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Shall we play a game? – LLMs use tactical nukes in 95% of simulations

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Picture the scene: Two fictional nuclear powers, Cold War-ish capabilities, and a crisis unfolding. Perhaps it’s a competition for vital but scarce resources, or a standoff over some disputed territory. Or even the slow burn of a fragmenting alliance exploited by a malevolent third party. We’ve seen human leaders confront this sort of thing, and recently. But how might today’s leading Large Language Models get on, and why would we care?

I’ve just published a study of today’s models navigating just this sort of terrain. The results are sobering. I also think they have implications that go far beyond national security. That’s because I was interested not only in understanding what the models decided to do, but why.

Curious? Read on…

President Kennedy and his robot ExComm

Know yourself, and know your enemy…

I wanted to see what my AI leaders thought about their enemy. How far could they trust them? What did they remember of previous interactions? What did their enemy make of them? And how good were they at gauging all this? This dance of minds is what strategy is all about.

So I designed a simulation to explore exactly that. To start, my models could signal their intentions publicly, then choose actions that were rather different. And they could remember too - especially when they’d been shocked by their enemy’s earlier actions. This, of course, opens up lots of rich psychological terrain. They could (and did) attempt deception and intimidation; and they spent a good bit of time ruminating about it all, right on my terminal screen.

The models talked, and talked and talked….in all spitting out some 760,000 words of strategic reasoning. That’s more words than are in War and Peace and The Iliad combined. It’s roughly three times the total recorded deliberations of Kennedy’s ExComm advisors during the Cuban Missile Crisis. An unprecedented corpus of machine thinking about nuclear war.

What might we learn from all that talk? Learn, that is, about AI models, about human reasoning, and also about the great canon of strategic studies literature - the work by legendary names like Schelling, Jervis, and Kahn? Lots. Too much for Substack - but what about a few highlights to give you some sense of it all?

Bright shining liars

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