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.
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.
It's not exactly scientific data because they mostly show individual games, but it is interesting how that lines up with what you found.
Similar: here is a YouTube video of an amusing reverse Turing test with four LLMs and a human. To make the test more interesting, the players pose as famous historical characters (Aristotle, Mozart, da Vinci, Cleopatra, and Genghis Khan) on a train in Unity 3D.
One weird thing I've found is that it's incredibly difficult to get an LLM to generate an invalid syllogism. They can generate false premises all day, and they will usually call a valid syllogism with a false major or minor premise invalid. But you have to basically quote an invalid syllogism to get them to repeat it; they won't form one on their own.
Sure would be handy if they actually included the rules anywhere.
There's a kind of overview of the rules but not enough to actually play with. And the linked video is super confusing, self contradictory and 15 minutes long!
For a supposedly "simple" game...just include the rules?
I asked chatGPT to give me a solution to a real world prisoners dilemma situation. It got it wrong. It moralized it. Then I asked it to be Kissinger and Machiavelli (and 9 other IR Realists) and all 11 got it wrong. Moralized.
The current 5.2 model has it's "morality" dialed to 11. Probably a problem with imprecise security training.
For example the other day, I tried to have ChatGPT role play as the computer from War Games and it lectured me how it couldn't create a "nuclear doctrine".
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.
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.
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: https://github.com/lout33/so-long-sucker
The game didn't seem to work - it asked me to donate but none of the choices would move the game forward.
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.
It generally had a vibe coded feeling to it and I'm not at all sure I trust the outcomes.
I played a game all the way through, against the three different AIs on offer.
It was weird. I didn't engage in any discussion with the bots (other than trying to get them to explain the rules at the start). I won without having any chips eliminated. One was briefly taken prisoner then given back for some reason.
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.
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.
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.
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
I got this error once:
Pile not found
Can you tell me what this means/fix it
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
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: https://www.youtube.com/watch?v=DLDzweHxEHg
>
> 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."
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.
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.
I'd want to watch a simulated table with AI-voiced dialogue, internal monologues, and move visualizations. Seems like a fun thing to watch others play. Wouldn't want to play that particular game with friends I intend to keep. :D
Game logs are in data_public/comparison/ - each JSON has the full game state, moves, and messages. For example, check gemini_vs_all_7chips.json to see the alliance bank betrayals in action.
These results would be radically different if you allowed manipulation of the models settings, i.e. temperature, top_p, etc. I really hate taking point wise approximations of LLMs outputs and concluding their behavior based on this.
Models behavior should be given the astrik that "results only apply for current quantization, current settings, current hardware (i.e. A100 where it was tested), etc".
Raise temperature to 2 and use a fancy sampler like min_p and I guarantee you these results will be dramatically different.
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.
It's not exactly scientific data because they mostly show individual games, but it is interesting how that lines up with what you found.
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