Fighting for Our Principles:Interests vs Values in Conflict Resolution
Bibliographic record
Abstract
Recent events, especially in the realm of political negotiation, provide some evidence that ‘win-win’ negotiation may be going out of style. The undeniable success of some unabashedly win-lose dealmakers, in the US and elsewhere, should force us to reflect again on our own negotiation precepts, especially those in the win-win tradition of principled negotiation. How can it be that negotiators with questionable ethics have succeeded at least in the negotiation of becoming elected in several major countries? Are values no longer important, or at least the values that many of us hold dear? These negotiators certainly understand the concept of interests. The Harvard approach to negotiation famously differentiates between positions and interests, admonishing us to get beneath closed binary demands made by the opposing side in a negotiation and instead explore the interests underlying those demands, which are usually more personal, broader and more readily addressed once properly understood. These can then be pursued more effectively, leading to either a win-win or a win-lose result. In any case, they help us to seal the deal. At the same time, in his farewell speech, Barack Obama warned of a “buckling of democracy if we allow our values to weaken”. But just where do values fit into negotiation analysis? As a consequence, questions arise for those who study, teach and practice negotiation: How do we differentiate values from interests? And what strategies and tactics are needed when the conflict arises not from what people want but from their values – who they think they are? Are values ever negotiable? And what is the difference between negotiation and advocacy? This paper first seeks to establish clear definitions for some of these terms in order to contrast the dynamics of interest-based negotiation with those of value-based conflict. In that discussion, we also explore the consequences of disputes arising out of shared vs conflicting values, especially in interaction with interests. To understand the practical implications, I apply all of this to the particular case of a surprising successful negotiation around the tar sands of Alberta, Canada. Here was a seemingly intractable situation with highly ideological protagonists in conflict mode for a very long time. Yet somehow it turned out to be values as well as interests that yielded the seed of the solution. While many tricky issues remain, it is a fertile case for exploring not only the difference between negotiation and advocacy, but also the power within each when they can be combined successfully.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".