Economic Multi Agent Systems

Design, Implementation, and Application

Gottfried Haber

• Publisher: Books on Demand
• Year: 2014
• Print ISBN: 9783735780348
• eBook ISBN: 9783735769312
• Edition: 1
• Topic: Business
• Subtopic: Business General
• Index: Business

The relatively new scientific field of economic agent-based systems is a promising, but yet not completely developed area of economics, at the frontier to business administration, computer sciences, and the social sciences. This book follows a rather interdisciplinary approach while maintaining a focus on the economic dimension of agent-based models.In contrast to other literature in this field, this book aims at developing a framework for modelling an economic system in the sense of a total model of the economy. The design and implementation of the agent-based framework AS1 are described in detail. Some sample applications, such as an analysis of the effects of the presence of different research institutes providing alternative inconsistent forecasts for the private agents, provide an overview of the strengths of an agent-based economic model.This 2nd edition is a reprint of the original Edition (from 2006).

1. Introduction

Economic multi-agent systems have gained more and more attention for the last decade. The basic idea of this innovative approach to model certain aspects of the economy is comparable to some well-known computer games³: By modeling individual economic agents on the micro level and subsequently aggregating their behavior on the macro level, artificial economies can be constructed. The main difference of this paradigm compared to the traditional modeling strategies is the absence of behavioral equations on the aggregate level and the ability to take into account different strategies regarding information processing and action patterns of the agents. Advantages of economic agent-based models are primarily (but not limited to):

• the possibility to model dynamic or evolutionary processes,
• the room for heterogeneity of the agents,
• the application of dynamically evolving expectation models embedded within the agents,
• the scaleable degree of detail in certain areas of the model, and
• the support of highly non-linear and/or probabilistic models.

Traditionally, agent-based economic research activities have been focused on isolated sociological or economic issues, following a partial modeling approach. On the other hand, toolkits for general purpose agent-based simulations have been developed using different IT platforms and computer languages.

The aim of this book is different from previous work in the field in the sense that the objective is arriving at a complete (total) macro model of the economy, which can be applied to different economic policy issues. The sections in this book are structured as follows:

In the first part, some theoretical considerations concerning economic modeling and especially agent-based modeling are presented. This chapter briefly describes the various alternative approaches to economic modeling with their advantages and limitations and then provides more detailed information on the multi-agent methodology. Agent-based models can overcome several of these traditional restrictions of the more traditional modeling methodologies, but on the other hand some specific characteristics have to be recognized. The key concepts of agent-based models, most prominent genetic algorithms (GA) and artificial neural networks (ANN) are described. Another focus of this chapter is the basic structure of agent-based models.

After having discussed the theoretical pros and cons of the methodological approach and the theoretical structure of an agent-based model, the following chapter throws some light on the technical dimension of implementing an agent-based model. First of all, the key concepts of agent-based programming are described. Naturally, this leads to a discussion of choosing the development and execution platform of such a model in a more technical manner. The next step is to choose an appropriate programming language, which is not an easy task, as there are several dimensions to keep in mind: code efficiency and speed, memory management and constraints, extensibility, usability, stability, etc. Finally the key advantages of choosing C# are described. This section also gives an overview of the most important C# 2.0 language features relevant to the agent-based programmer. Note that this part of the book is not intended as a C# primer, but rather as a discussion of C# programming concepts closely related to agent-based models.

The next section presents the design of the agent-based economic model AS1 developed by the author, which consists of an extensible and re-usable class library and a graphical user interface (GUI). The main focus of the discussion is on the structure and functioning of the core elements of AS1. Classes represent types of agents and are the basic elements of the artificial economic system. After showing the model structure, interfaces, classes, and the control flow in the model are presented. Due to the large number of classes and other elements, only the most important features of the most interesting classes are described in detail. A complete (but very short) class reference is given in the appendix. Additional information on all the classes can be found in the online documentation of AS1, available upon request from the author.

The last chapter in this book presents some hints on the possible applications of the previously developed model AS1. The exercises comprise the development of a model baseline that can be used for further analyses and an application on the information formation mechanisms of the agents. These applications should be some illustrative examples for the large number of possible research questions that could be treated with an agent-based model, but should be seen as only the beginning of a bunch of simulation experiments with AS1.

In the summary, the benefits of agent-based modeling as well as the special issues associated with this methodology are summed up. Extensions already in progress and further future enhancements of the agent-based model AS1 are presented.

The appendix contains a class reference of the AS1 core classes for economists and IT specialists who would like to use AS1 for their own experiments. AS1 is available as a compiled class library and a fully-featured application from the author upon request. Although the present version of AS1 is completely functional as a class library (including the GUI) no special efforts have been made testing the framework (and especially the installer) in different environments apart from the Microsoft Windows XP platform. Everybody planning to use AS1 is therefore encouraged to contact the author and/or visit the author’s website (http://www.haber.at/) for the most recent version.

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³ For example, “SimCity” was one of the first computer games to implement a micro model of a large number of agents.

2. Economic Modeling with Agent-based Models

2.1. Traditional Macroeconomic Modeling Approaches

2.1.1. General Considerations

The first choice when developing an economic model is to decide upon the general type of model. As this a rather complex decision with several dimensions, various aspects ranging from data availability to the application context have to be taken into account. To identify the specific strength and weaknesses of agent-based model, pros and cons of the other wide-spread modeling methodologies have to be investigated (see e.g. Haber, 2002).

Macroeconomic models are expected to resemble reality as close as possible while maintaining a high level of abstraction. Abstraction is necessary in order to focus on the most relevant features of the economic system, at the same time neglecting aspects which do not substantially influence overall economic output and other key variables in the model. The basic structure of an economic model is the first decision that has to be taken. When evaluating this decision regarding the modeling approach, several different paradigms can be chosen. Alternative modeling strategies differ with respect to premises, objectives and typical difficulties associated with each of the approaches. Several models used for empirical purposes follow very different methodologies and paradigms (for a hint on the range of different approaches see e.g. Bradley et al, 1995; Bundesbank, 1996; Fair, 1984 and 1994; McKibbin and Sachs, 1991; Url, 1998).

The most important traditional approaches for macroeconomic modeling are the Cowles Commission approach (Fair, 1992), several types of time-series models, vector auto-regressive models, and (computable) general equilibrium models.

There is another line of modeling methodology as well that focuses more on the derivation of stylized interdependencies than on empirically valid projections and forecasts: mathematical models with a limited number of equations and variables on the one hand, and Markov process type models on the other hand. Models involving game theoretic algorithms usually belong to the first kind, while statistical models of economic policy regimes often can be found in the latter category.

Agent-based models can be seen as an intermediate approach combining the philosophy of both lines of thought: While sticking to rather detailed microeconomic mechanisms and containing a large amount of stylized behavior with respect to the agents, these models generally aim at reproducing and revealing empirically valid economic mechanisms – enriched by partly rational expectations, selective perception, smart learning and strategic interaction.

2.1.2. The Cowles Commission Approach

In the 1970ies, the approach suggested by the Cowles Commission was very popular. This methodology still stands for medium to large structural macro-econometric models of the economy (see e.g. Fair, 1992). The general objective of this approach is to capture observable relations among the main economic variables in structural equation systems by applying econometric methods, thus identifying behavioral equations. Each endogenous variable in the model is explained by an equation in the model. Variables that cannot be observed, such as the deep parameters of utility functions or similar “hidden” variables are omitted in this approach by purpose. Modeling usually comprises these three steps:

• specification of the model equations
• estimation of the system
• evaluation of the model

When specifying the model, economic theory is used to derive the equations of the linear or non-linear system. The elements of the model are endogenous variables, pre-determined variables (exogenous and lagged endogenous variables), the unknown parameters and stochastic disturbances.

Estimation of the model is done by applying econometric methods and techniques. Originally, these methods were limited to traditional singleequation methods (such as ordinary least squares – OLS), but later on with the availability of sophisticated simultaneous methods, system estimation techniques have become more popular.

The last stage of modeling is evaluating the model. On the one hand, the consistency, significance, and compatibility of the estimated parameters with economic theory are checked. On the other hand, the empirical power (ex-post forecasting, ex-ante forecasting) of the model is evaluated.

All of the three stages might make it necessary to return to an earlier stage in the modeling process and to alter the structure of the model. But in principle, theory comes first and then econometric methods are applied. This is in contrast to “data mining”, which is the main feature in vector autoregressive models (VARs), which will be discussed below.

In spite of the fact that most models used for forecasting by various research institutes can still be seen as following this methodology, the Cowles Commission approach has become rather unpopular in economic modeling due to several issues that have been raised in the literature:

• Application of single-equation methods. OLS and other singleequation techniques do not account for restrictions and interdependencies of the parameters (Sims, 1980).
• Parameters are supposed to be constant (in the sense of “timeinvariant”) and invariant to changes in policy regimes, learning, etc. This is the classical “Lucas critique” raised by Lucas (1976).
• Identification issues, especially when including lagged endogenous variables and expectation variables as regressors (also raised by Sims, 1980).

The first issue can be regarded as more or less obsolete due to the development of powerful methods for simultaneous system estimation, such as seemingly unrelated regression estimators (SURE) two-stage least squares (2SLS), three-stage least squares (3SLS), limited information maximum likelihood (LIML), full information maximum likelihood estimators (FIML), and other even more sophisticated non-linear estimators. This is also true to some extent for the last issue, because there are some small-sample and largesample inference results together with system estimation techniques, cointegration and error correction approaches, that alleviate the identification problems to some extent. Especially the question, if lagged endogenous variables should be used for identification can be regarded as solved in the light of the development of error correction models (ECM). These considerations have been treated in detail e.g. in Engle and Granger (1987) or Taylor and Dixon (1997). Short-run dynamics (the error correction component) might be estimated separately from long-run co-integrating relations.

The traditional Lucas critique on the other hand is more important for structural models than the previous two concerns. The core argument can be formulated as follows: The parameters of the model are no longer constant, if backward-looking expectations are present and economic policy is changed. If this issue is taken very seriously and also applied to all parameters in the model, even to the deep parameters of the utility functions, the Lucas critique is purely destructive for structural models and leads to the conclusion that no econometric modeling is possible at all. Alternatively, the Lucas critique has been taken as a point in favor of structural models as opposed to reduced form approaches and has been also put forward as an argument against VAR models and in favor of the Cowles Commission approach (e.g. Sims, 1980 and 1996; Taylor, 1993).

Regardless of the interpretation of the Lucas critique, there still remains the problem of constant parameters, which might be less of a problem, when there is not much change within the economic environment. Reasons for a relative constancy of the environment might be the time-horizon or an economic situation close to a steady state or at least close to some stable adjustment path towards the steady state. Structural models with a clear inheritance from the Cowles Commission paradigm still exhibit some properties which make them first-choice for short-run or medium-run forecasts of the economy. This is the reason, why this model type is still very popular with research institutes all over the world. The drawbacks of this approach with respect to the forecasting power of the models are also well-known:

• In dynamically changing environments (e.g. catching-up processes in the transformation economies since the 1990ies), the assumption of constant parameters cannot be justified.
• Structural breaks inherently call for changes in the structural parameters of structural models. More over, the introduction of the common currency in Europe in 1999 has produced severe problems for structural model designers due to a small number of observations available since 1999, making it nearly impossible to estimate the most recent sub-period or to capture the structural break in the parameters.
• For transformation countries, there are limited an unreliable timeseries which render most of the large-sample results of the econometric techniques (such as super-consistency) useless and leave us with the most basic problems of multi-collinearity and auto-correlation.
• Structural models are very “sluggish” in forecasting changes in trends, such as the beginning of a boom phase or a recession. Best forecasts are obtained if there will be “business as usual”.
• Dynamic learning can be implemented in such models only to a very limited extent.
• Forward-looking expectations can be applied to structural models of the Cowles Commission type, but estimation and solution of these models is very complex and subject to a large number of restrictions, due to the arising two-point boundary issues. Moreover, there is no possibility to gain fine control over individual expectation formation and bounded rationality.

A more modern approach to large structural models can be seen in economic policy research and the application of dynamic stochastic optimization algorithms to represent the objective functions of the policy makers. When extending this analysis to dynamic games and coalitions, the Lucas critique becomes less important – on the other hand, the quality of forecasts depends more heavily on the assumptions for the objective functions (specification and parameter values).

2.1.3. Time-series Models

Small time-series models have become very popular in the 1990ies with the development of the theory around different time-series processes, such as the auto-regressive (AR) process, the integrated (I) process, and the moving average (MA) process. Co-integration theory and empirical methods, as well as the development of error correction models (ECM) have further promoted the succ...

Table of contents

• Preface
• Table of Contents
• 1. Introduction
• 2. Economic Modeling with Agent-based Models
• 3. Implementation Issues
• 4. Designing an Agent-Based Economic Model
• 5. Application of the Model
• 6. Summary and Concluding Remarks
• 7. Appendix
• 8. Literature
• Copyright