Who Says What to Whom? The Impact of Communication Setting and Channel on Exclusion from Multiparty Negotiation Agreements
Bibliographic record
Abstract
Previous research has argued that people exclude others in multiparty negotiations when their inclusion does not increase their payoffs. However, the majority of this research has been conducted in settings where participants do not interact person-to-person or where they communicate through highly restricted means. We argue that this view on exclusion needs to be modified and propose that communication can induce cooperation and thereby decrease exclusion from coalition agreements in multiparty negotiations. Data from two experiments examine how an opportunity to detect others' emotions, words, and behavior affects cooperation and exclusion in multiparty negotiations. Study 1 found that negotiators who communicate face-to-face or in the same (chat) room are less likely to exclude others from coalition agreements than negotiators who communicate in private and with computer mediated technology. Study 2 replicated this effect and also demonstrated that these effects are due to greater cooperation displayed in negotiators' language and behavior. Both studies consistently found that communication setting and channel were particularly impactful for the weakest party in the negotiation, suggesting that low power negotiators can decrease exclusion by altering the communication parameters.
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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 | 0.016 | 0.118 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".