Patches and Payoffs: A Pilot Study of Information Foraging and Forecasting Accuracy on Cultivate
Type
journal article
Date Issued
2026-04-28
Author(s)
Abstract
How people use Cultivate can predict how well they forecast. Using webcam eye and screen-tracking (7 participants, 44 sessions, approx. 1612 mins) on the online forecasting platform, Cultivate, we compared the top forecasters to other groups. Three effective forecasting behaviors emerged: 1) time-allocation, top forecasters spent a smaller proportion of time on-platform (64% vs. 71-86%) and avoided overinvesting in crowd rationales (14% vs. 23-54%); 2) execution, when they did log in, they were productive (1.5 forecast updates per session vs. <0.86); and 3) calibration, they made smaller adjustments (6% vs. 8-14%). Lower performers showed two potential traps: excessive forecast formulation and rumination, as well as endless crowd rationale engagement without further investigation. Our findings from the pilot study show that platform engagement does not necessarily translate to better forecasters; selective, integrative foraging does. We propose platform tweaks, including varied visual hierarchy, decay of scent for over-visited areas, re-entry and exit prompts, and "update assistants" to nudge advantageous behaviors.
Keywords
Forecasting platforms
Information Foraging Theory (IFT)
superforecasters
screen tracking
eye tracking
forecast calibration
strategic decision making
UI
UX
interface navigation
HSG Classification
not classified
Refereed
No
Subject(s)
Division(s)
File(s)![Thumbnail Image]()
open.access
Name
Thomas Li - Patches and Payoffs.pdf
Size
1.64 MB
Format
Adobe PDF
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