Thesis
Decision-model evaluation becomes more useful when it treats bounded choices as a separate task class and reports more than aggregate accuracy.
Observed facts
Mechanism
Decision models select among fixed options and provide a probability for each option, making calibration and operational cost relevant alongside correctness.
Why now
Builders increasingly use models for routing, classification, and tool selection, where outputs are constrained choices rather than open-ended prose.
Implications
Teams can use the index as a screening comparison, then test candidate models against their own task distribution and acceptance criteria.
Calibration, latency, and cost are relevant comparison dimensions in addition to aggregate correctness.
Evidence boundary
The source describes the benchmark and its displayed metrics; it does not establish external validation, representativeness for a given deployment, or causal links between index rank and business outcomes.
Watch conditions
Whether independent evaluations or production reports show that index scores predict performance on real bounded-decision workloads.