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Comment by c7b | Hacker News Reader

Fascinating as a paradigm. I wonder whether there's are ways to scale this to the level of agent-based simulations, ie models with populations of agents that let you study macroeconomic effects as emergent phenomena. You'd need to be able to scale those LLMs computations to many agent and find principled ways of encoding their interactions and decisions.

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You'd also have to fit the past interactions in the context window of the LLM, otherwise it wouldn't remember them.

Fine-tuning individual agents in order to move memories from the context window to the neural network weights, even if possible, would probably get too expensive.

yeah - so I think this is worth exploring. Given how many tokens you can jam in the prompt even w/ GPT3, I think could do some pretty complex game play, at least compared to what is typical in the lab e.g., I think could easily have it remember how 100 or so other agents behaved in some kind of public goods game.

Reminds me of Facebook's CICERO model:

> Facebook's CICERO artificial intelligence has achieved “human-level performance” in the board game Diplomacy, which is notable for the fact that’s a game built on human interaction, not moves and manoeuvres, like, say, chess.

It was the same scenario - many agents, multiple rounds, complex dialogue based interactions.

That's actually a pretty cool analogy, even the decisionmaking is arguably quite close to how human decision making actually happens (which involves a lot more exchange of words than just transmitting coded information like "accept proposal to exchange X of good Y for Z monetary units"). Might be a bit tricky to get an AI to really "understand" those implications of their response, but it's cool as a thought experiment.

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