giorgio Fagiolo

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Istituto di Economia

Scuola Superiore Sant’Anna

Piazza Martiri della Libertà, 33

56127 PISA (Italy)

Email: giorgio.fagiolo at sssup.it

Follow me on Twitter @giorgiofagiolo

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Short Bio

I am Full Professor of Economics at the Institute of Economics, Sant’Anna School of Advanced Studies.

My research interests include agent-based computational economics; empirics and theory of economic networks; and the statistical properties of microeconomic and macroeconomic dynamics.

My papers have been published in: Science, J of Economic Geography, World Development, J of Applied Econometrics, PLoS ONE, J of Economic Dynamics & Control, Nature Scientific Reports, Environmental Research Letters, Physica A, New J of Physics, Physical Review E, J of Economic Behavior & Organization, Macroeconomic Dynamics, Industrial & Corporate Change, Advances in Complex Systems, Frontiers in Human Dynamics, Global Environmental Change, J of Evolutionary Economics, J of Economic Interaction & Coordination, European Physical J B, Network Science, Regional Studies, Empirical Economics, Knowledge Engineering Review, Applied Network Science, The J of International Trade & Economic Development, J of Artificial Societies and Social Simulations, Applied Economics Letters, Cybernetics and Systems, Economics Bulletin, Eastern European Economics.

Click here to download my CV.

Current Research

Cresti, L. , Dosi, G. and Fagiolo, G. (2022), "Technological interdependencies and employment changes in European industries", forthcoming in Structural Change & Economic Dynamics. A previous version is available as LEM Working Paper Series, No. 2022/05.

Ferraresi, T., Ghezzi, L., Vanni, F., Caiani, A., Guerini, M., Lamperti, F., Reissl, S., Fagiolo, G., Napoletano, M. and Roventini, A. (2021), "On the economic and health impact of the COVID-19 shock on Italian regions: A value chain approach", LEM Working Paper Series, No. 2021/10.

Zema, S.M., Fagiolo, G., Squartini, T. and Garlaschelli, D. (2021), "Mesoscopic Structure of the Stock Market and Portfolio Optimization", LEM Working Paper Series, No. 2021/45. Also available as arXiv preprint.

Fagiolo, G. and Rughi, T. (2021), "Exploring the Macroeconomic Drivers of International Bilateral-Remittance Flows: A Gravity-Model Approach", LEM Working Paper Series, No. 2021/12.

Esposito, C., Gortan, M., Testa, L., Chiaromonte, F., Fagiolo, G., Mina, A. and Rossetti, G. (2022), "Venture capital investments through the lens of network and functional data analysis", Applied Network Science, 7, 42.

Fagiolo, G. (2022), "On the Coevolution between Social Network Structure and Diffusion of the Coronavirus (COVID-19) in Spatial Compartmental Epidemic Models", Frontiers in Human Dynamics - Social Networks, 4 March 2022. A previous version is available (with a different title) as LEM Working Paper Series, No. 2020/27 and arXiv preprint.

Reissl, S. , Caiani, A., Lamperti, F., Guerini, M., Vanni, F., Fagiolo, G., Ferraresi, T., Ghezzi, L., Napoletano, M. and Roventini, A. (2022), "Assessing the Economic Effects of Lockdowns in Italy: A Dynamic Input-Output Approach", Industrial and Corporate Change, 31, 2: 358–409. A previous version is available as LEM Working Paper Series, No. 2021/03 and SSRN paper.

Esposito, C., Gortan, M., Testa, L., Chiaromonte, F., Fagiolo, G., Mina, A. and Rossetti, G. (2022) "Can You Always Reap What You Sow? Network and Functional Data Analysis of Venture Capital Investments in Health-Tech Companies". In: Benito, R.M., Cherifi, C., Cherifi, H., Moro, E., Rocha, L.M. and Sales-Pardo, M. (eds) Complex Networks & Their Applications X. COMPLEX NETWORKS 2021. Studies in Computational Intelligence, vol 1015. Springer, Cham.

Research topics

Agent-Based Computational Economics (ACE)

Agent-Based Computational Economics (ACE) is a relatively recent field of research that addresses the study of economic dynamics by means of bottom-up micro-macro models with boundedly-rational, heterogeneous, interacting agents, often labeled as Agent-Based Models (ABMs). See Leigh Tesfatsion's ACE Website for introductory materials, papers, people and software related to ACE.

Within this broad field, my research interests focus in particular on:

  1. Development and analysis of micro-macro ABMs of industry and market dynamics. Applications include models of growth, labor market dynamics, firm investment and business-cycle theory.

  2. Methodology of ABMS. A peculiar feature of ABMs is that, in general, they are not analytically solvable. This means that computer simulations are required in order to analyze ABMs' outcomes. My research here aims at designing protocols and procedures required to analyze the output of ABMs and empirically validate them.

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 international trade, international migration, food and sustainability, financial flows across world countries.

  3. Impact of network structure on economic behavior. Currently, most of my empirical research deals with understanding the impact that observed network structures (and the role that nodes play in real-world networks) may have on the evolution of non-network node properties. For example, I am interested in exploring the impact that the position of World countries in the macro-economic networks of trade, finance, migration, mobility, etc. may have on country income, productivity and growth.

  4. Diffusion of economic shocks in meso- and macro-economic networks. I am also interested in assessing the way in which economic shocks spread in observed networks connecting industries or World countries. Applications here include the effect of supply and demand shocks in international input-output networks and the economic and health impacts of the COVID-19 pandemic on Italian regions.

Industrial Dynamics

My research on industrial dynamics has been focusing so far on two main topics:

  1. Industrial agglomeration and spatial concentration. This project aims at explaining the observed across-industry heterogeneity of firm geographical concentration. We employ a simple stochastic model based on Polya-Urn schemes where firms repeatedly choose their location. Their choice is affected by both some measure of the comparative advantage of the location, and the number of firms that are present in each location. The model can be analytically solved and delivers testable implications about the location distributions of firms belonging to different sectors. Implications can be tested against country-based data. Work in progress include the analysis of alternative formulations of the model, as well as empirical applications, such testing the model against alternative available databases.

  2. The impact of financial structure on firm size and growth dynamics. Standard empirical investigations of the determinants of firm growth have typically tested Gibrat-like equations, where firm growth is related to its size and age. This project tries instead to explore the extent to which the financial structure of the firm (e.g., its liquidity constraints, its debt structure, etc.) also affect the growth process through investment decisions. To that end, we apply standard panel-data tools and distributional analyses to large firm-level databases recording for each firm its production, employment and financial profile.

Statistical Properties of Micro and Macro-Dynamics

The philosophy underlying this broad research topics is rooted in the idea that economics must be primarily an empirical science. Broadly speaking, one can devise two alternative approaches to empirical economics. The first one (the most influential nowadays) suggests to always start from theory and then check if the empirical implications of the model are confirmed by the data. The problem with this approach is that by theory we mainly mean neoclassical theory, whose main assumptions and building block (rationality, equilibrium, etc.) are hardly confirmed by empirical and experimental findings. The second approach, pioneered by Kaldor and nowadays revived in the econophysics literature, suggests instead to always start from the data and extract in the most agnostic way stylized facts which should then be replicated and explained by theoretical models. Here by stylized facts we broadly mean any statistical regularity that is sufficiently robust across time, geographical space, etc..

Within this framework, my research activity has focused on two specific topics:

  1. Statistical properties of the distributions of country-output growth rates. In this project we explore some distributional properties of aggregate output growth-rate time series. We show that, in the majority of OECD countries, output growth-rate distributions are well-approximated by symmetric exponential-power densities with tails much fatter than those of a Gaussian (but with finite moments of any order). Fat tails robustly emerge in output growth rates independently of: (i) the way we measure aggregate output; (ii) the family of densities employed in the estimation; (iii) the length of time lags used to compute growth rates. We also show that fat tails still characterize output growth-rate distributions even after one washes away outliers, autocorrelation and heteroscedasticity.

  2. Statistical properties of household consumption patterns. This project aims at studying consumption patterns by empirically investigating distributions of household consumption expenditure. We employ data from the Survey of Italian Households' Income and Wealth provided by the Bank of Italy and we try to characterize the shape of consumption, income and budget shares distributions and their time evolution.

Publications

Books

Delli Gatti, D., Fagiolo, G., Gallegati, M., Richiardi, M. and Russo, A. (2018), Agent-Based Models in Economics: A Toolkit, Cambridge University Press, ISBN: 9781108400046.

Journal Articles

  1. Reissl, S. , Caiani, A., Lamperti, F., Guerini, M., Vanni, F., Fagiolo, G., Ferraresi, T., Ghezzi, L., Napoletano, M. and Roventini, A. (2022), "Assessing the Economic Effects of Lockdowns in Italy: A Dynamic Input-Output Approach", Industrial and Corporate Change, 31, 2: 358–409.

  2. Esposito, C., Gortan, M., Testa, L., Chiaromonte, F., Fagiolo, G., Mina, A. and Rossetti, G. (2022), "Venture capital investments through the lens of network and functional data analysis", Applied Network Science, 7, 42.

  3. Fagiolo, G. (2022), "On the Coevolution between Social Network Structure and Diffusion of the Coronavirus (COVID-19) in Spatial Compartmental Epidemic Models", Frontiers in Human Dynamics - Social Networks, 4 March 2022, doi: 10.3389/fhumd.2022.825665.

  4. Campi, M., Duenas, M. and Fagiolo, G. (2021), "Specialization in food production affects global food security and food systems sustainability", World Development, 141: 105411.

  5. Campi, M., Duenas, M. and Fagiolo, G. (2020), "How do countries specialize in food production? A complex-network analysis of the global agricultural product space", Environmental Research Letters, Volume 15, Number 12 https://dx.doi.org/10.1088/1748-9326/abc2f6.

  6. Fagiolo, G., Giachini, D. and Roventini, A. (2020), “Innovation, finance, and economic growth: an agent-based approach”, Journal of Economic Interaction and Coordination, 15, 703–736.

  7. Bonaccorsi, G., Riccaboni, M., Fagiolo, G. and Santoni, G. (2019), "Country centrality in the international multiplex network", Applied Network Science, 4: 126.

  8. Campi, M., Duenas, M., Barigozzi M. and Fagiolo, G. (2019), “Intellectual property rights, imitation, and development. The effect on cross-border mergers and acquisitions”, The Journal of International Trade & Economic Development, 28:230-256

  9. Calastri, C., Borghesi, S. and Fagiolo, G. (2019), “How do people choose their commuting mode? An evolutionary approach to travel choices”, Economia Politica, 36: 887-912.

  10. Abbate, A., De Benedictis, L., Fagiolo, G. and Tajoli, L. (2018) “Distance-varying assortativity and clustering of the international trade network”, Network Science, 6: 517-544.

  11. Torreggiani, S., Mangioni, G., Puma, M. and Fagiolo, G. (2017), “Identifying the community structure of the world food-trade multi-layer network”, Environmental Research Letters, 13: 5.

  12. Duenas, M., Mastrandrea, R., Barigozzi, M. and Fagiolo, G. (2017), “Spatio-Temporal Patterns of the International Merger and Acquisition Network”, Scientific Reports 7, doi:10.1038/s41598-017-10779-z.

  13. Fagiolo, G. and Roventini, A. (2017), “Macroeconomic Policy in DSGE and Agent-Based Models Redux: New Developments and Challenges Ahead”, Journal of Artificial Societies and Social Simulations (JASSS), 20 (1) 1.

  14. Molinari, M., Giannangeli, S., and Fagiolo, G. (2016), “Financial Structure and Corporate Growth: Evidence from Italian Panel Data”, Economic Notes, 45: 303-325.

  15. Fagiolo, G. and Santoni, G. (2016), “Revisiting the role of migrant social networks as determinants of international migration flows”, Applied Economics Letters, 23: 188-193.

  16. Jacob Leal, S., Napoletano, M., Roventini, A., and Fagiolo G. (2016), "Rock Around the Clock: An Agent-Based Model of Low- and High-Frequency Trading", Journal of Evolutionary Economics, 26: 49-76.

  17. Mastrorillo, M., Licker, R., Bohra-Mishra, P., Fagiolo, G., Estes, L.D., and Oppenheimer, M. (2016), “The influence of climate variability on internal migration flows in South Africa”, Global Environmental Change, 39: 155-169.

  18. Ferraresi, T., Roventini, A. and Fagiolo G. (2015), “Fiscal Policies and Credit Regimes: A TVAR Approach”, Journal of Applied Econometrics, 30: 1047-1072.

  19. Fagiolo, G. and Santoni, G. (2015), “Human-Mobility Networks, Country Income, and Labor Productivity”, Network Science, 3: 377-407.

  20. Mastrorillo, M. and Fagiolo, G. (2015), “International Migration and School Enrollment of the Left-Behinds in Albania: A Note”, Eastern European Economics, 53: 242-254.

  21. Ascari, G., Fagiolo, G., Roventini, A. (2015), “A Note on Fat Tail Distributions and Business Cycle Models”, Macroeconomic Dynamics, 19: 465-476.

  22. Dosi, G. Fagiolo, G, Napoletano, M. Roventini, A. and Treibich, T. (2015), “Fiscal and Monetary Policies in Complex Evolving Economies”, Journal of Economic Dynamics and Control, 52: 166-189.

  23. Alatriste Contreras, M.G. and Fagiolo, G. (2014), “Propagation of Economic Shocks in Input-Output Networks: A Cross-Country Analysis, Physical Review E, 90: 062812. Article covered as: “Focus: Heavily Interconnected Economies Are Vulnerable to Shocks” by Tamela Maciel, Physics 7, 130 (2014).

  24. Mastrandrea, R., Squartini, T., Fagiolo, G., Garlaschelli, D. (2014), “Reconstructing the world trade multiplex: The role of intensive and extensive biases”, Physical Review E, 90, 062804.

  25. Duenas, Marco and Fagiolo, Giorgio (2014), “Global Trade Imbalances: A Network Approach”, Advances in Complex Systems, 17: 1450014 (29 pages).

  26. Fagiolo, G. and Mastrorillo, M. (2014) “Does Human Migration Affect International Trade? A Complex-Network Perspective”, PLoS ONE 9(5): e97331.

  27. Mastrandrea, R., Squartini, T., Fagiolo, G. and Garlaschelli D. (2014), “Enhanced network reconstruction from irreducible local information”, New Journal of Physics, 16: 043022.

  28. Vitali, S., Napoletano, M. and Fagiolo, G. (2013) "Spatial Localization in Manufacturing: A Cross-Country Analysis", Regional Studies, 47:1534-1554.

  29. Fagiolo, G. and Mastrorillo, M. (2013), “International migration network: Topology and modeling”, Physical Review E, 88, 012812.

  30. Dosi, G., Fagiolo, G., Napoletano, M. and Roventini, A. (2013), “Income Distribution, Credit and Fiscal Policies in an Agent-Based Keynesian Model”, Journal of Economic Dynamics and Control, 37: 1598-1625.

  31. Chinazzi, M., Fagiolo, G., Reyes, J. and Schiavo, S. (2013), “Post-Mortem Examination of the International Financial Network”, Journal of Economic Dynamics and Control, 37: 1692-1713.

  32. M.D. Gerst, P. Wanga, A. Roventini, G. Fagiolo, G. Dosi, R.B. Howarth, M.E. Borsuk (2013), “Agent-based modeling of climate policy: An introduction to the ENGAGE multi-level model framework”, Environmental Modeling and Software, 44: 62-75.

  33. Duenas, M. and Fagiolo, G. (2013), “Modeling the International-Trade Network: A Gravity Approach”, Journal of Economic Interaction and Coordination, 8: 155-178.

  34. Squartini,T., Fagiolo, G. and Garlaschelli, D. (2013), “Null Models of Economic Networks: The Case of the World Trade Web”, Journal of Economic Interaction and Coordination, 8: 75-107.

  35. Fagiolo, G. and Roventini, A. (2012), " Macroeconomic policy in DSGE and agent-based models”, Revue de l’OFCE, 124: 67-116. [Get a PDF version]

  36. Napoletano, M., Dosi G., Fagiolo, G. and Roventini, A. (2012), “Wage Formation, Investment Behavior and Growth Regimes: An Agent-Based Analysis”, Revue de l’OFCE, 124: 235-262. [Get a PDF version]

  37. Fagiolo, G. and Roventini, A. (2012), "On the Scientific Status of Economic Policy: A Tale of Alternative Paradigms", Knowledge Engineering Review, 27: 163-185.

  38. Barigozzi, M., Alessi, L., Capasso, M. and Fagiolo, G. (2012), "The Distribution of Consumption-Expenditure Budget Shares. Evidence from Italian Households", Structural Change and Economic Dynamics, 23: 69–91.

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

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

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

  42. Dosi, G., Fagiolo, G. and Roventini, A. (2010), "Schumpeter Meeting Keynes: A Policy-Friendly Model of Endogenous Growth and Business Cycles", Journal of Economics Dynamics and Control, 34: 1748–1767.

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

  44. 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.

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

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

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

  48. Fagiolo, G., Alessi, L., Barigozzi, M. and Capasso, M. (2010), "On the distributional properties of household consumption expenditures. The case of Italy", Empirical Economics, 38:717-741.

  49. Fagiolo, G., Napoletano, M., Roventini, A. and Piazza, M. (2009), "Detrending and the Distributional Properties of U.S. Output Time Series", Economics Bulletin, 29, 4.

  50. 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.

  51. 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.

  52. Fagiolo, G. and Roventini, A. (2009), "On the Scientific Status of Economic Policy: A Tale of Alternative Paradigms", published in Russian as "O nauchnom statuse economicheskoy politiki: povest' ob al'ternativnykh paradigmakh", Voprosy Economiki, 6:24.

  53. Capasso, M., Alessi, L., Barigozzi, M. and Fagiolo, G. (2009), "On approximating the distributions of goodness-of-fit test statistics based on the empirical distribution function: The case of unknown parameters", Advances in Complex Systems, 12: 157–167.

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

  55. 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.

  56. Fagiolo, G., Napoletano, M. and Roventini, A. (2008), "Are Output Growth-Rate Distributions Fat-Tailed? Some Evidence from OECD Countries", Journal of Applied Econometrics, 23: 639-669.

  57. Dosi, G., Fagiolo, G. and Roventini, A. (2008), "The Microfoundations of Business Cycles: An Evolutionary, Multi-Agent Model", Journal of Evolutionary Economics, 18: 413-432.

  58. Bottazzi, G., Dosi, G., Fagiolo, G. and Secchi, A. (2008), "Sectoral and Geographical Specificities in the Spatial Structure of Economic Activities", Structural Change and Economic Dynamics, 19: 189-202.

  59. 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.

  60. Bottazzi, G., Dosi, G., Fagiolo, G. and Secchi, A. (2007), "Modeling Industrial Evolution in Geographical Space", Journal of Economic Geography, 7: 651-672.

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

  62. Dosi, G., Fagiolo, G. and Roventini, A. (2007), "Lumpy investment and endogenous business cycles in an evolutionary multi-agent model", Cybernetics and Systems, 38: 631-666.

  63. Fagiolo, G., Moneta, A. and Windrum, P. (2007), "A Critical Guide to Empirical Validation of Agent-Based Models in Economics: Methodologies, Procedures, and Open Problems", Computational Economics, 30:195-226.

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

  65. Fagiolo, G., Napoletano, M. and Roventini, A. (2007), "How Do Output Growth Rate Distributions Look Like? Some Time-Series Evidence on OECD Countries", European Physical Journal B, 57: 205-211.

  66. Windrum, P., Fagiolo, G. and Moneta, A. (2007), "Empirical Validation of Agent-Based Models: Alternatives and Prospects", Journal of Artificial Societies and Social Simulation, 10, 2, available at: http://jasss.soc.surrey.ac.uk/10/2/8.html .

  67. 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.

  68. Fagiolo, G. and Luzzi, A. (2006), "Do Liquidity Constraints Matter in Explaining Firm Size and Growth? Some Preliminary Evidence on the Italian Manufacturing Industry", Industrial and Corporate Change, 15, 1: 1-39.

  69. Dosi, G., Fagiolo, G. and Roventini, A. (2006), "An Evolutionary Model of Endogenous Business Cycles", Computational Economics, 27, 1: 3-34.

  70. Fagiolo, G. (2005), "A Note on Equilibrium Selection in Polya-Urn Coordination Games", Economics Bulletin, 3, 45: 1-14.

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

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

  73. 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.

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

  75. Fagiolo, G., Dosi, G. and Gabriele, R. (2004), "Matching, Bargaining, and Wage Setting in an Evolutionary Model of Labor Market and Output Dynamics", Advances in Complex Systems, 14: 237-273.

  76. Fagiolo, G. and Dosi, G. (2003), "Exploitation, Exploration and Innovation in a Model of Endogenous Growth with Locally Interacting Agents", Structural Change and Economic Dynamics, 14: 237-273.

  77. Aversi, R., Dosi, G., Fagiolo, G., Meacci, M. and Olivetti, C. (1999), "Demand Dynamics with Socially Evolving Preferences", Industrial and Corporate Change, 8, 2: 353-408.

Book Chapters

  1. Esposito, C., Gortan, M., Testa, L., Chiaromonte, F., Fagiolo, G., Mina, A. and Rossetti, G. (2022) "Can You Always Reap What You Sow? Network and Functional Data Analysis of Venture Capital Investments in Health-Tech Companies". In: Benito, R.M., Cherifi, C., Cherifi, H., Moro, E., Rocha, L.M. and Sales-Pardo, M. (eds) Complex Networks & Their Applications X. COMPLEX NETWORKS 2021. Studies in Computational Intelligence, vol 1015. Springer, Cham.

  2. Fagiolo G., Guerini M., Lamperti F., Moneta A. and Roventini A. (2019), “Validation of Agent-Based Models in Economics and Finance”. In: Beisbart, C. and Saam, N. (Eds) Computer Simulation Validation. Simulation Foundations, Methods and Applications. Springer, Cham.

  3. Fagiolo, G. (2017), “The International Trade Network: Empirics and Modeling”, in Jennifer Nicoll Victor, Alexander H. Montgomery, and Mark Lubell (Eds.), The Oxford Handbook of Political Networks, Oxford University Press, Oxford.

  4. Fagiolo, G. (2016), “The Empirics of Macroeconomic Networks: A Critical Review”, in P. Commendatore, M. Matilla-García, L. M. Varela and J.S. Cánovas (Eds.), Complex Networks and Nonlinear Dynamics: Social and Economic Interactions, Springer, Berlin.

  5. Fagiolo, G. and Mastrorillo, M., “Migration and Trade: A Complex-Network Approach” IEEE Computer Society, 2013 International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), Kyoto, Japan, 2-5 Dec. 2013, pp. 538 - 545, DOI: 10.1109/SITIS.2013.90.

  6. Dosi, G., Fagiolo, G., Napoletano, M. and Roventini, A. (2012), “Economic policies with endogenous innovation and Keynesian demand management”, in: Solow, R.M. and Touffut, J.-P. (2012), What’s Right with Macroeconomics, Edward Elgar, Cheltenham, U.K.

  7. Fagiolo, G., Reyes, J. and Schiavo, S. (2009), "Dynamics and Evolution of the International Trade Network", in: Fortunato, S., Mangioni, G., Mezenes, R. and Nicosia, V. (Eds.), Complex Networks, Studies in Computational Intelligence, Springer-Verlag Berlin Heidelberg, Vol. 207, pp. 1-14.

  8. Fagiolo, G., Valente, M. and Vriend, N.J. (2008), "A Dynamic Model of Segregation in Small-World Networks", 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.

  9. Fagiolo, G., Moneta, A. and Windrum, P. (2006), "Confronting Agent-Based Models with Data: Methodological Issues and Open Problems", in Bruun, C. (Ed.), Advances in Artificial Economics. The Economy as a Complex Dynamic System, Lecture Notes in Economics and Mathematical Systems, Springer-Verlag Berlin Heidelberg, Vol. 584, pp. 255-267.

  10. Dosi, G., Fagiolo, G., Roventini, A. (2006), "Lumpy Investment and Endogenous Business Cycles" in Trappl, R. (ed.), Cybernetics and Systems 2006, Vienna, Austrian Society for Cybernetic Studies.

  11. Bottazzi, G., Dosi, G. and Fagiolo, G. (2006), "On Sectoral Specificities in the Geography of Corporate Location", in Breschi, S. and Malerba, F. (Eds.), Clusters, networks and innovation, Oxford, U.K., Oxford University Press.

  12. Pyka, A. and Fagiolo, G. (2005), "Agent-Based Modelling: A Methodology for Neo-Schumpeterian Economics". In: Hanusch, H. and Pyka, A. (Eds.), The Elgar Companion to Neo-Schumpeterian Economics, Edward Elgar, Cheltenham.

  13. Dosi, G., Marengo, L. and Fagiolo, G. (2005), "Learning in Evolutionary Environments", in Dopfer, K. (Ed.), Evolutionary Principles of Economics, Cambridge, Cambridge University Press.

  14. Fagiolo, G., Dosi, G. and Gabriele, R. (2004), "Matching, Bargaining, and Wage Setting in an Evolutionary Model of Labor Market and Output Dynamics", in: Leombruni, R. and Richiardi, M. (Eds.), Industry and Labor Dynamics: The Agent-based Computational Economics Approach, Singapore, World Scientific Press.

  15. Fagiolo, G., Marengo, L. and Valente, M. (2004), "Population Learning in Random Games with Endogenous Network Formation", in Lux, T., Reitz, S.and Samanidou, E. (Eds.), Nonlinear Dynamics and Heterogenous Interacting Agents, Lecture Notes in Economics and Mathematical Systems, Berlin, Springer.

  16. Fagiolo, G., Dosi, G. and Gabriele, R. (2004), "Towards an evolutionary interpretation of aggregate labor market regularities", in: Cantner, U., Dinopoulos, E. and Lanzillotti, R.F. (Eds.), Entrepreneurship, the New Economy and Public Policy: Schumpeterian Perspectives, Berlin - Heidelberg, Springer Verlag.

  17. Bottazzi, G., Dosi, G. and Fagiolo, G. (2001), "On the Ubiquitous Nature of the Agglomeration Economies and their Diverse Determinants: Some Notes", in Quadrio Curzio, A. and Fortis, M. (Eds.), Complexity and Industrial Clusters: Dynamics and Models in Theory and Practice, Heidelberg, Physica-Verlag, 2002, p.167-191.

  18. Dosi, G., Fagiolo, G. and Marengo, L. (2000), "On the dynamics of Cognition and Actions. An Assessment of some Models of Learning and Evolution", in Pagano, U. and Nicita, A. (Eds.), The Evolution of Economic Diversity, Papers and Proceedings of the 10th International School of Economic Research on Evolution and Economics, Siena (Italy), June 27 - July 5, Routledge.

  19. Dosi, G. and Fagiolo, G.. (1998), "Exploring the unknown. On entrepreneurship, coordination and innovation-driven growth", in Lesourne, J. and Orléan, A. (Eds.), Advances in Self-Organizationand Evolutionary Economics, Paris, Economica. Also in: Dosi, G. (2000), Innovation, Organization and Economic Dynamics: Selected Essays, Cheltenham, Edward Elgar Publishing.

  20. Dosi, G., Fagiolo, G., Aversi, R., Meacci, M. and Olivetti, C. (1999), "Cognitive Processes, Social Adaptation and Innovation in Consumption Patterns: from Stylized Facts to Demand Theory", in Dow, S.C and Earl, P.E. (Eds.), Economic Organizations and Economic Knowledge: Essays in Honour of Brian Loasby, Cheltenham, Edward Elgar.

  21. Fagiolo, G. (1998), "Spatial Interactions in Dynamic Decentralised Economies",in Cohendet, P. , Llerena, P., Stahn, H. and Umbhauer, G. (Eds.), The Economics of Networks: Interaction and Behaviours, Berlin - Heidelberg, Springer Verlag.

Special Issues Edited

  1. Dawid, H. and Fagiolo, G. (Eds.), Special Issue on "Agent-Based Models for Economic Policy Design", Journal of Economic Behavior and Organization, 2008, Volume 67, Issue 2.

  2. Fagiolo, G., Birchenhall, C. and Windrum, P. (Eds.), Special Issue on "Empirical Validation in Agent-Based Models", Computational Economics, 2007, Volume 30, Issue 3.

Teaching

Agent-Based Computational Economics

This course is intended to serve as a broad introduction to the huge literature using agent-based computational approaches to the study of economic dynamics. It is organized in three parts. The first one (“Why?”) will discuss the roots of the critiques to the mainstream paradigm from a methodological, empirical and experimental perspective. We shall briefly review the building blocks of mainstream models (rationality, equilibrium, interactions, etc.) and shortly present some of the evidence coming from cognitive psychology and experimental economics, network theory and empirical studies, supporting the idea that bounded rationality, non-trivial interactions, non-equilibrium dynamics, heterogeneity, etc. are irreducible features of modern economies. In the second part (“What?”) we shall discuss what ACE is and what are its main tools of analysis. We will define an ABM and present many examples of classes of ABMS, from the simplest (cellular automata, evolutionary games) to the most complicated ones (micro-founded macro models).The third part (“How?”) aims at understanding how ABMs can be designed, implemented and statistically analyzed. We shall briefly present the basics of programming, by both discussing the pros and cons of using simulation platforms (Matlab, NetLogo, Swarm, LSD, etc.) vs. computer languages (Java, C++, etc.) and providing some simple “hands-on” applications to cellular automata. Finally, we will see how the outputs of ABMs simulation should be treated from a statistical point of view (e.g., Montecarlo techniques) and we will discuss two hot topics in ABM research: empirical validation and policy analysis.

Economic Networks: Theory and Empirics

This course introduces the “science of networks” for economists. The first part of the course discusses examples of real-world networks in hard and social sciences. We ask why networks are important for economists and what are the main network-related questions as far as models and empirical analyses are concerned. We then present more formally graph theory and explore network statistics. We finally move to models of network formation and present some relevant applications to economics (e.g. trade networks).

International trade, human mobility, and finance: Empirical Modeling

This course is an introduction to the theory and empirics of gravity models. We will start describing stylized facts in international trade data. Then, we will introduce the empirical gravity model of trade and we will explore its theoretical foundations. Next, we will go through issues on estimation with the help of empirical applications. Finally, we will see how the gravity model can be applied to international finance, human migration and temporary mobility. Advanced topics discussed in the course cover spatial econometrics techniques in panel data and gravity model estimation, multilateral resistance and the econometrics of networks.

Partial and General Equilibrium Theory

This course is an introduction to the neoclassical theory of competitive markets. We will cover issues about existence, uniqueness, and stability of competitive equilibria, as well as their efficiency properties, in both partial and general equilibrium settings. Furthermore, we will discuss market-failure issues in presence of externalities and public goods.

Last Update: March 2022