This paper examines issues in characterizing the performance of information sources as necessary for data fusion and coordination in a net-centric environment. In many practical applications, interacting agents have various degrees ? and possibly time-varying degrees ? of allegiance, common purpose, cooperativeness, information fidelity, controllability, etc. Agents share information with friends, foes and innocent bystanders alike, with varying degrees of cooperativeness and openness. In such cases, each network node needs to explicitly estimate the performance, trustworthiness and allegiance of all other contributing nodes as a part of the general multi-sensor/multi-target state estimation process. A sensor's or information system's reporting bias ? which may include intentional or unintentional human biases ? is distinguished from its measurement bias. The problem is compared with that of measurement bias estimation, e.g. in target tracking. Formulations for estimation of biases in discrete variable reporting ? e.g. in target classification or activity state reporting ? are explored.