Tackling information asymmetry in networks: a new entropy-based ranking index

TL;DR

This paper introduces InfoRank, a new entropy-based index to quantify information asymmetry in networks, improving the identification of the most informative nodes and aiding in better network structure reconstruction.

Contribution

The paper presents a novel entropy-based index, InfoRank, to measure node-specific information quality in networks, outperforming existing centrality measures in identifying key nodes.

Findings

InfoRank effectively quantifies information asymmetry.

It improves network reconstruction accuracy.

Outperforms traditional centrality measures.

Abstract

Information is a valuable asset for agents in socio-economic systems, a significant part of the information being entailed into the very network of connections between agents. The different interlinkages patterns that agents establish may, in fact, lead to asymmetries in the knowledge of the network structure; since this entails a different ability of quantifying relevant systemic properties (e.g. the risk of financial contagion in a network of liabilities), agents capable of providing a better estimate of (otherwise) unaccessible network properties, ultimately have a competitive advantage. In this paper, we address for the first time the issue of quantifying the information asymmetry arising from the network topology. To this aim, we define a novel index - InfoRank - intended to measure the quality of the information possessed by each node, computing the Shannon entropy of the ensemble conditioned on the node-specific information. Further, we test the performance of our novel ranking procedure in terms of the reconstruction accuracy of the (unaccessible) network structure and show that it outperforms other popular centrality measures in identifying the "most informative" nodes. Finally, we discuss the socio-economic implications of network information asymmetry.