  Crowdsourcing is now widely used to replace judgement by an expert authority
with an aggregate evaluation from a number of non-experts, in applications
ranging from rating and categorizing online content to evaluation of student
assignments in massively open online courses via peer grading. A key issue in
these settings, where direct monitoring is infeasible, is incentivizing agents
in the `crowd' to put in effort to make good evaluations, as well as to
truthfully report their evaluations. This leads to a new family of information
elicitation problems with unobservable ground truth, where an agent's
proficiency- the probability with which she correctly evaluates the underlying
ground truth- is endogenously determined by her strategic choice of how much
effort to put into the task.
  Our main contribution is a simple, new, mechanism for binary information
elicitation for multiple tasks when agents have endogenous proficiencies, with
the following properties: (i) Exerting maximum effort followed by truthful
reporting of observations is a Nash equilibrium. (ii) This is the equilibrium
with maximum payoff to all agents, even when agents have different maximum
proficiencies, can use mixed strategies, and can choose a different strategy
for each of their tasks. Our information elicitation mechanism requires only
minimal bounds on the priors, asks agents to only report their own evaluations,
and does not require any conditions on a diverging number of agent reports per
task to achieve its incentive properties. The main idea behind our mechanism is
to use the presence of multiple tasks and ratings to identify and penalize
low-effort agreement: the mechanism rewards agents for agreeing with a
`reference' rater on a task but also penalizes for blind agreement by
subtracting out a statistic term designed so that agents obtain reward only
when they put effort into their observations.
