Nonadditivity of Subjective Expectations over Different Time Intervals
Bibliographic record
Abstract
We examine the additivity of expectations over different time intervals. For example, when asked about 10-year stock price growth, survey respondents report an expected change that is not equal to, but closer to zero than, the sum of their expectations over two shorter time intervals that cover the same 10 years. Such subadditivity, which we also find in expectations for other economic variables, is irrational, as it cannot stem from aggregating short-term expectations. Model estimates show that the pattern is consistent with a time perception where shorter time intervals have a proportionally larger weight. We also find that the respondents’ degree of additivity is correlated with making larger financial investments. This paper was accepted by Manel Baucells, behavioral economics and decision analysis. Funding: This work was supported by Deutsche Forschungsgemeinschaft (the German Science Foundation) via CRC TRR 190 [Grant 280092119]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.01558 .
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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 | 0.013 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".