The promised operational benefits of the Internet of Things (IoT) are predicated on the notion that better decisions will be enabled through a multitude of autonomous sensors (often deployed by different firms) providing real-time knowledge of the state of things. This knowledge will be imperfect, however, due to sensor quality limitations. A sensor can improve its estimation quality by soliciting a state estimate from other sensors operating in its general environment. Target selection (choosing from which other sensors to solicit estimates) is challenging because sensors may not know the underlying inference models or qualities of sensors deployed by other firms. This lack of trust (or familiarity) in others’ inference models creates noise in the received estimate, but trust builds and noise reduces over time the more a sensor targets any given sensor. We characterize the initial and long run information sharing network for an arbitrary collection of sensors operating in an autoregressive environment. The state of the environment plays a key role in mediating quality and trust in target selection. When qualities are known and asymmetric, target selection is based on a deterministic rule that incorporates qualities, trusts, and state. Furthermore, each sensor eventually settles on a constant target set in all future periods, but this long run target set is sample path dependent and also varies by sensor. When qualities are unknown, a deterministic target selection rule may be suboptimal, and sensors may not settle on a constant target set. Moreover, the inherent targeting trade-off between quality and trust is influenced by a sensor’s ambiguity attitude. Our findings shed light on the evolution of inter-firm sensor communication over time, and this is important for predicting and understanding the inter-firm connectedness and relationships that will arise as a result of the IoT.
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