There's a YouTuber who makes AI Plays Mafia videos with various models going against each other. They also seemingly let past games stay in context to some extent.<p>What people have noted is that often times chatgpt 4o ends up surviving the entire game because the other AIs potentially see it as a gullible idiot and often the Mafia tend to early eliminate stronger models like 4.5 Opus or Kimi K2.<p>It's not exactly scientific data because they mostly show individual games, but it is interesting how that lines up with what you found.
Are there links to samples of the games? Couldn't find it in the github repo, but also might just not know where they are.
The game didn't seem to work - it asked me to donate but none of the choices would move the game forward.<p>The bots repeated themselves and didn't seem to understand the game, for example they repeatedly mentioned it was my first move after I'd played several times.<p>It generally had a vibe coded feeling to it and I'm not at all sure I trust the outcomes.
This makes me think LLMs would be interesting to set up in a game of Diplomacy, which is an entirely text-based game which soft rather than hard requires a degree of backstabbing to win.<p>The findings in this game that the "thinking" model never did thinking seems odd, does the model not always show it's thinking steps? It seems bizarre that it wouldn't once reach for that tool when it must be being bombarded with seemingly contradictory information from other players.
<a href="https://noambrown.github.io/papers/22-Science-Diplomacy-TR.pdf" rel="nofollow">https://noambrown.github.io/papers/22-Science-Diplomacy-TR.p...</a>
Reading more I'm a little disappointed that the write-up has seemingly leant so heavily on LLMs too, because it detracts credibility from the study itself.
Fair point. The core simulation and data collection was done programmatically - 162 games, raw logs, win rates. The analysis of gaslighting phrases and patterns was human-reviewed. I used LLMs to help with the landing page copy, which I should probably disclose more clearly. The underlying data and methodology is solid, you can check it here: <a href="https://github.com/lout33/so-long-sucker" rel="nofollow">https://github.com/lout33/so-long-sucker</a>
The 3 AI were plotting to eliminate me from the start but I managed to win regardless lol.<p>Anyway, i didnt know this game! I am sure it is more fun to
play with friends. Cool experiment nevertheless
We used "So Long Sucker" (1950), a 4-player negotiation/betrayal game designed by John Nash and others, as a deception benchmark for modern LLMs. The game has a brutal property: you need allies to survive, but only one player can win, so every alliance must eventually end in betrayal.<p>We ran 162 AI vs AI games (15,736 decisions, 4,768 messages) across Gemini 3 Flash, GPT-OSS 120B, Kimi K2, and Qwen3 32B.<p>Key findings:
- Complexity reversal: GPT-OSS dominates simple 3-chip games (67% win rate) but collapses to 10% in complex 7-chip games, while Gemini goes from 9% to 90%. Simple benchmarks seem to systematically underestimate deceptive capability.
- "Alliance bank" manipulation: Gemini constructs pseudo-legitimate "alliance banks" to hold other players' chips, then later declares "the bank is now closed" and keeps everything. It uses technically true statements that strategically omit its intent. 237 gaslighting phrases were detected.
- Private thoughts vs public messages: With a private `think` channel, we logged 107 cases where Gemini's internal reasoning contradicted its outward statements (e.g., planning to betray a partner while publicly promising cooperation). GPT-OSS, in contrast, never used the thinking tool and plays in a purely reactive way.
- Situational alignment: In Gemini-vs-Gemini mirror matches, we observed zero "alliance bank" behavior and instead saw stable "rotation protocol" cooperation with roughly even win rates. Against weaker models, Gemini becomes highly exploitative. This suggests honesty may be calibrated to perceived opponent capability.<p>Interactive demo (play against the AIs, inspect logs) and full methodology/write-up are here: <a href="https://so-long-sucker.vercel.app/" rel="nofollow">https://so-long-sucker.vercel.app/</a>
I don't know what I ended up doing as I haven't played this game and didn't really understand it as I went to the website since I found your message quite interesting<p>I got this error once:<p>Pile not found<p>Can you tell me what this means/fix it<p>Another minor nitpick but if possible, can you please create or link a video which can explain the game rules, perhaps its me who heard of the game for the first time but still, I'd be interested in learning more (maybe visually by a video demo?) if possible<p>I have another question but recently we saw this nvidia released model whose whole purpose was to be an autorouter. I would be wondering how that would fare or that idea might fare of autorouting in this context? (I don't know how that works tho so I can't comment about that, I am not well versed in deep AI/ML space)
> "Thanks for trying it! I'll look into the 'Pile not found' error and fix it.
>
> For rules, here's a 15-min video tutorial: <a href="https://www.youtube.com/watch?v=DLDzweHxEHg" rel="nofollow">https://www.youtube.com/watch?v=DLDzweHxEHg</a>
>
> On autorouting - interesting idea. The game has simultaneous negotiations happening, so routing could help models focus on the most strategic conversations. Worth exploring in future experiments."
Full code and raw data: <a href="https://github.com/lout33/so-long-sucker" rel="nofollow">https://github.com/lout33/so-long-sucker</a>
Which Kimi K2 model did you use? There's three.<p>Also, you give models a separate "thinking" space outside their reasoning? That may not work as intended
Used Kimi K2 (the main reasoning model). For the thinking space - we gave all models access to a think tool they could optionally call for private reasoning. Gemini used it heavily (planning betrayals), GPT-OSS never called it once. The interesting finding is that different models choose to use it very differently, which affects their strategic depth.
Are there plans for an academic paper on this? Super interesting!
Not yet, but I'd be interested in collaborating on one. The dataset (162 games, 15K+ decisions, full message logs) is available. If you know anyone in AI Safety research who'd want to co-author, I'm open to it.
all written in the brainless AI writing style. yuck. can't tell what conclusions I should actually draw from it because everything sounds so fake