Predicting our own and others’ future preferences: The role of social distance
Bibliographic record
Abstract
People underestimate how much their preferences will change in the future, a phenomenon sometimes referred to as a "presentism bias." Recently, we found that this presentism bias is attenuated when thinking about the preferences of other people. The aim of this study was to investigate whether predicting future preferences also differs depending on the level of social distance between self and other. A total of 67 participants completed a perspective-taking task in which they were required to think about their own preferences, those of a generic peer, and those of a close other both now and in the future. They were also asked to consider the preferences of an older adult now. Participants predicted less change between their current and future preferences than between the current and future preferences of a generic peer. Predicted change in preferences for a close other were similar, but not identical, to those made for the self. When considering relevant future preferences, participants predicted less change for themselves than for their close others and less change for close others than for generic peers. In other words, as social distance increases, the presentism bias decreases. Interestingly, participants estimated that both they and their peers would not change so much that they become similar to current older adults. Simulating the future perspectives of a generic peer or, even better, the current perspectives of an older adult may thus result in improved long-term decision-making, as it may enable a more realistic estimation of the magnitude of likely changes in the future.
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 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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".