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.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".