Qi Danyi, Roe Brian E, Apolzan John W, Martin Corby K
Dept. of Agricultural Economics & Agribusiness, Louisiana State University AgCenter.
Dept. of Agricultural, Environmental & Development Economics, Ohio State University.
J Agric Resour Econ. 2023 May;48(2):296-308. doi: 10.22004/ag.econ.320676.
The proliferation of personal, household and workplace sensors and devices has created individual environments rich with purposeful and incidental feedback capable of altering behavior. We formulate an empirical learning model suitable for understanding individual behavioral responses in such environments. We estimate this model using data collected about the joint personal decisions of food selection, intake, and waste during a study in which users photographed their meal selections and plate waste over the course of a week with a cell phone. Despite neutral recruitment language and no expectation that participants would alter food intake in response to the assessment procedures, we found a substantial learning-by-doing effect in plate waste reduction as those who document greater plate waste in their captured photographs waste less on subsequent days. Further we identified that participants reduced plate waste by learning to eat more rather than by learning to reduce the amount of food selected.
个人、家庭和工作场所传感器及设备的激增,创造了充满能改变行为的有意和偶然反馈的个体环境。我们构建了一个实证学习模型,适用于理解此类环境中的个体行为反应。我们使用在一项研究中收集的数据来估计该模型,在这项研究中,用户用手机拍摄他们一周内每餐的选择和餐盘剩余食物,以了解食物选择、摄入量和浪费方面的联合个人决策。尽管招募语言中立,且未期望参与者会因评估程序而改变食物摄入量,但我们发现,随着那些在拍摄照片中记录了更多餐盘剩余食物的人在随后几天减少了浪费,在减少餐盘剩余食物方面存在显著的边做边学效应。此外,我们发现参与者通过学会多吃而不是学会减少所选食物量来减少餐盘剩余食物。