Each augmentation module gives diminishing returns: pushing a module's investment x (0–1) toward its cap yields ever less extra capability, modeled as a saturating exponential:
effect(x) = 100 · (1 − e^(−k·x)), k = 2.4
The Enhancement Index is the weighted sum of the four module effects (weights reflect how much each domain contributes to overall human performance: exoskeleton 0.30, neural 0.25, metabolic 0.27, sensory 0.18).
Physiological strain grows superlinearly (quadratically) with investment — the body's tolerance to a single technology degrades fast as it's pushed toward its limit — plus a synergy penalty when several systems run hot at once (biological subsystems compete for the same metabolic and thermal budget):
strain = 100·Σ cost_i·x_i² + 15·max(0, modules_over_70% − 1)
The overload-risk curve is a logistic function of strain around a physiological threshold — risk stays low while strain is modest, then rises sharply once the body's buffering capacity is exceeded:
risk(strain) = 100 / (1 + e^(−(strain − 55)/12))
- Sliders — set each module's investment level; the figure's corresponding glow scales with it.
- Recovery Mode — all modules decay toward a low baseline over time and strain dissipates faster, modeling rest/downregulation between enhancement sessions.
- Spark particles — their count and speed scale with strain, a visual proxy for cumulative physiological load.
Real-world relevance: this is the same qualitative trade-off engineers and clinicians face when stacking real augmentation technologies — powered exoskeletons, BCIs, nootropic/metabolic interventions and sensory prosthetics — each adds capability but draws on shared, limited physiological budgets (energy, thermal dissipation, neural bandwidth, immune tolerance).