How analytic reasoning style and global thinking relate to understanding stocks and flows
Bibliographic record
Abstract
Abstract Understanding stock‐flow relationships is fundamental to the management of operational systems. In their most basic form, stock‐flow systems consist of resources that accumulate and flows that change their level. Managing stock‐flow systems is an indispensable part of operations management, including supply chain, inventory, and capacity planning. Previous studies have shown that most people, even experts and well‐educated individuals, make persistent errors when inferring the behavior of accumulation (i.e., stock) over time. However, little is known about what individual characteristics make a decision maker better or worse at understanding stock‐flows. In this paper, we report the results of investigating the relationship between analytical‐intuitive thinking and global‐local processing on performance in a simple stock‐flow problem. We find that individuals with an analytical thinking style, rather than an intuitive one, perform significantly better on a stock‐flow problem; whereas individuals with a global, rather than a local, thinking style do not necessarily perform better. However, even individuals who exhibit analytical thinking have a poor understanding of stock‐flow problems. Analytical thinking may be related to understanding stock and flows, but more work is needed to better understand what cognitive abilities are required to solve these problems.
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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| 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".