is the direct mapping to multi-agent reinforcement learning: the same metastable coalition dynamics explain why centralized optimizers in AI systems repeatedly collapse into local equilibria without explicit joint-agency mechanisms. This pattern echoes Tomasello's shared intentionality framework but extends it quantitatively, showing perpetual reconfiguration as evolution's adaptive engine rather than a transient phase. Limitations include the model's assumption of perfect anti-coordination payoffs and reliance on unvalidated simulation scale; no real-world longitudinal data tests whether coalition turnover rates match biological observations. A related 2023 study in Nature Communications on microbial cross-feeding networks (Mee et al.) reports similar coalition-driven efficiency gains in 47 experimental replicates, underscoring the preprint's broader applicability while highlighting its lack of stochastic perturbation analysis. In economics, the framework predicts cartel instability precisely at optimality, aligning with observed OPEC reconfiguration cycles but contradicting static game-theoretic models that ignore topological barriers.