Suppr超能文献

简化边际价值定理预测下的食品杂货购物

Grocery Shopping Under Simplified Marginal Value Theorem Predictions.

作者信息

Schlender Tabea, Rieger Alex, Eggert Frank

机构信息

Institute of Psychology, Faculty of Life Sciences / Fakultät für Lebenswissenschaften, Technische Universität Braunschweig, Braunschweig, Germany.

出版信息

Hum Nat. 2024 Dec;35(4):451-476. doi: 10.1007/s12110-024-09485-3. Epub 2025 Jan 17.

Abstract

This study examined whether supermarkets can be considered patches in the marginal value theorem (MVT) sense despite their particular features and whether they are models of human food foraging in resource-dense conditions. On the basis of the MVT, the quantitative relationship between gains in the Euro and patch residence time was modeled as an exponential growth function toward an upper asymptote, allowing the choice of an optimal strategy under diminishing returns. N = 61 participants were interviewed about their current shopping trip and contextual variables at a German supermarket and provided data to estimate relevant model parameters. A nonlinear model of the patch residence time and resulting gain based on an exponential function was fitted via nonlinear orthogonal distance regression. The results generally revealed the relationships predicted by the model, with some uncertainty regarding the estimation of the upper asymptote due to a lack of data from participants with long residence times. Despite this limitation, the data support the applicability of the MVT-based model. The results show that approaches from optimal foraging theory, such as the MVT, can be used successfully to model human shopping behavior even when participants' verbal reports are used.

摘要

本研究探讨了尽管超市具有其特殊特征,但从边际价值定理(MVT)的意义上讲,它们是否可被视为斑块,以及它们是否是资源密集条件下人类食物觅食的模型。基于边际价值定理,欧元收益与斑块停留时间之间的定量关系被建模为朝着上渐近线的指数增长函数,从而能够在收益递减的情况下选择最优策略。对61名参与者在一家德国超市进行了访谈,询问他们当前的购物行程和情境变量,并提供数据以估计相关模型参数。基于指数函数的斑块停留时间和所得收益的非线性模型通过非线性正交距离回归进行拟合。结果总体上揭示了模型所预测的关系,由于缺乏长时间停留参与者的数据,在上渐近线的估计方面存在一些不确定性。尽管存在这一局限性,但数据支持基于边际价值定理的模型的适用性。结果表明,即使使用参与者的口头报告,最优觅食理论中的方法,如边际价值定理,也可成功用于模拟人类购物行为。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2159/11836157/3250b6dc0b7c/12110_2024_9485_Fig1_HTML.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验