• Phone +44-020-7040-4575
  • Fax + 44-020-7040-8580

The study of socioeconomic networks is a burgeoning field in the physics and economics literature, with major progress having been attained over the last decade. Individuals and firms interact through networks to share information and resources, exchange goods and credit, make new friendships or partnerships etc. The structure of the network through which interactions take place may thus have an important effect on the success of the individual or the productivity of the firm. Furthermore, the network of interactions among socioeconomic agents plays an important role for the
stability and efficiency of socioeconomic systems. Theories
about how interaction networks form are thus essential for a deeper understanding of the development and organization of society as a whole.

We study a modified version of a model previously proposed by Jackson and Wolinsky to account for communicating information and allocating goods in socioeconomic networks. In the model, the utility function of each node is given by a weighted sum of contributions from all accessible nodes. The weights decrease with distance. We introduce a growth mechanism where new nodes attach to the existing network preferentially by utility. By increasing the weight of distant contribution, the network structure evolves from a power-law to an exponential degree distribution, passing through a regime characterised by shorter average path length, lower degree assortativity and higher central point dominance. We also compare different network structures in terms of the average utility received by each node. We show that power-law networks provide higher average utility than Poisson random networks. This provides a possible justification for the ubiquitousness of scale-free networks in the real world.

I used techniques from
random networks analysis to empirically analyse the
network of exchanges in the interbank market, in settlement systems and in the analysis of financial correlation matrices.