Title: Missing Data as Part of the Social Behavior in Real-World Financial Complex Systems,
Abstract: Many real-world networks are known to exhibit facts that counter our
knowledge prescribed by the theories on network creation and communication
patterns. A common prerequisite in network analysis is that information on
nodes and links will be complete because network topologies are extremely
sensitive to missing information of this kind. Therefore, many real-world
networks that fail to meet this criterion under random sampling may be
discarded.
In this paper we offer a framework for interpreting the missing observations
in network data under the hypothesis that these observations are not missing at
random. We demonstrate the methodology with a case study of a financial trade
network, where the awareness of agents to the data collection procedure by a
self-interested observer may result in strategic revealing or withholding of
information. The non-random missingness has been overlooked despite the
possibility of this being an important feature of the processes by which the
network is generated. The analysis demonstrates that strategic information
withholding may be a valid general phenomenon in complex systems. The evidence
is sufficient to support the existence of an influential observer and to offer
a compelling dynamic mechanism for the creation of the network.