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Record W4283361691 · doi:10.1177/00222437221112312

“It Could Be Better” Can Make It Worse: When and Why People Mistakenly Communicate Upward Counterfactual Information

2022· article· en· W4283361691 on OpenAlexaff
Xilin Li, Christopher K. Hsee, Ed O’Brien

Bibliographic record

VenueJournal of Marketing Research · 2022
Typearticle
Languageen
FieldDecision Sciences
TopicDecision-Making and Behavioral Economics
Canadian institutionsBooth University College
Fundersnot available
KeywordsCounterfactual thinkingReal estateIdeal (ethics)PsychologyComputer scienceSocial psychologyEconomicsEpistemologyPhilosophy

Abstract

fetched live from OpenAlex

Imagine you are a real estate agent and are showing a prospective buyer a house with a lake view, but it is foggy, and the view is less than ideal. Are you inclined to tell the prospective buyer, “Unfortunately, it is foggy outside. If it were not foggy, the view would be even better!”? Eight studies, spanning diverse domains, reveal a novel discrepancy: most presenters (e.g., the seller) choose to communicate such upward counterfactual information (UCI) to experiencers (e.g., the prospective buyer), believing it will enhance experiencers’ impressions (e.g., of the house)—yet UCI actually worsens their impressions. This discrepancy arises because presenters insufficiently account for the fact that they possess more knowledge about the presented target than experiencers do; they fail to realize that noting an imperfection reveals it. Accordingly, when experiencers are knowledgeable about the target, either because the imperfection is obvious or because they can easily envision the upward counterfactual, the discrepancy attenuates. Finally, the presenter–experiencer discrepancy occurs only when the counterfactual information is upward, such that presenters do not overcommunicate downward counterfactual information, which rules out a desire to share any information as an alternative mechanism for presenters’ communication decisions. Together, this research highlights the prevalence and costs of sharing UCI.

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.

How this classification was reachedexpand

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)0.084
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.390
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0840.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.268
GPT teacher head0.450
Teacher spread0.183 · 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

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations20
Published2022
Admission routes1
Has abstractyes

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