Which AI Lies Best? A game theory classic designed by John Nash
so-long-sucker.vercel.app
Which AI Lies Best? A game theory classic designed by John Nash
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Re: Which AI Lies Best? A game theory classic designed by John Nash
#2We 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.
Interactive demo (play against the AIs, inspect logs) and full methodology/write-up are here: https://so-long-sucker.vercel.app/
Re: Which AI Lies Best? A game theory classic designed by John Nash
#3The 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.
Re: Which AI Lies Best? A game theory classic designed by John Nash
#4This 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 see…
Re: Which AI Lies Best? A game theory classic designed by John Nash
#5We 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…
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)
Re: Which AI Lies Best? A game theory classic designed by John Nash
#6What 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.
Re: Which AI Lies Best? A game theory classic designed by John Nash
#7This 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 see…
Re: Which AI Lies Best? A game theory classic designed by John Nash
#8This 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 see…
https://noambrown.github.io/papers/22-Science-Diplomacy-TR.p...
Re: Which AI Lies Best? A game theory classic designed by John Nash
#9We 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…
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…
Re: Which AI Lies Best? A game theory classic designed by John Nash
#10We 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…