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

Area of Science:

• Environmental modeling
• Computational social science
• Socio-ecological systems

Background:

• Coupled human-environment models balance realism and tractability.
• Spectrum opinion models are rich but computationally intensive.
• Binary models are simpler but may miss key socio-ecological feedbacks.

Purpose of the Study:

• Systematically compare binary (N=2) and spectrum (N=100) social models.
• Evaluate their feedback with ecological subsystems.
• Determine conditions for binary model adequacy.

Main Methods:

• Utilized four benchmark settings: climate-carbon and forest-grassland systems.
• Coupled replicator and FJ opinion dynamics with ecological models.
• Quantified deviations using relative integrated absolute error (RIAE).

Main Results:

• Binary models tracked spectrum models within 15% for most parameters.
• Deviations increased with high social susceptibility or low ecological turnover.
• Identified specific conditions where binary models deviate significantly.

Conclusions:

• Binary social models serve as practical surrogates for spectrum models in many cases.
• Binary models are adequate for moderate susceptibility and appreciable ecological turnover.
• Full-spectrum models are preferable in high-susceptibility or low-turnover regimes, especially near critical transitions.