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The Value of Information and the Internet of Things 167

such as would be the case of the information provided by AI-enabled IoT,
the theoretical handling of clairvoyance changes. We see, as did Howard,
that knowledge about L is more important than knowledge about C when
it comes to maximizing EðVjbÞ. But we have shown that a knowledge of C in
a bid guarantees that a bidder will never have a negative expected profit.
Therefore, the VoI depends on what one is trying to do, or the contextual
objective. This qualitative consideration must be kept in mind for future
research on VoI. Existing work from the sensor network domain, specifi-
cally on QoI, and VoI may provide quantitative measures that can form a
probabilistic derivative VoI.
We explained the relevance of our approach in this chapter’s section on
IoT and AI. We have taken the opportunity to adjust Howard’s seminal the-
ory to provide an extended foundation for the VoI theory in the IoT. One
must keep in mind that AI techniques, such as machine learning and artificial
reasoning, when employed in the IoT for self-star system behaviors, will
require additional consideration for managing information provided to a
human or machine decision maker. While we continued with Howard’s
“market” context in this chapter for its explainability and theoretic continu-
ity, our future work will examine the implications of our theoretical VoI
guarantee, described herein, in an IoT-specific experimental simulation
or empirical study, that incorporates semantic notions of QoI.

ACKNOWLEDGMENTS
The authors acknowledge the assistance of Swmbo Heilizer. The authors also thank William
F. Lawless for his careful reading of the draft version of this chapter and his helpful
suggestions.

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