Appears in collection : Nexus Trimester - 2016 - Distributed Computation and Communication Theme

Social animals may share information to obtain a more complete and accurate picture of their surroundings. However, physical constraints on communication limit the flow of information between interacting individuals in a way that can cause an accumulation of errors and deteriorated collective behaviors. In this talk, I will theoretically discuss a general model of information sharing within animal groups, and take an algorithmic perspective to identify efficient communication schemes that are, nevertheless, economic in terms of communication, memory and individual internal computation. I will present a simple algorithm in which each agent compresses all information it has gathered into a single parameter that represents its confidence in its behavior. Confidence is communicated between agents by means of active signaling. It turns out that this algorithm competes extremely well with the best possible algorithm that operates without any computational constraints. The proofs rely on the Cramer-Rao bound and on our definition of a Fisher Channel Capacity. These concepts are used to quantify information flows within the group and to obtain lower bounds on collective performance. The results suggest confidence sharing as a central notion in the context of animal communication.