@article {1500622,
	title = {Every Team Deserves a Second Chance An extended study on predicting team performance },
	journal = {Journal of Agents and Multiagent Systems (JAAMAS) (To appear)},
	year = {2017},
	abstract = {Voting among different agents is a powerful tool in problem solving, and it has
been widely applied to improve the performance in finding the correct answer to complex
problems. We present a novel benefit of voting, that has not been observed before: we can
use the voting patterns to assess the performance of a team and predict their final outcome.
This prediction can be executed at any moment during problem-solving and it is completely
domain independent. Hence, it can be used to identify when a team is failing, allowing an
operator to take remedial procedures (such as changing team members, the voting rule, or
increasing the allocation of resources). We present three main theoretical results: (i) we
show a theoretical explanation of why our prediction method works; (ii) contrary to what
would be expected based on a simpler explanation using classical voting models, we show
that we can make accurate predictions irrespective of the strength (i.e., performance) of the
teams, and that in fact, the prediction can work better for diverse teams composed of different agents than uniform teams made of copies of the best agent; (iii) we show that the quality
of our prediction increases with the size of the action space. We perform extensive experimentation in two different domains: Computer Go and Ensemble Learning. In Computer
Go, we obtain high quality predictions about the final outcome of games. We analyze the
prediction accuracy for three different teams with different levels of diversity and strength,
and show that the prediction works significantly better for a diverse team. Additionally, we
show that our method still works well when trained with games against one adversary, but
tested with games against another, showing the generality of the learned functions. Moreover, we evaluate four different board sizes, and experimentally confirm better predictions
in larger board sizes. We analyze in detail the learned prediction functions, and how they change according to each team and action space size. In order to show that our method is
domain independent, we also present results in Ensemble Learning, where we make online
predictions about the performance of a team of classifiers, while they are voting to classify
sets of items. We study a set of classical classification algorithms from machine learning, in
a data-set of hand-written digits, and we are able to make high-quality predictions about the
final performance of two different teams. Since our approach is domain independent, it can
be easily applied to a variety of other domains.},
	author = {Marcolino, Leandro Soriano and Aravind S. Lakshminarayanan and Nagarajan, Vaishnavh and Tambe, Milind}
}
