Teaching Effective Negotiation Strategies with a Multi-Issue Negotiation Simulator
Bibliographic record
Abstract
This study explores the effectiveness of a negotiation simulator bot in teaching and assessing integrative, distributive, and compatible strategies for multi-issue scorable negotiations. Students, acting as company representatives, negotiated with a bot to purchase a car by balancing priorities across eight issues of varying importance. Using the simulator, they generated three Multiple, Equivalent, and Simultaneous Offers (MESOs), while the bot provided real-time counter-MESOs, enabling iterative learning and strategy refinement. Four strategy clusters were identified using k-modes clustering, based on 733 negotiation attempts by 55 students. Cluster 2 strategies yielded the highest median joint points (Md = 24.0) and average joint points (M = 24.2), approaching the joint optimum with a focus on mutual gains. Cluster 4 adopted a competitive but less effective approach, prioritizing buyers’ outcomes (Md = 19.8, M = 18.9) at the expense of sellers, resulting in lower joint outcomes (Md = 21.6, M = 22.3). Cluster 2 achieved the highest seller points (Md = 8.95), while Cluster 3, a self-focused strategy, resulted in the lowest seller points (Md = 6.20). This study highlights the potential of computational negotiation pedagogy to enhance adaptability and scalability. Future research will assess skill retention and explore AI for dynamic learning experiences.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".