Exploiting scale invariant dynamics for efficient information propagation in large teams

Large heterogeneous teams will often be in situations where sensor data that is uncertain and conflicting is shared across a peer-to-peer network. Not every team member will have direct access to sensors and team members will be influenced mostly by teammates with whom they communicate directly. In this paper, we investigate the dynamics and emergent behaviors of a large team sharing beliefs to reach conclusions about the world. We find empirically that the dynamics of information propagation in such belief sharing systems are characterized by information avalanches of belief changes caused by a single additional sensor reading. The distribution of the size of these avalanches dictates the speed and accuracy with which the team reaches conclusions. A key property of the system is that it exhibits qualitatively different dynamics and system performance over small changes in system parameter ranges. In one particular range, the system exhibits behavior known as scale-invariant dynamics which we empirically find to correspond to dramatically more accurate conclusions being reached by team members. Due to the fact that the ranges are very sensitive to configuration details, the parameter ranges over which specific system dynamics occur are extremely difficult to predict precisely. In this paper we (a) develop techniques to mathematically characterize the dynamics of the team belief propagation (b) obtain through simulations the relation between the dynamics and overall system performance, and (c) develop a novel distributed algorithms that the agents in the team use locally to steer the whole team to areas of optimized performance.