We simulate behaviour of independent reinforcement learning algorithms
playing the Crawford and Sobel (1982) game of strategic information
transmission. We show that a sender and a receiver training together converge
to strategies close to the exante optimal equilibrium of the game. Hence,
communication takes place to the largest extent predicted by Nash equilibrium
given the degree of conflict of interest between agents. The conclusion is
shown to be robust to alternative specifications of the hyperparameters and of
the game. We discuss implications for theories of equilibrium selection in
information transmission games, for work on emerging communication among
algorithms in computer science and for the economics of collusions in markets
populated by artificially intelligent agents.

我们模拟独立强化学习算法在 Crawford 和 Sobel (1982) 的战略信息传输游戏中的行为，显示出训练一起的发送方和接收方收敛到接近游戏先验最优均衡的策略，从而在代理之间的利益冲突程度给出的前提下，按照 Nash 均衡预测发生最大程度的通信。我们发现这个结论在超参数和游戏的替代规范下是稳健的。我们讨论了对信息传输游戏中均衡选择理论、计算机科学中算法间新兴通信的作用以及市场中由人工智能代理人组成的勾结经济的影响。