When simple is enough: Binary models capture social complexity in coupled human-environment systems

Models of coupled human-environment systems often face a tradeoff between realism and tractability. Spectrum opinion models, where social preferences vary continuously, offer descriptive richness but are computationally demanding and parameter-heavy. Binary formulations, in contrast, are analytically simpler but raise concerns about whether they can capture key socio-ecological feedbacks. Here we systematically compare binary and spectrum social models across four benchmark settings: (i) replicator dynamics coupled to a climate-carbon system, (ii) FJ opinion dynamics coupled to the climate-carbon system, (iii) replicator dynamics coupled to a forest-grassland ecological system, and (iv) FJ opinion dynamics coupled to a forest-grassland ecological system. We employ the relative integrated absolute error (RIAE) to quantify deviations between binary (N=2) and spectrum (N=100) formulations of social opinion dynamics in their feedback with ecological subsystems. Across systematic parameter sweeps of learning rates, reluctance, conformity, susceptibility, runaway amplitudes, and ecological turnover, the binary formulation typically tracks its spectrum counterpart to within  ≤ 15% for most parameter combinations. Deviations beyond this threshold arise primarily under very high social susceptibility or near-vanishing ecological turnover, where additional opinion modes and nonlinear feedbacks become consequential. We therefore present the binary formulation as a practical surrogate, not a universal replacement. As a rule of thumb, it is adequate when susceptibility is moderate, ecological turnover is appreciable, and runaway amplitudes are not extreme; in high-susceptibility or low-turnover regimes (especially near critical transitions), the full-spectrum model is preferable. This framing is intended to guide readers on when a binary reduction is sufficient versus when full-spectrum detail is warranted.