π€ AI Summary
This study addresses a key limitation of traditional multi-attribute decision-making models, which assume full compensability and thus fail to account for human tendencies to outright reject alternatives that perform poorly on critical attributes. To resolve this, the authors propose a bounded trade-off screening mechanism that formalizes tolerance for attribute trade-offs as a controllable parameter, capturing decision makersβ non-compensatory judgments across varying contexts. Through computational modeling and simulation experiments, they develop a lightweight choice model that successfully reproduces preference patterns distinct from those predicted by classical utility theory. The approach elucidates the computational underpinnings of non-compensatory screening and context-dependent preferences, while generating testable behavioral predictions that offer a novel mechanistic account of human multi-attribute decision making.
π Abstract
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.