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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Format: | eBook |
Language: | English |
Published: |
Cambridge :
Cambridge University Press,
2002.
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Subjects: | |
Online Access: | CONNECT 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.