Understanding the value of a forecast using an online game

Abstract

This research illustrates the use of an online game to study the value of forecasts under conditions of information uncertainty. The objective of the game is for players to plant crops in field locations in a way that maximizes a payoff, with the option of paying for forecasts that help make better decisions. We found that while players fall short of theoretically perfect play, they generally make decisions that improve their score. Players both under and overpay for forecasts that reduce uncertainty, but exhibit behaviour that shows they roughly understand the expected value of forecasts. The small sample size and demographic homogeneity of the participants limit our ability to generalize these results, but the results suggest that game environments can be useful platforms for augmenting existing primary data collection methods. This is especially poignant today since with the assistance of AI, researchers with little to no programming experience can now design and develop games in ways that would have been prohibitively costly less than a decade ago.