提出一种有限权衡筛选模型，解释人们如何在多属性选择中拒绝短板选项。
A Minimal Model of Bounded Trade-Off Screening in Multi-Attribute Choice
- 引入权衡容忍度参数，控制跨属性得失平衡的可接受范围。
- 模拟显示该模型能生成与传统效用模型不同的偏好模式。
- 适合研究决策中权衡行为受情境影响的实验心理学家。
人类决策常涉及多属性选项的选择，但经典模型假设完全补偿性效用聚合，而实证表明人们会因关键属性表现差而拒绝选项。本文提出一种受限权衡推理框架，决策由筛选过程决定，评估各属性间得失的平衡。模型引入权衡容忍度参数，控制可接受的不平衡程度，且该参数可在不同情境中变化。通过仿真，我们发现该机制产生与标准效用模型不同的偏好模式，并捕捉到权衡行为的情境依赖性变化。结果表明，受限权衡筛选是多属性选择的一种合理计算机制，为未来行为研究提供可检验的预测。
原文摘要 · Abstract (English)
Human decision-making often involves choosing between multi-attribute alternatives, yet classical models assume fully compensatory utility aggregation despite evidence that people reject options with poor performance on critical attributes. We propose a bounded trade-off reasoning framework in which decisions are governed by a screening process that evaluates the balance between gains and losses across attributes. The model introduces a trade-off tolerance parameter that controls acceptable imbalance and can vary across contexts. Through simulation, we show that this mechanism produces preference patterns that differ from standard utility-based models and captures context-dependent variation in trade-off behavior. These results establish bounded trade-off screening as a plausible computational mechanism for multi-attribute choice and generate testable predictions for future behavioral studies.
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