  We show that social learning is not useful in a model of team binary decision
making by voting, where each vote carries equal weight. Specifically, we
consider Bayesian binary hypothesis testing where agents have any
conditionally-independent observation distribution and their local decisions
are fused by any L-out-of-N fusion rule. The agents make local decisions
sequentially, with each allowed to use its own private signal and all precedent
local decisions. Though social learning generally occurs in that precedent
local decisions affect an agent's belief, optimal team performance is obtained
when all precedent local decisions are ignored. Thus, social learning is
futile, and secret ballots are optimal. This contrasts with typical studies of
social learning because we include a fusion center rather than concentrating on
the performance of the latest-acting agents.
