Learning and macro-economic dynamics

Publication Type:
Chapter
Citation:
Nonlinear Economic Dynamics and Financial Modelling: Essays in Honour of Carl Chiarella, 2014, pp. 109 - 134
Issue Date:
2014-05-01
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© 2014 Springer International Publishing Switzerland. All rights are reserved. 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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