面向可靠数据驱动时间使用优化的质量多样性方法
Quality Diversity for Reliable Data Driven Time-Use Optimization
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中文总结 AI 辅助
该研究针对时间使用优化中忽略预测不确定性的问题，提出不确定性量化质量多样性框架，平衡健康收益与模型置信度，生成更可靠的时间使用建议。
中文摘要 AI 辅助
每日有限的24小时时间预算分配与身体、心理及认知健康密切相关。尽管预测模型可估算时间使用构成与体重指数、生活满意度、认知等健康结果的关系，但多数优化方法仅聚焦最大化预期收益，未考虑数据驱动预测固有的不确定性，忽略健康相关决策中的不确定性会导致不切实际的时间使用建议。为解决这一缺口，我们提出一种用于更可靠时间使用推荐的不确定性量化质量多样性（QD）框架。通过对包含1000余名儿童的大型队列数据集进行成分数据分析推导目标函数，以捕捉每日活动构成与多个健康指标的关系。我们开发了一种将预测不确定性纳入QD过程的新方法，生成更可靠的建议，平衡预期健康收益与模型置信度。我们通过基于变量和基于目标的行为表征探索解空间，揭示多样的高质量时间使用构成及不确定性下健康结果间的明确关系。通过将不确定性直接嵌入优化，我们的框架将时间使用建议转向不确定性更低的区域，同时保留高质量结构，以支持行为健康领域更可靠的决策制定。
英文摘要
The daily allocation of the finite 24-hour time budget is strongly associated with physical, mental, and cognitive health. While predictive models can estimate the relationship between time-use compositions and health outcomes such as body mass index, life satisfaction, and cognition, most optimization approaches focus only on maximizing expected benefit and do not consider the uncertainty inherent in data-driven prediction. Ignoring uncertainty in health-related decisions can lead to unrealistic time-use recommendations. To address this gap, we introduce an uncertainty quantification Quality Diversity (QD) framework for a more reliable time-use recommendation. Objective functions are derived using compositional data analysis using a large child cohort dataset n > 1000, to capture the relationship between daily activity compositions and multiple health indicators. We develop a new approach that incorporates predictive uncertainty into QD processes and produces more reliable recommendations that balance the expected health benefits with the confidence of the model. We explore the solution space through variable-based and objective-based behavioral representations, revealing diverse high-quality time-use composition and explicit relationships between health outcomes under uncertainty. By embedding uncertainty directly into optimization, our framework shifts the time-use recommendations toward regions of lower uncertainty while preserving high-quality structures for more reliable decision-making in behavioral health.
发表机构
- Adelaide University（阿德莱德大学）
机构由 AI 辅助整理，请以论文原文为准。
