AI Predictions, Strategic Disclosure, and Optimal Coarse Bracketing

Participants will be exposed to different versions of a package-tracking interface on a digital logistics platform. The intervention consists of randomized variation in how predicted delivery information is presented to users.
The study varies three dimensions of the interface:
1. Prediction model type:
Users are assigned to different prediction systems, including a traditional statistical prediction model, a higher-accuracy AI-based model, a faster but lower-cost AI model, or a baseline non-model estimate.
2. Information display structure
Users are randomly assigned to either view only the final predicted delivery time or view additional intermediate shipment checkpoint information during the delivery process.
3. Time precision of prediction
The estimated delivery time is displayed at different levels of precision, including day-level, 6-hour window, 1-hour window, or 30-minute window.

After the initial randomized assignment, users may choose to adjust the displayed precision level according to their own preference. These choices are recorded as part of the study. The intervention is embedded in the platform’s normal shipment-tracking service and does not affect the actual delivery process.

2026-05-01

2026-06-01

1. User satisfaction / evaluation outcome:
Overall user rating of the package-tracking experience
Indicator for negative feedback or complaint submission
2. Tracking query behavior:
Total number of tracking queries per shipment
Average time interval between consecutive queries
3. User precision choice:
Indicator for whether the user changes the default prediction precision
Time spent by the user changing the prediction precision
Final selected precision level

1. Platform economic outcomes:
Estimated advertising impressions per shipment
Estimated advertising revenue per tracking session
Subsequent platform usage within the observation window
2. Feedback detail outcomes:
Specific reasons selected for negative evaluations
User response conditional on realized prediction

This study is a large-scale randomized field experiment embedded in the shipment-tracking interface of a digital logistics platform. The experiment uses a multi-arm factorial design that varies three dimensions of the user interface and prediction system:

Prediction model type (4 arms)
Users are assigned to one of four prediction approaches: a traditional model, a high-accuracy AI model, a fast AI model, or a baseline non-model estimate.
Intermediate shipment information (2 arms)
Users are randomly assigned to either view intermediate shipment checkpoint information or only the final estimated delivery time.
Prediction time precision (4 arms)
Estimated delivery times are displayed at one of four levels of precision: day-level, 6-hour window, 1-hour window, or 30-minute window.

These three dimensions generate a factorial treatment structure with multiple treatment combinations. Eligible users are randomly assigned at first exposure to one treatment condition by the platform’s backend experimental system.

After the initial assignment, users may choose to adjust the displayed precision level according to their own preferences. This feature allows the study to observe both the causal effects of default information presentation and users’ endogenous preferences for information granularity. The intervention affects only the presentation of predictive information and does not alter the actual logistics or delivery process.

Not available

Randomization is performed automatically by the platform’s backend computer system at the time of a user’s first shipment-tracking interaction during the study period. Upon first exposure, each eligible user is assigned by a computer-generated pseudo-random algorithm to one treatment cell in a factorial design defined by three dimensions: prediction model type, intermediate-node display, and time-precision level. The assignment procedure is implemented server-side and does not involve manual intervention, public lottery, or any physical randomization device.

The randomization algorithm uses uniform random assignment across all treatment arms and is logged in the platform’s internal experimental framework to ensure reproducibility and auditability.

The primary unit of randomization is the individual user.

Each user is randomly assigned to one treatment combination at the time of first exposure to the shipment-tracking interface during the experimental period, and this initial assignment remains the default treatment condition for that user throughout the observation window. Because multiple shipment queries may be observed for the same user, outcome data will be recorded at both the user level and the shipment-query level, but treatment assignment occurs at the individual-user level.

There is no higher-level cluster randomization (e.g., by city, courier company, or shipment route) in the baseline design.

No

Not clustered and 1,000,000 individual users (randomization units).

Approximately 1,000,000 user-level shipment-tracking sessions.

21 treatment arms with approximately 47,620 observations in each arm under equal random assignment.