An Explanation for the Efficiency of Scale Invariant Dynamics of Information Fusion in Large Teams

Abstract

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. Simple models of a large team sharing beliefs to reach conclusions about the world show that the dynamics of such belief sharing systems are characterized by information cascades. These are ripples of belief changes through the system caused by a single additional sensor reading. Glinton et al. showed that such a system will exhibit qualitatively different dynamics sensitive to ranges over system parameters. In addition they showed that In one particular range, the system exhibits behavior known as scale-invariant dynamics which was found empirically to correspond to dramatically more accurate conclusions being reached by team members. In this paper we provide an analytical explanation for the performance of scale invariant dynamics by leveraging signal processing concepts. We show that scale invariant dynamics behave as an adaptive information filter with a response that automatically adjusts to the accuracy of sensor inputs. This adaptation causes the performance gain.