On the size of the model selection problem in a conjoint measurement experiment

We examine how the number of models respecting marginality in model selection using Maximum Likelihood Conjoint Measurement (MLCM) varies with the number of dimensions tested and explain the logic for determining this number. The results are related to Dedekind numbers, which increase super-exponentially, and, thus, suggest a limit to the number of dimensions that can be practically tested using the MLCM paradigm.

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Publication Details

Published
2026-10-31
DOI
https://doi.org/10.31234/osf.io/qwh64_v1
Primary Topic
Economic and Environmental Valuation
Type
preprint
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preprint

On the size of the model selection problem in a conjoint measurement experiment

Kenneth Knoblauch, Frederic Devinck
Economic and Environmental Valuation
preprint

On the size of the model selection problem in a conjoint measurement experiment

Kenneth Knoblauch, Frederic Devinck
preprint en

Abstract

We examine how the number of models respecting marginality in model selection using Maximum Likelihood Conjoint Measurement (MLCM) varies with the number of dimensions tested and explain the logic for determining this number. The results are related to Dedekind numbers, which increase super-exponentially, and, thus, suggest a limit to the number of dimensions that can be practically tested using the MLCM paradigm.

Economic and Environmental Valuation
DOIOpen Access PDFOpenAlex
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On the size of the model selection problem in a conjoint measurement experiment — Kenneth Knoblauch, Frederic Devinck · (2026) | TGRS Research Map | TGRS