Perceived variance and preference for sequences of outcomes
Bibliographic record
Abstract
Purpose The purpose of this paper is to propose a new explanation for the well‐documented preference among individuals for sequences of increasing utility. It is put forward here that while there may be a preference for ascending‐utility sequences, this relationship is mediated by perceptions of variance. Specifically, there is reason to believe that sequences of ascending utility (e.g. receipt of payments) are perceived to be less variable than sequences of descending utility (e.g. prices). Design/methodology/approach Past and present price research supports the idea that perceived variance plays a key role in preference, which fits with established theory. This theory is examined and applied to a hypothetical scenario involving sequences of uncertain outcomes. The predicted effect is tested in two experimental studies. Findings The two studies lend support to the proposed explanation of sequence preference. Study one demonstrates that the effect of sequence direction on preference is mediated by perceptions of variability, and that individuals perceive a sequence of ascending utility to be less variable than an equivalent descending sequence. Study two shows that individuals prefer a sequence when it represents wages (ascending utility) than when it does prices (descending utility). Originality/value The paper provides a sound theoretical framework, as well as supporting evidence, for how sequences of outcomes, such as prices, are perceived by consumers. Given that such sequences are increasingly available thanks to information technology, and the increased use of yield management systems (leading to more price fluctuation), how they affect decisions is of obvious importance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".