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Record W4384297663 · doi:10.1177/00222437231190851

The Agent's Impatience: A Self–Other Decision Model of Intertemporal Choices

2023· article· en· W4384297663 on OpenAlexaff
Adelle Yang, Oleg Urminsky

Bibliographic record

VenueJournal of Marketing Research · 2023
Typearticle
Languageen
FieldDecision Sciences
TopicDecision-Making and Behavioral Economics
Canadian institutionsBooth University College
Fundersnot available
KeywordsIntertemporal choiceInterpersonal communicationAnticipation (artificial intelligence)PsychologyValue (mathematics)EconomicsDynamic inconsistencySocial psychologyMicroeconomicsComputer science

Abstract

fetched live from OpenAlex

Intertemporal choices represent one of the most prevalent and fundamental trade-offs in consumer decision making. While prior research on intertemporal choices has focused on choices for oneself, intertemporal choices often involve one individual choosing on behalf of another. How do intertemporal choices made for another person differ from otherwise identical choices made for oneself? This research introduces a self–other decision model that distinguishes reaction utility (derived from interpersonal feedback) from vicarious utility (derived from imagining the recipient's experience). The authors tested model-derived hypotheses in 13 experiments (N = 4,799) involving decisions between peers. Consistent with the proposed role of reaction utility in the model, they find that intertemporal choices made for others are typically more “impatient” than choices for oneself. Moreover, this “agent's impatience” is attenuated when contextual and individual differences weaken the anticipation of interpersonal feedback. Together, the theoretical model and experimental results highlight the rewarding value of interpersonal feedback in self–other decision making, shedding new light on interpersonal consumer choices.

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)0.125
metaresearch head score (Gemma)0.041
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.932
Threshold uncertainty score0.967

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1250.041
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.364
GPT teacher head0.527
Teacher spread0.164 · 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 designOther design
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

Citations3
Published2023
Admission routes1
Has abstractyes

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