Answer: C Quantify the social cost of forecast errors to guide investment in model accuracy.

Sun, 19 Apr 2026 18:32:09 +0000

**Why Quantifying the Social Cost of Forecast Errors Matters for Smarter Investment in Models** In an era driven by data and predictions, forecast accuracy isn’t just a technical benchmark—it’s a social imperative. What cost does misjudging what’s next incur, and how can this insight reshape decisions across industries? The answer lies in a powerful but underexplored concept: quantify the social cost of forecast errors to guide investment in model accuracy. Across sectors—healthcare, finance, urban planning, and public policy—forecasts shape life-altering choices. When estimates falter, communities feel the ripple effects: delayed medical interventions, unstable markets, or misallocated emergency resources. Understanding these consequences helps leaders decide where to invest in cleaner, more reliable models—not just for efficiency, but for equity and trust. Why is this gaining traction in the United States now? Growing awareness of algorithmic decision-making, rising stakes in AI adoption, and heightened demands for transparency have spotlighted the hidden toll of poor predictions. As data-driven systems grow more central to daily life, the need to measure and act on forecast reliability becomes urgent.