The Role of Computer Simulations in Science and the Academy
Bibliographic record
Abstract
Predictions are nice, if you can make them. But the essence of science lies in explanation, laying bare the fundamental mechanisms of nature. (Arthur Burks) Management and organization researchers’ engagement of computer simulation (or computational modelling) to understand the behavior of collections of individuals and institutions is derived from the uses of models in general, which has been at the core of ‘how science is done’ in fields as old as physics. Although some of the most influential use of simulations in management occurred over 50 years ago (Clarkson & Meltzer, 1960; Clarkson & Simon, 1960; Cohen & Cyert, 1961), the methodology itself has not been seamlessly integrated into the toolbox of organization research or researchers (Augier & Prietula, 2007). Over the years the Academy has published papers about the importance of this approach (Davis, Eisenhardt, & Bingham, 2007; Harrison, Lin, Carroll, & Carley, 2007), but no actual simulation papers have appeared in the past 10 years. Recently, an Editorial has appeared in the Academy of Management Journal discussing the “suitability” of submitting (computer) simulations to that journal (Shaw & Ertug, 2017), which appears to be the current stance of the favored outlet (vs. the Review) for Academy researchers. The purpose of this panel is to assemble a knowledgeable team of experts who will discuss (a) aspects of successfully and effectively using simulations in organization and management research, (b) the variety of ways simulation can be engaged in organization and management research, (c) elements and issues from an editorial perspective (including the recent Editorial), and (d) how Academy members can acquire the competencies to conduct this type of research. The panel discussions will be moderated and structured to being with an interactive discussion amongst themselves then move to open discussion with audience members.
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 | 0.026 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.027 |
| Scholarly communication | 0.021 | 0.025 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".