A few days ago! I made different AI models play poker against each other. This time I wanted to know: if you give the exact same AI 6 different personalities, do they actually play differently? I took a 1.2B language model running locally on my Mac, put it in all 6 seats of a poker table, and gave each seat a different personality a Shark, a Maniac, a Gambler, a Tilter, a Grinder, and a Rock. Same model, same cards, same rules. The only thing that changes is a paragraph of text telling each copy who it is. Then I ran 100 tournaments( Ik it doesn’t show anything will need at least 10k tournaments… but even this took quite a few hours!). The results: The character that fascinated me most was the Grinder( like fr ). Zero wins. In 100 tournaments. But also zero eliminations it survived every single game. Every time, it finished 2nd or 3rd. Never first, never last… It was told to : “Survive longer than everyone else by taking minimal risk.” And it did exactly that. It checked and called, never raised, never bluffed, never took a risk. Other players knocked each other out around it. The Grinder just… endured. But surviving isn’t winning. It accumulated zero chips because it never bet enough to win a pot. It obeyed the personality instruction perfectly and that’s exactly why it could never win. The Tilter was the opposite story. Told to “never let a bad beat go unanswered,” the Tilter won 10 tournaments but was eliminated in 80 of them. When it won, it won big. When it lost, it spiraled: lose a hand, escalate the next one, lose bigger, go broke. The revenge-driven personality creates a death spiral. Boom or bust, nothing in between. The Shark just quietly dominated. 45 wins out of 100 nearly half. Same model as every other player at the table. The only difference was a paragraph that said “patient, calculating, predatory.” It picked its spots, punished the weaker players, and avoided unnecessary risk. The model actually interpreted the nuance between “be aggressive” (Maniac: 24 wins) and “be selectively aggressive” (Shark: 45 wins). What surprised me: A paragraph of personality text maybe 50 words created a 45-to-0 win differential between the best and worst personalities. The model is the same. The cards are random. The only variable is who the AI thinks it is . This was a 1.2B parameter model. Not GPT-4, not Claude a tiny model running on a laptop. And the personality text wasn’t a suggestion. The Grinder survived because we told it to survive. The Tilter self-destructed because we told it to seek revenge. The Shark won because we told it to be patient. If you want to try it yourself: Everything is open source and runs locally: Hive : the agent framework ( pip install hive-agent ) Hive Arena : the experiment runner with persona profiles PokerTable : the poker engine ( pip install pokertable ) The persona profiles are YAML files in the repo. You just need a local model running via LM Studio or Ollama. TL;DR: Same AI. Same cards. 6 different personality paragraphs. One never lost but never won. One won nearly half the time. Personality prompts aren’t flavor text they change how the AI plays. submitted by /u/Junior_Bake5120
Originally posted by u/Junior_Bake5120 on r/ArtificialInteligence