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.

翻译：人类决策常涉及在多属性备选项中进行选择，然而经典模型假设完全补偿性效用聚合，尽管有证据表明人们会拒绝在关键属性上表现不佳的选项。我们提出了一种有限权衡推理框架，在该框架中，决策由评估跨属性得失平衡的筛选过程控制。该模型引入了一个权衡容忍度参数，该参数控制可接受的不平衡程度，并可随情境变化。通过仿真，我们证明这一机制产生的偏好模式与标准基于效用的模型不同，并能够捕捉权衡行为中的情境依赖变异。这些结果确立了有限权衡筛选作为多属性选择的一种合理计算机制，并为未来行为研究提供了可检验的预测。