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books and book chapters


  1. Delli Gatti D, Gallegati, Fagiolo G, Richiardi M, Russo A (2018). Agent-based Models in Economics: A Toolkit. Cambridge University Press.
  2. Neugart M, Richiardi M (2018). Agent-based models of the labor market. In: Chen S-H, Kaboudan M, Du Y-R (eds.). Handbook on Computational Economics and Finance, Oxford University Press, Oxford.
  3. Richiardi M, Richardson R. (2016). Agent-based Computational Demography and Beyond using JAS-mine. In: Grow A, van Bavel J. Agent-Based Modeling in Population Studies. Springer Series on Demographic Methods and Population Analysis. Springer.
  4. Berton F, Richiardi M, Sacchi S (2015). Non-standard work, low-paid work and employment dynamics in Italy: evidence from an occupational perspective. In: Eichhorst W, Marx P. (eds). Non-Standard Employment in Post-Industrial Labour Markets. Edward Elgar, Cheltenham, UK and Northampton, MA, USA.
  5. Richiardi M (2013). The missing link: AB models and dynamic microsimulation. In: Leitner S, Wall, F (eds). Artificial Economics and Self Organization. Agent-Based Approaches to Economics and Social Systems. Springer, Lecture Notes in Economics and Mathematical Systems, vol. 669, Berlin.
  6. Berton F, Richiardi M, Sacchi S (2012). The political economy of work security and flexibility: Italy in comparative perspective. Policy Press, Bristol.
  7. Grazzini J, Richiardi M, Sella L (2012). Small sample bias in MSM estimation of agent-based models. In: Teglio A, Alfarano S, Camacho-Cuena E, Ginés-Vilar M (eds). Managing Market Complexity. The Approach of Artificial Economics. Springer, Lecture Notes in Economics and Mathematical Systems, vol. 662, Berlin.
  8. Presbitero A, Amighini A, Richiardi M (2010). Delocalizzazione produttiva e mix occupazionale. In: Zazzaro A (ed.). Reti d'imprese e territorio. Il Mulino, Bologna.
  9. Berton F, Richiardi M, Sacchi S (2009). Flex-insecurity. Perché in Italia la flessibilità diventa precarietà. Il Mulino, Studi e Ricerche, Bologna.
  10. Berton F, Richiardi M, Sacchi S (2009). L’indennità di terminazione: una proposta pratica contro la precarietà. In: Dell'Aringa C, Treu T (eds.). Le riforme che mancano. Il Mulino, AREL, Bologna.
  11. Gallegati M, Richiardi M (2009). Agent-based Modelling in Economics and Complexity. In: Meyer B (ed.). Encyclopedia of Complexity and System Science, Springer, New York, pp. 200-224.
  12. Richiardi M, Leombruni R. eds. (2004), Industry and Labor Dynamics: The Agent-based Computational Economics Approach. Proceeding of the Wild@Ace 2003 conference, World Scientific Press, Singapore.
  13. Richiardi M, Fazio L (2004), UrbanSim: Microsimulazione urbana a Torino. In: Russo G, Terna P (eds.), I numeri per Torino, Otto Editore, Torino.

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


In contrast to mainstream economics, complexity theory conceives the economy as a complex system of heterogeneous interacting agents characterised by limited information and bounded rationality. Agent Based Models (ABMs) are the analytical and computational tools developed by the proponents of this emerging methodology. Aimed at students and scholars of contemporary economics, this book includes a comprehensive toolkit for agent-based computational economics, now quickly becoming the new way to study evolving economic systems. Leading scholars in the field explain how ABMs can be applied fruitfully to many real-world economic examples and represent a great advancement over mainstream approaches. The essays discuss the methodological bases of agent-based approaches and demonstrate step-by-step how to build, simulate and analyse ABMs and how to validate their outputs empirically using the data. They also present a wide set of applications of these models to key economic topics, including the business cycle, labour markets, and economic growth.

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Neugart M, Richiardi M. (2018). Agent-based Models of the Labor Market. In: Chen S-H, Kaboudan M, Du Y-R (eds.). Handbook on Computational Economics and Finance, Oxford University Press, Oxford.

Abstract: We review the literature on agent-based labor market models by tracing its roots to the
microsimulation literature, and surveying a selection of contributions made since the work by
Bergmann (1974) and Eliasson (1976). Agent-based models have been applied to explain stylized
facts of labor markets as well as for labor market policy evaluations. They also constitute a major
part in agent-based macroeconomic models. Besides reviewing the various results achieved, we
discuss modeling choices with respect to agents’ behavior and the structure of interaction. Our
overall assessment is that agent-based labor market models have given us valuable insights into the
functioning of labor markets and the consequences of labor market policies, and that they will
increasingly become an essential tool of analysis, in particular when the construction of large
macro-models is involved.

Working paper version: Neugart M, Richiardi M. (2012). Agent-based Models of the Labor Market. LABORatorio Revelli WP 125/2012.

[ read ]

Richiardi M, Richardson R. (2016). Agent-based Computational Demography and Beyond using JAS. In: Grow A, van Bavel J. Agent-Based Modeling in Population Studies. Springer Series on Demographic Methods and Population Analysis.

Abstract: In this chapter we provide a hands-on guide on how to build a microsimulation using JAS, a Java-based platform that provides unique simulation tools for discrete-event simulations, including both agent-based and microsimulation models. After presenting the rationale for the recent developments of the JAS project and the main architectural choices made, we illustrate a step-by-step implementation of a rich dynamic microsimulation, which includes demographic processes (birth, death, household formation and dissolution) and other life course events (educational choices, labour market participation and employment outcomes).

[ read ]

Berton F, Richiardi M, Sacchi S (2015). Non-standard work, low-paid work and employment dynamics in Italy: evidence from an occupational perspective. In: Eichhorst W, Marx P. (eds). Non-Standard Employment in Post-Industrial Labour Markets. Edward Elgar, Cheltenham, UK and Northampton, MA, USA.

[ read ]

Richiardi M (2013). The missing link: AB models and dynamic microsimulation. In: Leitner S., Wall, F. (eds). Artificial Economics and Self Organization. Springer, Lecture Notes in Economics and Mathematical Systems, vol. 669, Berlin

Abstract: In this note I pay tribute to two early works by Barbara Bergmann and Gunnar Eliasson which, though firmly grounded in the dynamic microsimulation literature, can be considered as the first examples of large-scale agent-based models. These attempts at building complete micro-to-macro computational models of the economy are important not only in a history of economic thought perspective, but also to encourage convergence of the two approaches in developing credible alternatives to DSGE models.

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Grazzini J, Richiardi M, Sella L (2012). Small sample bias in MSM estimation of agent-based models. In: Teglio A., Alfarano S., Camacho-Cuena E., Ginés-Vilar M. (eds). Managing Market Complexity. The Approach of Artificial Economics. Springer, Lecture Notes in Economics and Mathematical Systems, vol. 662, Berlin

Abstract: Starting from an agent-based interpretation of the well-known Bass innovation diffusion model, we perform a Montecarlo analysis of the performance of a method of simulated moment (MSM) estimator. We show that nonlinearities of the moments lead to a small bias in the estimates in small populations, and prove that our estimates are consistent and converge to the true values as population size increases. Our approach can be generalized to the estimation of more complex agent-based models. 

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Berton F, Richiardi M, Sacchi S (2012). The political economy of work security and flexibility: Italy in comparative perspective. Policy Press, Bristol



The recent economic crisis has shown us the dark side of deregulating the labor market: rising unemployment, limited access to social security and, due to low wages, depleted savings to rely upon in bad times. An emphasis on flexibility has led to inequality and insecurity, Fabio Berton, Matteo Richiardi, and Stefano Sacchi reveal in this compelling study of Italy’s embrace of nonstandard work contracts. Analyzing outcomes at the individual level, the authors contrast Italy with Germany, Spain, and Japan as they demonstrate the social and economic fallout of pursuing flexibility merely as a tool to reduce costs.

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Presbitero A, Amighini A, Richiardi M (2010). Delocalizzazione produttiva e mix occupazionale. In: Zazzaro A. (ed.). Reti d'imprese e territorio. Il Mulino, Bologna

Working paper versions:

  • Presbitero A., Amighini A., Richiardi M. (2010). Delocalizzazione produttiva e mix occupazionale. LABORatorio Revelli WP 104/2010
  • Presbitero A., Amighini A., Richiardi M. (2010). Delocalizzazione produttiva e mix occupazionale. MOFIR WP n. 42
[ read ]

Berton F, Richiardi M, Sacchi S (2009). Flex-insecurity. Perché in Italia la flessibilità diventa precarietà. Il Mulino, Studi e Ricerche, Bologna



Da dove viene la precarietà? Dal crollo di tre argini: continuità dell'impiego, salari adeguati, un welfare ben funzionante. Tre fattori sulla base dei quali è possibile stabilire, come fa questo volume, quanti e chi sono i precari in Italia. Scopriamo così che esistono molti lavoratori "tipici" (a tempo pieno e indeterminato) precari, accanto ad alcuni "atipici" niente affatto precari. Ma soprattutto scopriamo che molto spesso la flessibilità - per il modo in cui è stata introdotta - porta alla precarietà. Dove e come intervenire, dunque, in un momento in cui la recessione incalzante minaccia di far deflagrare il fenomeno? Gli autori suggeriscono di aggredire tempestivamente tutte e tre le questioni - carriere, salari, welfare - e avanzano, quantificandone i costi, concrete proposte di riforma: contribuzione unica, salario minimo, indennità di terminazione e, in particolare, una riforma degli ammortizzatori sociali che li renda universalmente accessibili, trasformandoli da privilegio di pochi in diritto di tutti.

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Berton F, Richiardi M, Sacchi S (2009). L’indennità di terminazione: una proposta pratica contro la precarietà. In: Dell'Aringa C., Treu T. (eds.). Le riforme che mancano. Il Mulino, AREL, Bologna

Abstract: La flessibilità del lavoro non conduce necessariamente alla precarietà dei lavoratori. Quest’ultima dipende dalla capacità di un lavoratore – occupato o disoccupato, tipico o atipico – di provvedere al proprio sostentamento attraverso la partecipazione al mercato del lavoro oppure attraverso la fruizione delle prestazioni di mantenimento del reddito quando il lavoro viene a mancare. Per valutare la precarietà occorre quindi considerare la continuità occupazionale, il livello delle retribuzioni, l’accesso agli schemi di mantenimento del reddito in caso di non lavoro. Allo stesso modo, per combattere la precarietà (che, è opportuno dirlo, si associa empiricamente ai contratti atipici più che a quelli tipici) occorrono interventi indirizzati a ciascuna di tali tre determinanti: carriere, salari e protezione sociale. In questo saggio intendiamo illustrare la situazione del mercato del lavoro italiano e come nel nostro paese la flessibilità diventi precarietà, mostrare come dovrebbero (e, soprattutto, come non dovrebbero) essere congegnati degli interventi di riforma volti a fornire un pavimento di garanzie per tutti i lavoratori, per poi avanzare una proposta pratica che potrebbe contribuire a ridurre la segmentazione del nostro mercato del lavoro e a combattere la precarietà in Italia: l’introduzione di un’indennità di terminazione.


Gallegati M, Richiardi M (2009). Agent-based Modelling in Economics and Complexity. In: Meyer B. (ed.). Encyclopedia of Complexity and System Science, Springer, New York, pp. 200-224

Abstract: A crucial aspect of the complexity approach is how interacting elements produce aggregate patterns that those elements in turn react to. This leads to the emergence of aggregate properties and structures that cannot be guessed by looking only at individual behaviour. It has been argued (Saari, 1995) that complexity is ubiquitous in economic problems (although this is rarely acknowledged in economic modelling), since (i) the economy is inherently characterized by the interaction of individuals, and (ii) these individuals have cognitive abilities, e.g. they form expectations on aggregate outcomes and base their behaviour upon them: “Imagine how hard physics would be if electrons could think” is how the Nobel prize winner Murray Gell-Mann, a physicist, has put it (as reported by Page, 1999). Explicitly considering how heterogeneous elements dynamically develop their behaviour through interaction is a hard task analytically, the equilibrium analysis of mainstream (neoclassical) economics being a shortcut that in many cases is at risk of “throwing the baby out with the bath water”. On the other hand, explicitly considering the dynamics of the process started to be a feasible alternative only when computer power became widely accessible. The computational study of heterogeneous interaction agents is called agent-based modelling (ABM). Interestingly, among its first applications a prominent role was given to economic models (Anderson et al., 1988), although it was quickly found of value in other disciplines too (from sociology to ecology, from biology to medicine). Goal of this chapter is to motivate the use of the complexity approach and agent-based modelling in economics, by discussing the weaknesses of the traditional paradigm of mainstream economics, and then explain what ABM is and which research and policy questions it can help to analyse.

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Leombruni R, Richiardi M (eds.) (2004). Industry and Labor Dynamics: The Agent-based Computational Economics Approach. Proceeding of the Wild@Ace 2003 conference, World Scientific Press, Singapore



This book presents the contributions to the first Wild@Ace conference. The acronym stands for "Workshop on Industrial and Labor Dynamics - The Agent-Based Computational Aproach," and it has been the first event ever focusing on the very promising use of the agent-based simulation approach for investigation of labor economics and industrial organization issues. Agent-based models are computer models in which a multitude of agents -each embodied in a specific software code- interact. These agents can represent individuals households, firms, institutions, etc. Moreover, "special" agents can be added to observe and monitor individual and collective behavior. One of the main purpose of writing an ACE model is to gain intuitions on the two-way feedback between the microstructure and the macrostructure of a phenomenon of interest. How is it that simple aggregate regularities may arise from individual disorder? Or that a nice structure at an individual level may lead to a complete absence of regularity in the agregate? How is it that the complex interaction of very simple individuals may lead to surprisingly complicated aggregate dynamics? Or that sophisticated agents may be unable to organize themselves in any interesting way? The book includes contributions by some of the most distinguished researchers in the field, such as the economists Alan Kirman. Giovanni Dosi. Leigh Tesfatsion and Mauro Gallegati, and the sociologist Nigel Gilbert.

Richiardi M, Fazio L (2004). UrbanSim: Microsimulazione urbana a Torino. In: Russo G., Terna P. (eds.). I numeri per Torino. Otto Editore, Torino

Abstract: Questo lavoro, pur con limitate risorse, ha portato alla creazione e ad una prima validazione di un modello operativo integrato della città di Torino, ovvero un modello di simulazione che coglie le interazioni tra i diversi sotto-sistemi che compongono la città: la demografia, l’economia, il traffico, il mercato immobiliare, le scelte di pianificazione, l’ambiente. Il modello deve quindi intendersi come uno ‘studio di fattibilità’, che ha dato esiti sostanzialmente positivi. Si è infatti dimostrato in grado di replicare in buona misura le scelte localizzative delle famiglie, cogliendo il fenomeno di ‘fuga dal centro’ che ha caratterizzato il decennio 1991-2001.  Inoltre, il modello è stato utilizzato per una prima analisi di scenario, permettendo di ricostruire un controfattuale – l’evoluzione della città in assenza dei trend demografici e macroeconomici che invece l’hanno caratterizzata – da cui possono essere tratte alcune indicazioni. Tra queste, un certo maggior appeal del centro, collegato proprio alla minor congestione determinata da una popolazione in calo. Se la popolazione fosse rimasta costante, la fuga dal centro sarebbe stata ancora maggiore. Infine, con l’addizionale cautela derivante dal fatto che non è stato possibile validare il modello sul fronte delle scelte localizzative delle imprese per l’indisponibilità dei dati disaggregati del Censimento 2001, il modello ha individuato una tendenza all’espansione della zona nord-est della città.