Multiple Equivalent Simultaneous Offers in Negotiations:Effects on Individual and Joint Gain
Bibliographic record
Abstract
Package offers – presenting multiple issue positions at once –are typically recommended to manage uncertainty and gain value in multi-issue negotiations, but we propose that a superior strategy is starting negotiations with a choice among two or more package offers equivalent in value to the negotiator presenting them. For the first time, we argue that this strategy, which we term multiple equivalent simultaneous offers (MESOs), will yield greater gains for the offerer because recipients will perceive MESOs as a more legitimate first offer, leading them to adjust less from MESOs’ initial positions as they make counteroffers and reach agreement. The first experimental tests of MESOs supported predictions, revealing greater gains for the offerer (Study 1–3) because recipients perceived MESOs to be more legitimate (Study 3) and adjusted less from the offers’ initial positions (Studies 1, 2, 3). Also, MESOs led to greater joint gain (Study 1, 2) because it began the negotiation with a more efficient starting point (Study 2). This research offers new insight for multi-issue negotiations and the psychology of choice, and contributes to the decision-making literature on anchoring by focusing on the process of adjustment.
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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 | 0.009 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 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".