Missing Data as Part of the Social Behavior in Real-World Financial Complex Systems

TL;DR

This paper introduces a framework for understanding non-random missing data in real-world financial networks, highlighting how strategic information withholding influences network formation and analysis.

Contribution

It presents a novel methodology for interpreting missing data as part of social behavior, demonstrated through a case study on financial trade networks.

Findings

Strategic withholding of information affects network structure.

Non-random missingness can be an influential factor in network dynamics.

Evidence supports the presence of an influential observer in financial 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.