Rank-Based Alternatives to Mean-Based Ensemble Models of Satisfaction With Earnings: Comment on Putnam-Farr and Morewedge (2020)

How much satisfaction do we derive from a new salary or from receiving a bonus payment in an experiment? People do not judge monetary amounts in isolation but compare them to other amounts-judgments are context sensitive. A key question is, however, how context affects judgment. Across eight experiments, Putnam-Farr and Morewedge (2020) showed that people's self-reported satisfaction with a sum of money is predicted by the difference between that amount and the highest or lowest amount received by others. The authors found no evidence that people's judgments are sensitive to the ranked position of a monetary amount among other rewards. Putnam-Farr and Morewedge explained their results with reference to the ensemble representation literature, which shows that people can accurately estimate summary statistics, such as the maximum or mean, of stimulus distributions. In this commentary, we argue that their proposed interpretation is inconsistent with extensive theoretical and empirical research showing that judgments of stimuli reflect the relative ranked position of those stimuli within a comparison context. Building on this research, we show that the experimental results reported by Putnam-Farr and Morewedge can be explained on the assumption that people use contextual information to infer a distribution of monetary amounts and judge individual amounts by their relative ranked position within that inferred distribution. This inferred distribution theory accounts for empirical results reported in the original study while remaining consistent with the general and well-established principle of rank-based judgment.