List of Figures = xi List of Tables = xiii List of Contributors = xiv Preface = xvii 1 Introduction = 1 1.1 Hard Times for Dr Pangloss = 1 1.2 The Complexity View = 3 1.3 Heterogeneity in a Neoclassical World = 4 1.4 Agent-Based Models (ABMs) = 6 1.5 Plan of the Book = 8 2 Agent-Based Computational Economics : What, Why, When = 10 2.1 Introduction = 10 2.2 Features of Agent-Based Models = 11 2.2.1 Scope of Agent-Based Models = 12 2.2.2 The Whole and Its Parts = 13 2.2.3 The Dual Problem of the Micro-Macro Relationship = 14 2.2.4 Adaptive vs. Rational Expectations = 15 2.2.5 Additional Features of Agent-Based Models = 17 2.3 The Development of ACE = 20 2.3.1 Evolutionary Roots = 20 2.3.2 The Santa Fe Perspective : The Economy as an Evolving Complex System = 21 2.3.3 AB Models as Dynamic Microsimulations = 24 2.3.4 The Experimental Machine = 25 2.4 Why Agents = 27 2.5 An Ante Litteram Agent-Based Model : Thomas Schelling''s Segregation Model = 29 2.6 Conclusions = 32 3 Agent-Based Models as Recursive Systems = 33 3.1 Introduction = 33 3.2 Discrete-Event vs. Continuous Simulations and the Management of Time = 33 3.3 The Structure of an AB Model = 37 3.4 Obtaining Results in AB Models = 41 4 Rationality, Behavior, and Expectations = 43 4.1 Introduction = 43 4.2 Certainty = 44 4.3 Uncertainty = 45 4.3.1 Risk Neutrality = 46 4.3.2 Risk Aversion = 47 4.3.3 Optimal Choice in a Multi-Period Setting = 50 4.4 Adaptation in Expectation Formation = 55 4.5 Riding at Full Gallop through the History of Macroeconomics = 56 4.5.1 The Neoclassical-Keynesian Synthesis = 57 4.5.2 Expectations Enter the Scene = 58 4.5.3 Adaptive Expectations = 59 4.5.4 Rational Expectations = 62 4.5.5 The New Neoclassical Synthesis = 68 4.6 The Limits of Rational Expectations = 71 4.7 Heterogeneous Expectations : A Very Simple Introduction = 72 4.7.1 Heterogeneous-Biased Expectations = 72 4.7.2 A Convenient Special Case : Two Types = 74 4.7.3 Heterogeneous Adaptive Expectations = 76 4.8 Heterogeneous Expectations in ABMs = 76 4.9 Conclusions = 79 5 Agents'' Behavior and Learning = 81 5.1 Introduction = 81 5.2 Full and Bounded Rationality = 82 5.2.1 Empirical Microfoundations of Individual Behavior = 85 5.2.2 Agents'' Behavior and Heuristics = 90 5.3 Learning = 93 5.3.1 Individual Learning 1 : Statistical Learning = 95 5.3.2 Individual Learning 2 : Fitness Learning = 97 5.3.3 Social Learning = 105 5.3.4 Individual vs. Social Learning = 107 5.4 Conclusions = 108 6 Interaction = 109 6.1 Introduction = 109 6.2 Modeling Interactions = 110 6.2.1 Local Exogenous Interaction = 114 6.2.2 Endogenous Interaction = 118 6.3 Networks : Basic Concepts and Properties = 125 6.4 Static and Dynamic Networks = 133 6.4.1 Static Networks = 133 6.4.2 Dynamic Networks = 137 6.5 Conclusions = 141 7 The Agent-Based Experiment = 143 7.1 Introduction = 143 7.2 Long-Run and Transient Equilibria = 144 7.2.1 Definitions = 144 7.2.2 Uniqueness and Multiplicity of Equilibria = 146 7.2.3 Implications of Stationarity and Ergodicity = 150 7.3 Sensitivity Analyis of Model Output = 151 7.3.1 Settings for SA = 152 7.3.2 Strategies for SA = 152 7.3.3 SA and AB Modelling : Some Applications = 156 7.3.4 A Simple Example : SA on a Bass Diffusion Model with Local Interaction = 156 7.4 Conclusions = 161 8 Empirical Validation of Agent-Based Models = 163 8.1 Introduction = 163 8.2 The Methodological Basis of Empirical Validation = 165 8.2.1 Tractability vs. Accuracy = 166 8.2.2 Instrumentalism vs. Realism = 167 8.2.3 Pluralism vs. Apriorism = 167 8.2.4 The Identification Problem = 168 8.3 Input Validation of Agent-Based Models = 169 8.4 Output Validation of Agent-Based Models = 172 8.5 Qualitative Output Validation Technqiues = 176 8.5.1 The Indirect Calibration Approach = 178 8.5.2 The History-Friendly Approach = 180 9 Estimation of Agent-Based Models = 183 9.1 Introduction = 183 9.2 Taking the Model to the Data = 185 9.2.1 Comparing Apples with Apples = 185 9.2.2 Preliminary Tests = 187 9.2.3 Simulation-Based Estimation = 189 9.2.4 Consistency = 191 9.2.5 Calibration vs. Estimation = 192 9.3 Simulated Minimum Distance = 195 9.3.1 The Method of Simulated Moments = 195 9.3.2 Ergodicity and an Application to a Simple AB Model = 203 9.4 Bayesian Estimation = 210 9.4.1 Estimating the Likelihood = 211 9.4.2 Sampling the Posterior Distribution = 214 9.4.3 Approximate Bayesian Computation = 216 9.4.4 ABC Estimation of the Segregation Model = 219 9.5 Conclusions = 221 10 Epilogue = 222 Bibliography = 224 Index = 240