Antonio Palestrini – författare

Introduction to Agent-Based Economics describes the principal elements of agent-based computational economics (ACE). It illustrates ACE's theoretical foundations, which are rooted in the application of the concept of complexity to the social sciences, and it depicts its growth and development from a non-linear out-of-equilibrium approach to a state-of-the-art agent-based macroeconomics. The book helps readers gain a better understanding of the limits and perspectives of the ACE models and their capacity to reproduce economic phenomena and empirical patterns.

Reviews the literature of agent-based computational economics Analyzes approaches to agents' expectations Covers one of the few large macroeconomic agent-based models, the Modellaccio Illustrates both analytical and computational methodologies for producing tractable solutions of macro ACE models Describes diffusion and amplification mechanisms Depicts macroeconomic experiments related to ACE implementations

This book offers a practical guide to Agent Based economic modeling, adopting a “learning by doing” approach to help the reader master the fundamental tools needed to create and analyze Agent Based models. After providing them with a basic “toolkit” for Agent Based modeling, it present and discusses didactic models of real financial and economic systems in detail. While stressing the main features and advantages of the bottom-up perspective inherent to this approach, the book also highlights the logic and practical steps that characterize the model building procedure. A detailed description of the underlying codes, developed using R and C, is also provided. In addition, each didactic model is accompanied by exercises and applications designed to promote active learning on the part of the reader. Following the same approach, the book also presents several complementary tools required for the analysis and validation of the models, such as sensitivity experiments, calibration exercises, economic network and statistical distributions analysis.By the end of the book, the reader will have gained a deeper understanding of the Agent Based methodology and be prepared to use the fundamental techniques required to start developing their own economic models.Accordingly, “Economics with Heterogeneous Interacting Agents” will be of particular interest to graduate and postgraduate students, as well as to academic institutions and lecturers interested in including an overview of the AB approach to economic modeling in their courses.

This book offers a practical guide to Agent Based economic modeling, adopting a “learning by doing” approach to help the reader master the fundamental tools needed to create and analyze Agent Based models.

by exploiting the disequilibrium and non linear relationships among economic aggregates. Prom an empirical point of view, this approach resemblaces the old NBER view, according to which: "the business cycle [. . . ] consists of - pansions occurring at about the same time in many economic activities, f- lowed by similairly general recessions, contractions, and revivals which merge into the expansion phase of the next cyxle" (Burns and Mitchell, 1946). They add that the movement, although recurrent, is not periodic, lasting from 1 to 12 years, and it is not divisible into shorter cycles. Of course, both approaches are not free from limits and inconsistencies. In spite of the equilibrium approach having nowadays became the workhorse of modern macroeconomics, for example, their users still find enormous d- ficulties in explaining why small shocks produce large fluctuations. A well known argument in multi sector real business cycle models (see e. g. Long and Plosser, 1983) is that as the number of sectors or industries considered in the analysis becomes large, aggregate volatility must tend to zero very quickly. This result, which follows directly from the Law of Large Numbers (LLN), rests on the hypothesis that each sector is periodically buffeted with idiosyncratic, identically and independently distributed shocks to Total F- tor Productivity (TFP).