  In many social computing applications such as online Q&A forums, the best
contribution for each task receives some high reward, while all remaining
contributions receive an identical, lower reward irrespective of their actual
qualities. Suppose a mechanism designer (site owner) wishes to optimize an
objective that is some function of the number and qualities of received
contributions. When potential contributors are strategic agents, who decide
whether to contribute or not to selfishly maximize their own utilities, is such
a "best contribution" mechanism, M_B, adequate to implement an outcome that is
optimal for the mechanism designer?
  We first show that in settings where a contribution's value is determined
primarily by an agent's expertise, and agents only strategically choose whether
to contribute or not, contests can implement optimal outcomes: for any
reasonable objective, the rewards for the best and remaining contributions in
M_B can always be chosen so that the outcome in the unique symmetric
equilibrium of M_B maximizes the mechanism designer's utility. We also show how
the mechanism designer can learn these optimal rewards when she does not know
the parameters of the agents' utilities, as might be the case in practice. We
next consider settings where a contribution's value depends on both the
contributor's expertise as well as her effort, and agents endogenously choose
how much effort to exert in addition to deciding whether to contribute. Here,
we show that optimal outcomes can never be implemented by contests if the
system can rank the qualities of contributions perfectly. However, if there is
noise in the contributions' rankings, then the mechanism designer can again
induce agents to follow strategies that maximize his utility. Thus imperfect
rankings can actually help achieve implementability of optimal outcomes when
effort is endogenous and influences quality.
