Bibliographic record
Abstract
CSBILEs allow the compression of time and space and provide an opportunity for practicing managerial decision making in a non-threatening way (Issacs & Senge, 1994). In a computer simulation-based interactive learning environments (CSBILEs), decision makers can test their assumptions, practice exerting control over a business situation, and learn from the immediate feedback of their decisions. CSBILE’s effectiveness is associated directly with decision-making effectiveness; that is, if one CSBILE improves decision-making effectiveness more than other CSBILEs, it is more effective than others. Despite an increasing interest in CSBILEs, empirical evidence to their effectiveness is inconclusive (Bakken, 1993; Diehl & Sterman, 1995; Moxnes, 1998). The aim of this article is to present a case for HCI design principles as a viable potential way to improve the design of CSBILEs and, hence, their effectiveness in improving decision makers’ performance in dynamic tasks. This article is organized as follows: some background concepts are presented first; next, we present an assessment of the prior research on (i) DDM and CSBILE and (ii) HCI and dynamic decision making (DDM); the section on future trends presents some suggestion for future research. This article concludes with some conclusions.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".