Knowledge Acquisition by Networks of Interacting Agents in the Presence of Observation Errors

TL;DR

This paper studies how networks of agents learn about a complex system under observation errors, revealing how an agent's error rate and network structure influence the overall knowledge quality.

Contribution

It introduces a model of interacting agents with observation errors on different network topologies and provides analytical solutions for the system's behavior.

Findings

Influence of a high-error agent scales linearly with its degree.

Degree as a fitness parameter causes superlinear effects.

Inter-community links with high-error agents degrade overall knowledge quality.

Abstract

In this work we investigate knowledge acquisition as performed by multiple agents interacting as they infer, under the presence of observation errors, respective models of a complex system. We focus the specific case in which, at each time step, each agent takes into account its current observation as well as the average of the models of its neighbors. The agents are connected by a network of interaction of Erd\H{o}s-Renyi or Barabasi-Albert type. First we investigate situations in which one of the agents has a different probability of observation error (higher or lower). It is shown that the influence of this special agent over the quality of the models inferred by the rest of the network can be substantial, varying linearly with the respective degree of the agent with different estimation error. In case the degree of this agent is taken as a respective fitness parameter, the effect of the different estimation error is even more pronounced, becoming superlinear. To complement our analysis, we provide the analytical solution of the overall behavior of the system. We also investigate the knowledge acquisition dynamic when the agents are grouped into communities. We verify that the inclusion of edges between agents (within a community) having higher probability of observation error promotes the loss of quality in the estimation of the agents in the other communities.