Load-Bearing Node Theory (LBNT): Identifying Structurally Decisive Nodes in Drift-Amplifying Systems

Local misalignment does not contribute equally to system-wide drift: in many complex systems, some components are structurally decisive in the sense that small degradations there disproportionately change whether the entire ecology amplifies or damps misalignment. This paper introduces Load-Bearing Node Theory (LBNT): a structural-sensitivity layer for drift models that ranks nodes by how strongly their local drift dynamics affect global amplification. We formalize two complementary notions. First, growth load-bearingness measures the sensitivity of the system’s global amplification factor (the spectral radius) to local degradation at each node. Second, outcome load-bearingness measures the sensitivity of a downstream-weighted drift objective to local degradation in stable regimes. Under mild Perron–Frobenius conditions, growth load-bearingness admits a closed-form expression in terms of left/right Perron eigenvectors. Finally, we give a worked toy simulation extending an SDT-style Enron network. As structural insulation increases, load-bearingness concentrates in executive leadership while the global amplification factor remains above unity, producing an endogenous runaway drift regime.