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This paper proposes a model of behavior for agents who answer questions; the model works similarly to the way in which people interact in social networks and the agents behave differently depending on who asks a question; this behavior modulates the effort utilized in finding better answers for a given question. Our model also avoids consulting all acquaintances fact that can overload or overburden the contacts. However, since reducing the number of recipients might result in a poorer answer, we propose a behavior of consulting a small set of contacts and adding more recipients only if no relevant answer is found. The most promising result is that the first answer-in is probably the most relevant. The ordering the answers simply as they arrive gives the best ranking of answers. The new ranking is well-suited for real time question answering and avoids costly methods associated with re-ranking results.