Weighting or aggregating? Investigating information processing in multi-attribute choices

Multi-attribute choices are commonly analyzed in economics to value goods and services. Analysis assumes individuals consider all attributes, making trade-offs between them. Such decision-making is cognitively demanding, often triggering alternative decision rules. We develop a new model where individuals aggregate multi-attribute information into meta-attributes. Applying our model to a choice experiment (CE) dataset, accounting for attribute aggregation (AA) improves model fit. The probability of adopting AA is greater for: homogenous attribute information; participants who had shorter response time and failed the dominance test; and for later located choices. Accounting for AA has implications for welfare estimates. Our results underline the importance of accounting for information processing rules when modelling multi-attribute choices.

Automated summary

The study suggests that individuals aggregate information into meta-attributes and adopt different decision rules in multi-attribute choices. Individuals with homogeneous attribute information, shorter response times, and who fail the dominance test are more likely to adopt attribute aggregation. Considering attribute aggregation has significant implications for welfare estimates.