Modeling aggregate behavior and fluctuations in economics : stochastic views of interacting agents /

This book has two components: stochastic dynamics and stochastic random combinatorial analysis. The first discusses evolving patterns of interactions of a large but finite number of agents of several types. Changes of agent types or their choices or decisions over time are formulated as jump Markov...

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Bibliographic Details
Main Author: Aoki, Masanao (Author)
Format: eBook
Published: Cambridge : Cambridge University Press, 2002.
Online Access:CONNECT
Table of Contents:
  • Our Objectives and Approaches
  • Partial List of Applications
  • States: Vectors of Fractions of Types and Partition Vectors
  • Vectors of Fractions
  • Partition Vectors
  • Jump Markov Processes
  • The Master Equation
  • Decomposable Random Combinatorial Structures
  • Sizes and Limit Behavior of Large Fractions
  • Setting Up Dynamic Models
  • Two Kinds of State Vectors
  • Empirical Distributions
  • Exchangeable Random Sequences
  • Partition Exchangeability
  • Transition Rates
  • Detailed-Balance Conditions and Stationary Distributions
  • The Master Equation
  • Continuous-Time Dynamics
  • Power-Series Expansion
  • Aggregate Dynamics and Fokker-Planck Equation
  • Discrete-Time Dynamics
  • Introductory Simple and Simplified Models
  • A Two-Sector Model of Fluctuations
  • Closed Binary Choice Models
  • A Polya Distribution Model
  • Open Binary Models
  • Two Logistic Process Models
  • Model 1: The Aggregate Dynamics and Associated Fluctuations
  • Model 2: Nonlinear Exit Rate
  • A Nonstationary Polya Model
  • An Example: A Deterministic Analysis of Nonlinear Effects May Mislead!
  • Aggregate Dynamics and Fluctuations of Simple Models
  • Dynamics of Binary Choice Models
  • Dynamics for the Aggregate Variable
  • Potentials
  • Critical Points and Hazard Function
  • Multiplicity--An Aspect of Random Combinatorial Features
  • Evaluating Alternatives
  • Representation of Relative Merits of Alternatives
  • Value Functions
  • Extreme Distributions and Gibbs Distributions
  • Type I: Extreme Distribution.