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Agent Based Models in Economics and Complexity

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Complex Systems in Finance and Econometrics

Article Outline

Glossary

Definition of the Subject

Introduction

Some Limits of the Mainstream Approach

The Economics of Complexity

Additional Features of Agent-Based Models

An Ante Litteram Agent-Based Model: Thomas Schelling's Segregation Model

The Development of Agent-Based Modeling

A Recursive System Representation of Agent-Based Models

Analysis of Model Behavior

Validation and Estimation

The Role of Economic Policy

Future Directions

Bibliography

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Abbreviations

Abduction:

also called inference to the best explanation, abduction is a method of reasoning in which one looks for the hypothesis that would best explain the relevant evidence.

Agents:

entities of a model that (i) are perceived as a unit from the outside, (ii) have the ability to act, and possibly to react to external stimuli and interact with the environment and other agents.

Agent-based computational economics (ACE):

is the computational study of economic processes modeled as dynamic systems of interacting agent.

Agent-based models (ABM):

are models where (i) there is a multitude of objects that interact with each other and with the environment; (ii) the objects are autonomous, i. e. there is no central, or top-down control over their behavior; and (iii) the outcome of their interaction is numerically computed.

Complexity:

there are more than 45 existing definitions of complexity (Seth Lloyd, as reported on p. 303 in [97]). However, they can be grouped in just two broad classes: a computational view and a descriptive view. Computational (or algorithmic) complexity is a measure of the amount of information necessary to compute a system; descriptive complexity refers to the amount of information necessary to describe a system. We refer to this second view, and define complex systems as systems characterized by emergent properties (see emergence).

Deduction:

the logical derivation of conclusions from given premises.

Economics:

is the science about the intended and unintended consequences of individual actions, in an environment characterized by scarce resources that both requires and forces to interaction.

Emergence:

the spontaneous formation of self‐organized structures at different layers of a hierarchical system configuration.

Evolution:

in biology, is a change in the inherited traits of a population from one generation to the next. In social sciences it is intended as an endogenous change over time in the behavior of the population, originated by competitive pressure and/or learning.

Heterogeneity:

non‐degenerate distribution of characteristics in a population of agents.

Induction:

the intuition of general patterns from the observation of statistical regularities.

Interaction:

a situation when the actions or the supposed actions of one agent may affect those of other agents within a reference group.

Out-of‐equilibrium:

a situation when the behavior of a system, in terms of individual strategies or aggregate outcomes, is not stable.

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Author information

Authors and Affiliations

  1. Università Politecnica delle Marche, Ancona, Italy

    Mauro Gallegati & Matteo G. Richiardi

  2. Collegio Carlo Alberto – LABORatorio R. Revelli, Moncalieri, Italy

    Matteo G. Richiardi

Authors
  1. Mauro Gallegati
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  2. Matteo G. Richiardi
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Editor information

Editors and Affiliations

  1. RAMTECH LIMITED, 122 Escalle Lane, Larkspur, CA, 94939, USA

    Robert A. Meyers Ph. D. (Editor-in-Chief) (Editor-in-Chief)

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© 2009 Springer-Verlag

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Gallegati, M., Richiardi, M.G. (2009). Agent Based Models in Economics and Complexity. In: Meyers, R. (eds) Complex Systems in Finance and Econometrics. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-7701-4_3

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  • DOI: https://doi.org/10.1007/978-1-4419-7701-4_3

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4419-7700-7

  • Online ISBN: 978-1-4419-7701-4

  • eBook Packages: Business and Economics

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