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

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