Covers statistical models of heterogeneity, artificial consumer markets, models of adaptive expectation formulation in financial markets, and agent-based models of industry evolution, product diversification and energy markets. This book provides a toolbox for building agent-based models of learning and adaption in a variety of settings.

Learning and adaption are key features of "real economies". Studying interesting real phenomena like innovation, industry evolution or the role of expectation formulation in financial markets thus necessitates novel methods of data analysis and modelling. This title covers statistical models of heterogeneity, artificial consumer markets, models of adaptive expectation formulation in financial markets, and agent-based models of industry evolution, product diversification and energy markets. The joint findings are presented in a manner that is interesting both for readers with a background in economics/management and mathematics and statistics, and also for non-expert readers, because it allows them to grasp the ideas of modern management science. This book thus provides a unique integrated toolbox for building realistic agent-based models of learning and adaption in a variety of settings based on sound data analysis.

Introduction: General scientific concept: aims of SFB 010.- Modeling Consumer Behavior: Basic Concepts and a Discrete-Time Model
A Continuous-Time ACM Model and Experiment
Capturing Unobserved Consumer Heterogeneity Using the Bayesian Heterogeneity Model.- Modeling Financial Markets: Non-linear Volatility Modeling in Classical and Bayesian Frameworks with Applications to Risk Management
Expectation Formation and Learning in Adaptive Capital Market Models.- Agent-Based Simulation Models: The Artificial Economy: A Generic Simulation Environment for Heterogeneous Agents
Disruptive Technologies: the Threat and its Defense
Agent-Based Simulation of Power Markets
A Simulation Model of Coupled Consumer and Financial Markets
Product Diversification in an Artificial Strategy Environment.- Statistical Modeling and Software Development: Parameter Estimation and Forecasting under Asymmetric Loss
Identification of multivariate state-space systems
Factor Models for Multivariate Time Series
Detecting Longitudinal Heterogeneity in Generalized Linear Models
Ensemble Methods for Cluster Analysis
Open and Extensible Software for Data Analysis in Management Science.