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

Mesfin G Genie,Nicolas Krucien,Mandy Ryan

doi:10.1002/hec.4245

Mesfin G Genie, Nicolas Krucien + Show 1 more

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https://doi.org/10.1002/hec.4245

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Abstract

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.

Highlights

Based on Samuelson's theory of revealed preferences (Samuelson, 1938), both actual and hypothetical choices are frequently used in applied economics to measure sensitivities of the demand to marginal changes in goods and services
The results for the reference attribute weighting (AW)‐multinomial logit (MNL) model is presented in Table 3, column 2
Using a log‐likelihood ratio test (LR test: Deviance = 2.2; dof = 3; p‐value = 0.532), we found no evidence of differences in respondents' choices between the two models

Summary

Introduction

Based on Samuelson's theory of revealed preferences (Samuelson, 1938), both actual and hypothetical choices are frequently used in applied economics to measure sensitivities of the demand to marginal changes in goods and services. When actual choices ( referred as revealed preferences) are either unavailable (e.g., no market data for an innovation) or imperfect (e.g., patients' choices are often driven by medical recommendations and/or regulatory limitations), nonmarket valuation techniques are used to observe hypothetical consumption decisions ( referred as stated preferences) (Ben‐Akiva et al, 1985; Louviere et al, 2000). Both actual and hypothetical choice data are analyzed within the random utility maximization (RUM) framework to obtain marginal utilities (McFadden, 1974). The utility an individual derives from a particular good, depends on the relative importance of its features.

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Discussion

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