Economics as an Agent-Based Complex System: Toward Agent-Based Social Systems Sciences

269 Pages·2004·9.89 MB·English

by  Hiroshi Deguchi (auth.)

Springer Japan KK
H. Deguchi
Economics
as an Agent-Based
Complex System
Toward Agent-Based
Social Systems Sciences
With 91 Figures
Springer
Hiroshi Deguchi
Department of Computational Intelligence and Systems Science
Interdisciplinary Graduate School of Science and Engineering
Tokyo Institute of Technology
4259 Nagatsuta-cho, Midori-ku
Yokohama 226-8502, Japan
ISBN 978-4-431-67965-3 ISBN 978-4-431-53957-5 (eBook)
DOI 10.1007/978-4-431-53957-5
Printed on acid-free paper
© Springer Japan 2004
Originally published by Springer-Verlag Tokyo in 2004
Softcover reprint ofthe hardcover lst edition 2004
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Preface
This book is a condensed and revised version of the original Japanese edition
that was published in 2000 by Union of Japanese Scientist and Engineers
(JUSE), Tokyo. The purpose of this English version is to introduce a new
research program in the area of agent-based modeling (ABM) and its applica
tion to socioeconomic systems.
In the last decade, several ABM research programs have been developed
around the world. In Japan, since the early 1990s some groups have been explor
ing an agent-based approach in the social sciences. In the area of agent-based
modeling the main research programs have developed from distributed artificial
intelligence (AI) or complex adaptive systems (CAS). One of the most impor
tant influences on agent-based modeling comes from the CAS paradigm, with a
focus on learning and the e•.rolution of agents.
At the same time, other research programs in the area of complex systems
with decision makers were being developed by two different scientific
approaches. One comes from the study of dissipative structure and synergetics
as an extension of statistical physics. From the very earliest stages, ABM has been
influenced by the concept of self-organization, structural change as bifurcation
of dynamical systems. However, genetic algorithm and the evolutional approach
became more popular, partly because they do not require the construction of a
dynamical systems model to explore an agent-based approach.
The other research program developed from traditional systems sciences that
includes cybernetics, operations research, general systems theory, and the soft
systems approach. Systems scientist H.A. Simon and his colleagues have devel
oped a computational approach at Carnegie Mellon University and other places.
In particular, K. Carley and her group have developed a mathematical and com
putational organization theory and an agent-based approach at Carnegie
Mellon. At the University of Michigan, R. Axelrod and M. Cohen started their
research program under the influence of J.H. Holland, who is a founder of the
CAS paradigm. In Europe, N. Gilbert and the Simulating Society (SIMSOC)
group also devised a simulated approach in the social sciences in the early
1990s.
In Japan, Y. Takahara and his colleagues, who with M.D. Mesarovic were
v
VI Preface
founders of a mathematical general systems theory, have developed a hierarchi
cal systems and a mathematical organization theory. I am also a pupil of Y.
Takahara and can state that in Japan the research program of ABM has been
influenced by this mathematical general systems approach.
Hard systems approaches such as cybernetics or operations research provide
problem-solving tools and methods under the given aims of systems. In the social
sciences we have to start from a definition of the problem in human activity
systems. In other words, systems sciences provide epistemic devices to compre
hend human activity systems as ideal types.
P.B. Checkland and his group have developed a soft systems methodology.
This approach gives a framework to contain and solve the problems of human
activity systems. It became popular in the United Kingdom and Japan but not
in the United States. Gaming simulation is a different soft research program for
complex systems with decision makers. ABM also must be combined with an
interpretative approach such as gaming simulation or soft systems methodology
for comprehensive real-world grounding. In the early stages of our research
program of agent-based modeling in Japan, gaming simulation was introduced
as an essential tool for real-world grounding. In this book we focus on gaming
simulation.
In ABM the stress is on a bottom-up approach that is strongly connected to
a kind of reductionism. If we are interested in designing socioeconomic systems,
this approach must be combined with a macro functional system.
In this book we present a research program for a socioeconomic system as a
science of autonomous agents with deviations from the standard research pro
grams of neoclassical economics. Traditional economists are not interested in
ABM as a research program, because they adhere to strong academic conven
tions for treating rational agents. They cite the early works of the repeated pris
oner's dilemma by R. Axelrod again and again but never cite his recent work
with ABM, as it deviates from their paradigm. In the last decade they have tried
to expand their framework of rational decision making, and evolutional game
theory is a typical development. More recently they have introduced learning
theories such as reinforcement learning into their framework. Nevertheless, the
gaps between these different paradigms are deep and wide. It would seem that
to effectively analyze a competitive multiagent system and its institutional design
we require a new paradigm. In this book we construct a research program for a
socioeconomic system as a science of autonomous agents, with deviations from
the standard research programs of neoclassical economics or standard rational
decision theory. In ABM the focus is very much on agent-based simulation, as
simulation is an important tool for ABM. We also use agent-based simulation in
this book with an emphasis on the mathematical foundation of ABM. We intro
duce two original mathematical frameworks, a theory of dynamical social learn
ing and a descriptive theory of economic exchange among agents. We focus on
the theoretical, mathematical, and methodological aspect of ABM.
In Chapter 1 we set forth the basic methodology for ABM as used through
out this book. Repeatability and refutability are important criteria for scientific
Preface VII
theories. In soft systems methodology the focus is also on the traceability of the
analysis. A computational approach satisfies these three methodological require
ments in the context of simulation, but computational formulation does not give
mathematical formulations and theorems even when grounded in the Keep It
Simple and Stupid (KISS) principle. A computational model might become valid
under the docking method of modeling, but it does not directly provide inter
pretive or other ways of real-world grounding. We investigate the epistemolog
ical foundation of ABM.
In Chapter 2 we investigate the dynamic decision-making model of social
science and its bifurcation analysis that provides a mathematical foundation for
some ABM. The bifurcation model provides hard-core theory for structural
change and self-organization. In this chapter we also focus on the concept of
indirect control where we control the boundary conditions of a system to manage
the steady state of the system by introducing social learning dynamics (SLD).
SLD provides a framework for structural change that creates the theoretical
framework of ABM simulation. We also introduce the concept of indirect control
of socioeconomic systems by using the SLD framework to manage structural
change and its stability.
In Chapter 3 we apply SLD to the problem of norm formation and the col
lapsing process. The model was originally formulated and analyzed by R.
Axelrod in the form of agent-based simulation as a norm game and meta-norm
game (Axelrod, 1997). We include a reconstruction of a norm and meta-norm
game by SLD and also provide other models of SLD in socioeconomic systems.
In Chapter 4 we focus on the fact-finding ability of agent-based simulation,
introduce agent-based simulation with reinforcement learning, and show the
emergence of interesting strategies in the problem of the tragedy of the
commons. We also compare agent-based simulation with gaming simulation. We
introduce the concept of indirect control into the gaming simulation framework
and show how to improve the tragedy of the commons.
In Chapters 5 and 6 we have provided a mathematical foundation for a
national economy from a descriptive point of view. In Chapter 5 we give the
mathematical basis for a bottom-up construction of an economic state. We
include an axiomatic foundation for a state description of economic exchange,
in which we introduce the concept of a minus state of stock quantity with an
input-output description. As traditional structuralism theory states, we have
abstracted a deep axiomatic structure, known as exchange algebra, from the
repeated surface structure of a bookkeeping system. The System of National
Accounts (SNA) and the System of Social Demographic Statistics (SSDS) are
both important in giving a descriptive foundation of economic and social
systems.
In Chapter 6 we give a mathematical reconstruction of the SNA from a
bottom-up point of view. Macroeconomic variables for the stock and flow of a
national economy are formulated from our exchange algebra.
Chapter 7 provides an application of gaming simulation on a small-scale model
of a national economy known as a virtual economy. This virtual economy is
VIII Preface
constructed using the framework explained in Chapters 5 and 6. We also try to
combine agent-based simulation with gaming simulation.
In Chapters 8 and 9 we give an application of social learning dynamics for ana
lyzing the lock-in process of network- or platform-oriented industrial structures.
We extend the concept of network externality to the concept of platform exter
nality, and we treat the dynamic change of the utility of agents under the plat
form structure of industry.
In conclusion, the chapter construction flows as follows:
~ Chapter 3 ~ Chapter 8 ~ Chapter 9
I
Chapter 1 ~ Chapter 2 ~ Chapter 4 ~ Chapter 7
I i
~ Chapter 5 ~ Chapter 6
Agent-based social systems sciences have started to consolidate their long
research history. In the United States the North American Association for Com
putational Social and Organizational Science (NAACSOS) was established in
June 2003 (www.dis.anl.gov/naacsos/home.html). In Japan the Pacific Asian
Association for Agent-based Social Sciences (PAAA) was also established in
June 2003 (www.paaa.econ.kyoto-u.ac.jp), and in September 2003 the European
Social Simulation Association (ESSA) was established (http://essa.cfpm.org).
These associations are connected with one another and will contribute much to
the design of our global society in the twenty-first century.
I hope this book will make a valuable contribution to the field and will provide
a conceptual, theoretical, and practical framework to aid the study, understand
ing, and planning of new solutions that meet the challenges of our rapidly chang
ing world.
I express my gratitude for the support provided by the Japan Society for the
Promotion of Science in the form of 2002/2003 Grants-in-Aid for Publication of
Scientific Research Results.
I am also very grateful to the staff of Springer-Verlag Tokyo, and to Mrs.
Masayo Whitaker and Mr. Andi Whitaker for their helpful editorial support.
This book is dedicated to my beloved wife Fusae and to the memory of my
parents, Shigeru and Kimiko Deguchi.
Hiroshi Deguchi, DSc, DEc
December 2003
Tokyo, Japan
Contents
Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . V
Chapter 1
Systems Description and Models in Economics
Purpose of This Chapter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 A Systems Description of Economics Systems . . . . . . . . . . . . . . . . . 1
1.2 Paradigm Shift . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Conception of This Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.4 Methodological Issue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.5 Three Types of Realities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.6 Beyond the Systems View of Natural Science . . . . . . . . . . . . . . . . . . 9
1.7 An Epistemological Framework for a Poly Agent System . . . . . . . . 10
Chapter 2
Mathematical Research of Evolution and Structural Change
-Social Learning Dynamics and Its Bifurcation-
Aim of This Chapter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.1 Structural Change and Bifurcation in Social Sciences . . . . . . . . . . . . 13
2.2 Game Theory and Evolutionary Games . . . . . . . . . . . . . . . . . . . . . . 16
2.3 Social Learning Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.4 Asymmetrical Social Learning Dynamics . . . . . . . . . . . . . . . . . . . . . 22
Chapter 3
A Social and Organizational Learning Model of
Decision Making
-Application of SLD and Agent Based Simulation-
Purpose of This Chapter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.1 Indirect Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
IX
X Contents
3.2 Norm Formation, Mutual Commitment and Structural Change 28
3.3 Social Learning Dynamics of the Norm Game . . . . . . . . . . . . . . . . . 30
3.4 Centralized and Hierarchical Commitment and Support . . . . . . . . . 35
3.4.1 Type of Commitments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.4.2 Educational Effect and Supporting Commitment . . . . . . . . . . 36
3.5 The Historical Change of Social Commitment Structure . . . . . . . . . 39
3.6 Commitment and Learning Rationality . . . . . . . . . . . . . . . . . . . . . . . 41
Chapter 4
The Tragedy of the Commons and Its Agent Based and
Gaming Modeling
Aim of This Chapter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
4.1 Gaming Simulation and ABM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
4.2 Environment Management Gaming . . . . . . . . . . . . . . . . . . . . . . . . . 44
4.3 How to Play Environment Management Gaming . . . . . . . . . . . . . . . 45
4.4 The Facilitator's Guide of Environment Management Gaming . . . . 47
(1) Purpose of the Facilitator's Guide . . . . . . . . . . . . . . . . . . . . . . . . 47
(2) Assumption . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
(3) Preparation of Gaming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
(4) Reproduction Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
(5) Gaming Termination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
( 6) Institutional Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
(7) Debriefing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
4.5 Human Gaming Simulation of the Commons . . . . . . . . . . . . . . . . . . 52
4.6 Agent Based Simulation and Classifier . . . . . . . . . . . . . . . . . . . . . . . 54
4.7 Agent Based Simulation of the Commons . . . . . . . . . . . . . . . . . . . . 56
4.8 Agent Based Simulation of the Commons . . . . . . . . . . . . . . . . . . . . 58
4.8.1 Agent Based Simulation and Fact Finding . . . . . . . . . . . . . . . . 58
4.8.2 Change of Value . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
4.8.3 Tax and Subsidy as Institutional Mechanisms of Agent
Society . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
4.8.4 Random Strategy and Docking Method . . . . . . . . . . . . . . . . . . 64
Chapter 5
State Space Complexity and Exchange Algebra
Purpose of This Chapter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
5.2 Axiomatic Foundation of Economic Exchange . . . . . . . . . . . . . . . . . 70
5.3 Exchange Algebra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
5.3.1 Concept of Exchange and Economics . . . . . . . . . . . . . . . . . . . 84
5.3.2 Redundant Algebra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
5.3.3 Exchange Algebra......... . .... ......... . . . . . . . . . . . . 88
5.4 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
Contents XI
Chapter 6
The National Economic Field and Algebraic Reconstruction
ofSNA
Aim of This Chapter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
6.1 The Virtual Economy.. .... . .. . .. ........ ... . . . . . . . . . . . . . . 95
6.2 The Structure of Economic Field and SNA . . . . . . . . . . . . . . . . . . . . 96
6.3 Classification of Transactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
6.3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
6.3.2 Main Decision Making . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
(6.3.2.1) [Price[e](t): Decision Making] . . . . . . . . . . . . . . . . . . 103
(6.3.2.2) [Material Purchase, Production, Selling:
Decision Making] . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
(6.3.2.3) [Equipment Investments: Decision Making of
Buying Machinery] . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
(6.3.2.4) [Employment: Decision Making for Wages and
Numbers in Employment] . . . . . . . . . . . . . . . . . . . . . 106
(6.3.2.5) [Financing: Decision Making] . . . . . . . . . . . . . . . . . . 107
(6.3.2.6) [Financial Parameters: Decision Making] . . . . . . . . . 110
(6.3.2.7) [Government: Policy Making] . . . . . . . . . . . . . . . . . . 110
6.3.3 Accompanying Decision Making . . . . . . . . . . . . . . . . . . . . . . . 110
(6.3.3.1) [Government Production and Consumption] . . . . . . . 110
(6.3.3.2) [Household Consumption Expenditure] . . . . . . . . . . 110
(6.3.3.3) [Depreciation and Scrapping of Equipment] . . . . . . . 110
(6.3.3.4) [The Adjustment of Value Added Depending
on the Change of Product Stock Price] . . . . . . . . . . . 111
(6.3.3.5) [Income Transfer by the Payment of Interest
and Tax] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
(6.3.3.6) [The Issue and Redemption of Central Bank
Notes] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
(6.3.3.7) Transfer Transactions . . . . . . . . . . . . . . . . . . . . . . . . . 114
6.3.4 Bottom Up Construction of Macro Economic Variables . . . . . 127
(6.3.4.1) [Sector-Deposit: S(ro)] . . . . . . . . . . . . . . . . . . . . . . . . 127
(6.3.4.2) [Sector-Investment: I(ro)] . . . . . . . . . . . . . . . . . . . . . . 129
(6.3.4.3) [Sector-Value added: Y(ro)] . . . . . . . . . . . . . . . . . . . . 131
(6.3.4.4) [Sector-Consumption: C(ro)] . . . . . . . . . . . . . . . . . . . . 131
(6.3.4.5) [Sector-Export & Import: EX[e](ro), IM[e](ro)] . . . . . 131
(6.3.4.6) [Macro National Economic Variables] . . . . . . . . . . . . 132
6.4 National Economic Field: Aggregation . . . . . . . . . . . . . . . . . . . . . . . 132
6.5 National Economic Field and Bottom Up Reconstruction of
SNA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134
6.5.1 Sector-National Economy Field: Flow Quantities . . . . . . . . . . 134
6.5.2 Sector-National Economy Field: Stock Quantities . . . . . . . . . . 162
6.6 Integrated National Economic Field . . . . . . . . . . . . . . . . . . . . . . . . . 164
6.6.1 Integrated National Economy Field: Flow Quantities . . . . . . . 164