The Virtually Intelligent Negotiator: Building Trust and Maximizing Economic Gain in E-Negotiations
Bibliographic record
Abstract
Virtual intelligence is “the ability to communicate and navigate relationships and achieve business goals when engaging with others who are not physically co-present.” Virtual intelligence is particularly critical in e-negotiations because negotiators compete to achieve economic goals but must cooperate to reach mutual agreement and maintain social relationships. I review key research findings on the advantages and disadvantages of virtual and in vivo negotiations. I make the point that in vivo negotiation does not always result in more trust and mutually beneficial outcomes than virtual negotiations. I use insights from research on e-negotiations and virtual communication to identify skills that facilitate trust and information sharing and lead to more desirable negotiation outcomes. I organize my discussion of virtual intelligence in terms of four key challenges that confront negotiators: relational concerns (building trust), conveyance (transmitting and receiving information), convergence (reaching a shared understanding of the situation), and achieving instrumental goals (negotiating a favorable outcome).
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".