Judgments Based on Stocks and Flows: Different Presentations of the Same Data Can Lead to Opposing Inferences
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.
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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 | 0.032 | 0.248 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".