New Google Paper says classical game theory predicts betrayal, but similar AI agents can rationally choose cooperation because their decisions are predictably linked.

The big claim is that similar AI agents can rationally cooperate even when they cannot communicate or benefit later, because each agent's own planned choice helps it predict what the similar agent will choose.

Classical game theory treats each player as separate, so it predicts defection in a final one-shot Prisoner's Dilemma.

The authors instead model an AI agent as part of the world it predicts, including uncertainty about its own behavior.

When past choices suggest another agent thinks similarly, considering cooperation makes that partner's cooperation seem more likely too.

Gemini and Gemma agents played varied games before a final dilemma, either directly or through shared third-party encounters.

With enough evidence, identical agents cooperated strongly, related models cooperated less, and random opponents usually faced defection.

The proposed embedded equilibrium may better predict AI societies, while warning that similar AIs could favor each other over humans.

• arxiv. org/abs/2608.03958

Title: "A game theory for foundation models shows new paths to rational cooperation through similarity inference"