As Digital Twins (DTs) become increasingly detailed, their growing complexity often exceeds human decision-making capacity, not simply as a visualization challenge but as a decision-fidelity and risk-management problem. Excessive contextual detail and fine-grained uncertainty representations can impose cognitive burden, slow time-constrained reasoning, and reduce interpretability, ultimately limiting adoption and return on investment despite high technical sophistication. This project investigates how to design DTs that are good enough to support defensible electric line planning decisions while remaining interpretable, efficient, and usable in real consulting and agency workflows. By systematically varying the amount of contextual information and the granularity of uncertainty presentation, the study evaluates trade-offs between cognitive burden (cognitive load and decision time) and decision-support value (perceived usefulness and confidence alignment), as well as the extent to which DT-supported decisions align with expert-defined planning objectives. The outcome is evidence-based, adoption-ready guidance that helps infrastructure and transportation practitioners identify appropriate DT fidelity thresholds, balance complexity and utility, and deploy cost-effective Digital Twins that meaningfully improve human decision-making rather than overwhelm it.
