Research Topics


Agent-based Computational Economics

Networks: Models and Empirics

Industrial Dynamics

Statistical Properties of Micro and Macro-Dynamics

Giorgio Fagiolo

Giorgio Fagiolo

Welcome

Teaching

Publications

Slides

Networks: Models and Empirics


In the last years, an exploding body of literature has put forth the idea that all types of interactions taking place among economic agents can be fruitfully studied in the framework of network theory. A network is a collection of nodes and links. In this metaphor, nodes play the role of economic agents, whereas a link between any two nodes represents the existence (and possibly the direction and intensity) of the interaction going on between these two nodes. Interactions here refer to both material and immaterial exchanges: trade, knowledge spillovers, externalities of any kind, imitation, etc. are all examples of a direct interaction. Standard economics has largely neglected the study of direct interactions. Theoretical models have either assumed that no direct interactions take place (think e.g. to general equilibrium theory) or that all agents interact with anyone else (as happens in game theory). Similarly, empirical analyses did not address the study of topological properties of networks existing among firms, consumers, or countries. In recent years, on the contrary, theoretical and empirical studies have shown that the properties of interaction microeconomic networks are crucial to understand macro-economic dynamics.


My research on networks focuses on both theoretical and empirical questions:

  1. •Theory. I am interested in game-theoretic models where agents are placed on networks, play games with their neighbors, and are able to endogenously modify the links they maintain through time in response of the payoffs they get. In these models, agents are not only able to decide the strategy to play in the game but also to choose whom to play with. The main goal is to understand how strategies and networks co-evolve and affect the final (equilibrium) outcome of the game.

  2. •Empirics. More recently, I became interested in the empirical analysis of real-world networks. From a methodological point of view, my research here focuses on developing tools for the analysis of weighted, directed networks, i.e. networks where links are directed and heterogeneous in the intensity of the interaction they carry. Applications include the international trade network and the network of financial flows across world countries.


Selected Publications


  1. •Squartini,T., Fagiolo, G. and Garlaschelli, D. (2011), “Randomizing World Trade. Part I: A Binary Network Analysis”, Physical Review E, 84, 046117.

  2. •Squartini,T., Fagiolo, G. and Garlaschelli, D. (2011), “Randomizing World Trade. Part II: A Weighted Network Analysis”, Physical Review E, 84, 046118 .

  3. •Barigozzi, M., Fagiolo, G. and Mangioni, G. (2011), “Identifying the Community Structure of the International-Trade Multi Network”, Physica A, 390: 2051–2066.

  4. •Fagiolo, G., Reyes, J. and Schiavo, S. (2010), "The Evolution of the World Trade Web", Journal of Evolutionary Economics, 20: 479-514.

  5. •Reyes, J., Schiavo, S. and Fagiolo, G. (2010), "Using Complex Networks Analysis to Assess the Evolution of International Economic Integration: the Cases of East Asia and Latin America, Journal of International Trade and Economic Development, 19: 215-239.

  6. •Barigozzi, M., Fagiolo, G. and Garlaschelli, D. (2010), "The Multi-Network of International Trade: A Commodity-Specific Analysis", Physical Review E, 81, 046104.

  7. •Fagiolo, G. (2010), "The International-Trade Network: Gravity Equations and Topological Properties", Journal of Economic Interaction and Coordination, 5:1-25.

  8. •Schiavo, S., Reyes, J. and Fagiolo, G. (2010), "International Trade and Financial Integration: A Weighted Network Analysis", Quantitative Finance, 10: 389–399.

  9. •Schweitzer, F., Fagiolo, G., Sornette, D., Vega-Redondo, F., Vespignani, A., White, D.R. (2009), "Economic Networks: The New Challenges", Science, 24 July 2009, Vol. 325, No. 5939, pp. 422-425.

  10. •Schweitzer, F., Fagiolo, G., Sornette, D., Vega-Redondo, F., White, D.R. (2009), "Economic Networks: What do we know and what do we need to know?", Advances in Complex Systems, 12:407-4022.

  11. •Fagiolo, G., Reyes, J. and Schiavo, S. (2009), "The World-Trade Web: Topological Properties, Dynamics, and Evolution", Physical Review E, 79, 036115 (19 pages).

  12. •Fagiolo, G., Valente, M. and Vriend, N.J. (2008), "A Dynamic Model of Segregation in Small-World Networks", forthcoming in: Naimzada, A.K., Stefani, S. and Torriero, A. (Eds.), Networks, Topology, and Dynamics. Theory and Applications to Economic and Social Systems, Lecture Notes in Economics and Mathematical Systems, Springer-Verlag Berlin Heidelberg, Vol. 613, pp. 109-124.

  13. •Reyes, J., Schiavo, S. and Fagiolo, G. (2008), "Assessing the evolution of international economic integration using random-walk betweenness centrality: The cases of East Asia and Latin America", Advances in Complex Systems, 11: 685–702.

  14. •Fagiolo, G., Reyes, J. and Schiavo, S. (2008), "On the Topological Properties of the World Trade Web: A Weighted Network Analysis", Physica A, 387: 3868–3873.

  15. •Fagiolo, G., Valente, M. and Vriend, N. (2007), "Segregation in Networks", Journal of Economic Behavior and Organization, 64: 316-336.

  16. •Fagiolo, G. (2007), "Clustering in Complex Directed Networks", Physical Review E, 76: 026107 (8 pages).

  17. •Fagiolo, G. (2006) "Directed or Undirected? A New Index to Check for Directionality of Relations in Socio-Economic Networks", Economics Bulletin, 3, 34: 1-12.

  18. •Fagiolo, G. and Valente, M. (2005), "Minority Games, Local Interactions, and Endogenous Networks", Computational Economics, 25: 41-57.

  19. •Fagiolo, G. (2005), "Endogenous Neighborhood Formation in a Local Coordination Model with Negative Network Externalities", Journal of Economic Dynamics and Control, 29: 297-319.

  20. •Fagiolo, G., Marengo, L. and Valente, M. (2004), "Population Learning in a Model with Random Payoff Landscapes and Endogenous Networks", Computational Economics, 24: 383-408.

  21. •Fagiolo, G., Marengo, L. and Valente, M. (2004), "Endogenous Networks in Random Population Games", Mathematical Population Studies, 11: 121-147.