Mega-Simulations in Negotiation Teaching: Extraordinary Investments with Extraordinary Benefits
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract A mega-simulation is a complex-negotiations teaching exercise involving complicated issues and challenging conditions that is undertaken by three or more teams of students. In this article, I draw on two decades of teaching with mega-simulations in international business negotiation courses to discuss potential learning goals for this type of experiential exercise, effective ways to organize the experience, challenges for the instructor, and the distinctive educational benefits that justify the substantial investment of time and resources required to implement these mega-simulations. These simulations can help students to develop greater sophistication in basic negotiation skills, become more extensively exposed to complex skill sets, and develop a deeper understanding of negotiation subject matter and complex processes than they would by conducting standard role plays. Mega-simulations offer major opportunities for students to move to advanced levels of negotiation skill not just in international business, but in diplomacy, law, engineering, and a host of other professional arenas.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it