Bibliographic record
Abstract
Managers face problems that are increasingly complex and dynamic. Decision support systems (DSS) are designed to assist them make better decisions. However, the empirical evidence concerning the impact of DSS on improved decision making and learning in dynamic tasks is equivocal at best (Klabbers, 2003; Sharda, Steve, Barr, & McDonnell, 1988; Sterman, 2000; Todd & Benbasat, 1999). Over four decades of dynamic decision making; studies have resulted in a general conclusion on why people perform poorly in dynamic tasks. In dynamic tasks, where a number of decisions are required rather than a single decision, decisions are interdependent, and the decision-making environment changes as a result of the decisions or autonomously or both (Edwards, 1962), most often the poor performance is attributed to subjects’ misperceptions of feedback. That is, people perform poorly because they ignore time delays between their “actions and the consequences” (Sterman, 2000) and are insensitive to the feedback structure of the task system (Diehl & Sterman, 1995). Decision maker’s mental models about the task are often inadequate and flawed (Kerstholt & Raaijmakers, 1997; Romme, 2004). In this paper we argue that system dynamics based interactive learning environments (ILEs) could provide effective decision support for dynamic tasks by reducing the misperceptions of feedback.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".