  When harvesting collective intelligence, a user wishes to maximize the
accuracy and value of the acquired information without spending too much time
collecting it. We empirically study how people behave when facing these
conflicting objectives using data from Yahoo Answers, a community driven
question-and-answer site. We take two complementary approaches. We first study
how users behave when trying to maximize the amount of the acquired
information, while minimizing the waiting time. We identify and quantify how
question authors at Yahoo Answers trade off the number of answers they receive
and the cost of waiting. We find that users are willing to wait more to obtain
an additional answer when they have only received a small number of answers;
this implies decreasing marginal returns in the amount of collected
information. We also estimate the user's utility function from the data. Our
second approach focuses on how users assess the qualities of the individual
answers without explicitly considering the cost of waiting. We assume that
users make a sequence of decisions, deciding to wait for an additional answer
as long as the quality of the current answer exceeds some threshold. Under this
model, the probability distribution for the number of answers that a question
gets is an inverse Gaussian, which is a Zipf-like distribution. We use the data
to validate this conclusion.
