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In this paper we demonstrate how to use graph matching touncover heterogeneity in the structure of preferences acrossa population of decision-makers. We propose a novel non-parametric approach to formally capture the concept of pref-erence structure using preference graphs, thereafter clusteringdecision-makers based on graph embedding methods. We ex-plore the approach with simulated choice and empirical datafrom the most common classes of economic and psychologicalmodels. The approach uncovers heterogeneity in preferencestructure across a variety of dimensions, without requiring anyprior knowledge of those structures.
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Stored only when both papers are in the corpus — 4 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.