一种确定性序贯渐近学习协议
A Deterministic Protocol for Sequential Asymptotic Learning
摘要：
在经典的羊群模型中，智能体接收关于潜在的二元自然状态的私人信号，并在观察其前任的行为后，依次采取行动，选择两种可能的行为之一。我们研究什么类型的行为导致渐近学习，代理最终将收敛到正确的行动的概率。众所周知，对于理性主体和有界信号，不会有渐近学习。如果代理人能够合作而不是自私，这会有帮助吗？如果允许代理使用随机协议，则这很容易实现。在本文中，我们提供了第一个确定性协议下，渐近学习发生。此外，我们的协议的优点是比以前的协议简单得多。
英文摘要：
In the classic herding model, agents receive private signals about an underlying binary state of nature, and act sequentially to choose one of two possible actions, after observing the actions of their predecessors. We investigate what types of behaviors lead to asymptotic learning, where agents will eventually converge to the right action in probability. It is known that for rational agents and bounded signals, there will not be asymptotic learning. Does it help if the agents can be cooperative rather than act selfishly? This is simple to achieve if the agents are allowed to use randomized protocols. In this paper, we provide the first deterministic protocol under which asymptotic learning occurs. In addition, our protocol has the advantage of being much simpler than previous protocols.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
