A Minimal Model of Bounded Trade-Off Screening in Multi-Attribute Choice
For researchers in decision science, this provides a plausible computational mechanism for multi-attribute choice, though it is an incremental contribution as it builds on existing bounded rationality concepts.
The paper proposes a bounded trade-off screening model for multi-attribute choice that uses a tolerance parameter to control acceptable attribute imbalance, and shows through simulation that it captures context-dependent preference patterns distinct from standard utility models.
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.