01243nam2-2200385---450-99000112490020331620030708100727.088-7107-107-7000112490USA01000112490(ALEPH)000112490USA0100011249020030115d2002----km-y0enga50------baitaITy|||z|||001yyCampania[a cura di] Regione Campania, Assessorato musei e bibliotecheRomaICCUMilanoBibliografica20022 v. (X, 675 p. compless.)25 cm.0010001125492001Catalogo delle biblioteche d'Italia027.045Regione CampaniaAssessorato musei e bibliotecheITsalbcISBD990001124900203316XIII D 296/1166030 L.M.XIII D00095402XIII D 296/2166031 L.M.XIII D00095401BKUMAPATRY9020030115USA010959PATRY9020030123USA011200RENATO9020030708USA011006RENATO9020030708USA011007PATRY9020040406USA011718Campania65662UNISA03320nam 22006495 450 991102114360332120260216162749.03-031-73354-110.1007/978-3-031-73354-3(CKB)40378593700041(MiAaPQ)EBC32265665(Au-PeEL)EBL32265665(DE-He213)978-3-031-73354-3(OCoLC)1534190618(EXLCZ)994037859370004120250819d2025 u| 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierMachine Learning Perspectives of Agent-Based Models Practical Applications to Economic Crises and Pandemics with Python, R, Netlogo and Julia /edited by Pedro Campos, Anand Rao, Joaquim Margarido1st ed. 2025.Cham :Springer Nature Switzerland :Imprint: Springer,2025.1 online resource (449 pages)Mathematics and Statistics Series3-031-73353-3 Agent-Based Models and the Economics of Crisis -- The Machine Learning perspective -- Setting up Agent-Based Models of Crisis (Microeconomic Model of Crisis; Virus on a Network Spread Model) -- Developing models with Python and R.This book provides an overview of agent-based modeling (ABM) and multi-agent systems (MAS), emphasizing their significance in understanding complex economic systems, with a special focus on the emerging properties of heterogeneous agents that cannot be deduced from the characteristics of individual agents. ABM is highlighted as a powerful tool for studying economics, especially in the context of financial crises and pandemics, where traditional models, such as dynamic stochastic general equilibrium (DSGE) models, have proven inadequate. Containing numerous practical examples and applications with R, Python, Julia and Netlogo, the book explores how learning, particularly machine learning, can be integrated into multi-agent systems to enhance the adaptation and behavior of agents in dynamic environments. It compares different learning approaches, including game theory and artificial intelligence, highlighting the advantages of each in modeling economic phenomena.StatisticsBiometryStatisticsStatistical Theory and MethodsBiostatisticsStatistics in Business, Management, Economics, Finance, InsuranceAprenentatge automàticthubSistemes multiagentthubLlibres electrònicsthubStatistics.Biometry.Statistics.Statistical Theory and Methods.Biostatistics.Statistics in Business, Management, Economics, Finance, Insurance.Aprenentatge automàticSistemes multiagent519.5Campos Pedro1844459Rao Anand1844460Margarido Joaquim1844461MiAaPQMiAaPQMiAaPQBOOK9911021143603321Machine Learning Perspectives of Agent-Based Models4427118UNINA