Learning and Macro-Economic Dynamics
- Publication Type:
- Nonlinear Economic Dynamics and Financial Modelling, 2014, pp. 109 - 134
- Issue Date:
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.
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