Learning and Macro-Economic Dynamics

  • Simone Landini
  • Mauro Gallegati
  • Joseph E. Stiglitz
  • Xihao Li
  • Corrado Di Guilmi


This chapter focuses on the relevance of the learning activity in an economy populated by many heterogeneous and interacting financially constrained firms. The economy is represented as an Agent-Based Model (ABM), which constitutes the data generating process (DGP) of the aggregate observables. Following the line of a companion chapter Landini et al. 2014, agents learn and make decisions, according to the notion of “social atom”. The artificial economy is a complex system whose evolution can be predicted inferentially. The analysis of the ABM-DGP aggregate observables is analysed by means of master equations and combinatorial master equations. Inference results confirm the relevance of learning providing insights in two main directions: (a) a new perspective for the micro-foundation of macro models; (b) an interpretation of the system phase transitions.


Master Equation Labour Demand Financial Soundness Behavioural Rule Virtual Agent 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors thank an anonymous referee for suggestions and remarks; the discussants and the participants to the EEA conference in NYC in May 2013, PRIN Bologna, June 2013, for suggestions. The financial support of the Institute for New Economic Thinking grant INO1200022, EFP7, MATHEMACS and NESS are gratefully acknowledged.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Simone Landini
    • 1
  • Mauro Gallegati
    • 2
  • Joseph E. Stiglitz
    • 3
  • Xihao Li
    • 2
  • Corrado Di Guilmi
    • 4
  1. 1.I.R.E.S. PiemonteTurinItaly
  2. 2.DiSESUniversità Politecnica delle MarcheAnconaItaly
  3. 3.Columbia Business SchoolColumbia UniversityNew YorkUSA
  4. 4.UTS Business SchoolUniversity of Technology, SydneySydneyAustralia

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