Judgments Based on Stocks and Flows: Different Presentations of the Same Data Can Lead to Opposing Inferences
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Time-series data—measurements of a quantity over time—can be presented as stocks (the quantity at each point in time) or flows (the change in quantity from one point in time to the next). In a series of six experiments, we find that the choice of presenting data as stocks or flows can have a consequential impact on judgments. The same data can lead to positive or negative assessments when presented as stocks versus flows and can engender optimistic or pessimistic forecasts for the future. For example, when employment data from 2007 to 2013 are shown as flows (jobs created or lost), President Obama’s impact on the economy during his first year in office is viewed positively, whereas when the same data are shown as stocks (total jobs), his impact is viewed negatively. The results highlight a challenge that accompanies the growing reliance on data and analytics for decision making within organizations: seemingly benign choices—such as that between two informationally equivalent data presentations—can substantively impact how data are interpreted and used, even though the underlying information is the same. This paper was accepted by Yuval Rottenstreich, decision analysis.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it