Computational concept for human food choice and eating behaviour

IF 0.4 Q4 MATHEMATICS, APPLIED
V. Istratov
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引用次数: 0

Abstract

An inadequate diet can cause a number of illnesses with some of them posing major threats for humanity. Poor diet largely originates from behavioral and social issues rather than environmental factors. With simulation being a grand tool to analyze and address behavior issues, relatively few studies focus on computational modeling of nutrition at behavioural level. Furthermore, we have overviewed several popular approaches to computational modeling and simulating dietary decision-making and found no clear favorite. Further still, modelers rarely pay attention to one of the key behavioural factors – motivation. In the vast majority of models, either motivation is assumed to be exogenously given and, hence, is left out of the model, or motivation is not taken into account in any form, even though ignoring incentives significantly reduces adaptive capabilities of any human-like goal-directed model entity. We aimed to outline a modelling approach that would fit into the food choice topic and would improve on the available models. This implies offering an intelligible algorithm that would be easily applied to statistical data yet offering a depth of analysis despite its seeming simplicity. Thus, we present our view of the food choice simulation problem which employs eating incentives and an original choice mechanism that is different both from traditional maximizing approaches common to economics and artificial intelligence research and from the dominant psychological computational approaches. We outlined the programming conceptual algorithm that involves sequential incentive (which can result from the biological necessities, social, intellectual or spiritual needs alike) selection, incentive-foodstuff coupling (a relation can be either fixed or dynamic) and elimination of undesirable food options based on incentives ranking (qualitative ranking seems to be preferable over quantitative ranking, forasmuch as it resembles the way of thinking of a regular person more closely) supplemented by pseudocode segments. The algorithm suits agent-based simulation paradigm, yet it is not tied to it and can be fitted with other simulation approaches as well. The algorithm is supposed to be implemented in Java. Since the offered algorithm is conceptual it requires an implementation to bring about robust conclusions which is our goal to reach next.
人类食物选择和饮食行为的计算概念
不适当的饮食会导致许多疾病,其中一些对人类构成重大威胁。不良饮食主要源于行为和社会问题,而不是环境因素。虽然模拟是分析和解决行为问题的重要工具,但相对较少的研究集中在行为水平的营养计算模型上。此外,我们概述了几种流行的计算建模和模拟饮食决策的方法,并没有找到明确的最爱。此外,建模者很少关注一个关键的行为因素——动机。在绝大多数模型中,要么假设动机是外生给定的,因此被排除在模型之外,要么不考虑任何形式的动机,尽管忽略激励会显著降低任何类人目标导向模型实体的适应能力。我们的目标是概述一种适合食物选择主题的建模方法,并改进现有的模型。这意味着提供一种易于理解的算法,可以很容易地应用于统计数据,同时提供深度分析,尽管它看起来很简单。因此,我们提出了我们对食物选择模拟问题的看法,该问题采用了进食激励和原始选择机制,既不同于经济学和人工智能研究中常见的传统最大化方法,也不同于主流的心理计算方法。我们概述了规划概念算法,该算法涉及顺序激励(可能来自生物必需品,社会,智力或精神需求)选择,激励-食物耦合(关系可以是固定的或动态的)以及基于激励排名(定性排名似乎优于定量排名)消除不受欢迎的食物选择。因为它类似于普通人的思维方式(更接近),辅以伪代码段。该算法适合基于智能体的仿真范式,但不受其约束,也可以与其他仿真方法相适应。该算法应该在Java中实现。由于所提供的算法是概念性的,因此需要一个实现来带来稳健的结论,这是我们下一步要达到的目标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
0.70
自引率
0.00%
发文量
0
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