Distributed Decision-Making over Adaptive Networks

TL;DR

This paper introduces a distributed decision-making algorithm for networks where agents observe data from two unknown models, enabling them to identify the correct model and reach consensus collaboratively.

Contribution

It proposes a novel approach to unbiased distributed estimation and model classification in multi-agent networks with unknown data sources.

Findings

Algorithm successfully identifies the correct data model.

Agents reach consensus on the model through collaborative decision-making.

Application to fish foraging demonstrates practical effectiveness.

Abstract

In distributed processing, agents generally collect data generated by the same underlying unknown model (represented by a vector of parameters) and then solve an estimation or inference task cooperatively. In this paper, we consider the situation in which the data observed by the agents may have risen from two different models. Agents do not know beforehand which model accounts for their data and the data of their neighbors. The objective for the network is for all agents to reach agreement on which model to track and to estimate this model cooperatively. In these situations, where agents are subject to data from unknown different sources, conventional distributed estimation strategies would lead to biased estimates relative to any of the underlying models. We first show how to modify existing strategies to guarantee unbiasedness. We then develop a classification scheme for the agents to identify the models that generated the data, and propose a procedure by which the entire network can be made to converge towards the same model through a collaborative decision-making process. The resulting algorithm is applied to model fish foraging behavior in the presence of two food sources.