Errors in Aggregation and Adaptive Choice

Most economic decisions require aggregating multiple attributes into an overall evaluation. We develop and experimentally test a theory where aggregation is error-prone and, in response, attributes are weighted optimally given an environment of likely options. We characterize these decision weights, which generically differ from attributes’ impact on utility, and the bias they induce in choice. When attributes are independently distributed among likely options, higher-variance attributes are over-weighted, and this bias increases in the level of aggregation errors. We test the theory in an experiment by first manipulating between subjects which attribute varies more in choice options, and then comparing choices between options that are identical across subjects. Consistent with the model’s predictions, choice probabilities shift toward the option favored by the high-variance attribute, in our baseline treatments by about 10 percentage points. We also test the effect of aggregation complexity by lowering the number of attributes. Compared to the baseline, this reduces the shift in choice probabilities by about 72%. We apply our model to purchases where base and add-on prices may be displayed separately. A consumer reacts to a short-term change in the display as if it were a price change, while her reaction to a long-run change depends on the details of the environment. Consumer welfare is generally maximized by a fully aggregated price.