{"blocks": [{"key": "480960f6", "text": "Scenario", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "3a725b14", "text": "Airport pickups: comparing a new model that predicts walk-time from order location to pickup zone against the current ETA model.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "a9e9f87a", "text": "Question", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "e193607e", "text": "What metrics would you use to evaluate the new airport ETA model’s impact on rider and driver experience? Design an experiment to test the new model against the baseline. Riders may speed up or slow down based on displayed driver arrival time; how would you quantify this behavioral feedback loop and separate it from model accuracy?", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "4367498f", "text": "Hints", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "952d4f96", "text": "Consider rider wait, driver idle, cancellations; use staged rollout, delayed predictions, or instrumental variables to isolate behavioral response.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}], "entityMap": {}}