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Record W2334115192 · doi:10.1002/bdm.1794

True Context‐dependent Preferences? The Causes of Market‐dependent Valuations

2013· article· en· W2334115192 on OpenAlex
Nina Mažar, Botond Kőszegi, Dan Ariely

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Behavioral Decision Making · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEconomicsContext (archaeology)Product (mathematics)Value (mathematics)ReservationMicroeconomicsDistribution (mathematics)Market priceEconometrics

Abstract

fetched live from OpenAlex

ABSTRACT A central assumption of neoclassical economics is that reservation prices for familiar products express people's true preferences for these products; that is, they represent the total benefit that a good confers to the consumers and are, thus, independent of actual prices in the market. Nevertheless, a vast amount of research has shown that valuations can be sensitive to other salient prices, particularly when individuals are explicitly anchored on them. In this paper, the authors extend previous research on single‐price anchoring and study the sensitivity of valuations to the distribution of prices found for a product in the market. In addition, they examine its possible causes. They find that market‐dependent valuations cannot be fully explained by rational inferences consumers draw about a product's value and are unlikely to be fully explained by true market‐dependent preferences. Rather, the market dependence of valuations likely reflects consumers' focus on something other than the total benefit that the product confers to them. Furthermore, this paper shows that market‐dependent valuations persist when – as in many real‐life settings – individuals make repeated purchase decisions over time and infer the distribution of the product's prices from their market experience. Finally, the authors consider the implications of their findings for marketers and consumers. Copyright © 2013 John Wiley & Sons, Ltd.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.241
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.179
GPT teacher head0.307
Teacher spread0.128 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it