Taboo "equilibrium": Less confused frames for research on AI bargaining
To understand why powerful AIs might get into conflict, and ways to mitigate it, we need to understand bargaining problems: situations where multiple agents have different preferences over Pareto-efficient outcomes. I’ve come to suspect that certain common frames on bargaining problems are confused. Here, I’ll explain why, and which frames I think are better.
- ▪To understand why powerful AIs might get into conflict, and ways to mitigate it, we need to understand bargaining problems: situations where multiple agents have different preferences over Pareto-efficient outcomes.
- ▪I’ve come to suspect that certain common frames on bargaining problems are confused.
- ▪Here, I’ll explain why, and which frames I think are better.
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| Original publisher | Lesswrong |
| Canonical URL | https://www.lesswrong.com/posts/KDq5aXwanvH5YoZYs/taboo-equilibrium-less-confused-frames-for-research-on-ai |
| Publication time | Sat, 01 Aug 2026 11:02:26 +0000 |
| Retrieval time | 2026-08-01T11:20:58.276Z |
| Last seen | 2026-08-01T11:20:58.276Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | rQmKFQ3-Djxv · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
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Opening excerpt (first ~120 words) tap to expand
To understand why powerful AIs might get into conflict, and ways to mitigate it, we need to understand bargaining problems: situations where multiple agents have different preferences over Pareto-efficient outcomes. I’ve come to suspect that certain common frames on bargaining problems are confused. Here, I’ll explain why, and which frames I think are better. One motivation for this is to hopefully help others make progress in research on safe Pareto improvements (SPIs), which are among the most promising approaches to mitigating the downsides of AI conflict, in my view.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Lesswrong.
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