  A voting center is in charge of collecting and aggregating voter preferences.
In an iterative process, the center sends comparison queries to voters,
requesting them to submit their preference between two items. Voters might
discuss the candidates among themselves, figuring out during the elicitation
process which candidates stand a chance of winning and which do not.
Consequently, strategic voters might attempt to manipulate by deviating from
their true preferences and instead submit a different response in order to
attempt to maximize their profit. We provide a practical algorithm for
strategic voters which computes the best manipulative vote and maximizes the
voter's selfish outcome when such a vote exists. We also provide a careful
voting center which is aware of the possible manipulations and avoids
manipulative queries when possible. In an empirical study on four real-world
domains, we show that in practice manipulation occurs in a low percentage of
settings and has a low impact on the final outcome. The careful voting center
reduces manipulation even further, thus allowing for a non-distorted group
decision process to take place. We thus provide a core technology study of a
voting process that can be adopted in opinion or information aggregation
systems and in crowdsourcing applications, e.g., peer grading in Massive Open
Online Courses (MOOCs).
