  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.
