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dissertation entitled
The Development of a System Dynamics
Simulation Model of National Park
Regions for Educational Use.

presented by
Kuan-Chou Chen

has been accepted towards fulfillment
of the requirements for

Ph.D. degree in Park, Recreation and
Tourism Resources

WM

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THE DEVELOPMENT OF A SYSTEM DYNAMICS SIMULATION MODEL OF
NATIONAL PARK REGIONS FOR EDUCATIONAL USE

By

Kuan-Chou Chen

A DISSERTATION

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY
Department of Park, Recreation and Tourism Resources

1995

ABSTRACT
THE DEVELOPMENT OF A SYSTEM DYNAMICS SIMULATION MODEL OF
NATIONAL PARK REGIONS FOR EDUCATIONAL USE
By

Kuan-Chou Chen

Problems related to National Parks regional planning and economicdeveIOpment
that must be addressed by educators today have become increasingly complex, ambiguous,
and interrelated. Traditional classroom teaching styles, including lecture and discussion
which are passive and often neglect real world issues, have proven inadequate to address the
growing complexity of National Park regional systems, particularly with regard to regional
economic development concerns. Learning is most effective as part of an interactive,
discovery-oriented process, and, in order to more effectively understand complex real world
systems, teaching styles that encourage hands-on, experiential learning should be used.

The System Dynamics modeling approach described and employed in this
dissertation effectively demonstrates how System Dynamics can be used to construct a
model of complex systems as a pedagogical tool to understand National Park planning,
economic development processes and dynamic system behavior.

The interrelationship of seven primary sectors that are at the foundation of National
Parks regional development are modeled in this research: population, capital allocation,

industry, social overhead, environment, economics, and tourism and recreation.

Particular emphasis is directed at enhancing understanding of environmental impact and
economic development which are of growing concern in both park system planning and
the education of future park managers.

Two System Dynamics methods, Qualitative System Dynamics and Quantitative
System Dynamics are used in model development. The resulting model facilitates
understanding of how the processes and other elements of systems interact to create
system behavior. The model also establishes a set of National Parks regional system
variables that can be manipulated by students and teachers through an interactive gaming
simulation interface to experiment with different variables and outcomes.

The interactive experiential teaching and learning tool developed and described in
this study can be used effectively by educators and students in the learning process. A
gaming simulation application, teaching procedures, and a simulation case study are
develOped and explained to demonstrate ways in which the interactive gaming simulation
application can be used effectively by instructors and students in the classroom. In addition,
academic programs and curricular plans that utilize the interactive simulation mOdel are
discussed to assist educators in developing related courses and integrating these tools and

information into existing courses and curricula

h
e

Copyright by
Kuan-Chou Chen

1995

ACKNOWLEDGMENTS

During the past four years, I have worked in three programs at Michigan State
University simultaneously (Park, Recreation & Tourism, Economics, and Resource
Economics). Without the encouragement, assistance, and support from my family,
friends, committee members, and fellow students the work I have completed would not
have been possible.

I would like to express my sincere gratitude to my major advisor and committee
chairman, Dr. Donald F. Holecek for his encouragement, suggestions, and assistance
throughout my study at Michigan State University. Dr. Ralph Levine, a committee
member during the early stages of model development, also provided valuable expertise
and assistance in strengthening my System Dynamics knowledge. Valuable insight and
suggestions also were provided by Dr. Joseph Fridgen and Dr. Larry Leefers.

I would also like to thank to Dr. Bob LaPrad, Director of Academic and Student
Affairs for the College of Agriculture and Natural Resources, for his continued support
and friendship throughout my final years at the Michigan State University. Others that I
am especially grateful to include: Dr. van der Smissen, for her assistance in dealing with
the many financial and academic difficulties I had to endure as a student; Professor Louis
Twardzik, for his assistance and suggestions; Dr. Chamg-Homg Hsieh, former
Chairperson of the Institute of Management Science at Chiao Tung University in Taipei,

for offering important reference material regarding System Dynamics; Dr. Peter Forsberg,

for his help. and support; my two friends, Dr. Gui-Lin Cui and Mr. David Reynolds, for
their friendliness and encouragement. Special thanks are extended to Mr. Michael Kelly
and Dr. Dan Dizon for their friendship and patience in reviewing and editing various
drafts of the dissertation and their input and discussion regarding educational instrument
development.

And, of course, words cannot express my appreciation and gratitude for the love,
sacrifice, support, and encouragement so generously given by my wife, Keh-Wen, and my
lovely daughter, Emily.

Finally, I would like to dedicate this dissertation to my parents, Kuo-Chien and
Ya-Hwa Chen. Without their moral support, I may not have been able to complete my

graduate education.

vi

TABLE OF CONTENTS

 

LIST OF FIGURES

 

LIST OF TABLES

 

CHAPTER I
INTRODUCTION

 

 

CHAPTER II
GENERAL DESCRIPTION OF THE SYSTEM MODEL

The Problem
The Meaning of National Park Regional Development and Planning

 

 

Role of Economics in Regional Development

 

The Gaming-simulation Model as an Educational Tool

 

Objectives of this Study
Potential Uses of the Study

 

\DOOOQMUJ

 

Organization of the Study

12

 

12

 

12

 

Introduction
General Focus of the Model

12

 

l3

 

General Structure of the National Park Region

16

 

Specific Sectors

 

The Population Sector

16

 

The Social Overhead Sector

17
2O

 

The Economic Sector

21

 

The Tourism and Recreation Resource Sector

24

 

The Environmental Sector

25

 

The Capital Allocation Sector
vii

26

 

Intersector Dependencies

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

The Modeling Approach 26
Brief Review of the System Modeling Approach 26
System Dynamics 29
Rationale for Selecting the System Dynamics Method 32
Review of Previous Research 34

The Modeling Process 41

CHAPTER III 43
FORMULATION OF THE MODEL 43

Introduction 43

Qualitative System Dynamics 45
Causal Feedback Loops 46
Summary of Causal Feedback Loops of the Model 64

Quantitative System Dynamics 66
Computer Software 66
General Structure of Equations 67
Key Variables Formulation 75

CHAPTER IV 106
SYSTEM MODEL AS AN EDUCATIONAL TOOL 106

Introduction 106

Gaming Simulation Application 106

Case Study of Tourism and Recreation Sector 113
Model Base Run of the Tourism and Recreation Sector 114
Change in Tourism and Recreation Policy Variables 116
Policy for Tourism and Recreation Resources Development 116
Results of Simulation Scenarios for Tourism and Recreation Resources
Development 1 19

viii

Academic Programs in National Park Regional System Dynamics

 

 

Using this Study as an Instructional Tool

 

Curricular Plan

 

CHAPTER V

 

CONCLUSIONS

 

Summary of the Study

 

Conclusions

 

Study Limitations

 

Recommendations

BIBLIOGRAPHY

 

APPENDIX A: SUGGESTED READINGS

 

APPENDIX B: LIST OF VARIABLES AND EQUATIONS

 

ix

131
132
134

140

140
140
146
148
150

154

165

167

LIST OF FIGURES

FIGURE 1. INTER-SECTOR DEPENDENCIES IN THE NATIONAL PARK REGIONAL MODEL---- 27

 

 

 

 

 

 

 

 

 

 

FIGURE 2. THE PROCESSES OF THE SYSTEM DYNAMICS MODELING 42
FIGURE 3. SYSTEM DYNAMICS ..- AN OVERVIEW OF THE PROCESS 44
FIGURE 4. A CONCEPTUAL CAUSAL EFFECT DIAGRAM OF THE SYSTEM MODEL ----------- 47
FIGURE 5. THE SYMBOLS m THE CAUSAL EFFECT DIAGRAM 48
FIGURE 6. INFRASTRUCTURE CAPACTTY - INFRASTRUCTURE INVESTMENT LOOP ------------ 49
FIGURE 7. SUPERSTRUCTURE CAPACTTY - INVESTMENT LOOP 51
FIGURE 8. MANPOWER MANAGEMENT - RESOURCE MAGNTTUDE LOOP 53
FIGURE 9. VISTTOR - A'I'I'RACI'IVENESS - LOAD INDEX LOOP 55
FIGURE 10. TRANSPORTATION CAPACITY - DEMAND/CAPACTTY LOOP 56
FIGURE 1 l. RURAL POPULATION -INCOME - MIGRATION LOOP 57
FIGURE 12. POPULATION - NET BIRTHS LOOP 57
FIGURE 13. POPULATION - LABOR FORCE - UNEMPLOYMENT - MIGRATION LOOP -------- 58
FIGURE 14. WAGE RATE- REGIONAL ATTRACTIVENESS -EMPLOYMENT LOOP -------------- 59
FIGURE 15. HEALTH SERVICES - DEATHS - POPULATION LOOP 60

FIGURE 16. SCHOOL ENROLLMENT - LITERACY RATE - PRODUCTIVTTY - INCOME LOOP»- 61

FIGURE 17. POLLUTION - CONCERN FOR QOL - CONCERN FOR ECONOMIC GROWTH LOOP63

 

FIGURE I 8. A SUMMARY OF CAUSAL EFFECT LOOPS 65

FIGURE 19. A SIMPLIFIED STRUCTURAL DIAGRAM 68

 

 

FIGURE 20. OUTPUT OF VARIOUS ORDERS OF DELAY 73

 

 

 

 

 

 

FIGURE 21. EXAMPLE OF A TABLE FIJNCITON 74
FIGURE 22. A STRUCTURAL DIAGRAM OF THE TOURISM AND RECREATION SECTOR ------- 77
FIGURE 23. NATIONAL PARK CHARACTERISTICS INDEX 81
FIGURE 24. INFRASTRUCTURE INVESTMENT CRITERION BASED ON CONGESTION ---------- 82
FIGURE 25. INFRASTRUCTURE INVESTMENT CRITERION BASED ON LOAD INDEX ----g----- 82
FIGURE 26. SUPERSTRUCI'URE INVESTMENT BASED ON USE RATE 84
FIGURE 27. A STRUCTURAL DIAGRAM OF THE EMPLOYMENT SUBSECTOR 87
FIGURE 28. A STRUCTURAL DIAGRAM OF THE LAND USE SUBSECTOR 88
FIGURE 29. LAND VALUE INCREASE MULTIPLIER FOR URBAN LAND 92
FIGURE 30. A STRUCTURAL DIAGRAM OF THE ENVIRONMENTAL SECTOR 94

 

FIGURE 31. CONCERN FOR QUALITY OF ME vs. CONCERN FOR ECONOMIC GROWTH ---- 95

 

FIGURE 32. A STRUCTURAL DIAGRAM OF THE POPULATION SECTOR 96

FIGURE 33. A STRUCTURAL DIAGRAM OF THE HEALTH SERVICES AND POPULATION

PROGRAMS SUBSECTOR 99

 

 

FIGURE 34. A STRUCTURAL DIAGRAM OF THE EDUCATION SUBSECTOR 100
FIGURE 35. HOSPITAL BED INCREASE vs. MEDICAL PERSONNEL DEFICIENCY RATIO ---- 101

FIGURE 36. A STRUCTURAL DIAGRAM OF THE CAPITAL ALLOCATION SECTOR ----------- 104

 

FIGURE 37. THE MAIN SCREEN OF THE GAMING SIMULATION MODEL 109

 

FIGURE 38. THE POLICY OPTIONS SCREEN l 10

xi

FIGURE 39. THE TIME TABLE SCREEN 1 1 1

 

 

 

 

FIGURE 40. THE TIME GRAPHIC SCREEN 1 12
FIGURE 41. BASE RUN OF THE TOURISM AND RECREATION SECTOR 1 15
FIGURE 42. COMPARISON OF NUMBER OF VISITORS IN THREE SCENARIOS 120
FIGURE 43. COMPARISON OF THE CONSERVATIVE INVESTMENT AND BASE RUN --------- 124
FIGURE 44. COMPARISON OF THE AGGRESSIVE INVESTMENT AND BASE RUN -------------- 125

FIGURE 45. COMPARISON OF NUMBER OF VISITORS UNDER ALTERNATIVE

 

SUPERSIRUCTURE INVESTMENT CRITERIA 129

FIGURE 46. COMPARISON OF SUPERSTRUCTURE CAPACTTY UNDER ALTERNATIVE

 

INVESTMENT CRITERIA 130
FIGURE 47. RELEVANT COURSE AND SUBJECTS ADDRESSED BY THE STUDY --------------- 135

FIGURE 48. THE CURRICULAR PLAN 137

 

xii

LIST OF TABLES

TABLE 1. [NM PARAMETER VALUES USED IN THE NATIONAL PARK REGIONAL MODEL 18

 

TABLE 2. PARAMETERS FOR TESTING OF INFORMATION DELAYS 122

TABLE 3. TI-IEINFRASTRUCI'URE INVESTMENTCRITERIA 123

 

TABLE 4. THE SUPERSTRUCTURE INVESTMENT CRITERIA 128

 

xiii

CHAPTER I
INTRODUCTION

The Problem

National Park regions contain a number of natural resources such as beautiful
scenery, diverse wildlife, and vegetation most of which provide great opportunities for
recreation and create substantial economic impacts on local communities. Also, due to the
rural nature of many regions which contain National Parks, the economies of these regions
tend to rely heavily on one or a few major industries and thus lack the economic diversity of
metropolitan areas. In these areas, tourism and recreation generally play a large role in local
economies. III other words, the National Park systems of such regions are tightly
interrelated with their socio-economic systems. Indeed, National Park systems will usually
affect the way in which related socio-economic systems grow and change. And changes in
socio—economic systems will in turn call forth changes in National Park systems. This
interrelationship is fundamental to the view of National Park system analysis taken in this
dissertation.

How can (should) a National Park region gain or use comparative advantage to
obtain economic revitalization and/or use natural resources, while also maintaining the
environmental integrity of the related land resources and preserving the park’s unique

character? The answer to this question surely must incorporate information about the local

1

economy and its regional setting. In any such answer, economic development and
protection of the natural environment should be mutually supportive goals. On the other
hand, economic development increases the complexity of not only the interrelationships
with other regional subsystems but the interdependencies between its subsystems.

In most classroom settings, instructors typically use a combination of lecture and
discussion to teach natural resources based regional economic development topics. While
both the lecture and discussion formats are useful to explain complex subsystems, they
often can fall short of realizing their potential. Lectures can easily become one-way
avenues for communication. Students can rapidly lose sight of the big picture, as they
furiously strive to take notes on the specific information elements being presented. Many
instructors lack the knowledge and experience to facilitate truly effective classroom
discussion, and as a result, classroom discussion can also easily lose its focus. A handful of
students can gain dominance over the discussion, causing it to drift into narrow tangential
areas while the remainder of the students lose interest in the material being discussed and its
broader implications. Educators must accept and respond to the need for effective
analytical frameworks that help students understand key concepts within the context of real
world problems and concerns (Meyers, 1986). In the past ten years, it appears that the use
of “hands-on experimentation” in the social sciences, natural resources and humanities, has
improved the discipline and rigor of the thinking processes of students in these disciplines.

Research during this time suggests that teaching styles that encourage and utilize hands-on

experimentation are more effective than lecture or discussion style in these disciplines
(Richmond, 1985; Youtz, 1984; Perry, 1981; Morecraft, 1982). Educational tools such as
the System Dynamics model developed as part of this study offer a practical, real-world
framework for students that encourages critical exploration and effective classroom
discussion.

Instructors of natural resource management classes must understand the regional
economy, the implications of alternative economic policies, and answer questions raised by
students. In addition to compiling and analyzing basic facts, they must develop effective
leaming/teaching models and strategies. To effectively address these broad issues, there is
an acute need to develop a theoretical analysis framework, a set of tools that can open new
avenues for focused communication. Similarly, a sound and practical holistic model for the
simultaneous analyses of National Park regional development options with emphasis on
economic development issues is necessary to engage students in active learning

experiences.

The Meaning of National Park Regional Development and Planning

In essence “development” means “resource development” achieved through a
production process which increases the utility of resources. Manufacturing (the
conversion of raw materials to final goods) is an obvious example of production; but so
are transportation (the conveyance of materials from one place to another), inventory or

storage (the retention of materials from one time to another), and construction (the

combination of materials into building components and systems). Thus, value added to
the resource may be accomplished through the creation of form utility (manufacturing
and construction), Space utility (transportation), or time utility (inventory or storage).

National Park regional development, then, is a process by which the natural
endowments of a region are captured by one or all of the following: appropriate
management, skill, technology, and socio-cultural appreciation. The end uses generally
include such things as household consumption, public service, capital accumulation,
conservation and preservation, tourism and recreation, education, and self-satisfaction.

It is clear that development is an evolutionary process. The phenomenon of
development always exists in any human related system, such as a nation, a region, or a
city. The point is not to identify the importance of development, but to determine how
development can be matched to goals.

Planning is recognized as a complex and many-Sided phenomenon. It is, or at
least should be, an organized, intelligent attempt to select the best available alternatives to
achieve specific goals. The goals may range from putting men into outer space to the
management of an enterprise, a city, or a whole country. Planning can be temporary, as
in planning in times of emergencies such as war or national disaster, or it can be
permanent and long term, as in national planning for economic development.

Regional planning seeks to bring the physical environment in which men live

under the controlling influence of the public interest. In other words, the purpose of

regional planning has usually been interpreted as achieving economic progress through
physical resource development. Both regional development and regional planning imply
the effort to search for the optimum utilization of the natural endowments of a region to
attain economic and social progress. Planning for development is pervasive--- it is a most

urgent task facing the countries of the world.

Role of Economics in Regional Development

The economic system can be divided into two broad categories, macroeconomics
and microeconomics. Macroeconomics in general considers the broad reach of economic
theory as developed at a regional, national or international scale. Components of this
economic system category are represented by an aggregation of the actions of a multitude
of individual elements such as firms, persons or groups. The observation period used to
gather information is, in comparison to most other systems, quite long, varying from one
quarter year to an entire year. In microeconomics, the economic system of concern is
much smaller in scale than are macroeconomic systems and might represent a particular
firm to a producer-distributor-consumer sector in a given geographical region. The focus
of economics in regional development has been on big economic issues in terms of what
can happen to the economy and why. Each of these issues involves the overall economic
performance of the region, rather than that of particular individual sectors or economic
units. For instance, do regional residents find it easy or difficult to find jobs? How much

total income is the region producing, and how rapid is income growing year after year?

By applying macroeconomics theory to regional economic development, it is
easier to see more detailed characteristics of that region. For example, it might be found
that tourism and recreation is the most important sector stimulating economic
development in a particular National Park region.

Economic development is a process whereby an economy’s real income increases
over a long period of time. And, if the rate of development is greater than the rate of
population growth, the per capita real income will increase (Meier & Baldwin, 1957).
“Process” implies the operation of certain forces; these forces operate over a long period
and embody changes in certain variables. Details of the process vary over differing space
and time conditions, but there are some common features. The general result of the
process is growth in an economy’s product—«in itself a particular long-run change.

Regional economic development in the sense in which it has been described
above, necessarily involves certain fundamental changes in the Structure of the economy.
These changes occur both on the demand and supply side. These changes include: ( l)
the discovery of additional resources, (2) capital accumulation, (3) population growth, (4)
introduction of new and better techniques of production, (5) improvement in skills, and
(6) other institutional and organizational modifications (Meier & Baldwin, 1957;

Fernando & Kambli, 1971; Donaldson, 1984).

The Gaming-simulation Model as an Educational Tool

Gaming and simulation are experimental activities that have gained acceptance in
classrooms at all levels of education and training and in a variety of subject areas
(Gredler, 1992). In practice, a “game” refers to a teaching exercise in which interaction
among learners is an important component. More specifically, a “game” usually means
an exercise, defined by a set of rules, which can be played by two or more participants.
The rules define how the game is supposed to be played and how it is ended.

The term simulation is used to describe a variety of attempts to represent “real
world” activities in such a way that “players” may manipulate selected variables and
experiment with their interrelationships. A simulation may focus on only a few
components of a system or process, or it may entail a complex set of variables. Some
simulations are physical representations of systems as illustrated by flight trainers and
architectural models. Simulations of management processes and transportation systems
are usually represented mathematically and generally embedded in computer programs.
Land use simulations often rely upon graphic representations. The simulation in this
research is implemented in a computer graphic-based system and is designed in gaming

style.

Objectives of this Study

The purpose of this study is to design a theoretical and applicable model using the

System Dynamics approach that can be used in natural resources management training

and instruction. This model can assist natural resources administrators, economists,

planners and classroom instructors in organizing the regional economic development

elements in National Park regions. The educational uses of the generic model, rather than

focusing on presentation of facts, focuses on building skill in the use of facts. The

specific objectives are as follows:

1.

To provide sufficient tools and guides to permit the widespread use of the System
Dynamics simulation model for educational uses in National Park regional planning.
These tools and knowledge can be used for simulation experimentation and as the
basis for comprehensive System Dynamics modeling and regional planning courses.
To apply a suitable methodology for establishing an integrated model to organize
expert knowledge and theory into a meaningful scheme for educational purposes, and
to serve as a development planning laboratory to perform policy formulation and
testing by evaluating performance and behavior of significant variables.

To construct a dynamic, graph-based operational computer model that can simulate
the process of National Park regional planning and development. In the process of

gaming simulation, this model will demonstrate for students the impact of recreation

and tourism and population and natural resources issues on the regional planning and

development process.

Potential Uses of the Study

The development of a National Park regional Simulation model based on the
domain of economic development would provide a means to:
1. Identify elements in National Park regions that may be controlled to improve
economic development.
2. Identify and structure National Park system planning rules that determine the
inferential processes of economic development.
3. Model how changes in economic development policy may change the responsibilities

of both providers and consumers of tourism and recreation.

Organization of the Study

This dissertation consists of five chapters. In Chapter I, the problem, the relevant
terminology, and the objectives and potential uses of the study are presented.

In Chapter II, a description of the system model and its components is provided
without extensive use of mathematics. This chapter also serves to discuss the modeling
approach employed in this research. Approaches to system modeling are briefly
reviewed. The reasons why System Dynamics simulation was selected for use in this

study are presented. Reviews of related applications of System Dynamics in economic

10

development are presented to increase the understanding and familiarity with the
methodology used.

In Chapter III, the concept of System Dynamics is presented in conjunction with
Qualitative System Dynamics of causal feedback loops used to describe the basic
structure underlying the development process. The overall Quantitative System
Dynamics of the regional model, upon which the dynamic behavior of regional economic
development is based, is introduced. Finally, connections between verbal descriptions of
the behavior of some of the key variables to the System Dynamics equations is given.

In Chapter IV, a basic simulation run of the National Park regional model is
presented. The development of the region is represented by some of the most significant
variables. The consequences and implication of these outputs are discussed. The
application of gaming simulation, teaching procedures, and a simulation case study are
explained and developed to demonstrate ways in which the interactive gaming simulation
application can be used effectively by instructors and students in the classroom. In addition,
academic programs and curricular plans that utilize the interactive simulation model are
discussed to assist educators in developing related courses and integrating these tools and
information into existing courses and curricula.

The last chapter, Chapter V, functions as a recapitulation of preceding chapters. It
also reports this author’s perceptions of the overall usefulness and success of this

research effort. Finally, the limitations of this study and recommendations are made for

ll

improvements and additions that should increase the utility of this and similar models that

may be developed in the future.

 

CU

CHAPTER II
GENERAL DESCRIPTION OF THE SYSTEM MODEL

Introduction

In this chapter, the National Park regional model will be described in general
terms. This chapter begins with a discussion of the general focus of the model. The
processes that have been modeled and the variables used in the model will be discussed
for each of the components. The intersectoral dependencies of the model will also be
reviewed. Chapter III will cover the same material but in more detail. It will use the
System Dynamics method to develop the causal effect loops and mathematical
relationships that are implemented in the computer program. The present chapter will be

limited to a verbal discussion of the modeling of components.

General Focus of the Model

The term “National Park,” in this study, means any area expressly reserved,
acquired, controlled or managed primarily for recreation or preservation of the natural
environment. Since the purpose of this research is to develop a generic model, the
conceptual framework of the mOdel structure is such that it could be used with minor

modifications for analyzing any natural resources based regional system.

12

13

General Structure of the National Park Region

In constructing a computer model of a National Park system, the selection and
arrangement of information about the real system is crucial. The selection of information
involves identifying relevant and measm'able variables that afiect the National Parks
Regional Planning process. The arrangement of information involves accurate
representation of the relationships among the various information variables. The sources of
information and the basis for variable selection and modeling include the relevant literature,
existing economic and social development theory, the author’s expertise and experts’
knowledge and comments.

Key literature resources providing the basis for this study include the
groundbreaking work of Jay Forrester regarding industrial dynamics, urban dynamics, and
world dynamics. Additional literature utilized extensively to support this study include
research by Nathan Forrester, 1973 regarding the modeling of economic development
processes using System Dynamics theory. Vital contributions regarding the proper
selection of model variables were provided by Joseph Fridgen (Dimensions of Tourism,
1991), and Donald Holecek, from his work regarding a model of the state of Michigan’s
tourism system. System Dynamics theory applied in this study, particularly in regard to the
application of Quantitative System Dynamics approach, also is based on research and
teaching by Ralph Levine regarding System Dynamics approaches to planning, forecasting,

and analysis for recreational usage. System Dynamics theory pertaining to Qualitative

 

ICE

0P:

14

Dynamics applied in this study is supported by the work of Peter Senge, as described in The
Fifth Discipline, 1990. In addition to the author’s extensive coursework regarding National
Parks planning, resource development, economic development, and general systems theory,
he has significant previous experience designing and developing system models, including a
coastal community options model used to help simulate the community development
decision process (Daniel Talhelrn, et al, 1991 ). A comprehensive list of resources used to
develop the National Parks Regional Planning model and this study is provided in the

‘ bibliography.

In general , the National Park Regional Model is composed of seven sectors. These
sectors are industry, recreation and tourism resources, economic, capital allocation,
population, social overhead , and environment. Industry is classified into four basic types:
agriculture, manufacturing, mining, and services. In this study, the services sub-sector is
divided into household and business services. Only services class is modeled in detail
because of its relative importance in the region. The manufacturing and mining sub-sectors
are combined into a single sector in the model.

Considered in the tourism and recreation resources sector are tourism and recreation
resources, management, infrastructure, superstructure, visitors, and transportation. This
sector is the most crucial one in the overall model since it is the central focus in this study.
The efficient allocation of scarce recreation resources is the most important means of

optimizing economic and social development in the context of this model. In the

 

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15

transportation sub—sector, only the roadway system is modeled because of its relative

importance in the region (Foresta, 1985).

The economic sector in the model considers wages, incomes, employment, labor
force participation and use, tax effects, household service and industrial production, and the
region’s attractiveness as a place to work, live and/or operate a business.

The capital allocation sector considers the receipt of loans, aid, taxes, and savings
which go into a capital pool from which allocations are made to different activities in the
region. It is here that trade-offs between allocations to different programs and sectors can

be examined in the context of the model yielding insights of great potential value to

decision makers.
Population is entered into the system via the following groupings: children, adults

and older adults. Rural and urban populations are considered separately. Births, deaths and
migration, the three determinants of population change, are modeled. Educational levels,
income, health and population programs and practices affect these determinants, and their
ialpact on population change is taken into account. Health service, population programs
and practices, and education are modeled in the social overhead sector. The primary,
secondary, and tertiary levels of rural and urban education are modeled separately.
The environmental sector considers the trade offs between concern for quality of life
and concern for economic growth. Pressure for protecting environmental quality and

government action to control pollution contribute to pollution abatement. The main sources

 

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16
for the generation of pollution in the model are in the industry and the tourism and
recreation sectors.

Specific Sectors
This section will provide a description of the content of each sector as it has been
modeled. The major variables used, the physical basis for the model, and the assumptions
made in developing the model will be discussed for each sector. These are the sectors that
comprise National Park regions. National Park regions may vary in size, geographic
location, and other factors, but each region possesses characteristics that can be modeled
using these sectors. The characteristics and values of variables within each sector for a

typical National Park region may look like the hypothetical region outlined in Table 1.

The Population Sector
The population in the National Park Region is divided into rural and urban, as well

as into the following three age groups: children (0-14), adult (15-64), and older adult (over

65). The three determinants of population are births, deaths, and migration.

Births The number of births is obtained by multiplying the number of adults by a

birth rate which is assumed to be affected by programs and practices affecting population

and by the literacy rate. The rate of these factors is modeled by means of multipliers which

are applied to the normal birth rate.

l7

Deaths The death rate is the product of the basic death rate and new births. It is
also affected by the status of health facilities which depend upon the relationship between
the death rate and the region’s hospital bed - population ratio.

Migration The major balancing mechanism tying supply of labor and demand for
labor is migration. The rate of migration is related to the difference between the regional
unemployment rate and the long-term national unemployment rate. Children and older
adult migration is assumed to depend upon the adult migration rate.

Migration from rural to urban areas is attributed to three factors: employment,
income, and education. Employment also affects inter-regional migration. If urban per
capita income is substantially higher than rural per capita income, there will be higher

migration out of the rural area.

We:

The social overhead sector in the model describes the social infrastructure in the
region. The objective of investment in social infrastructure is to increase the quality of life
and the general welfare of society. Both are important components of economic
development. The success of social infrastructure development reflects not only the success
of economic development, but also the potential to overcome the challenges of endless

development problems.

18

Table 1. Initial Parameter Values Used in the National Park Regional Model

 

 

System Variables Initial Value
Regional Population 2, 964,000 (persons)
Infrastructure Capacity 32,620 (persons/day)
Supersu'ucture Capacity 4,400 (persons/day)
Developed Area 31,000 (acres)
Potential UndevelOped Area 31,000 (acres)
Average annual number of Visitors 3,513,000 (persons/year)
Accumulated load index 0.05
Infrastructure Investment Period 12 Months
Infrastructure Depreciation Period 120 Months
Superstructure Use Rate 20 %
Average Resources Decline Period 60 Months
Per Capita Income 20,000 (dollars)
Average Manpower Adjustment Period 1 Month

 

19

The model considers only health programs and practices affecting population, and
education. It is believed that health and education are the most Significant indicators of
social progress while population programs and practices constitute society’s strategy for
coping with existing with population problems.

Health Services The number of hospital beds in the region is’used to express the
state of health services. Its adequacy is represented by the hospital bed to population ratio.
The demand for medical personnel is also considered.

Population Proms and Practices The success of programs, policies, and practices
that affect population is indicated by the number of Obstetricians and other professionals
engaged in this program. The model estimates the demand for population planning
personnel based upon the funds available for such programs and activities.

Education The education system in the National Park region includes primary
education, intermediate education, secondary education, vocational education, university
education, and graduate education. The model, however, considers the education system as
an aggregated 3—tier system: primary, secondary, and tertiary. Urban and rural education is
treated separately. Student enrollment in any level is increased by new entrants and
decreased by graduating students, drop-outs, and migrating students. In computing the
literacy rate, the population group which has completed primary education is considered to

be literate.

20

The Economic Sector

The economic sector in the model describes the key indicators of economic
development in the region. The model considers employment, wage rate, and per capita
income. Employment and wages in manufacturing and services are computed separately.
Per capita incomes are determined separately for the urban and rural pOpulationS.

Emploment Employment in industry and services (including health, population
programs and practices, education, tourism and recreation, transportation, and government
administration) are computed separately to arrive at total regional employment.
Employment in each activity is determined by average labor productivity. A good indicator
of economic development that is easily obtained is the fraction of employees in agriculture,
mining, manufacturing, and services. Also, the local unemployment ratio is obtained by
dividing the difference between available labor force and total employment of the available
labor force.

m The ratio of regional average wage rate to the national average wage rate, the
relative wage rate, depends upon the target relative wage rate and the time to attain it. The
target relative wage rate is the eventual relative wage. The target relative wage rate is a
function of the average wage rate, the lagged unemployment ratio and the minimum wage

rate. The total wages rate is the sum of wages in all employment categories.

21

M Per capita rural and urban incomes are determined by dividing industry
wages and services wages by their respective populations. For this model, it was assumed
that wages are the only source of income, or that other sources are negligible.

Labor Force Participatio_n The labor force is defined as the number of persons 18
years of age or over who are either working or are unemployed (Hall & Taylor, 1991). The
labor force participation rate is the percentage of the working-age population that is in the
labor force.

Land Use and Tax Effect The term "land use" represents all land use categories in
urban areas while in rural areas it represents residential land use only. The model
accounts for expansion of the urban area and rural residential land use change which results
from the pressure of population growth. The effects of urban land expansion on property

values and property tax collections are recognized in the model.

The Tourism and Recreation Resource Sector

In this sector, the development potential of the National Park's resources for
recreational activities and the resulting impacts of tourism and recreational development on
the local economy have been included. Attendance at recreational sites throughout the
region is expressed in terms of visitors and iS assumed to be significantly influenced by
regional accessibility and the recreational infrastructure and superstructure capacities. The

definitions of infraStructure and superstructure are explored in detail below.

22

Tourism and Recreation Resources The function of the tourism and recreation
sector in this study can be viewed as the process of making the raw materials (i.e. natural
resources) into products (i.e. tourism and recreation resources). Tourism and recreation
resources are the main reasons why visitors access the National Park region. The
development of potential natural resources into valuable tourism and recreation reSources is
crucial for economic development in the National Park region. However, tourism and
recreation resources are not infinite. Thus, the allocation and sound management of
developed resources are key elements to maintain the use of the resources at a useful and
lasting level. However, the development and use of resources often conflicts with the need
to protect the long-term use of the resources. For this reason, it is necessary to establish a
development standard. The standard is called “carrying capacity,” or the capacity to
maintain development and use of a particular resource over the long run.

Infrastructure Attractions and facilities are not accessible to tourists until
infrastructure needs have been met (Mill & Morison, 1985). The infrastructural sub-sector
of a National Park region is taken here to include physical elements such as communication
networks, water supply, electric power, and housing. All of these physical elements are
combined to create an infrastructure capacity element for the purpose of modeling the
infrastructure sub-sector of the model. Infrastructure capacity is defined as the capacity of
infrastructural facilities to serve a set number of visitors during a specifically period of

time.

23

Superstructure Superstructure is not a necessary condition for developing a tourism
and recreation area, but it is a positive contributor to an area’s attractiveness as a travel
destination. In general, the superstructure includes lodgings, restaurants and recreation
facilities, etc. (Fridgen, 1990). Superstructure attractiveness is not only measured by
capacity, but also by the level of services available. Large capacity is but one measure of
the attractiveness of the recreation sub-sector. Visitors' satisfaction with the leVel of
services also contributes to overall attractiveness of the recreation sub-sector. In this study,
the concept of level of services is indicated by a special relationship between service quality
and superstructure capacity.

Visitors The number of visitors was selected as the measure of the performance of
the recreation sub—system. However, an increasing number of visitors alone does not
indicate better performance of the recreation sub-sector. In the short run, excess visitors
would destroy recreation resources and create a barrier to resource development in the long
run. Offering high quality service to increasing numbers of visitors, without reducing future
resource development possibilities, is an important responsibility of National Park region
managers.

Manmwer Management In relation to the tourism and recreation resources sub—
sector, the main duty of National Park managers is to develop new resources and maintain
existing developed resources. Thus, manpower in this model includes technology

manpower and management manpower. Technology manpower includes manpower

24

necessary for the development of new resources. Management manpower includes
manpower necessary for the maintenance of existing developed resources.

Transmrtation Transportation is one of the most important recreational
infrastructural elements in the regional development process. In the transportation sub-
sector, only the roadway system is modeled because of its relative importance to the region.
The road network in the region is classified into highway and feeder roads. The road
inventory is increased by new construction. Reduction of road inventory is not permitted in
the model because it is assumed that all roads will be at least minimally maintained as
needed to accommodate expected traffic. The construction rate depends on available funds
for construction, unit costs, and construction delays. The demand for transportation services

is determined from considerations of recreational and industrial use.

The Environmental Sector

The environmental sector of the planning model considers problems related to the
industrial and tourism/recreation sector’s generation of pollution as well as the natural
occurrence and reduction of pollution and their affect on the availability of resources for
development. To decrease the generation of pollution, government must regulate firms and
visitors discharging pollutants exceeding acceptable levels. Thus, in this sector the
pollution index, the number of people concerned with environment quality, the number of

people concerned with economic growth, and the investment in pollution abatement by

25

government and private firms are indicators which represent the trade-offs between quality

of life and economic development in the region.

The Capital Allocation Sector

The allocation of scare capital resources to competing activities in the region is of
paramount importance in striving to achieve economic and social development in the
National Park region.

Capital Pool In the model, all external loans, public and private savings, and
internal loan repayments accumulate in a variable called the capital pool. Government
savings are the excess of incomes over expenses. Government income is obtained through
taxation on personal incomes, regional production (e.g. industry, and tourism and
recreation), and real property and land holdings.

Capital Allocation Available capital is allocated to two major classes of activities:
tourism and recreation, and social overhead. The allocations are shown as constant
fractions of the capital which can be varied to test the trade-offs between capital allocation
to different activities. The social overhead allocation is further distributed to education,
health, and population programs and practices. The activities in the model which compete
for capital with tourism and recreation development are infrastructure and superstructure

development needs.

26

Intersector Dependencies

In Figure 1, seven sectors of the model are displayed: [1] Tourism and Recreation
Resources; [2] Capital Allocation; [3] Economic; [4] Industry and Services; [5] Population;
[6] Social Overhead; and [7] Environmental. Each sector has a function in regional
development, and the sectors fit together in a structural network.

It is evident that policies adopted in one sector will influence all other sectors either
directly or indirectly, and hence, it is necessary to consider all of them in a comprehensive
model. The complete discussion of the conceptual model and the emphasis of the System
Dynamics model will be introduced in Chapter III, where the core of the integrative model

is completed.

The Modeling Approach

Brief Review of the System Modeling Approach
Models are a rational attempt to describe the relationships between the variables
entering into a problem. Models are particularly important when the system to be studied is
complex or inaccessible. The use of models in planning for regional development can be
justified because: [1]regional development planning involves complex technological,
economic and social considerations, and [2] such planning deals with the future which,

because of uncertainty, is inaccessible.

27

 

Figure l. Inter-sector Dependencies in the National Park Regional Model

Modeling in planning consists of the use of either sub-models or system-wide
models. Limitations in the modeling of regional problems in the past have tended to lead to
the construction of sub-models with outputs from one being used as the inputs to another,
rather than trying to deal with all aspects of the system simultaneously, as with the use of a
system-wide model.

The simplest attempt at system-wide modeling in the planning process is
mathematical programming. The general mathematical programming model strives to

facilitate the selection of an optimal growth pattern for future development by determining

28

the investment mix which Optimizes some criteria (e. g. minimizes total cost). The problem
with this approach when applied to multi-sectoral regional analysis is that, because of the
need for simplifying assumptions, little realism is achieved.

Although explicit quantitative techniques are preferable to the non-quantitative
methods of regional analysis, the former have suffered as in the case of mathematical
programming because of the need to alter the problem to fit the procedure. Of all the
quantitative methods available, simulation places the least restrictions on problem
representation. Therefore, Simulation offers the most promise as a tool for allowing the
planner to act out in the computer laboratory the behavior of a complex regional system
under varying conditions. Through simulation, the planner can observe directly the
potential effects of policy changes, alternative planning strategies, or new development
programs.

The conventional system wide simulation approach is a kind of event simulation. It
reproduces the microstructure of the system being Simulated in the model. To try to apply
this type of simulation to a region and still consider all the major sectors would be
prohibitive. Essentially, conventional system wide simulation models are equilibrium
analysis models. Equilibrium assumes that there exists a balance or Stability between the
inputs and outputs and that a "prediction" merely involves the specification of the inputs at
the assigned point in time. The models are essentially a temporal device because they do

not consider the process of moving from one situation to another and because the length of

 

29

time required for the system to adjust to the new inputs remains unspecified. In other
words, this type of event simulation or comparative-static approach has some obvious
disadvantages in a planning context. First, it is an incorrect simulation of the historical
process of development. Secondly, the whole focus of policy testing is on marginal change.

Lastly, it is impossible to study the phasing of the effects of policies through time.

System Dynamics

The field of System Dynamics began with the founding of the Sloan School of
Management at Massachusetts Institute of Technology by Jay W. Forrester in 1956. System
Dynamics is a way of analyzing the behavior of complex systems to show how systems are
structured and how policies used in decision making govern the behavior of the system.
"Structure" in this context is defined to include the components of the system and the
communication channels by which information is made available at decision-making points.
"Policies" is defined as the rationale that influences how decisions are reached such as
natural-generating processes, experience, laws, rules of thumb, etc. System Dynamics
modeling strives to express, in the form of causal feedback loops and mathematical
equations, the Structure of the system upon which policy acts.

In essence, five characteristics of the System Dynamics method arise from its
holistic view and wide applicability. The five characteristics are as follows:

1. System Thinking. The methodology of System Dynamics is based on a philosophy

that views a problem globally, i.e., from the broadest possible perspective. The

30

problem can be either one of design of a new system or of control of an existing
system. To design a new system or to control an existing system can help the
designer, planner, manager, and the ultimate user have the opportunity to study
developing goals, confirm the components, and understand the sub-system
interrelationships, as well as decide control options.

' Information Feedback Control Theory. Real world systems are usually characterized
by circular causality. The feedback loops represent the transmitting, the dynamic
behavior of one attribute to the next until the circle is closed and the Signal, in a
modified form, is fed back to its origin. Such loops have a tendency to stabilize or to
destabilize a system. When system planners try to manage a feedback system, their
actions are typically amplified or counteracted, depending upon which feedback
structures are dominating the system at the time.

Delay Concepts. The emphasis on understanding the impacts of lag and delays in a
system is one of the important characteristics of System Dynamics. How long
something takes to occur can profoundly impact the overall behavior of a system
(Levine & Fitzgerald, 1992). Delays are an ubiquitous feature of dynamic systems;
they are present at every stage of an action. Time is required to recognize a problem,
to decide what to do about it, and to implement action once a decision is made. Many

decisions turn out to be faulty because people underestimate the length of delays.

31

Thus, policy-makers and planners must understand delays if they are to predict the
consequences of their actions.

Simulation and Gaming. Simulation is one of the most powerful techniques available
for solving a problem. It involves the construction of a replica or model of the
problem on which researchers experiment and test alternative courses of action.
Operational games based on System Dynamics models have been used in System
Dynamics teaching Since the beginning (Meadows, 1989). Hence, the latest trend in
using computers to simulate dynamic systems is to develop user oriented system
dynamics-based games. With this gaming package, decision makers who will
eventually be using the model as a policy aid can participate in the modeling process
right on the computer screen. Also, the educator can adopt the gaming simulation
model as a classroom laboratory to teach and experiment with human behavior
(Richmond, 1987).

Computer Technology. Following the previous point, computers play an important
role in building a System Dynamics simulation model. Computers enable researchers
to handle complex non-linear relationships and to model the system realistically from
the start. Computer technology allows users to modify parameters quite quickly to
see what would happen under changing conditions. Advances in computer and

software technology continue to create easy to use and graphical gaming interfaces.

32

The application of these advanced gaming interfaces enables users to interact directly

with system models with little or no prior training (Anderson & Richardson, 1990).

Rationale for Selecting the System Dynamics Method

 

The modeling approach to be selected for any analysis should depend upon the

problem to be investigated and the purpose of the Study. In the previous section, the

characteristics of System Dynamics as a modeling approach were explored in the previous

section. Several reasons for selecting the System Dynamics approach for this research are

discussed below:

1.

National Park regions are complex. National Park regions are complex systems
involving demographic, economic, social, natural resources, environment, and tourism
and recreation elements. Complex regional systems are systems of high-order,
multiple-loop, non-linear feedback structure, and have delays or lags. Non-linearity
implies that system attributes influence each other in a non—proportional way and that
they interact so that their partial effects, playing out over time, cannot easily be
distinguished. Such interactions may cause shifts in the structural dominance of a
system over time. That is, substructures that have dominated a system's behavior for
some time may, suddenly or gradually, loose their influence while other substructures
gain influence. This typically causes dramatic modification of the system's dynamic
behavior. Delays distribute the effects of changes in variables throughout a system

over time and often cause information to arrive at its destination in an untimely, and

33

hence, harmful manner. Delays and lags lead researchers to discover and give priority
to Short-run gains and to ignore and postpone actions against future losses. Dynamic
simulation is a means to understand how complex geographical regions evolve and
change through time.

The feedback nature of development problems. The regional development process,
like many processes in the world of nature and society, involves several variables
mutually affecting each other so as to form causal effect loops. Feedback loops
exhibit behavior that can only be analyzed by studying the complete system;
analyzing the separate components of the loop in isolation from one another does not
even hint at the sorts of behavior that might arise.

Regional planning is a circular process and strongly depends upon feedback. The
planner should be in a position to match changes in the system structure by altering
the planning trajectory as well as by adjusting operating policies. The relevant models
for planning are therefore unlikely to come from the processes behind the construction
of physical artifacts or physical planning, but rather from their management which
consists of those activities or sciences concerned with the management of feedback
systems. A
Economic development is a multifaceted issue. Any economic development program
is judged by its ability to create jobs, promote economic stability, increase property

values and expand opportunities to achieve "quality of life." Many factors must be

34

included in a system to develop an economic development action plan, and any one of
them can spell success or failure to economic development. System Dynamics lends
itself well to the "options-open" adaptive strategy for accomplishing the economic
development action plan as well as a framework to act as a guide in the plan's
development.

5. The inadequacy of data in regional analysis is due to poor information bases and rapid
changes. Application of dynamic simulation to a National Park region is beginning to
expose the myth that the first Step in planning must be the extensive collection of vast
amounts of statistical data whose value does not equal a fraction of the cost. A
realistic system wide model should come first to determine what data should be

collected.

Review of Previous Research

While there is a considerable amount of general literature which separately deals
with urban planning, physical system design and economic development policy design,
there is little literature on the simultaneous determination of the economic development
plan and tourism and recreation resources, demographic considerations, and the
environment in one paradigm. This section will present a brief description of the
previous models which are related to this study; gaming simulation models built using the

System Dynamics method are included.

35

Initial application of the System Dynamics mechanism to social behavior was
developed in Jay W. Forrester’s first book (1961). Forrester first called his approach to
system analysis Industrgtl Dynamics, but it was later re-named System Dynamics (Levine
& Fitzgerald, 1992). An Industrial Dynamics study covers the production, distribution,
and retailing of a commodity in order to interrelate all the components (i.e., men,
materials, money, orders, equipment and information), of a business system. The
approach is one Of building models of companies and industries to determine how
organizational structural, amplification (in policies) and time delay (in decisions and
action) interact to influence the success of the enterprise. An Industrial Dynamics
simulation aims to demonstrate the characteristic behavior of the system rather than to
predict specific events. The result is a “management laboratory” in which proposed
changes in organizational structure or policy can be tested and the results used as a guide
to better management.

Jay W. Forrester (1969) published the book Urfln Qvnamics. which extended the
System Dynamics approach to describing the growth and Stagnation characteristics of
urban areas. This study simulates the life cycle of an American city over a 250-year
period by examining three systems: business enterprises, housing, and labor forces. Each
of these systems contains three classes: business enterprise consists of new enterprises,
mature business, and declining industry; housing comprises premium housing, worker

housing, and underemployed housing; the labor force consists of managerial-professional

36

workers, labor, and underemployed persons. Then, the life cycle of an urban area is
generated by the Simulation model and shows how growth gives way to maturity and then
stagnation. The urban area is a complex, self-regulating system that creates internal
pressures to modify economic activity and shift the uses of land, structures, and people.
These changes are dominated by the construction, aging, and demolition of industry and
housing combined with concurrent population movements. Forrester draws several
general conclusions: complex systems are counter-intuitive and are strongly resistant to
most policy changes but highly influenced by some if they can be found. Moreover, the
short-term response to a policy change in a complex system is often in the opposite
direction from the long-term effect.

Hamilton, et al. (1969) couples an economic model for a river basin with a model
of the water sector treating demand, cost, quality, quantity, and the like. It is the first
regional simulation model employing the System Dynamics technique. The model not
only ties the demographic, economic, and water sector together, but also incorporates
important dynamic elements (delay, feedback, accumulation, etc.). The three main
elements of the model are: [1] the demographic sector, [2] industry (employment), and
[3] water supply. In addition, there are submodels for recreation, income and electric
power requirements.

Dennis L. Meadows (1970) published his first book on System Dynamics in 1970

titled Dynamics of Commodity Production Cycles. He criticized the classical cobweb

37

theorem and some of its modifications which he claims cannot represent the dynamic
relationships in actual commodity systems adequately. Employing System Dynamics
methodology, Meadows develops a general dynamic model considering the economic,
biological, technological, and psychological factors which lead to the instability of
commodity systems.

Jay W. Forrester (1971) published another distinguished System Dynamics
application book, World Dynamics, which offered a dynamic model of world scope. His
objective was clearly to illustrate a more rational, more analytical and more thorough
analysis of the factors which combine to produce a “quality of life.” Five levels were
chosen as the cornerstones on which to build the world system structure: [1] population,
[2] capital investment, [3] natural resources, [4] fraction of capital devoted to agriculture,
and [5] pollution. The computer model interconnects concepts from demography,
economics, agriculture, and technology. From these five levels and their interactions
appear to come the dynamics of changes in the world system. Rising population creates
pressures to increase industrialization, grow more food, and occupy more land. But more
food, material goods, and land tend to encourage and permit larger populations.
However, in time, growth encounters limits set by nature. Land and natural resources
become exhausted and the pollution-dissipation capacity of the earth becomes

overloaded. The model describes a world system that shows a variety of alternative

38

behaviors. Which behavior is the most likely scenario for the future depends on policies
that man may still be able to choose.

Nathan B. Forrester (1972) extended the application of the System Dynamics
methodology to economic development on a national level with the publishing of the
book titled The Life Cycle of Economic Development. In this model, economic activity
is divided into five production sectors -- agriculture, goods, services, capital, and
resources. Each sector has a functional niche in the economy, and the sectors fit together
in a structural network. The model deals with the shifting allocation of labor and capital
between major production sectors to balance the needs of capital accumulation which is
controlled by the relative marginal productivity of capital and labor. Various possible
strategies of economic development are analyzed. Therefore, the model serves in effect
as a laboratory for policy experimentation.

Nathaniel J. Mass (1975) developed a sequence of System Dynamics models to
analyze and explore the economic processes underlying short-term and long-term
business-cycle behavior. Business cycles are recurring fluctuations in total production,
wages, price, employment, inventories, and capital investment. Such fluctuations have
been observed and charted in the United States for over one hundred years. In general,
Mass’s model overviews existing theories of the economic cycle and provides a general
framework for evaluating the impact of social and economic factors on economic cycles

of various periodicities.

39

Alfeld and Graham (1976) published the book, Introduction to Urban Dynamics,
which provides a step-by-step approach to understanding the ideas and concepts
introduced in urban dynamics. It explains the complexity of the urban system by
examining simpler urban subsystems one at a time. The book contains eleven urban
models. Each model is slightly more complex and realistic than the one before it. This
book illustrates some principles of urban behavior, and discusses each model in terms of
real events and policy issues. Land use, attractiveness, and aging and obsolescence are
three major concepts of urban dynamics. They provide a framework to View the behavior
of the entire city.

SimCity (1989) is a computer entertainment game. The underlying structure of
this game is a system simulation. Although the principal purpose of this type of game is
to build a “dream city” for the players’ challenge and amusement, its concepts of
simulation and scenario planning for a city have been employed for some educational
uses. It provides the players with a set of rules and tools that describe, create and control
a city system. The challenge of playing this game is to figure out how the system works
and take control. As master of the city system, the player is free to use tools to create and
control a city within the framework and limits provided by the rules. The rules are based
on city planning and management variables, including human factors, economic factors,
survival factors, and political factors. The tools provide the player with the ability to

plan, lay out, build, bulldoze, re-zone, and manage a city.

4O

SimCity is designed primarily as a game and not as an educational tool, and
players are exposed only to processes of allocating and managing resources and relative
system components. The game does not expose users to the qualitative issues related to
building, designing, and analyzing a system. Therefore, SimCity has limited educational
and academic utility.

In his book, The Fifth Discipline (1990), Peter M. Senge extends the applications
of Qualitative System Dynamics methodology to help understand the organization of the
social phenomena. In this book, Senge suggests that several learning disabilities result
when learners continually apply trial and error methods. Attempting to understand the
future by relying on the past only forces the learners to try and solve the same problems.
However, the primary threats to learners’ survival today come not from events, but from
gradual processes to which most people are blind. The author offers causal feedback
training methods to help learners avoid these learning pitfalls. This book furnishes a
good picture of Qualitative System Dynamics thinking for empirical studies.

Meadows, Fiddaman and Shannon (1993) developed a micro-computer game
named Fish Bank, an assisted group simulation that teaches principles of sustainable
management of renewable resources while illustrating the interaction of economics,
biology, and group behavior. System Dynamics methods were used to design the

computer model, which was proposed as an operational gaming tool.

41

The Modeling Process

Figure 2 is a flow chart describing the modeling process. The process is carefully
designed to divide a larger problem into more manageable components that can be
described in causal feedback loops. In conjunction with the appropriate computer
program, these feedback loops can be utilized to develop a system structural model and a

model evaluation strategy to facilitate effective model use.

42

r Problem ,
Recognition
E

 

 

 

 
 
    

(National Park
Systems)

 

 

 

 

 

 

eal World Symptom
r Variables
r Selection

 

 

 

  
   
    
   
 
     
  
    
 

 
 

 

   
   
    

 

 

System
Dynamics
System Map
(Causal Feedback Loops)
( Computer
Programming
(Powersim)

 

Structural
Diagram

 

System
Behavior

 

Model
Evaluation

 

Figure 2. The Processes of the System Dynamics Modeling

CHAPTER III

FORMULATION OF THE MODEL

Introduction

The System Dynamics method used in this study is comprised of two separate
phases which are referred to as Qualitative and Quantitative System Dynamics
(Wolstenholme, 1990; Levine & Fitzgerald, 1992). Figure 3 Shows a summary of the
steps involved in these two methods and their purposes. The Qualitative System
Dynamics phase is based on creating cause and effect diagrams according to precise and
rigorous rules and using these to explore and analyze the system. The Quantitative
System Dynamics phase involves representing quantifiable relationships between the
causal variables involved. In it, a set of simulation difference equations are written and
then used for experiments.

This chapter begins with a discussion of Qualitative System Dynamics using the
causal-effect diagram to describe the interrelationships of the variables in this study.
Next, the model used is presented in quantitative terms. However, for the purpose of
keeping the presentation manageable, modeling will be kept simple, and sophisticated

discussions of calibration techniques will be avoided.

43

 

Qualitative System
Dynamics

Quantitative System Dynamic

 

(Diagram construction and
analysis phase)

Create and examine
feedback loop structure of
system using resource
flows, represented by level
and rate variables and
information flows,
represented by auxiliary
variables.

Provide a qualitative
assessment of the

relationship between system

processes (including
’ delays), information,
organizational boundaries
and strategy.

Estimate system behavior
and postulate strategy
design changes to improve
behavior.

(Simulation phase)
Stage 1

Examine the quantitative
behavior of all system
variables over time.

Examine the validity and
sensitivity of system
behavior to change

(1) information structure
(2) strategies

(3) delays/uncertainties

stage 2

Design alternative system
structures and control
strategies based on

(1) intuitive ideas

(2) control theory analogies
(3) control theory
algorithms

in terms of non-optimizing
robust policy design.

Optimize the behavior of
specific system variables.

 

Figure 3. System Dynamics --- an overview of the process

Source: Wolstenholme, 1990

45

Having provided an overview of the model, its various components will be
examined in detail. Considerable attention will be given to some of the subtle elements

of the model as well as to methods actually used to quantify the key inputs to the model.

Qualitative System Dynamics

The major use of a qualitative System Dynamics diagram is to identify
information feedback loops which have been created by linking resource and information
flows. It is the analysis of such loops which facilitates understanding of how the
processes, organizational boundaries, delays, information, and strategies of systems
interact to create system behavior. A causal feedback loop consists of two or more
variables that close back on themselves. The sign "S" identifies changes in the same
direction. The sign "0" identifies changes in the opposite direction]. In other words, if
any variable in an “S” loop changes, then the loop causes that variable to change even
more in the same direction. If any variable in an “0” loop changes, then the loop causes
that variable to correct itself and to readjust in the opposite direction. There are two
kinds of feedback loops: reinforcing and balancing. Reinforcing loops create exponential

growth, while balancing loops are self-regulating and bring the system towards

 

' Some academic research uses the “+” and “-” to represent the causal direction. However, it is easy to
confuse these signs as “good" or “bad" impacts. Thus, this study adopts the “S"(change same direction)
and “O”(change opposite direction) to display the variables’ causal direction since change in either
direction can be either good or bad in their impacts.

46

equilibrium when they dominate. The Sign of a causal feedback loop is obtained by
counting the number of “O” causal relationships around the loop. If there iS an odd
number of “O” causal relationships, the loop is a balancing feedback loop; if there is an
even number, the variables are connected by a reinforcing feedback loop. Figure 4.
exhibits a comprehensive picture of the causal feedback loops of the National Parks
regional system. The arrows indicate the direction of causation. The symbols used in the
causal feedback loops are summarized in Figure 5. The following is a description of each

feedback loop identified in the diagram.

Causal Feedback Loops
Mstmcture Capacity - Infrastructure Investment Loop
The three feedback loops in Figure 6 communicate with each other through
infrastructure capacity. As visitors increase, infrastructure congestion goes up; this tends
to decrease regional accessibility. If infrastructure capacity congestion achieves a certain
level, the manager must make the decision to invest in new infrastructure according to the
infrastructure investment criteria; then, infrastructure capacity will increase. This is a

balancing loop which is described in loop 1 (infrastructure capacity - infrastructure

47

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51-3 and/or B#
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48

The surrounding causal loop is a Balancing loop, possibly identified by a
number.

 

% and/or R#

Reinforcing Loop

The surrounding causal loop is a Reinforcing loop, possibly identified by a
number.

 

 

>

Influence

An arrow represents a causal link from one variable to another. The arrow's
label signifies whether the variable at the arrow tail will influence the variable
at the arrow head positively (S) or negatively (O).

 

\_—-—-\\——~A

Delayed Influence

A double line across the link signifies that the influence is significantly
delayed.

 

—@+

Change in the Same
Direction

Given that all other influences are constant, a change in the variable at the
arrow tail will change the variable at the arrow head in the Same direction.

 

Change in the
Opposite Direction

Given that all other influences are constant, a change in the variable at the
arrow tail will change the variable at the arrow head in the Opposite direction.

Figure 5. The Symbols in the Causal Effect Diagram

Source: Powersim User’s Guide and Reference, 1993

49

Visitors/@)m—_\A Infrastructure
Congestion
Infrastructure %zj ’:
Ca t
p acr y Regional
Accessibility
1

Infrastructure I
Depreciation 3

Load Index »
Infrastructure
\@\J Investment
' Rate
Carrying Infrastructure
Capacity Investment
Criteria

Figure 6. Infrastructure Capacity - Infrastructure Investment Loop

50

congestion - regional accessibility - infrastructure investment rate - infrastructure
capacity).

As a matter of fact, the decision to invest in infrastructure is not only a response to
congestion, but also a response to load index. In loop 3, load index has an Opposite
impact on infrastructure investment. When the infrastructure capacity goes up, visitors
will increase. An increase in visitors will cause the load index to increase. At the same
time, in loop 1 the infrastructure capacity increase will allow the regional accessibility
index to go up. In other words, the load index and regional accessibility index will
increase simultaneously. This means that the two indexes must be considered in the
feedback of the investment policy and decision. The causal feedback loops are exhibited
in the balancing loop 3 (load index - infrastructure investment rate - infrastructure
capacity - visitors - load index) and reinforcing loop 2 (visitors - infrastructure congestion

- regional accessibility - infrastructure investment rate - infrastructure capacity - visitors).

Supprstructure Capacig — Suparstructure Investment Loop

Actually, there are two loops coupled together in Figure 7. High quality level of
service makes a positive contribution to regional attractiveness. When the regional index
goes up, visitor numbers will increase. An increase in visitors will increase the

SUPCI'SII'UCIUI'C USC

51

WSuperstructure—g—p Superstructure
I .

nvestment Rate Capacity
Superstructure "T“ 2
Level of Service

kg? 1 Superstructure

Use Rate
Superstructure

Depreciation
Regional

Attractiveness
Visitors

Figure 7. Superstructure Capacity - Investment Loop

rate (i.e. Visitors/superstructure capacity). Therefore, an increase in the use rate will
stimulate investment and promote the construction of superstructure. This feedback
behavior is exhibited in reinforcing loop 1. In the balancing loop 2 (superstructure use
rate - superstructure investment rate - superstructure capacity - superstructure use rate), it
is demonstrated that an increase in the superstructure use rate will result in an increase in
superstructure capacity. However, an increase in superstructure capacity has an opposite

impact on superstructure use rate.

52

. Manp_ower Management - Resources Magnitude Loop

The manpower management sub-sector is displayed in Figure 8. AS mentioned in
Chapter H, manpower in the National Park region can be divided into two categories:
technology and management manpower. Technology manpower includes manpower
necessary for the development of new resources. Management manpower includes
manpower necessary for the maintenance of existing developed resources.

Management manpower is a variable that is based on seasonal adjustments.
Management manpower, therefore, becomes a controllable variable. The magnitude of
recreation resources to be managed determines the level of potential maintenance
employment. If there is a gap between potential management manpower and real
management manpower, then recreation resources will begin to decline. This relationship
is reflected in balancing loop 1 (resources magnitude - potential maintenance employment
- gap - resource decline - resource magnitude). As shown in balancing loop 2 ( real
maintenance employment - gap - adjustment - real maintenance employment), when a gap

occurs, the manager must adjust real maintenance employment in order to reduce the

gap-

 

53

Potential
K Load Index
Visitors

Figure 8. Manpower Management - Resource Magnitude Loop

Another relationship between the magnitude of tourism and recreation resources
and resources maintenance and development is demonstrated in this loop. The magnitude
of tourism and recreational resources will increase due to tourism and recreation
resources development. Unless the developed resources are well maintained, they will
decline gradually. The larger the magnitude of recreational resources, the more attractive

the region to visitors. An increase in the magnitude of recreational resources will

54

increase the carrying capacity. By dividing the number of current visitors by the carrying

capacity, the load index can be calculated ( current visitors / carrying capacity).

Visitor - Attractiveness — Load Index Loop

The feedback loop illustrated in Figure 9 indicates the relationship of visitors,
attractiveness, and infrastructure capacities. In this model, the basic assumption is that
regional attractiveness is the indicator which reflects the potential number of visitors who
would like to access this region. If infrastructure capacity is available, then potential
visitors will become real visitors to the region. Also, note that regional attractiveness
depends upon both resource magnitude and load index in the loop. In summary, this
balancing loop demonstrates that when infrastructure capacity increases, the number of
visitors and the load index increase. This causal feedback is demonstrated, therefore,

when the regional index decreases and potential visitors declines.

Transpprtation Capacity - Demand/Capacity Ratio Loop

 

 

As transportation capacity increases, due to investment in transportation or other
related variables, the demand-capacity ratio falls. Inadequate investment in
transportation, therefore, may result in a transportation Shortage. A lower transportation

capacity, on the other hand, results in more funds being diverted to transportation. This

55

loop, which is shown in Figure 10, will not let capacity overshoot demand. This is a

balancing loop controlling investment in transportation.

Resources @N Regional @\

Magnititude Attraction Potential

Visitors

579
Load Index

\G‘D/ Visitors

Infrastructure
Capacity

Figure 9. Visitor - Attractiveness - Load Index Loop

56

Transportation
Capacity

Demand-
Capacity
Ratio

Investment in J
Transportation

Figure 10. Transportation Capacity - Demand/Capacity Loop

Rural Population - Per Capita Income - Rural-Urban Migration Loop

This balancing loop controls the growth of rural population. An increase in
population will decrease per capita income, which then induces the population to migrate
to the urban area. This migration keeps the population rise in check in the region of the

park. The structure of this loop is shown in Figure 11.

Population - Net Birth Loop
A traditional, reinforcing growth process is demonstrated in Figure 12. This loop
shows that population influences net births per year (births minus deaths) which, in turn,

influences population.

Rural Per
Capita
Income

Rural
Population

nae

Rural Urban Per Capita
Income Difference

Rural-Urban J

Migration

Figure 11. Rural Population -Income - Migration Loop

Population % Net Births

Figure 12. Population - Net Births Loop

58

Population - Labor Force - Unemployment - Migration Loop

An increase in population implies a large labor force which results in a higher
unemployment rate assuming that at some point population growth overcomes the
economy’s job creating potential. This encourages out-migration of the population. This

loop is illustrated in Figure 13.

Labor Force

Population

Unemployment

Out-migration

Figure 13. Population - Labor Force - Unemployment - Migration Loop

59

Wages - Regional Attractiveness - Industry Production - Employment Loop

Industrial production and employment changes in the same direction. An increase
in industrial output requires more labor, thus employment increases. An increase in
employment (lower unemployment rate) results in higher wages as it becomes
increasingly difficult to find labor which stimulates large increases in wages. Higher
wages reduce the region’s attractiveness for new businesses or expansion of existing
businesses. As regional attractiveness decreases, capital investment in industry declines
and thus industrial output is constrained. The structure of this loop is shown in Figure 14.

h m...
Employment

Wage

i Industrial

Production

Regional
Attractiveness

©\> Capital Add Capital in

Rate \@__.’V Industry

Figure 14. Wage Rate- Regional Attractiveness Employment Loop

6O

Hfllth Services- Deaghs - Population Loop

The extent of health services in the region can be expressed in terms of the
hospital bed-population ratio. Increasing this ratio leads to a reduction in the death rate
which results in the increasing of population and, therefore, the hospital bed-population

ratio is decreased. The Structure of the loop is shown in Figure 15.

Hospital Bed to
Population Ratio

73%

Deaths

Population

Figure 15. Health Services - Deaths - Population Loop

School Enrollment - Literacy Rate - Productivity - Income Loop
Four loops are integrated in Figure 16. Three of them are reinforcing loops, and

one is a balancing loop. Intersectoral dependencies are represented through two of the

V LIteracy Rate —'@——> Productivity

233/
School
Enrollment "T“
“a
\‘@/ Births %

8‘3

Per Capita
Income

Figure 16. School Enrollment - Literacy Rate - Productivity - Income Loop

reinforcing loops and the balancing loop. As school enrollment increases, the literacy
rate goes up; this tends to change society’s attitude toward education and school
enrollments increase. When the literacy rate goes up, productivity will increase because a
better educated work force permits the increased use of new production technologies.
Thus, the economic situation in terms of per capita income is improved along with
educational opportunities. At the same time, the higher literacy rate will increase the
acceptance of population programs and practices and decrease the birth rate. Reducing

total births and population increases per capita income and school enrollment. Both

62

loops are reinforcing in nature. The balancing loop is the school enrollment - literacy rate
- births - school enrollment loop. It is evident that the natural growth of population will
increase the demand for education, while the increased literacy rate will depress the birth

rate.

Pollution - Concern for Quality of Life - Concern for Economic Growth Loop

There are four causal loops included in Figure 16. Three balancing loops and one
reinforcing loop. Per capita income grows in the same direction as production and
visitors. As investment in tourism/recreation and investment in industrial production
increase, regional per capita income will go up. However, pollution will increase as well.
On the other hand, because the self-abatement ability of nature is limited by its carrying
capacity, when pollution effluent increases over the carrying capacity, the self-abatement
ability in nature will decay. Therefore, if more and more pollution is generated without
investments in pollution control, degradation of environmental quality will become more
and more serious.

In National Park areas, park management policy makers search not only for
economic welfare but also environmental quality. When regional per capita income
increases coupled with decreasing environmental quality, the demand for environment

quality become higher. As the number of people concerned with environmental quality

63

Per Capita @\ @.__
vuwpimome Concern for l \) Concern for
V Quality “W Economic

Industrial Life Growth
Potential Production
Visitors
Pressure for

  
 
 

®\VPollndon
Regional
AM‘M QM” at we
Costs Government
‘15 3 t Intervention
Superstructure
Private
Investment W Invesanent in Government
Pollution Inveetrnent in
Abatement Pollution
Abatement

Figure 17. Pollution - Concern for QOL - Concern for Economic Growth Loop

64

increases, the number of people concerned with economic growth decreases. The result is
that this public concern will lead to more pressure to improve environmental quality. On
the other hand, pollution intervention by government will increase the pollution effluent

standard and force investment in pollution abatement by private industry.

Summgy of Causal Feedback Loops of the Model

Twenty-two causal feedback loops are identified in the intersectoral relationships
in Figure 18. In addition, each of the sectors represented in Figure 18 consist of
numerous feedback loops. The twenty-two loops include eleven reinforcing and eleven
balancing causal feedback loops. Each of the loops has a separate purpose and may
compete with other loops to dominate overall behavior of the National Park regional
system. The development of a regional economy is generated by shifting the dominance
from one loop to another. The growth phase of development is dominated by reinforcing
loops. During the transition from growth to equilibrium, dominance shifts from
reinforcing to balancing loops. Balancing loops determine the nature of the post-growth
and equilibrium state (Forrester, 1973). In other words, the equilibrium state of an
economy can take various forms depending upon which balancing loops ultimately

suppress growth.

65

 

 

 

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Figure 18. A Summary of Causal Effect Loops

66

Quantitative System Dynamics

This section discusses quantitative computer simulation modeling using dedicated
modeling software. This is a more conventional and traditional phase of System
Dynamics and involves deriving a system model through the shaping of relationships
between all variables within the diagrams, the calibration of parameters, and the
construction of simulation equations, and experiments. In this section, the calibration of
parameters and the construction of simulation equations will be presented. As noted
earlier, for the purpose of keeping the presentation manageable, sophisticated and
complex calibration will be avoided. A computer-assisted simulation experiment and

model behavior will be presented in Chapter IV.

Computer Software

As mentioned in the previous chapter, computers play a vital role in System
Dynamics model implementation. It is very important to emphasize that the simulation of
System Dynamics models can be carried out in any computer language. High level
computer languages are most appropriate and better still is the use of purpose built
software, since these encompass algorithms which facilitate the construction of dynamic
models. The Powersim software was employed to execute Quantitative System
Dynamics in this study. Powersim is for the IBM PC range of computers and takes full

advantage of the Windows and the hard-wired graphics interface designed for these

67

particular computers. This software is designed so that diagrams representing models can
be drawn directly on the computer screen using a predefined tool kit. The variables of the
diagrams are presented by icons which can be opened to insert parameter values and
relationships between variables. This program is excellent for demonstrating the
relationship between system feedback structures and system behavior, and for involving
system actors more closely in the model-building and analysis process. Also, this
software was developed in order to provide ease of interaction for gaming simulation

models and to build user interfaces for data input and output.

Genegtl Structure of Equations

In converting the causal effect loops introduced previously to a computer
structural diagram, all assumptions made in the model are expressed in terms of
mathematical equations or table lookup functions. Basically, the structure of the system
is perceived as consisting of three basic components: levels, flows and auxiliaries. Also,
the delay concept and function will be discussed in this section. A simplified structural

diagram is shown in Figure 19.

68

LeveI_Variable

 

Auxiliary_Variable

Figure 19. A Simplified Structural Diagram

Level . Flow and Auxiliary
In mathematical terms, it is noted that level equations are "integrating" equations
that accumulate the net result of past rate of change in the level. If a differential equation

formulation is used, these equations would appear as integrals:

69

LEVEL .., = LEVEL.“ J:(RA — RS)dt

That is, the level at time t+l(for example, number of employees) is the level at t=0
plus the cumulative flow [input (RA) less output (RS), e.g., hiring (RA) less turnover
(RS)] to date. The time (dt) quanta is being used. In other words, the RA-RS can be
represented as a rate variable. This rate variable denotes that the net rate of change of the
level variables is the inflows minus the sum of the outflows. The logic here is that level
variables are calculated at the current time and the rates are projected for the forthcoming
time period. Time is then moved forward in the simulation software by dt. On the other
hand, level variables represent an accumulation of flows into the system such as
inventories of goods, population, recreation resources, etc. Rates of flow represent the
activities and decision functions in the system such as the movement of goods, migration,
demolition of housing, as well as generation and depreciation of capital, etc.

When the variable is expressed in the Powersim computer program, the equation for

a level variable can be displayed as follows:

LEVEL...l = LEVEL:0 ........... (1)
+(dt)*(RA) ........... (2)
-(dt)*(RS) ............ (3)

The meaning and dimensions of these variables are provided below.

IEVEL.=0 : The initial value of level variable.

7O

dt : The term dt denotes "delta time", which is the integration interval
in the simulation.

RA : Rate being added to the level variable.

RS : Rate being subtracted from the level variable.

The first part of a level equation defines the level's initial value. In this case it can
be represented as LEVEleo, The second part of a level equation defines the inflows
(+dt*...) and outflows (-dt*...). For each new time step (dt) in the simulation, each level
variable is assigned a value which is equal to the level's previous value plus dt multiplied by
the sum of the flows. In this case, the level is increased by RA and decreased by RS.

Levels and rates form the fundamental building blocks for the modeling of a
system, but it is rarely feasible or desirable to specify all rates solely in terms of the levels
in a system. To make the definition of the rate variables clearer and easier, a class of
auxiliary variables is used.

In terms of the feedback structure of a system, auxiliary variables simply bridge
the gap between levels and the rate changing other levels. In the practice of modeling,
however, auxiliary variables tend to be most numerous and represent important concepts
in the system under consideration. Auxiliary variables tend to be based on information

within a system and act to control the physical components of the system.

Delay

One of the most important features of the systems approach to modeling is the

recognition of the saliency of lags and delays in the ways systems respond to changes

71

within the system as well as to external simulation (Levine, 1993). Some forms of delays
which are used in this study will be discussed in this sub-section. A “shorthand” notation
is introduced to represent the actual level and rate equations for delay.

According to Forrester (1961), delay is a conversion process that accepts a given
inflow and delivers a resulting flow rate as the output. The content of the delay increases
whenever the inflow exceeds the outflow. In empirical research, not all delays in a model
need to be estimated. It is often the case that System Dynamics models are completely
insensitive to a delay and occasionally the case that even the time length is unimportant.
Therefore, simplifying the delay used is necessary. Two kinds of simplifications are always
used to reduce the number of points at which delays must be introduced into a model
formulation. First, many system delays will be judged to be too short to affect the system’s
behavior. Such delays are negligible compared with the other longer or more significantly
. located delays. Second, some delays arise from separate, processes which are cascaded one
after the other and can often be combined into a single delay representation.

There are two classes of delays: material and information. The difference between
these classes does not affect the numerical results unless the length of the delay is changing.
Levine (1993) demonstrated that in many recreation, physical, and economic systems, delay
processes entail holding, manufacturing, building, growing, and other activities which take
time. A good example is that it might take 12 weeks to retrain a group of workers who have

been laid off their previous jobs. Unlike the information delay, there are material things

72

stored in stock for that length of time. In other words, in the material delay, whatever is
being delayed is considered a physical substance and is conserved as the length changes; in
the information delay it is not.

When applying delay concepts in a flow channel within a mathematical model,
several computational processes might be used to create a delay form. In System Dynamics
modeling, exponential delays are commonly adopted in the application. There is no need to
exclude other kinds of functions that could be used to create delays in a flow; however,
exponential delays are simple in form, and they have adequate scope to fit modelers’ usual
degree of knowledge about the actual systems to be represented (Forrester, 1961).

Delays can be characterized by their “order,” the number of levels through which
the variable being delayed passes. The “order” of a delay is an integer greater than or
equal to 1 that determines the shape of the delay. A first-order delay of a variable
produces an exponential average of past values of that variable; an nth-order delay is a
sequence of n first-order delaysz. The graph shown in Figure 20 represents the response
of delays of increasing order, all having a delay time of 10. Lower-order delays have an
immediate response that is dispersed. Hi gher-order delays have a deferred response that
is more concentrated. The choice of the order of delay depends upon the modeling

context, but a third-order form is commonly used to create an approximately normal

 

2 The detail mathematical computational processes of “order” of delays can be found in Rander (1980) Elements of

System Dynamics method, pp. 162-183.

73

distribution of output from a given input. In the Powersim computer program, the
SMOOTH function may be used to represent first-order delays. To access the total

quantity contained in a third-order delay, the DELAYINF (nth order information delay)

and DELAYMTR (nth order material delay) are used.

 

 

 

 

TIflE
OUT FOR FIRST ORDER DELAY
OUT FOR THIRD ORDER DELAY
OUT FOR SIXTH ORDER DELAY

Figure 20. Output of Various Orders of Delay

Source: Wolstenholme, 1990

74

Iagble function

Although equations are one way to represent the relationship between variables,
many systems contain quantities that are related to each other in ways for which algebra is
not well suited. These are situations where the modeller knows how two variables are
related at a causal level and can express the relationship graphically. In other words, table
functions are useful when modellers do not know the exact mathematical formula of a
function or when modellers want to use statistical or empirical data to express a
relationship between two variables. Figure 21 represents an example of a table function

for two different seasonal patterns in two National Park regions.

Characteristics Index

    
 

 

 

1.2 -
1.0 -
/ NationalParkl
0.8 -
0.6 -
NationaIPark 2

0.4 -—
0.2 -
0 0 l r r I r r r

0 2 4 6 8 10 12 14

M onths

Figure 21. Example of a Table Function

75

Key Variables Formulation

In the previous section, the general equations for level variables and rate equations
are explored. In this section, the structural diagrams for separate sectors and some
selected key variables formulations which are associated with regional economic
development will be discussed in detail. A full set of the equations for each model
appears in the equation listings in the text and in the complete model listing in appendix
B.

Tourism and Recreation Resources Sector
Recreation Resources

It is very difficult to quantify recreation resources without specifying the
characteristics of a specific recreation region. For this model, it was assumed that the
main resources that are considered to attract visitors include visually pleasing scenery and
recreational opportunities, such as forest recreation, lake shore frontage, wilderness
landscape, etc... Thus, in a park region, the magnitude of recreation resources is
represented by positive contributions to the resources of the region that have been
developed or managed to attract visitors. Hence, in this model, the area developed for
recreational use within the particular region being modeled represents the magnitude of
the recreation and tourism resources.

The structural model diagram of recreation resources is shown in Figure 22. The

rate of recreation resource development is calculated as potential recreation resource

76

divided by the time period required for developing recreation resources, and then times
the ratio of technology manpower. In other words, technology manpower is considered a
controllable variable in recreation resource development. The equation can be

represented as follow:

ResDevRate = (PotDevRes / DevPeriod ) *RatioTecMan

where

ResDevRate: recreation resources development rate

PotDevRes: potential recreation resources (represented by acres)

DevPeriod: the time period for development of recreation resources (years)

RatioTecMan: ratio of technology manpower

The recreation resources decline rate can be calculated as, the recreation resource

magnitude divided by the average time period for resources to decline, multiplied by the
gap in management manpower. The gap in management manpower is only a control
variable intended to maintain the management manpower needed to achieve the desired
management manpower level. Without adequate management manpower, resources

decline may become very small, however, it is impossible to lower the decline rate to

zero. The calculation is as follow:

77

 

 

 

 

 

 

 

 

 

 

upUnn
r' “I
, D D
dlust UnltDepre Unitlnve L
MagtPeopFract UsaRate
AdjustRat SupServiceLev |I :
t' 1 \ SuplnvP :
Att 0 I. .I ‘
AdluotTlme N Month DopTlme Suplnv X
rlndex Av lnvPa
Potentla anp Che ‘upCap g
PerManRos - - - ‘
tenvtslt I' 'I D SUSR
s VIsA SupDe s
7 ‘ ~ ._ D '- " I' " smv
AttrDumrnylndex SAttrD ' Po Days
u n
I. .I
V O AvglnvPenod N SuplnvPar
r ‘I
AocuLoadlndex supc‘pp" 5“ N
VorRate u Ianate L J
t' CriticalPoint VisitorPerYear p
RecoveTIme I. .I
ResDevRat L oadlnde
lncR DecR
PotDevRos ecRosM gnitude UseRate
Decline MR
AngecPer _‘ r 1 Visitors
L _, ‘ AveUserRatlo
RatloTochMan DevPorlod L -’ Potenvis'
MagtPoopF ction Congestion G
CarryCapc IntraCapPorI Month
RosCap '
r ParlodDays
Coun
‘ v D ‘ v I‘
I. .1 Loadlndex .
Penodoayc ’ ' ‘

 

I
r 1 I
' ' 2
r' 1 ‘ L.J ‘ . ‘
L \\ AvglnvPerlod : r'

.1
Loadlndo ‘\ InvesRate e L.

  

.I
' 6 Parameter P DepTime
"‘\
AvoLoad ' ‘ 1
lnvesParameter ‘- ~.
‘ I. .1
. ”00°09“ Congestion
Acces

Figure 22. A Structural Diagram of the Tourism and Recreation Sector

78

ResDecline = (RecResMagnititude / DevPeriod) * GapManRate

where

ResDecline: rate of resources decline

RecResMagnititude: developed recreation resource magnitude

DevPeriod: average time period for resource decline

GapManRate: gap in rate of maintenance manpower

Carrying capacity and load index are two important indicators for measurement of

the acceptable space for visitors. In this model, they are represented by the concept of
density. The two indicators are also used to control resource decline and feedback

' concerning visitors” quality of experience. The carrying capacity for a recreation area is

given by:

CarryCapac = ResCap * RecResMagnititude (persons/time period)
where:
CarryCapac: carrying capacity (persons/time period)

ResCap: space standard for one person (person/acre)
RecResMagnititude: recreation resource magnitude (acre/time period)

LoadIndex= Potenvisit / CarryCapac
where:

LoadIndex: Load Index
Potenvisit: Potential Visitors
CarryCapac: Carrying Capacity

79

According to the equation, if the load index is greater than 1, the carrying capacity
cannot accommodate an increase in number of visitors; therefore, the attractiveness of
recreation resources will decline. In the model, a dummy variable named "accumulated
load index" is used for estimating the degree of decline of recreation resources. If the
accumulated load index is greater than 1 in a continuous time period, then the regional
attractiveness of recreation resources will decline throughout the time period. This
continuous time period is called a critical period. If the load index is less than 1,
recreation resources will recover gradually and the accumulated load index will decrease.

The time period for recreation resources recovery is called recovery time.

Visitor Attractiveness

The function of tourism and recreation demand is reflected in the visitor
attractiveness index (VisAttrIndex) and potential visitors displayed in the causal effect
diagram. In the equation used to depict this in the model, the first order delay exponential
average function (DELAYINF) is used to represent the delayed value of information. The
delayed value of information considers the time lag between increases in recreation

resources and/or level of services and the resulting increase in the number of visitors.

80

Recreation Area Characteristics

Recreation and tourism primarily are seasonal businesses (Mill & Morrison,
1985). Thus, visitors have a unique distribution according to each area’s particular
characteristics and the particular season of interest. If the area has different
characteristics, it will have a different visitor distribution. The TABLE function of the
relationships between visitors and season (time) is shown in Figure 23. These

relationships are represented by the CharIndex variable.

Infrastructure Capacity

The construction of infrastructure has delay information value, so it will be
represented by a third order delay exponential function. This means that only when
decision makers sense that the over crowding has occurred, do the processes of budgeting
and planning for improvements begin. Infrastructure depreciation can be calculated as

follows:

Depreciation = InfraCap / DepTime
Where

Depreciation: depreciation of infrastructure
InfraCap: infrastructure capacity
DepTime: average depreciation time period

81

Characteristics Index

1.2-

1.0“

0.8 —

0.6 -

0.4 -

0.2 —

 

 

0.0

d
-4
-
ad
—I
—
—I

Months

Figure 23. National Park Characteristics Index

Investment decisions are made according to the congestion level and load index value
achieved. The TABLE functions of infrastructure investment criteria are shown in Figure
24 and Figure 25. The relationships among average congestion, average load index and

the investment coefficient are exhibited in these two figures.

82

laIrasIrucrure lavesIIII eat Pararn eIer

0.30 1
0.25 — F
0.20 -1
0.15 «-
0.10 -J
0.05 -

0.00 —

 

 

r
0 1

A veraage Congestion

Figure 24. Infrastructure Investment Criterion Based on Congestion

Infrastructure Investrn ent

 

 

 

0 I I I I T I
0.2 0.3 0.4 0.5 0.6 0.7 0.8

A verage Load Index

Figure 25. Infrastructure Investment Criterion Based on Load Index

83

Superstructure Capacity and Level of Service

For the model, it is assumed that not all visitors will use the superstructure. The
proportion of visitors (represented by variable AveUserRatio) represents the ratio of
visitors (less than 100%) that actually use the superstructure. The superstructure use rate

can be calculated as follows:

SUseRate = AveUserRatio * visitors / SupCapPerProd
where
SUseRate: superstructure use rate
Visitors: numbers of visitors (persons/time period)
SupCapPerProd: superstructure capacity per time period (persons/time period)
AveUserRatio: average ratio of visitor use of the superstructure
Decision makers will determine investment in superstructure according to the
superstructure’s use rate and investment criteria. Investment criteria represent the level of
investment for superstructure to accommodate a given level of superstructure use. The

relationship of use rate and investment criteria is presented by a TABLE function and is

shown in Figure 26.

84

Equvalent Based
Investment Parameter

0.7 — /

 

 

fi $
0.6 4
0.5 -*
Capacity Based
0.4 "1 /
0.3 4
0.2 ~
0.1 -
0.0 —
I I I I I 7 I I

 

 

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Superstructure Use Rate

Figure 26. Superstructure Investment Based on Use Rate

85

Another key variable in the model is level of service (SupServiceLev). Level of
service is the overall measure of all superstructure service characteristics that affect
visitors. Level of service is a basic element in attracting potential visitors to the National
Park regional system. For simplicity , the superstructure level of service in the model
only assumes two kinds of superstructures, which are A and B. Each is weighted to
represent the proportion of superstructure level of service in this area. The level of

service index can be calculated:

SupServiceLev = (Ca * SCAP, + Cb * SCAPb) / SCAPa + SCAPb= SupUnit / SupCap

where
SupServiceLev: superstructure level of service index
SupUnit: Superstructure Equivalent Unit
SupCap: Total Superstructure Capacity
Ca: weight of A superstructure capacity
Cb: weight of B superstructure capacity
SCAPaz capacity of A superstructure
SCAPb: capacity of B superstructure

The model assumes that C. has a value of 2, and Cb has a value of 1. The resulting
level of services of superstructure A is double that of the level of services of
superstructure B. The existing superstructure level of service index assumed for this area

is close to 2. In other words, the level of services of superstructure A is increasing more

rapidly than that of superstructure B. Similarly, if the index is close to 1, the level of

86

services of superstructure B is increasing gradually. In summary, the superstructure level

of service index reflects the superstructure quality change for the area.

Economic sector

The economic sector can be described in two figures. In Figure 27, the
employment subsector is an important indicator in guiding economic development
decisions in the National Parks region. Included in this sector are employment, per capita

income, and wage rate. In Figure 28, the land use subsector is presented.

Wage, Per capita income, and Employment

The relative wage rate is an important variable in Figure 27. It gives an indication
of the difference between the national average wage rate and the region’s average wage
rate. The relative wage rate in the region is thus the ratio of the regional average wage
rate to the national average wage rate. The relative wage rate depends upon the target
relative wage rate and the time required to attain it. The target relative wage rate is
defined as that relative wage rate which will eventually become the relative wage rate
after an appropriate time lag. The target relative wage rate is a function of the average
wage rate, lagged unemployment ratio and minimum wage. If the target wage rate is
higher than the minimum wage rate, the target relative wage rate is determined by the

average wage rate and lagged local unemployment ratio, otherwise the target relative

NorInWagoA LaborOutFac

 

 

 

  
 

RelativeWage

NormWageBS

 

 

- OutpuBua
L J L J FUDPIP 0 FIG
GovtEmp EduEmp MedPerson
r 1
W m... e e 0
v L .I
‘ ’ - ' 113005005109 , Tm .. GDVIEI‘I'ID
' - .‘FacHo * ' '
mwmm“ 8‘.“me > . - ‘ .- 1
. W C v - r . .
CLOIItFacl-Iou _ , EduEmp ,. . L .I
HousEInp

TerEduEInp

 

-
Manpower I...I

BuaEmp
o .-..
M15019 L J
. LaborForee
.

I. .I

LUnempRate T oIE that t

Figure 27. A Structural Diagram of the Employment Subsector

~88

 

 

       

AcoeptRResDen

DesirUubanPopD

U anLandVelue .
I Distance

I
UrbanLand ‘ .

UrbanRadius

 

Figure 28. A Structural Diagram of the Land Use Subsector

89

wage rate is given by the ratio of the minimum wage rate to the national average wage
rate. The national wage rate is assumed to be fixed. Average wage rate is total wages
divided by total employment. The following conditional equations give the target relative

wage rate.

TargetRw = IF (T arWage > MiWage, TargetRWl, TargetRW2)
where

TargetRw: target relative wage rate

TarWage: target wage rate (Dollars/Persons/Day)

TargetRWl: target relative wage rate]

TargetRW2: target relative wage rate2

MiWage: minimum wage rate (Dollars/Persons/Day)

From the above, it can be seen that if the target wage rate is greater than minimum '
wage rate, the target relative wage rate will equal target relative wage rate]; otherwise,
the target relative wage rate will equal the given target relative wage rateZ.

It is assumed that only wages comprise income. Property income is assumed to be
negligible. In fact, wages in many cases represent disposable income. Hence, it is
assumed that the indicator of regional economic development is per capita income arrived
at by using total wages and that per capita income affects other economic variables. Per

capita income is total wages divided by total population. Rural per capita income is

wages that accrue to the rural population divided by the number of rural residents in the

90

region. Urban per capita income is sum of business and service wages divided by the

number of persons living in the urban region.

Land use

The land use sub-sector in the economic sector is shown in Figure 28. It is
concerned with human activity in a very broad sense. It is concerned with the living
patterns of households, productive patterns of industries, selling patterns of retail and
personal service establishments, and the many other classes of activity patterns that exist
and interact as elements in the regional social system. Thus, from this viewpoint, "land
use" means a great deal more than existing or proposed improvements visible on the
ground. Another very important element of the land use subsector is that it is intended to
capture a constantly evolving and continuously changing phenomenon. Thus, the model
is designed to mimic the dynamics of expansion of land areas in the region.

Urban land areas and rural residential lands are represented by level variables and
which are increased by expansion rates. The land area increase rate both in urban and
rural areas is a first order delayed function of the indicated increase rate. It is much easier
to obtain land in the rural area; therefore, a longer delay time for urban land expansion is
assumed. Because the acceptable population density indicates the point at which
expansion of urban areas and rural residential land starts, as population density

approaches acceptable limits, the impact of crowding and higher density and pressure to

91

expand becomes more severe and urgent. It is assumed that the rural acceptable
population density is 1.2 times the actual population density and that the urban acceptable
density is 1.5 times the actual density. Next, the value of the indicated expansion rate is
to be determined. It is assumed that required land for expansion in rural area is the
difference between the desired land area and the acceptable land area. This relation is

expressed as follow:

RMunicAreaInc = RPOP / RuResDensity - RuralMuniArea [1.2
where:

RMunicAreaInc: required land for expansion in rural area (Hectares)

RPOP: rural population (Thousand Persons) ’

RuResDensity: desired rural residential area population density (Thousand

Persons/Hectare)

RuralMuniArea: rural residential land area (Hectares)

Before considering the urban expansion rate, it must be noted that it was assumed
that the land needed for urban expansion is always available during the simulation period.
Thus, the indicated urban expansion rate depends not only upon the population density
but also upon average land value. The model assumes that the average urban land value
is the principal factor affecting the quantity of land available for expansion. A multiplier
is used to specify this effect. The assumed relationships between urban population

density and, land area expansion, and land value is included in the model as a TABLE

function which is shown in Figure 29.

Figure 29.

92

 

 

Land Value Increase Multiplier for Urban Land

It is evident that initially land value increase has a positive influence on urban

land, but, after a certain level of land value is reached, the trend is reversed. The average

urban land value follows Mills’ land rent theory (Owen, 1964) that:

mm = Roe'AU

where

R(U): land rent at distance (U) from city center
R0: a constant of integration interpreted as land rent at the city center
0: natural base of logarithms

A:

U:

exponent specifyingthe land rent change which depends upon the size,
pattern, and function of the city
distance from city center

93

Applying Mills’ theory, the model assumes that the land value in the urban center
changes when the urban population density is greater than the desired population density
and there is an increase in proportion to the ratio of actual to the desired population
density. The actual population density is simply the ratio of current population to current

urban land acreage.

The Environmental Sector

The structural diagram of the environmental sector is displayed in Figure 30. The
environmental sector plays a trade-off role between economic growth and environmental
pollution. Pollution is a level variable. It is in essence a pool in which pollution
discharge accumulates and from which pollution then empties. The pollution index is an
indicator to represent the trend in pollution accumulation. The index is based on the ratio
of the pollution pool (Pollution) to pollution standards (PollInStandard). Also, concern
for economic growth and concern for quality of life are two indicators which represent the
trade-off relationships involved in the environmental sector of the model. The latter

relationship is defined in a TABLE function which is displayed in Figure 31.

94

 

         

 

 

 

 

ConEc
Rate
PollStandard
Govinterv 'l J
Pm nves QolPoll PolluRate
Pollution
I. .I
QOL PO'lNO'm IncPolI DecPoll N
r 'I ’V PoliAboTIme
PerCaplnL _, PollProM N
ollVisiM (V
" L
QolPoll
L _, CocQOL
Visitors
N N

QOLln me QOLStand

 

ConEc
Rate
.1
N PerCapln

Figure 30. A Structural Diagram of the Environmental Sector

95

Concern Economic Growth

1.40 T
1.35 —‘
1.30 '-

1.25j

1.20

I

1.15 -

1.10 -*

1.05 —

1.00 -*

 

 

0.95 I I I I f

Concern QOL

Figure 31. Concern for Quality of Life vs. Concern for Economic Growth

Population Sector

The population sector is shown in Figure 32. Development of the population
sector basically follows the cohort-survival method. Here, the components of the method
are birth rate, death rate, and aging rate. The birth rate is calculated by dividing the

number of live births in a year by population in the same year. Similarly, the death rate

96

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

r' 1
| J
RPOP
Bom_UC U -UOid MDie I
UAdIlt TotPOP
UBkIhRate It
Dead d DAP
UCDeath .
0mm U' 0' 00W
DCP
RMIII
9
Op NoChiPA A
r 1 (V r 1
TotaiWorkPop N
J LIDOme L LFPRM L J M
I igMutl
IRMIR
t. .1 i
UEmpM
L “L otalFIWorkPop no”
I ”I d | Ra Reid
RIFPRI '
1d
Bom_
Mb
I" 1
r 1 11000th L J
DCP
I. .I
Rt»!
1' 1
(V n 7
I. .I
mom L -'

 

 

 

 

 

L .I
WNOCNPA

Figure 32. A Structural Diagram of the Population Sector

97

is calculated by dividing the number of deaths in a year by population in the same year.
Aging rate to the next cohort is a reciprocal number of the number of people within each
age group. It is assumed that the number of people of each age within an age group is the
same. For instance, the number of individuals 13 years old within the 12-17 year old age
group is assumed to be 1/6 of the total 12-17 year olds.

The cohort-survival technique starts with age cohorts at the base year. By adding
births and subtracting deaths and then aging the residual to the beginning of the next age
group during the simulation period, yields the population in the next year by age and age
groups. Births, deaths, and aging are calculated applying assumed age-specific fertility,
survival, and aging rates to the population. These rates are determined exogenously. The
model is recursive; i.e. population of one age group in a given year is a function of
population of the same age group in the previous year and the appropriate fertility, survival,
and aging rates. It is noted that the model cannot take into account unpredictable social and
economic changes such as war and recession, which would cause significant deviations

from the recent past trends in births, deaths, and migration.

The Social Overhead Sector
Three main components are included in the social overhead sector. These are:

health services, population programs and practices, and education. The related structural

98

diagrams are shown in Figure 33 (health service and family subsectors) and Figure 34

(education subsector).

Health Services

Health services in the region are expressed in terms of hospital beds. Two level
variables are used to represent hospital beds in both urban and rural areas. In both cases
the level of hospital beds is increased by a rate derived from the actual-indicated—
possible-desired mechanism. The desired hospital bed increase rate is proportional to the
products of the medical personnel deficiency multiplier times the difference between

desired hospital beds and actual beds. The relationship can be shown as follow:

UrbanHospBedDesir = ((DUHBPR * UPOP - UHB) / UHDRT) * MPDM
where

UrbanHospBedDesir: desired urban hospital bed increase rate (Bed/Year)
DUHBPR: desired urban hospital bed population ratio (Bed/Thousand Persons)
UPOP: urban population (Thousand Persons)

UHB: urban hospital beds (Bed)

UHDRT: urban hospital development realization time (Year)

MPDM: medical personnel deficiency multiplier

99

 

FamPerPopRatio

FamiinianPersonne
‘ r 1
‘ ‘1
D I. .I
\ TotPOP
F3 P178109? FamPPRetired
I

 

 

 

 

FamiPlaPerTraTime '
’ FamPlanPerSetTime
PPFtealizationTime
FamPPTrainlnd

I" 1

FamPPTrainD sir
FamPPTPoss L _,

TotPOP
CapRequrFPP '1

I. .I DesirFPPerPopRatio
FPBudAllocat

Figure 33. A Structural Diagram of the Health Services and Population Programs Subsector

IOO

 

,6)

n'.‘ m

, re

a”: .. .

Figure 34. A Structural Diagram of the Education Subsector

101

The expansion of hospital beds has to take into account the availability of medical
personnel. The TABLE function ( Figure 35 ) shows the relationship between hospital

bed increases and the medical personnel deficiency ratio.

Medical Personnel Deficiency Rate

1.1'1

 

 

 

0.2 I I I I O

0.0 0.2 0.4 0.6 0.8 1.0

II ospital Bed Increase

Figure 35 . Hospital Bed Increase vs. Medical Personnel Deficiencnyatio

The possible hospital bed increase rate is equal to the health development budget

allocation divided by the cost per hospital bed. The actual hospital bed increase rate is

l02

the delayed value of the indicated rate which is the smaller of the desired and possible
increase rates.

The demand for medical personnel is the product of medical personnel to hospital
bed ratio times the number of hospital beds. Based on the report of Popoy (1971), a
medical personnel to hospital ratio of 3 persons per bed is applied to this model. The
medical personnel available in the region is increased by medical graduates or
vocationally trained personnel, and decreased by retirement rate. Medical personnel
includes all levels of vocationally and professionally trained employees in hospitals.

These include doctors, nurses, technicians, and pharmacists.

Programs and Practices Affecting Population

It is rather difficult to quantitatively formulate programs, policies, and practices
that affect population into the regional development model. However, based on the
assumption that population program personnel are the principal force carrying out the
population-related programming and practices, the model simulates the demand for such
population program personnel. The influences of a population practices in the recurring
natural birth rate are considered in the demographic sector of the model and will not be

discussed here.

103

Population program personnel, referred to as population programs and practices
personnel (FPP) in the model, include obstetrician, physicians, and professional family
planning personnel.

It is obvious that the success of a population control programs depend principally
upon the achievements of these population program personnel. The level of population
personnel is increased by a training or recruiting rate and decreased by a retirement rate.
It is assumed that five population program personnel per 10,000 population are needed to
ensure the success of the whole program. The actual-indicated-possible-desired
mechanism is applied to determine the population personnel (FPP) recruiting rate. The
ratio of population personnel to regional population indicates the degree of influence on

the regional birth rate.

Education

Education is the basis of manpower development. The level of education
achieved by the population to a large extent determines the potential success of economic
development programs. The model classifies the educational system in the National
Parks region as primary, secondary, and tertiary. - Pre-primary and graduate education are
excluded from the model structure for the reason that they are less important to

manpower development. Intermediate education is included in the primary level as is

104

customary in the US. Vocational, college, and university education are aggregated in the

tertiary level.

Capital Allocation
The structural diagram of the capital allocation component of the model is
exhibited in Figure 36. The principal indicator is the capital pool. The capital pool is

treated as a level wherein all external loans, external aid, internal loan repayments and

L J
TotWaoe

. ParsonlncomoTax

. PersontnoomeTaxRate

 

t'
O “W
L J W
UPetCann

Figure 36. A Structural Diagram of the Capital Allocation Sector

105

savings are accumulated. Allocations are made out of this pool for various activities

during each time period iteration of the model.

CHAPTER IV

SYSTEM MODEL AS AN EDUCATIONAL TOOL

Introduction

This chapter is divided into three main sections: [1] Gaming Simulation
Application; [2] Case Study of the tourism and recreation sector model implemented in a
classroom setting; and [3] Academic Programs, including teaching procedures and
curricula planning. This chapter explains how gaming simulation can be incorporated into
the learning process and illustrates the classroom uses of the National Parks regional
model by providing a case study of a simulation experimental problem with

accompanying suggested academic programming strategies.

Gaming Simulation Application

In an educational context, a simulation model is a powerful tool for teaching
aspects of the world by limiting or replicating it. Students are not only motivated by
simulations, but they also learn by interacting with simulations in a manner that
approximates real situations. A simulation model simplifies reality by omitting or
changing details. In this simplified world, the student solves problems, learns procedures,
understands the characteristics of phenomena and how to control them, and recognizes

alternative actions for different situations.

106

107

The teaching style that utilizes gaming simulation has four phases: presenting the
student with information; guiding the student in acquiring information and skills;
providing practice to enhance retention and fluency; and assessing learning.

Gaming screens play a dynamic role by connecting complex models with game-
players who may be unfamiliar with the inner operations of models. Through gaming
screens, game-players receive information on the major variables in the National Park
regional system. The information received by game-players, which is enhanced and
presented by appropriate game screens and interfaces, is an important basis for making
policies or changing policies in response to particular regional development problems and
circumstances. Each game-player can actualize his policy changes by clicking on
appropriate policy buttons and by changing the position of policy levers that appear on
the gaming screens.

Gaming screens provide direct interaction with the National Park regional model.
They display, graphically or numerically, the changing trends of major variables in the
National Park regional model. Gaming screens also convert the policy changes made by
game-players into numerical changes within the regional model.

The gaming screen consists of five separate screens: [1] Policy Option; [2] Time
Table; [3] Time Graphic; [4] Equation; and [5] Main Model. Convenient access to each
screen is facilitated by clicking a small icon. The Main Model gaming screen is exhibited

in Figure 37. The policy option screen, shown in Figure 38, contains a set of parameters.

108

In this policy change mode, the learner can use the pointer tool to adjust parameters of the
model by moving the buttons that represent policy objective parameters.

Activating the Time Table icon on the main screen brings the user to the output
screen containing scrollable fields that accumulate numerical data representing major
variables. The Time Table screen is shown in Figure 39. A double click on the Time
Graphic icon on the main screen reveals graphic time-series phase plots based on the
numerical data. The Time Graphic screen is displayed in Figure 40. Target variables for
observation can be accessed through the variables selection screens by double clicking on
the screens of Figure 39 or Figure 40.

The Main Model screen is composed of the structural diagrams for each sector
that is described in Chapter IH. This screen helps users understand the inner relationships
and workings of the model, and it helps with sensitivity analyses of the variables. A
double click on the Equation icon reveals the mathematical presentations and variable
definitions for the whole model.

Instruction and learning are enhanced by utilizing the gaming simulation in the
classroom. The gaming simulation application allows groups of students to use the model

to create and interact with various policy and management scenarios that can serve as a

109

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113

basis for discussions and further studies. This type of interaction provides an
experimental learning experience for students that is different from a traditional lecture.
A case study demonstrating the use of the interactive gaming simulation model as an
educational tool is presented below. Further information regarding the integration of
modeling concepts and the gaming simulation interface in various academic programs

and within course curricula is also discussed below.

Case Study of Tourism and Recreation Sector

To illustrate how the National Park regional planning model can perform in a
classroom setting, various scenarios focusing on one of the sectors of the model will be
demonstrated. This case study, of the Tourism and Recreation sector, will effectively
demonstrate the gaming simulation interface, student interaction and experimentation,
and how dynamic system behavior can be understood through the use of the model (for
any National Park regional planning sector). The following scenarios are derived from
three alternative policy development options that provide the instructor with a framework

to present the material in model form to students.

114

Model Base Run of the Tourism and Recreation Sector

The gaming simulation interface was developed to enable users easy access and
better understanding of the National Park regional model. For example, the Policy
Options screen in Figure 38 requires the user to slide the bar to different options to
observe its effect. Prior to implementing the model, a model base run is necessary to
establish a point of reference (baseline) to accommodate policy comparison. The model
base run does not require any interactive policy involvement or input. The data in Figure
41 show the simulation results of the base run of the Tourism and Recreation sector. The
number of visitors increased during the first 7 years and then drops dramatically,
primarily because of the discrepancy in the development of recreation resources after 7
years and the decreasing growth of superstructure capacity.

In Figure 41, infrastructure capacity growth rate rises steeply during the first 9
years. However, after the 9th year, the growth rate begins to decline. The superstructure
capacity increases during the first 3 years, but steadily drops until the year 10. In Figure
42, a typical S-shape curve growth trend can be observed. According to Bronson, et al
(1988), the S-shape curve is a valid representation of the fundamental relationship
between variables in macrosociological theory. The value representing superstructure
level of service exhibits sustainable growth during the first 8 years but declines during

years 9 and 10.

 

 

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The development of recreation resources is concentrated within the first 8 years.

The development rate is greater than the decline rate over this time period. Beyond year

116

8, the development rate is less than the decline rate, so developed area is decreasing after

year 8.

Change in Tourism and Recreation Policy Variables

The following scenarios are derived from three alternative development policies:
Tourism/Recreation Resources Development; Infrastructure Investment; and
Superstructure Investment. Each of these scenarios are examined through the dynamic
behavior of the National Parks regional model. The adoption of each policy is
represented in the model by particular changes in the controllable parameters of the
structure, i.e. changing variables through the Policy Option gaming screen. The
discussions are limited to the examination of the probable consequences of adopting that
particular policy and investigating the issues, if any, which might arise from taking a

particular course of action. Each policy can be treated independently using the model.

Policy for Tourism and Recreation Resources Development
The scenarios used to test this particular Tourism and Recreation Development
policy involve examining the effect of the policy on the number of visitors, infrastructure

capacity, superstructure capacity, and level of services.

117

Scenario 1

Assumption: The resources development rate is equal to the resources decline rate. In this
case, the resources development strategy is intended to maintain the tourism/recreation
resources at a constant level.

In the base run, the model uses the following resources development rate and
resources decline rate:

ResDevRate = (PotDevRes / DevPeriod) * RatioTechMan
ResDecline = (RecResMagnititude / AveDecPer) * (1.2 - MagtPeopFraction)

In order to modify the model to simulate the desired scenario, the resources
development rate must equal the resources decline rate. That is, the model must reflect a
situation where the development of natural resources related to tourism and recreation is
equal to the loss of tourism and recreation resources. To accomplish this, the above
equations are replaced by the equations shown below:

ResDevRate = ResDecline = (RecResMagnititude / AveDecPer) * (1.2 - MagPeopFraction)
where

ResDevRate: potential rate of tourism and recreation development

ResDecline: tourism and recreation extraction rate

RecResMagnitude: base magnitude of tourism and recreation resources

AveDecPer: average resources development rate
MagtPeopFraction: the proportion of management manpower to total manpower

118

Scenario 2
Assumption: The resources development rate is less than the resources decline rate. In
this case, the tourism and recreation resources are experiencing a declining trend.

In the base run, the model uses the following ratio of technology manpower:
RatioTechMan = 1.0

Since alternative scenarios are based upon the assumption that the resources
development rate is less than the resources decline rate, the base model’s fraction of the
technology manpower input, which directly affects new resource development, must be
altered. Thus, the above equation is replaced by the equation shown below:
RatioTechMan = 0.2
where

RatioTechMan: the fraction of technology manpower

Scenario 3
Assumption: The resources development rate is greater than the resources decline rate. In
this case, the tourism and recreation resources variable is experiencing a positive growth
fiend.

In the base run, the model uses the following resources development time:

DevPeriod = 150 (days)

119

When the resources development rate is greater than the resources decline rate, the
value of the resources development time period in the base run, which directly affects the
resources decline rate, is decreased. Thus, the equations are replaced by the equation
shown below:

DevPeriod = 60 (days)
where

DevPeriod: the resources development time period.

Results of Simulation Scenarios for Tourism and Recreation Resources Development

To facilitate comparisons among the three alternative scenarios, the number of
visitors is plotted in Figure 42. Under scenario 1, the number of visitors declines steadily
but at a slow rate because the development rate lags behind the resources decline rate.
Furthermore, this decreasing trend becomes more severe in scenario 2 due to the lower
ratio of technology manpower input. Under scenario 3, due to the higher resources
development rate, the number of visitors trend reverses to a steady growth trend.

From these three scenarios, it can be concluded that resources development policy is
a very sensitive factor in National Park regional development. Shortening the development
time period and inputting more technology manpower into the system, therefore, are two
strategies for accelerating resource development. Relationships such as these can help
students understand the importance of potential tourism/recreation resources in the planning

process.

Number of Visitors (1000 Persons)

120

Res. Devp. Rate > Res. Decline Rate

 

 

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3500 - /
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O 6 8 1 0 1 2

Years

Figure 42. Comparison of Number of Visitors in Three Scenarios

121

Policy for Infrastructure Investment

Investment in infrastructure is a critical factor in the development process of
regions. As an educational tool, the simulation experimentation can help students
understand the degree of sensitivity of information delay and investment criteria among
infrastructure investment alternatives. As discussed in Chapter III, experimentation with
information delay can provide basic information for making efficient policy decisions.
Testing investment criteria in the context of the model can provide the information

needed for students to observe tourism/recreation development trends.

Test of Information Delay

The parameter change for testing the role of information delay in the National Park
system is shown in Table 2. Students are shown that decreasing the information delay
results in a large growth of infrastructure capacity; however, the natural resources
themselves suffer from over use. Chronic over use eventually causes the number of visitors

growth to become less than that of the model base run.

122

Table 2. Parameters for Testing of Information Delays

 

 

 

Testing Items Average Information Delays
Base Run 12 Months
Scenario - Decrease Information Delay 6 Months

 

 

 

 

In conclusion, lessening the information delay promotes the construction of new
infrastructure. This increase in infrastructure capacity contributes to an increase in the
number of visitors. The increases in the number of visitors leads to over use of resources,
which then becomes a barrier to the further development of tourism and recreation

TCSOUI'CCS .

Test of Investment Criteria

The original investment criteria and two new investment criteria are shown in
Table 3. The data in Table 3 shows that the proportion of infrastructure investment based
upon an average crowding index ranging from 0 to 1. The assumption in scenario 1
involves conservative investment of development resources, where resources for
development are applied and increased in relatively small and gradual increments. The

assumption in scenario 2 involves aggressive investment of development resources,

123

where resources for development are applied in relatively large increments. The

simulation scenario results are displayed in Figure 43 and Figure 44. These results show
that the infrastructure capacity is high in scenario 2 and low in scenario 1. However, the

number of visitors under scenario 2 is always lower than under scenario 1.

The data in Figure 44 demonstrate the negative effects of over investment in

infrastructure. A comparison of the model base run and scenario 1 in the first simulation

Table 3. The Infrastructure Investment Criteria

 

Testing Items

If Average Crowding Index (0 - l)

 

 

 

Investment

 

 

 

 

 

 

0.2 0.3 0.4 0.5 0.6
(Investment Parameter)
Base Run 0 0 0.08333 0.1667 0.25
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Investment
Scenario 2 - Aggressive 0 0.04167 0.0833 0.1667

0.333

 

 

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126

time period shows that the number of visitors growth of the scenario 1 is worse than the
base run. However, the number of visitors growth of scenario 1 is better than base run
after 7 years. These results demonstrate that the interrelationships and combinations of
identified variables have a significant causal effect on the behavior of the regional system.
These changes only affect one sub-sector of the entire model, and such sub-optimization
may not represent the best alternatives for the whole system. The above tests can offer

students an opportunity to observe change in system behavior under different conditions.

Policy for Superstructure Investment

Although most superstructure investment resources are contributed by the private
sector, decisions regarding superstructure investment are made by public sector managers.
This is an important consideration when teaching students about the consequences and
decision making processes of various superstructure investment policies. The results of
superstructure investment policy experimentation can be used by managers to evaluate
the alternatives for encouraging or restricting private sector superstructure investment.

The decision criteria for superstructure investment are based on the average I
superstructure use rate. (Superstructure use rate is a function of the superstructure
capacity divided by the number of visitors--see Chapter III). Decisions to increase

superstructure investment are made only when the use rate reaches a specific level. These

superstructure use rate levels vary across the base run, scenario 1, and scenario 2. The

127

original superstructure investment criteria and two new superstructure investment criteria
represented by the different simulation scenarios are shown in Table 4. The data in
Table 4 shows that the proportion of superstructure invetment based upon average use
rates ranging from 0 to 1. Scenario 1 assumes that the superstructure use rate is higher
than the use rate in the base run and that public managers are willing to allow high quality
private-sector superstructure investment at this higher level of use. Scenario 2 assumes
that the superstructure use rate is higher than the use rate in the base run and in scenario
1, and superstructure investment is initiated by public managers only when use rates reach
these much higher rates. The resulting amount of superstructure capacity, which is
displayed in Figure 45, is less under scenario 2 than under scenario 1, partly because
superstructure investment in scenario 2 is made only when use rates are much higher.

The unique and somewhat counter-intuitive relationship under scenario 2 between
superstructure capacity and the number of visitors, displayed in Figure 46, is better
understood using the simulation capability of the system model. Comparison between the
base run, scenario 1, and scenario 2 helps to demonstrate that as decision makers raise
investment criteria (i.e. investment at higher superstructure use rates), the amount of
superstructure capacity is reduced. Because scenario 2 has higher investment criteria, it
takes longer for investment levels to reach those displayed under scenario 1, and less
superstructure capacity is developed as a result. This relationship is clearly shown in

Figure 46. However, despite lower superstructure capacity, the number of visitors is

128

higher under scenario 2 than under scenario 1. This unique relationship indicates to
students that other factors must influence the number of visitors and associated
superstructure use rates. In this model, additional variables affecting the number of
visitors include superstructure level of services as well as regional attractiveness. The
model’s superstructure causal loop and related variables are illustrated in Figure 7 and
explained in Chapter III. The simulation model effectively teaches students, for example,
that the conservative development of superstructure capacity can encourage a higher level

of services that appeal to and attract park visitors.

Table 4. The Superstructure Investment Criteria

 

 

 

 

 

 

Testing Items If Average Use Rate (0 - 1)
0.3 0.4 0.5 0.6
(Investment Parameter)
Base Run 0.08333 0.1667 0.25 0.3333
Scenario 1 0 0.08333 0.1667 0.25
Scenario 2 0 0 0.1667 0.0833

 

 

 

 

 

 

 

129

Number of Visitors

 

 

 

4400 *
Start to invest when use rate very high
4200 -
4000 - Base Run
3800 ~
3600 -
Start to invest when use rate is median

3400 -
3200 I I l I I I

0 2 4 6 8 1 0 12

Years

Figure 45. Comparison of Number of Visitors under Alternative Superstructure Investment

Criteria

Superstructure Capacity

7500 T
7000 ~
6500 -
6000 -
5500 -
5000 ~

4500 -*

 

4000

130

Base Run

Start to invest when use rate median

.‘O...

Start to invest when use rate is very high

 

Years

Figure 46. Comparison of Superstructure Capacity under Alternative Investment Criteria

13]

Academic Programs in National Park Regional System Dynamics

Natural resource management can be viewed as the organization and control of a
system. The use of models to permit system behavior experiments can provide an
integrating structure for the teaching of natural resource management. The use of models
motivate students and provide “synthetic experiences” in policy making. The study of
National Park regional System Dynamics can be introduced at the beginning of an
undergraduate academic program and continue through doctoral and executive
development programs. At the Master’s and doctoral thesis levels, it provides a means
for experimenting with new hypotheses and concepts of natural resource management
organization and policy.

National Park regional System Dynamics in the natural resource management
curriculum integrates the other subject matter of natural resource or recreation
management. It ties together functional areas and adds the time dimension by creating an
understanding of growth and of system-created change in natural resource operations.

System Dynamics and the National Park regional models introduce causal effect
feedback within the educational process. As an idea is developed, it can be set up
immediately as an experiment, and the results can be evaluated and revised. This
sequence of invention, experiment, evaluation, and review is facilitated by the modeling

process described in Chapter I through Chapter IV.

132

This work provides an environment and teaching tools that make it possible to
observe and evaluate personal characteristics and learning styles of students in ways that
are not always available in traditional classroom environments. To convert knowledge
into a dynamic model, students must identify a problem, visualize the dynamic concepts
which are at work, show initiative and judgment in selecting factors that must be
incorporated, cope with uncertainty and incomplete information, and supply

inventiveness in seeking system improvements.

Using this Study as an Instructional Tool

This study provides two separate but related expositions: [1] the development of
concepts of National Park systems behavior, which is described in Chapters I, H, and III,
and [2] a discussion of the implementation of instructional process and computer
simulation applications. Natural resource management, like engineering, medicine and
architecture, is a practical profession dedicated to addressing unique needs and achieving
specific goals. The successful practitioner, in any profession, must be highly motivated to
address these needs successfully. Providing the opportunity for students to recognize and
understand these needs and associated goals as soon as possible in the learning process,
through the study of and interaction with models such as the one described in this study,
is vital to their success as students and practitioners.

The experimental study of the National Park regional model does not require

advanced mathematical knowledge. By using the information contained in this study,

133

gaming simulation application, education and learning in natural resource management

can begin with an understanding and application of basic Quantitative System Dynamics

and Qualitative System Dynamics issues and concepts. These concepts can be readily
understood through the interpretation and use of information from numerous familiar and
accessible information sources, including history texts and current natural resources
publications. Such educational tools enable students to focus on the most relevant
educational topics and enhance motivation in the study of natural resource management,

National Park options, and regional economic development.

The following three suggestions outline general instructional principles for the

National Park regional System Dynamics model:

1. When implementing the National Park regional System Dynamics model, the
instructor should focus on the concepts of the National Park system and how to
express and interrelate the natural resource factors that comprise this system.
He/She should not focus on the techniques and the methodology of System
Dynamics.

2. Classroom simulation, with students playing the part of various components of the
system, can be used to demonstrate the policy impacts of economic development
in National Park regions. Group simulation offers a more personal and dramatic
way of demonstrating and learning important concepts. Simulation also can

demonstrate convincingly that prevailing environmental factors are a primary

134

determinant in the decision-making process. Decisions regarding the best action
are often so powerfully influenced by available information that intuitive
decisions by different people are surprisingly similar.

3. The availability of a modem high-speed computers with complex graphical user
interfaces offers drama, vitality, and scope to experimental System Dynamics.
Instructors considering using System Dynamics in their courses will likely be
experienced or inexperienced modellers. Inexperienced modellers are not advised
to consider System Dynamics unless they have access to a previously developed
model. Even inexperienced instructors can quickly master the use of an existing
System Dynamics model, such as that developed in this study, and use it

effectively in their classes.

Curricular Plan

This study and the gaming simulation interface it describes can be used to enhance
courses in both Park, Recreation and Tourism Resources as well as Resource
Development. Relevant course topics and subjects that are addressed by the study are

listed in the Figure 47.

135

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136

National Park regional economic development issues, such as those identified in Figure
47, are most effectively taught within lecture and discussion formats when combined with
various forms of hands-on laboratory work. Such laboratory work can include concept
expression of the causal feedback interrelationships, the study of more complicated
systems by using the interactive gaming simulation interface, and individual and group
projects in formulating models of natural resources situations. This study and gaming
simulation interface can be used either for independent study or as a supplement to other
classroom material and activities. In Figure 48 , the curricular plan for single and
multiple sessions, one semester and two semesters are demonstrated. The minimum
classroom resource requirements necessary to utilize the study and the gaming simulation

interface as an educational tool are listed below.

1. Powersim software (ModellData, Inc.) and an IBM—compatible personal computer

with 386 or greater processor and 4MB or more random-access memory (RAM).

2. Appropriate presentation equipment, such as an overhead projector and LCD

panel, to view instructor-led classroom discussion and interaction.

3. Student access to IBM-compatible personal computers and the gaming simulation
software. Students could access the computer systems and/or software in the

classroom, in a computer laboratory, on a network, or via other means.

137

 

 

Time Required Users Style Subjects Objectives
Single/Multiple Instructor Lecture Describe modeling Introduce modeling
Session (Interactive) process and scenarios process and application in
Natural resource and
National Park regional
planning processes
One Semester Instructor Lecture & Qualitative System Understand system process
& Student Experiment Dynamics, Gaming- and variable relationships
simulation Interface and estimate system
behavior", hands-0n use of
the gaming simulation
interface
Two Semester Instructor Lecture & Entire study, Master System Dynamics
& Student Experiment including System modeling methodology
Dynamics approach , and construct models of
and supplementary natural resources and
material park, recreation and

Figure 48. The Curricular Plan

tourism management and
plmfigprocesses.

Discussion regarding the modeling process and the use of the gaming simulation

interface can be used on a limited basis to supplement existing course topics. For a

relatively quick review of the modeling process and to demonstrate its use in a simple

policy planning scenario, instructors may expose students to relevant portions of the study

and provide an interactive demonstration of the gaming simulation interface in class. For

138

single or multiple sessions, instructors and students can read the verbal-description
sections in Chapters I and H and the policy-implications section in Chapter IV. An
instructor in one or two lectures, for example, could introduce the modeling process and

implement a policy-implication scenario with his/her class.

For a more complete analysis and understanding of National Parks regional
planning problems, teachers and students in one-semester course can read and discuss the
part of the study that addresses planning/policy variables and other Qualitative System
Dynamics issues. The instructor can select the model’s sectors that emphasize the
analysis of National Park regional problems that will help students understand the
relationship between system processes and variables and test strategy design changes. In
this case, the gaming simulation interface becomes a more integral part of the students’
learning process--students can utilize the hands-on, experiential interface to explore
different scenarios related to policy implementation and to become more knowledgeable

about dynamic systems behavior.

To address all aspects of the study, including National Parks regional planning
problems as well as Qualitative and Quantitative System Dynamics issues, in the most
comprehensive manner, a two-semester course would offer students the best opportunity
to master the material and its applications. Recommended two-semester course topics

include all of the study’s concepts, including model formulation, behavior, and

139

methodology as well as its application in National Parks and natural resources regional
planning processes. A comprehensive study of these System Dynamics issues also
requires the use of appropriate supplementary material, and a list of suggested readings in
the Appendix A to assist instructors and students. The instructor should use primary and
supplementary material regarding the modeling process and policy implications to
provide students a realistic context for System Dynamics modeling and natural resources
planning. By understanding all of the concepts addressed in the study and suggested
readings, students will be able to create their own models to assist in natural resource and

tourism, park, and recreation management and planning processes.

140

CHAPTER V

CONCLUSIONS

This chapter contains a summary of the preceding four chapters, conclusions
drawn from the development and implementation of the model, the limitations of this
study, recommendations for using it as an educational tool, and recommendations for
improvement of the model. The summary includes a brief explanation of the need for
effective teaching tools that enhance critical exploration and discussion in the classroom,
the use of the System Dynamics model and gaming simulation, the goals and objectives

of the model, and the System Dynamics modeling process used in this study.

Summary of the Study

National Parks regional planning and economic development problems facing
instructors, managers, and policy makers today have become increasingly complex,
ambiguous and interrelated. The traditional teaching style, lecture and discussion, often
can fall short of their potential as a strategy for teaching classroom concepts. Traditional
styles are especially limited when the classroom focus is on complex systems. The lecture
style of teaching does not allow meaningful two-way communications between the
instructor and the learners. Discussion style may address this basic communication

problem. However, because it is often facilitated in a manner that does not encourage full

141

participation, discussion usually only engages a few students. Furthermore, analysis
regarding critical thinking and exploration by students demonstrates the importance of
teaching key concepts and issues within the context of real problems and concerns (Meyers,
1986.)

Thus, what is needed is a systematic and organized approach to teaching that can
exercise and develop the problem solving, critical thinking, and discussion skills of
students. In such an approach, concepts introduced in the classroom can be applied to real
life situations or to controlled experiments. These applications can better facilitate the
learning process by providing the proper analytical framework for understanding and
discussion of key t0pics. An old Chinese proverb says, “ I hear - I forget, I see - I
remember, I do - I understand.” Its message is that “hands-on” experience can be the best
way to master concepts taught in the classroom.

The System Dynamics National Park regional system model was developed
primarily to make learning of the regional economic development process in the natural
resources area easier by providing a real world context within which learning can take
place. Relationships, dynamic processes, and policy impacts are modeled so that they- can
be explored critically and dynamically. The model can be used for simulation
experimentation and as the basis for comprehensive System Dynamics modeling and

regional planning courses. In the process of gaming simulation, this model will

142

demonstrate for students the impact of recreation and tourism and population and natural
resources issues on the regional planning and development process.

Goals of the National Parks regional planning model include aiding students in
identifying key elements of economic development, understanding the interrelationship of
these elements, and describing planning and development processes. In order to effectively
meet these goals, a number of considerations were made when developing the model. The
resulting objectives are identified below.

1. To provide sufficient tools and guides to permit the widespread use of the System
Dynamics simulation model for educational uses in National Park regional planning.

2. To apply a suitable methodology for establishing an integrated model to organize
expert knowledge and theory into a meaningful scheme for educational purposes.

3. To construct a dynamic, graph-based operational computer model that can simulate
the process of National Park regional planning and development.

The modeling process used to develop the National Park regional system model
involves two System Dynamics tools—Qualitative System Dynamics and Quantitative
System Dynamics. Qualitative System Dynamics is a based on creating cause and effect
diagrams according to theories, academic references and rules of thumb, and using these
to explore and analyze the system. Quantitative System Dynamics, the conventional and

traditional modeling tool, involves deriving with system actors the shape of relationships

143

between all variables within the diagrams, and the calibration of parameters and the
construction of simulation equations and experiments.

These System Dynamics tools were used to develop a system model consisting of
multiple classifications of system interactions. These interactions provide the primary
organizational framework for integrating the many diverse dimensions of the National Park
region. The modeling process used to build the System Dynamics model of a hypothetical
National Parks regional system and its contribution to the creation of an effective
educational tool, is described below.

Qualitative System Dynamics was applied to formulate a systematic analysis of
National Park systems to examine the scope of the analytical task. The process began by
setting out the general assumption of the study, namely, the explicit treatment of the total
recreation/tourism system of a National Park and the interrelations between
recreation/tourism and its socioeconomic context. The overall system was divided into
seven major sectors. These sectors were industry, recreation and tourism resources,
economic, capital allocation, population, social overhead ,and environment. The
information was shared among these sectors. Some information needed in each sector must
be produced by other sectors, and each sector could count on getting some of its basic
information from other sectors. The process then identified those sectors of the system that

consisted of variables that could be manipulated as part of a learning process.

144

The resulting organizational framework was engaged to examine the feedback and
boundary of the National Parks regional system. The components in each sector and
sector-to-sector information requirements are defined in a causal feedback loops diagram.
The main causal feedback 100ps analysis was demonstrated in Chapter III. In this model,
twenty-two causal feedback loops were evident in the intersector relationships of National
Parks region with emphasis on tourism/recreation and economic development.

The causal effect diagrams create a forum for translating thoughtsand
assumptions about the system by individual actors into useable ideas which can be
communicated to others. The affect is to broaden the understanding of each person by
making them aware of the system as a whole and their role within it. In addition, through
simulation, learners can rigorously experiment with concepts and processes regardless of
their time frame. For example, students can quickly and easily manipulate various
control variables to modify population size and growth or accelerate the development of
tourism and recreation resources.

Quantitative System Dynamics guided the conversion of system causal feedback
loops to a structural diagram that could be utilized as an effective educational tool.
Powersim, an IBM personal computer program, was used to develop a structural diagram
for the seven National Park region sectors and their sub-models. The structure of the
system is perceived as consisting of three basic components: levels, rate, and auxiliary.

Basic data were obtained to verify the parameters’ values and the variables’ relationships.

145

The structural diagram was then used to demonstrate how policies for economic
development can be simulated and evaluated using the regional planning model. In order to
place the policy runs in proper perspective, a basic simulation run describing the behavior of
the National Parks region without policy inferences was presented and discussed. In
addition, policy analysis was employed to assess the impact of variable model parameters
on the behavior of the system.

The National Park regional simulation model is designed with an appropriate
graphic-based gaming simulation interface. Gaming screens enable dynamic interaction
between complex models and game-players who may be unfamiliar with the inner
operations of the models. Through gaming screens, game-players receive information on
the major variables in the National Park system. Game-players can actualize their policy
changes by clicking on policy buttons or by changing the position of policy levers
appearing on the game screens.

This study, the National Park regional planning model, and the gaming simulation
interface can be used to create an educational tool that can be used to enhance a variety of
courses in Park, Recreation and Tourism as well as Resource Development. A curricular
plan and teaching procedures designed to accommodate different levels of use, as well as
an example of a classroom simulation experiment, are provided to guide educators in

using the study and the regional planning model in the education process.

146

Conclusions

This System Dynamics modeling technique was selected because it treats at a
National Park region as a "system" and analyzes the relative components simultaneously.
The approach can make controversial assumptions explicit, and provide a common
framework for learners to develop a shared understanding of the problem. In addition, the
modeling processes can illustrate basic concepts and theories, and provide a richer and
deeper understanding of dynamic phenomena. Through the process of creatively exploring
the basics of a subject, understanding of the subject matter is deepened, and critical drinking
and problem solving skills are enhanced.

As a result, there are many different possible uses and applications of this type of
model. These applications are briefly reviewed below.

1. Much of academic research in natural resource management has been following
the tradition of the liberal arts and the social sciences-«gathering data about the
past and seeking explanations for the present. The present can use more than
explanation. It needs more effective concepts for natural resource management
and economic leadership. Hence, an academic program built around System
Dynamics simulation model can be dramatic, challenging, and intellectually
demanding. The impact on academic research in natural resources management
can be vast, moving research away from mere data collection and explanation, and

into a position of leadership toward the design of more effective system.

147

The main goals of this study for the gaming-simulation model player are not to
have players grapple with the particular substantive issue loaded in the framework
(i.e. the school system issues). Rather, the primary objective is to have the players
learn enough about the National Park regional structure and economic
development process to be able to design a new version, content-specific to their
own needs.

Using the National Park regional simulation model will strengthen the educational
experience in the classroom. A sound educational experience will emerge when
the teacher/trainer pays attention to simple recommendations concerning
preparation, operation, and leading discussion/critique.

Even if the model consists of complicated and complex phenomena, such as
feedback, delays, and non-linearity’s, which normally require very sophisticated
mathematics, System Dynamics models can be created that are “user friendly.”
Such models can be used by instructors and students who are not modellers to
explore and learn about complex systems.

The results of the simulation runs presented here show that the author has to a
great extent managed to portray the basic patterns of behavior that can be
observed during tourism/recreation and economic development. Both the
assumptions expressed in the model equations and the simulated results are

consistent with the descriptions given in the literature. Assessing the model

148

revealed certain elements that could be improved through further refinements and
by gathering improved data on which to base these refinements. With the
completion of these refinements, as outlined in the following sections, the model

could meet minimal requirements to serve as a helpful teaching tool.

Study Limitations

This study has some limitations that should be addressed. These limitations are
related to the selection and arrangement of information and variables for the model and
the use of the model as an educational tool.

A primary limitation involves model testing. Many interesting scenarios can be
played out by experimenting with various settings of the model parameters. The model
lets students see the effects of tourism and recreation development policy decisions,
combinations of economic variables, and various future developments in the National
Park regions. However, the study does not exhaustively document simulation runs and
sensitivity analysis. Sensitivity analysis is best facilitated as the model is applied and
tested. It would be desirable to repeat sensitivity analysis for selected model variables as
the model is used in the future. The result of sensitivity analysis will assist in refining
and enhancing the level of confidence in the use of the National Park regional model.
These results also can effectively help the instructor and student focus on the critical

variables for experimentation and discussion.

149

An additional limitation involves an inherent problem with the use of models--
that the model users may misperceive or rely too much on a model as an actual 100%
realistic reproduction of real world systems. Therefore, it must be emphasized that a
model is always a simplified representation of reality that should be used as a tool in the
decision making process. In this sense, the simplified nature of a model becomes its
major strength by allowing the student to focus less on the least important aspects and
more on the vital topics of discussion. To simplify reality is, however, not without
problems. How do the users know which aspects are important and which are not? This
implies that model users and instructors should be very careful and considerate of how
forecasted values of variables are used in the planning process. Although the behavior
pattern of a model may be accurate and reliable, there is no guarantee that simplified
system representations will accurately reflect how a real-world system actually develops.

A third limitation of the model developed in this study is related to the fact that
the person who learns the most from such a model tends to be the person who built it.
Therefore, any strategy for introducing the use of National Park regional models as a
means for gaining and conveying insight into economic and social principals should
include not only the use of ready-made models, like the one presented in this study, but

also the model building process itself.

150

Recommendations

This research represents an initial attempt to assist educators to effectively teach
economic development issues regarding natural resources and National Park regional
development. The comprehensive regional planning model developed requires additional
work to maintain and enhance its reliability, validity, and relevance as an educational
tool. These additional efforts support the use of study concepts and the gaming
simulation model for other educational purposes as well, including career planning for
students, professional development for educators, and academic program planning for
educators and practitioners.

Additional work is recommended to enhance the continued utility of the National
Parks Regional Planning Model. Although the model can be used effectively in its
current form, gradual improvements can be made as better information becomes
available. Ongoing activities that will improve the model are identified below.

1. The structure implemented in the system should be fully developed and tested to
enable a complete assessment of the validity and reliability of the approach. A
follow-up study of surveys of the National Park regions, therefore, is necessary to
improve estimates of the parameters, constants, initial values, and table values.

2. Continuous model calibration is necessary to keep the model up-to-date and to
accommodate changes in economic development policies and environmental

conditions.

151

3. Efforts should be maintained to test the model in real classroom settings and other
settings and uses for which the model can be of assistance to determine if the
model actually performs as expected.

4. Use ongoing tests and implementation analysis to improve the model as well as
the training process to help instructors understand and master its use.

5. Additional assistance is necessary to help instructors access and use the model,

including the development of a user’s manual.

Career Planning, Professional Development, Academic Program Planning

In addition to providing a valuable teaching and learning tool for instructors and
students, the National Parks regional planning model and gaming simulation interface
described in this study is particularly well suited to assist in other academic areas as well,
including career planning, professional development, and academic programming.

The process of identifying and modeling associated societal and environmental
factors provides for students a comprehensive view of the complex and interesting
aspects, knowledge, skills, and professions that are a part of Natural Resource and _.
National Parks regional management and planning. These insights offer valuable
assistance to students as they explore career planning alternatives.

The study also enhances professional development activities for educators by

directly addressing a number of evolving needs and priorities within the education

152

profession. By offering for educators a hands-on demonstration of an interactive,
experiential learning tool, the planning model and gaming simulation interface can be
used effectively to help educators perceive the benefits of alternative teaching styles.
Experiential and active learning opportunities for students, which can be demonstrated
and taught through appropriate professional development activities, allow students to
build their own knowledge structures in their own contexts. “Constructive” professional
development approaches, which include hands-on action research, group problem
solving, peer interaction, and curriculum development, are encouraged by the use of the
study and its gaming simulation interface. These approaches may help educators
understand and focus more effectively on learner needs and outcomes.

In addition, the study can be used to help educators obtain a deeper understanding
of the modeling process and National Parks regional planning issues and the most
effective pedagogical approaches for teaching related concepts and topics. Incorporating
key modeling concepts and the gaming simulation interface into professional
development activities also encourages systems thinking among educators. Systems
thinking is playing an increasingly important role in curriculum development, academic
program planning, and educational reform (Sparks, 1995).

Academic program planning and curriculum development can be enhanced
significantly by educators who are familiar with the modeling process and the real world

application and interaction of Natural Resources and National Park regional planning

153

processes and variables. Properly maintained models can help academic and curricula
planners understand the most critical and important issues for students to learn, and the
use of such models necessitate productive interaction between educators and practitioners

in the field.

154

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Stynes, D. J ., & Spotts, D. M. (1980). A simulation model for forecasting downhill
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Talhelm, D. R., Chen, K.-C., & Holecek, D. F. (1991). Do communities have options?
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12th Annual CenState Travel and Tourism Research Association Conference.
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Tilden, F. (1976). The National Parks. New York: Alfred A. Knopf, Inc.

Tinker, R. F. (1990). Teaching theory building modeling: Instructional materials and

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164

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165

APPENDIX A: Suggested Readings

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Wright-Allen Press, Inc.

Forrester, J. W. (1961). Industrial dynmics. Cambridge: The M.I.T. Press.

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Forrester, J. W. (1968b). Principles of systems. Cambridge: Wright-Allen Press.
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Greenblat, C. S. (1988). Designing games and simulations: An illustrated handbook.
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Levine, R. L., & Fitzgerald, H. E. (1992). Analysis of Dynamic Psychological System_s;
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166

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Meadows, D. L. (1970). Dmamics of commodity production cycles. Camgridge: Wright-
Allen Press, Inc.

Meadows, D. L. (1972a). The limits to growth. New York: Universe Books, Publishers.

Meadows, D. L. (1972b). Toward global Quilibrium: Collected papars. Cambridge:
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Meadows, D. L., Fiddaman, T., & Shannon, D. (1993). Fish Banks, Ltd. In Durham:
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Randers, J. (1980). flements of the system dynamics method. Cambridge: The MIT
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Robert, N ., Anderson, D. F., Deal, R. M., & Garet, M. S. (1983). Introduction to

computer simulation: The system dynamics approach. Cambridge: Addison-
Weslsy Publishing Company. .

Senge, P. M. (1990). The fifth discipline: The art & practice of the learning organization.
New York: Doubleday/Currency.

Wolstenholme, E. F. (1990). System enguig: A System Dynamics approach. New York:
John Wiley & Sons.

APPENDIX B: List of Variables and Equations

In this appendix a list of variables and equations of the National Park regional
model is presented. This information can help the user understand the assumptions that
form the basis for simulating the consequences of alternative strategies, policies, and

programs for promoting the economic and social development of the National Park

 

region. The Appendix has been divided into four sections: level variable ( D ), flow
variable ( 0" ), auxiliary variable ( O ) and constant variable ( O ).

In each section, the variables are listed alphabetically.

167

APPENDIX B: List of Variables and Equations

[3 AccuLoadlndex
@ 0.05
w +dt°(VarRate)
Accumulate load index
D CapBusServ
@ 500000
‘10 +dt‘(AddRBus)
-dt‘(OutBus)
Capital in Business Services (1000 Dollars)
[:l CapHouServ
80000
40 -dt‘(0utHou)
+dt‘(AddRHous)
Capital h Household Services (1000 Dollars)
[:l CapitatPool
30000
a” -dt‘(CapOut)
+dt‘(Capln)
Regional Capital Pool (1000 Dollars)
D CapOutputBus
2 '
sh +dt‘(ChCapBus)
Capital Output Factor in Business Services (Yrs)
[:l CapOutputl-lou
1.25
#0 +dt‘(ChCapHou)
Capital Output Factor in Household Services (Yrs)
ConEc
E] 0.2
=80 -dt‘(Rate)
[:1 Com
ED 12
#0 +dt‘(Recoun)
Dummy Variable
[j DemPerBus
250000
ISO +dt'(Busln)
Demand Perceived in Business Services (1000 Dollars/Yr)
DemPerl-tous
250000
40 +dt‘(Housln)
FaminlanPersonnel
0.1
'3» -dt‘(FamPPRetired)
+dt‘(FamPPTrained)
Famiy Flaming Personnel (1000 Persons)
[3 InfraCap
32620
slab +dt‘(lFiniRate)
-dt‘(Depfeciation)
CED Personleay ~
Wrastmcttxe Capacity. The level of infrastructure Capacity in the region is initialized as increase investment rate
and decrease depreciation rate.“
D LaborOutFac
5000
«a» +dt‘(Changel)
® Labor Output Factor in Business Services (”Y r)
[:1 LOFHS
4000
€89 +dt‘(Clianch)
@ Labor Output Factor in Household Scrvrccs (1000 Dollar/Pcrson/Yr)

C]

DE]

168

[:J Manpower
177
1%" +dt‘(Adjust)
1% National Park current real employment for the management and mainlainence work.
[:] MedPerson
E] 0.32
18$ +dt'(Medlnc)
-dt‘(MedRet)
Medical Personnel (1000 Persons)
[:1 Pollution
100000000
1%» -dt‘(DecRate)
*dt‘(lncPoll)
-dt‘(DecPol)
[:1 PotDevRes
31
1&0 -dt‘(ResDevRate)
Potential recreation resources
[:3 RAdult
339
$0 +dt‘(Grow_Ad)
+dt‘(MRadlt)
-dt‘(RAdead)
-dt‘(Grow_Old)
C] RChlld
31s
c8» -dt‘(RCdead)
+dt‘(MRehild) -
-dt'(Grow_Ad)
+dt‘(Bom_RCh)
[:j RecResMagnitude
13131 31
4H0 -dt‘(Decflne)
+dt‘(ResOevRate)
The Ievle of recreation resources magnititude as the increase the developing the potential recreation resources and
decline due to the resources extraction.
D RelativeWage
EB 0.3
#0 +dt‘(WRate)
Relative Wags( This is taken to represent the ratio of regional average wage to national average wage)
[j RHospiBed
1,52
1%» +dt'(RHBedlnR)
Urban Hospital Bed (Bed)
[:l ROM
45
ISO +dt‘(MRold)
-dt'(ROAdDie)
+dt°(Grow_Otd)
[3 RSS
21 .
40 +dt‘(NRSS)
-dt‘(fl.USS_1)
Student Enrolment h Rural Secondary School (Thousand Students)
C] RuEnrolPrim
110
slab -dt‘(TLURS)
+dt‘(NRPS)
[2.1) Student Enrolment in Rural Primary School (Thousand Students)

169

[_j RuLitcracy
595
<80 +dl‘(RLPln)
-dl‘(RLPOut)
+dt'(OMRLP)
Rural Literacy Population (1000 Persons)
[3 RuralMuniArea
7000
slab +dt‘(RuralMaAarlncR)
E Rural Municipality Area (Acre)
[:l SLUR
0.1
#0 +dt‘(SRate)
Smoothed Local Unemployment Ratio
[:] SupCap
m 4400
'80 -dt'(SupDep)
+dt‘(SFHR)
[E The level of superstructure capacity is initialized by in crease superstructure investment and decrease in
depreciation.
[:1 SupUnit
E] 6400
118$ -dt‘(UnilDepre)
+dt‘(Unitlnves)
m Persons/Periods
1% The level of superstructure capacity equivalent is defined as increase in investment and decrease in depreciation.
The function of this variable is connecting the level of services and superstructure capacity.
I: Tertiary
El 9
s8» +dt‘(NTSE)
-dt‘(OUT)
[Q Tertiary Student Enrolment (Thousand Students)
C] UAdult
120
also +dt‘(MUadtt)
-dt°(UAdead)
+dt‘(Grow_UAd)
-dt‘(Grow_UOld)
C] UChild
112
s8» +dt‘(MUchld)
-dt°(UCOead)
+dt‘(Bom_UCh)
-dt‘(Grow_UAd)
[:] UHospiBed
E1 320
=80 +dt‘(UHBedlnR)
Urban Hospital Bed (Bed)
C] ULiteracyP
m 221
#0 +dt°(OMULP)
-dt°(ULPOut)
+dt‘(ULPln)
@ Urban Literacy Population (1000 Persons)
C] uow
15.0
439 +dt'(MUold)
-dt‘(UOAdDie)
+dt‘(Grow_UOld)

170

C] UPrimS
56
$0 -dt‘(LeavRPrim)
+dt‘(NUPS)
1% Student Enrolment in Urban Primary School (Thousand Students)
[3 UrbanLand
1500
$0 +dt‘(UrbanLandGrow)
Urban Land Area (Acre)
D USecond
11
#0 +dt‘(NUSS)
-dt‘(TLUSS)
Student Enrolment in Urbans Secondary School (Thousand Students)
C] VisitorPerYear
0
#0 +dt‘(lncR)
-dt‘(DecR)
m Persons
[Q The annual visitors is determined by the difference between increase rate and decrease rate.
0» AddRBus
= DELAYMTR(AddBus,DeICap)
Capital Add Business Services (1000 Dollars Nr)
«09 AddRHous
= DELAYMTR(AddHou.DelCapH)
Capital Add Household Services (1000 Dollars er)
0° Adjust
= DELAYMTR(AdjustRate. 2 )
Adjust rate when the manpower adjust rate goes through first order delay. The average delay time is 2.

Ole Bom_RCh

= RAdult'RBirthRate

0° Bom_UCh

= UAdult‘UBirthRate

'09 Busln
= DemBus-DemPerBus
-O° Capln
= ExternalAid+GovSav+LoanRecR+PSav
Capital Inflow
“0° Capo“!
= lnfraAll+LandRetorrnA+LoanRepayRate+SocCapAl
Capital Outflow
-O¢ Changel
= LaborOutFac'CLOFRBS
[3 Change in Labor Output in Business Services (1/Yr)
'0" Changez
= LOFHS'CLOFRHS
Change
00 ChCapBus
= CapOutputBus‘ChF actor
Change in Capital Output Factor in Business Services
{)9 ChCapHou
= CapOutputHou'ChFactorH .
@ Change in Capital Output Factor in Business Services
0" Decline

= (RecResMagnitudelAngecPer)‘(1.2-MagtPeopFraction)

[3 Recreation resources extraction rate. It is defined as recreation resources magnitilude devided by average decline
time, and regarding the fraction of manpower for management. The difference between 1.2 and the lraction of
management personnel demonstrates that even if the fraction of management personnel achieves 100%, the
resources would decline to minimum but not all disapear.

O: DecPoll

= Pollution/PollAboTimc

171

“03 DccR

= |F(1.S>=Month, VisitorPerYear, 0)
[2:3 Decrease rate. A dummay variable for calculating the annual visitors.
=06 DecRate
= DELAYINF(PrilnveRate,3)
O» Depreciation
= InfraCap/DepTime
[25 Infrastructure depreciation is defined as the infrastructure capacity divided by the average of depreciation time
period.
{)9 FamPPRetired
= FamityPlanPersomellFamPlanPerSerTlme
1% Family Planning Personnel Retained Rate (1000 Personler)
'O‘ FamPPTrained
= DELAYMTR(FamPPTrainlnd,FamlPlaPerTraTlme)
1% Family Planting Personnel Trained (1000 Person/Yr)

“0° Gr0w_Ad

-O° Grow_UOld
= UAdult/DAP

-O-° Housln

= DemHous-DemPerHous
0o iFiniRate
= DELAYMTR(InvesRate.24,3)
Current finish rate of new infrastructure. It is the three order delay of investment rate.

~00 lncPoll

= PollProM‘PolMsiM‘PollNorm
«Oo lncR
= MR'Visitors
[Q Dummy variable for calculating the annual visitors.
0 LeavRPrim
= NoSLeavUP+OutMigUprim+UPrimDrop -
E Total Leave Rate of Student Leave the Primary School (1000 Students Nro
()o Medlnc
= FMSCVG’GTS
Medical Personnel increased (1000 Persons/Yr)
'O'° MedRet
= MedPerson/ASLMP
Medical Personnel Retained (1000 Persons/Yr)
On MRadlt
= (IRMlR'UEmpMig-RUMl‘lncDifMigMutl)‘RAdult
[a Migrant to Rural, Adult (1000 Persons)
.0. MRchild
= MRadlt-‘NoChiPA
{)9 MRold ,
= MRadlt‘NoOldPA
'0’ MUadlt
= lRMlU'UEmpMig'UAdult+RUML°tncDifMigMuti‘RAdult
Migration to Urban, Adult(1000 PersonleR)
{)9 MUchtd
= McCNPA‘MUadtt
[a Migrant to Urban, Child (1000 Persons)
0 MUold
= MUadlt‘NoOldPA
1% Migrant to Urban, Old Adult (1000 Persons)
0: NRPS
= MlN(DERPS,PERPS)
1% New Enrolment in Rural Primary School (1000 Students/Yr)

172

r0: NRSS

= MIN(ADERSS,PERSS)

ET.) New Enrolment in Rural Secondary School ( 1000 Students/Yr)
=0» NTSE

= MlN(DTSE,PTSE)

Ea) New Tertiary School Enrolment (1000 Students/Yr)
0° NUPS

= MlN(DesirEnroUPrim,PossEnrolUPrim)

[3 New Enrolment in Urban Primary School (1000 Students/Yr)
«O» NUSS

= MlN(DEUSS.PEUSS)

New Enrolment in Urban Secondary School (1000 Students/Yr)

{)9 OMRLP

= (lRMlR‘UEmpMig'RAdult)‘(NoChiPA+NoOldPA+1)‘RLR

.0. OMULP

= (lRMlU'UEmpMig'UAdult)'(NoChiPA+NoOldPA+1)‘ULR
0" our
= GTS+TSDO
- Total Leave Rate of Student Leave the Primary School (1000 Students er0
=00 OutBus
= CapBusServ‘CapDisRatio
1% Capital Discard in Business Services (1000 Dollars Nr)
.0. OutHou
= CapHouServ‘CapDisHRatio
[a Capital Discard in Business Services (1000 Dollars Nr)

09 RAdead

= RAdUll'RADeath
'0‘ Rate '

= (ECStand‘lncMuli)-OOL

{)9 RCdead

= RChiId‘RCDeath
0o Recoun

= lF(TlME>=Coun,12,0)

Dummy variable
=06 ResDevRate

= (PotDevRes/DevPeriod)‘RatioTechMan

@ Developing rate of the potential recreation resources
=0: RHBedlnR

= DELAYMTR(RHBedlncl,RHCDelay,3)

Rural Hospital Bed Increase Rate (Bed/Yr)

«O» RLPln

= NSLRPS-RULP
()6 RLPOut

= RDeathRate'RLR

Death of Rural Literacy Population ( 1000 Persons/Yr)
O» ROAdDie

= ROId'RODeath
«O»: RuralMaAarlncR

= DELAYMTR(|ncRuraIMAreIR, DelayRMAl)

@ Rural Municipality Area Increase Rate

0 SFHR

= DELAYMTR(SINV, 24)

{)9 SRate
= (LUnempRate-SLURVLUnempRTime
«()9 SupDep
= SupCap/DepTime
@ Superstrucutre Depreciation Rate.
Os TLURS
= NSLRPS+OMRPS+RPSDO
[2.) Total Leave Rate of Student Leave the Primary School (1000 Students er0

173

()9 TLUSS

= NSLUSS+USSDO

1% Total Leave Rate of Student Leave the Secondary School (1000 Students [WC
{)9 TLUSS_1

= NoSLeavR+RSSDO

[E-b Total Leave Rate of Student Leave the Secondary School (1000 Students IYrO

{)9 UAdead

= UAdult'UAdeath
{)0 UCDead
= UChild'UCDeath
-O° UHBedInR
= OELAYMTR(UHBedIncl,UHCDeIay.3)
Urban Hospital Bed Increase Rate (Bed/Yr)

Os ULPln

= NoSLeavUP+RULP
«O» ULPOut
= UDeathRate‘ULR
Death of Urban Literacy Popdation (1000 Persons/Yr)
«Oo UnitDepre '
= SupUnit/DepTime
[Q Superstructure equivalent unit depreciation equal to current superstructure equivalent divided by average
depreciation time
-O¢ Unitlnves
= DELAYMTR(Suplan,24)
1:22 Fraction
Superstructure equivalent unit investment rate after three order delay
«0- UOAdDie
= UOld'UOdeath
=09 UrbanLandGrow
= DELAYMTR(IndULand,DdlayULAI)
Céfi Urban Land Area increase Rate (Acre/Yr)
=00 VarRate
= lF(Loadlndex>=1,1/CriticalPoint, (-1)‘(AccuLoadlndex/RecoveTime))
Rate of accumulate the load index.
«Os WRate
= (T RW-RelativeWageyRWAT
O AcceptRResDen
= 1.2‘RuResDensity
@ Acceptable Rural Resident Density ( 1000 Persons/Acre)
O AccepUrbanPopD
= 1.5‘UrbanPopDensity
[3 Acceptable Urban Population Density (1000 Person/Acre)
O Acces
= tlAveConges
[9 Regional Accessibility
O AddBus
= CapBusServ‘CapAddMuti‘CapAddRatio
Capital Add in Business Services (1000 Dollars M)
O AddHou
= CapHouServ‘CapAddHMuti‘CapAddRatio_1
g Capital Add in Household Services (1000 Dollars Nr)
0 ADERSS
= DERSS'(1-FRSUSS)
g: Actural Desired Enrolment in Rural Secondary School (1000 Students M)
O AdjustRate -
= (PotentialManp-ManpoweryAdjustTime
[Q The manpower adjust rate is defined the difference between the potential need of management personnel and
current real employment.
0 AduPop
= RAdult+UAduIt

OO‘OOOOOOOOOOOOOOO

000 O

174

Angagc

= RelativeWage‘AgriEmp‘Averageray‘NWGAG

[Q Agricultural Wages (10000 Dollars/Yr)

ASR

= GRAPHLINAS(SUSR,0.2,0.I,[0,0.083333.0.16667.025,0.3333"Min:0;Max:0.4"])
AttrDummylndex

= RecResMagnitude'U+SupServiceLev)!38

Attrlndex

= SAttrDuln‘CharIndex‘( 1 -AccuLoadIndex)

Attraction Index of the Region
AveConges

= DELAYINF(Congestion, 12)

Infrastructure average congestion degree is determined by the average exponentional (smooth) of congestion.
AveLoad

= DELAYINF(LoadIndex,12)

Average load index is determined by the average exponentional of load index.
AveWage

= TotWagefTotEmptoyment

Q Average Wages (Dollars/Person/Yr)

BRPS

= PEBA-Bungrim

(a: Budget for Rural Primary School (1000 Dollars/Yr)

BRSS

= SEBA-BUSS

[E3 Budget for Rural Secondary School (1000 Dollars/Yr)

BTE

= EduBudgA‘FBTE

[Q Tertiary Education Budget Allocation ( 1000 Dollars/Yr)

Bungrim

= PEBA'FBUPS

Budget for Urban Primary School (1000 Dollars/Yr)

BusEmp

= OutputBus/LaborOutFac

Employment in Business Services (1000 Persons)

BUSS

= SEBA'FracBugUSec

[3 Budget for Urban Secondary School (1000 Dollars/Yr)

BusWage

= RelativeWage'BusEmp'Averageray‘NWBS

Wages In Business Services (1000 Dollars/Yr)

CapAddHMuti

= GRAPH(DemCapRatioH,0.5,0.5.[0.5,1,1.2,1.4'Min:0;Max:1.5'])

1% Capital Add Multiplier in Household Services

CapAddMuti

= GRAPH(DemCapRatio,0.5,0.5,[0.5.1.12,1.4'M‘n:0;Malc1.5'])

Capital Add Multiplier in Business Services

CarryCapc

= RecResMagnitude‘ResCap‘PeriodDays

The regional carrying capacity is determined by the regional recreation resource magnititude multiplied the per unit

resources carrying capacity in a given time period.

Charlndex

= GRAPHLINAS(Month,1,1,[0.2.02,0.33.0.4,0.22,0.4,0.4,0.4,0.2,0.27.0.29.0.29'Min:0;Max:1'1)
[El The characteristics of the region

ChiPop

= RChiId+UChiId

CocOOL

= GRAPHCURVE(ConEc,0,0.16,[1,0.S3,0.2,0.11,0.08,0.05,0.01,0.0'Min:O;Max:t'])
Congestion

= tOO‘Msitors/lnfraCapPeriod)

GD Persons

The regional cojestion is defined as the visitors is divided by the infrastructure capacity in period.

OOOOOOOOOOOOOOOOOOOO

175

DcmBus

= (DMFIBS‘OutputHou)+(DMFZBS'OutpulBUS)+SROAG‘SRQMI
Demand in Business Services (1000 Dollars/Yr)

DemCapRatio

= DemPerBus/OutCapacity

1%) Demand Capital Ratio in Business Services

DemCapRatioH

= DemPerHous/OutCapHous

@ Demand Capital Ratio in Household Services

DemHous

= TotPOP°(DMF1HS+DMF2HS‘PerCapIn)

Demand in Household Service ( 1000 dollars/Yr)

DERPS

= RChild‘RLREM'(RPCIEM/15)+IF(MRchild>=0.MRchiId,0)

(é) Desired Enrollment in Rural Primary School (1000 Students IYr)
DERSS

= MUchId‘NoSLeavUP‘ULREM‘UPerCaplncEM‘FPSSEN
Desired Enrollment in Urban Secondary School (1000 Students Nr)
DesirEnroUPrim

= UChild‘ULREM'(UPerCapIncEM/15)+lF(MUchld>=0,MUchld,0)
@ Desired Enrollment in Urban Primary School (1000 Students IYr)
DEUSS

= MUchld‘NoSLeavUP'ULREM‘UPerCapIncEM‘FPSSEN

[5:3 Desired Enrollment in Urban Secondary School (1000 Students er)
DTSE

= RDTSE+UDTSE

EL; Desired Enrollmentin Urban Primary School (1000 Students er)
DURLR

= ULR-RLR

@ Difference between Urban and Rural Literacy Rate

EduBudgA

= SocCapAI‘EduBudgAF

[E9 Education Development Budget Allocation

EduEmp

= PSEMP+TEEMP

Educational Employment (1000 Persons)

EduWage

= RelativeWage'EduEmp'Averageray‘NWageED

CED Wages in Education (1000 Dollars/Yr)

ExtemalAid

= ExtAidliti‘(STEP( 1 ,0)+STEP(O.2,5)+STEP(-12.6))

[25 External Aid (1000 Dollars/Yr)

FaleVage

= NWageF'(MedPerson+FamilyPtanPersonneO'Averageray
[Q Wage in Health and Family Planning (1000 Dollars/Yr)
FamPerPopRatio

= FamilyPtanPersonnelfTotPOP

® Family Planning Personnel-Population Ratio
FamPlanBudAllocat

= SocCapAI‘FIamPIanBAF

@ Family Planning Budget Allocation (.1000 Dollars/Yr)
FamPPTPoss

= FamPlanBudAIIocat/CapRequrFPP

Family Planning Personnel Trained, Possible (1000 Person/Y r)
FamPPTrainDesir

= (DesirFPPerPopRatio'TotPOP-FamilyPIanPersonnel)lFPPRealizalionTime
[3 Family Planning Personnel Trained, Desired (1000 Person/Yr)
FamPPTrainlnd

= MIN(FamPPTrainDcsir,FamPPTPoss)

[3.2) Family Planning Personnel Trained, Indicated (1000 Persons/Yr)

OOOOOOOOOOOOOOOOOO

176

F RSUSS

= GRAPH(DURLR,O,2,[0,0.2,0.406,085,1'Min:0;Max:1'])
Fraction of Rural Student to Urban Secondary School
FundPExpa

= Bungrim-MainEprPrim

Urban Primary School Expansion Fund (1000 Dollars/Yr)
GoveEexp

= GovtEmp‘PerGovEmpExp

1% Government Expendiature (1000 Dollars/Yr)

Govlnterv

= GRAPHCURVE(ConEc,0,0.2,[1,0.65,0.33,0.2,0.19,0.03.0,0,0'Mil:0;Max:1'])
GovSav

== TaxIncome-GoveEexp

[Eb Government Saving (1000 Dollars/Yr)

GovtEmp

= GRAPHUIME.O,5,[0.8,1,1.2,1.5,1.8,2.1,2.5‘Min:0;Max:3'D
Gothage

= RelativeWage‘Averageray‘NWGG‘GovtEmp

@ Wages in Goemment (1000 Dollars/Yr)

GTS

= Tertiary/UTE

[21 Graduate of Teratiry School (1000 Students! Yr)

HeaIBuAI

= SocCapAI'HeaIthBudgetAF

Health Budget Allocation

HousEmp

= OutputHou/LOFHS

Employment in Household Services (1000 Persons)
HousWage '

= RelativeWage'HousEmp'Averageray‘NWl-IS

[21 Wages in Household Services (1000 Dollars/Yr)
IncDifMigMuti

= GRAPH(IncomDiff,0,0.1,[0,0.5,1,1.5,2,3"Min:0;Max:5'1)
1% Income Difference-Migration Multiplier

IncMuli

= GRAPHCURVE(PerCapln,0,200,[1.01 ,0.74,0.53,0.44,0.23,0.04,0,0,0"Min:0;Max:1.5'1)
IncomDiff

= ((UPerCapIn)'(AIDRUHRPerCapIn))/RPerCapIn

Income Difference Ratio

lncRuralMAreIR

= IF(AcceptRResDen>=DesirRResDen,RMunicAreaInc,0)
Incficated Rural Municipality Area Increase Rate (Acre/Yr)
IndULand

= IF(AccepUrbanPopD>=DesirUubanPopD,UrbanAreaInc,0)
Indicated Urban Land Area Increase Rate (Acre/Yr)
InfraAIl

= FraclnfraAlIocation'(CapitaIPooI/DelayCapA)
Infrastructure Allocation (1000 Dollars/Yr)

InfraCapPeriod

= InfraCap‘PeriodDays

ml Persons/Periods

[Q Infrastructure Capacity in Period. ‘

InvesParameter

= GRAPHCURVE(AveLoad,0.2,0.t,[1,1,I,0.73,0.27,0.06,0,0,0'Min:0;Max:1'j)
g Infrastructure Investment Parameter. It is associated with average load index.
lnvesRate

= InvesParameter‘InfraCap‘((Parameter/AvglnvPeriod)+(1IDepTime»

Ei—b Investment Rate of New Infratructure is equal to the current infrastructure capacity divided the average investment

time period, and then, time the two investment parameters, and plus current depreciation.
LaborForce
= LFParRate‘TotaIWorkPop
[.25 Labor Force (1000 Persons)

OOOOOOOOOOOOOOOOOOOOC

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177

LandReIorlnA
2' FracLandRA'(CapitalPooI/DelayCapA)
[E] Land Reform Budget Allocation (1000 Dollars/Yr)
LandVaIueMuI
= GRAPH(UrbanLandVaIue,t00,100,[0.75,0.9,1.05,1 2.1.16.1 ,0.88,0.78,0.72,0.65'Min:0.5;Max:1.2'1)
[Q Land Value Multiplier
LFParRale
= LFPRM‘LFRI
E9 Labor Force Participation Rate
LFPRM
. = GRAPH(PerCapIn,500,500,[1.1,1.07,1.05,1 .02,1'Min:1;Max:1.15"])
[g1 Labor Force Particiaption Rate Multiplier
LiterRate
= (RuLiteracy+ULiteracyP)/TotPOP
[3 Literacy Rate
LoadIndex
= Visitors/CarryCapc
@ Regional Load Index is defined as the visitors divided by carrying capacity.
LoanRecR
= LoanRRIiti‘(STEP(1,0)+STEP(2,5)+STEP(6,—1.2))
[3) Loan Receive Rate (1000 Dollars/Yr)
LoanRepayRate
= DELAYMTR(LoanRecR,LoanTime)
Loan Repayment Rate
LUnempRate
= (LaborForce-TotEmployment)/LaborForce
[3 Local Unemployment Rate
MagtPeopFraction
= Manpower/PotentiaIManp
MainEprPrim
= UPrimS/CostPrim
lg Maintenance Expendiature of Urban Primary Schools (Dollars/Yr)
MedicaIPersonnelDM
= GRAPH(MPDR,0,0.2,[1,0.85,0.7,0.5,0.3"Min:0;Max:1'1)
éj Medical Personnel Deficiency Multiplier
MedPersonDem
= (RHospiBed+UHospiBed)/MPBR
1% Medical Personnel Demand (1000 Persons)
MERPS
= RuEnroIPrim/CostPrim
E9 Maintenance Expendiature of Rural Primary Schools (Dollars/Yr)
MERSS
= RSSIPPSMC
Maintenance Expendiature of Rural Secondary Schools (Dollars/Yr)
METS
= Tertiary‘PTSMC
Ea) Maintenance Expendiature of Teratiry School (1000 Dollars/Yr
MEUSS
= USecond/PPSMC
1% Maintenance Expendiature of. Urban Secondary Schools (Dollars/Yr)
MinWage
= RelativeWage’MinEmp‘Averageray‘NWGM
[a Wages in Mining (1000 Dollars IYr)
Month
= TIME-Coun+12
MPDR
= (MedPersonDem-MedPerson)lMedPersonDem
Ext) Medical Personnel Deffrcicncy Ratio
NoChiPA
1' ChiPop/AduPop
NOOIrIPA
- Olrll’rlp/Arllll)t)p

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O

178

NoSLeavR

: RSSIDSE

Number of Student Leaveing Rural Primary (1000 Students er)
NoSLeavUP

= UPrimS/DurPrim

E2) Number of Student Leaveing Urban Primary (1000 Students er)
NSLRPS

= RuEnroIPrim/DurPrim

[i=3 Number of Student Leaveing Rural Primary (1000 Students IYr)
NSLUSS

= USecond/08E

[3 Number of Student Leaveing Urban Primary (1000 Students IYr)
OIdPop

= ROId+UOId

OMRPS

= IF(MRchild>=0,0,-MRchiId)°6lI5

[2% Out-Migration of Rural Primary School Student (1000 Studentsl Yr)
OutCapacity

= CapBusServ/CapOutputBus

Output Capacity in Business Services ( 1000 Dollars/Yr)
OutCapHous

= CapHouServ/CapOutputHou

[2 Output Capacity in Household Services (1000 Dollars/Yr)
OutMigUprim

= lF(MUchld>=0,0,-MUchId)‘6/t5

Out-Migration of Urban Primary School Student (1000 Studentsl Yr)
OutputBus

= MIN(DemPerBus,OutCapacity)

[2 Output of Business Service (1000 Persons/Yr)

OutputHou

= MlN(DemPerHous,OutCapI-Ious)

Output of Household Service (1000 Persons/Yr)

Parameter

= GRAPHLINAS(AveConges,0,0.2,[0,0,0.1 19,0.249,0.249,0.249,0.25,0,0"Min:0;Max:0.3"])
[i=3 Infrastructure Investment Parameter. It is associated with average congetion degree.
ParkWage

= RelativeWage'Averageray‘NWGP‘Manpower

1% Wages in National Park (1000 Dollars/Yr)

PEBA

= EduBudgA'FPEBA

[2 Primary Education Budget Allocation (1000 Dollars/Yr)

PerCapIn

= TotWagefTotPOP

[2 Per Capita Income (Dollars/Personer)

PERPS

= NSLRPS+OMRPS+RPSDO+RPSE

[i=3 Possible Enrollment in Rural Primary School (1000 Students IYr)
PersonlncomeTax

= TotWage‘PersonlncomeTaxRate

[3 Personal Income Tax (1000 Dollars/Yr)

PERSS .

= NoSLeavR+RSSDO+RSSE

[3 Possible Enrollment in Urban Secondary School (1000 Students IYr)
PEUSS

= NSLUSS+USSDO+USSE

[2) Possible Enrollment in Urban Secondary School (1000 Students er)
PoIIAboTime

= GRAPH(PoIIuRaIe,0,IO,[0.6,2.5,5,8,1 I .5, I S.5,20'Min:0;Max:20"])
[g Pollution Absorption Time

PoliProM

= GRAPHCURVE(PerCann0200010 070 I 5025040560740990 86,I"Mln.O,Maxl"])

[:5 Pollution from Production Multiplier

OOOOOOOOOOOOOOOOOOOOO

O

179

PoIIuRate

= Pollution/PollStandard

PoIlVlsiM

= GRAPHCURVEMsitors,O,200,[0.03,0.01,0,0.1,0.2,0.28,0.39,0.57,t“Min:0;Max:1"])

[Q Pollution from Tourism Multiplier

PossEnroIUan

= NoSLeavUP+OutMigUprim+UPrimDrop+UPrimExpan

[Q Possible Enrollment in Urban Primary School ( 1000 Students IYr)

PotentialManp .

= RecResMagnitude/PerManRes .

The potential manpower management personnel is determined by the recreation resources magnititude divided by
the maintinence ability per manpower.

Potenvisit

= Attrlndex‘AttV’lsitor

Pn’lnveRate

= Privlnves/IO

PrivateSavingR

= GRAPH(UPerCapIn,500,500.[0.01 ,0.02,0.035,0.055,0.08'Min:0;Max:0.t '1)

Private Saving for Reinvestment Rate

Privlnves

= IF(QOL>=QolPoII, 0, Govlnterv)

Investment on Pollution Abatement from Private

PSav

= PrivateSavingR‘TotWage

[3 Private Saving (1000 Dollars/Yr)

PSEMP

= (RuEnroIPrim+RSS+UPrimS+USecond)/ASPEDP

Primary-Secondary Educational Employment (1000 Persons)

PTSE

= GTS‘TEE'TSDO

E=§ Possible Teratiry School Enrolment (1000 Students er)

QOL

= CocOOL‘QOLIncome‘QoIPoIl‘QOLStand

QOLIncome

= GRAPHCURVE(PerCapIn,0,2000,[0.03,0.13,0,0.14,0.35,0.69,0.91,0.97,1"Min:0;Max:1'1)

[3 Quality of Ute From Income Increase .

OoIPoII

= GRAPHCURVE(POIIuRate,0,10,[1.12,0.52,0.3,0.22,0.18,0.09,0'Min:0;Max:1 .5'])

Quality of Life from Pollution

RDeathRate

= RAdead+RCdead+ROAdDie

Rural Population Death Rate

RDTSE

= NoSLeavR'RLREM'FSSGTEN'RPCIEM

[Q Demand for Tertiary School Education, Rwal (1000 Students/Yr)

ReaIPropertyTax

= LandTax+TaxHous

Real Property Tax (1000 DollarsIYr)

RHBedIncI

= MIN(RuraIHBIncRD,RHBIRP)

[3 Urban Hospital Bed Increase Ratio, Indicated (Bed/Yr)

RHBIRP

= RuralHBCapRat‘HealBuAl/CostPerRHospBed

[Q Urban Hospital Bed Increase Rate, Possible (Beder)

RHBPR

= RHospiBed/RPOP

é) Rural HOSpital Bed Population Ratio (Bed/1000 PersonsO

RLaborForce

= RLFPR‘TotaIRWorkPop

RLF PR

2 LFPRM‘RLFPRI

 

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180

RLR

= RLPl/RPOP

Rural Literacy Rate

RLREM

= GRAPH(RLR,0,0.2,[0.4,0.7,0.95,0.98,0.99,1 'Min:O;Max:l '1)
Rural Literacy Rate-Education Multiplier
RMunicArealnc

= (RPOP/RuResDensityHRuraIMuniArea/LZ)

Rural Municipality Area Increase (Acre/Yr)

RPCIEM

= GRAPH(RLR,500,500,[0.9,0.980990995,1'Min:0.9;Max:1"])
Rural Per Capita Income-Education Multiplier
RPerCapIn

= (Angage+MinWage)/RPOP

Rural Per Capita Income (Dollars/Personer)

RPOP

= RAduIt+RChiId+ROId

RPSDO

= RuEnroIPrim'RPSDORN

@ Rural Primary School Drop out (1000 Students)
RPSE '

= DELAYINF(RPSEI,DPSD)

[3 Rural Primary School Expansion (1000 Students IYr)
RPSEF

' = BRPS-MERPS .

[Q Rural Primary School Expansion Fund (1000 Dollars/Yr)
RPSEI

= RPSEF/PPSFC

1% Urban Primary School Expansion, Indicated (1000 Students/Yr)
RSSDO

= RSS‘RSSDORN

Rural Secondary School Drop out (1000 Students)

RSSE '

= DELAYINF(RSSEI,DSSD)

[Q Rural Secondary School Expansion ( 1000 Students IYr)
RSSEF

= BRSS-MERSS

[9 Rural Secondary School Expansion Fund (1000 Dollars/Yr)
RSSEI

= RSSEFIPSSFC

Rural Primary School Expansion, Indicated (1000 StudentsIYr)
RSUSS

= DERSS‘FRSUSS

Rural Student to Urban Secondary School (1000 Students IYr)
RULP

= RUM'RUSE'RUTE

RUM '

= (RUMI‘lncDifMigMuti‘RAdult)°(1+NoChiPA+NoOIdPA)
Rural-Urban Migration ( 1000 Personler)

RuraIHBIncRD ‘

= ((DesirRuraII-lBPopRatio'RPOP-RHospiBed)IRuraIHDRT)'(MedicalPersonneIDM)
[Q Urban Hospital Bed Increase Rate, Desired (Breder)
RuResDensity

= RPOP/RuralMuniArea

Rural Resident Density ( 1000 Persons/Acre)

RUSE

= NUSS‘(RSUSS/OEUSS)

RUTE

= NTSE°(RDTSEIDTSE)

SAItrDuIn

“-5 DELAYINF(AterummyIndex,I2)

0000000

0

0000000000000

181

SEBA

= EduBudgA°FSEBA

@ Secondary Education Budget Allocation (1000 Dollars/Yr)

ServiceEmp

= FamilyPlanPersonnel+MedPerson+Manpower+BusEmp+EduEmp+GovtEmp+HousEmp+TranspotEmp

[Q Servic Employment (1000 Persons)

SerWage

= BusWage+EduWage+Gothage+HousWage+ParkWage+TransWage+FamiWage

[3 Service Wages (1000 Dollars/Yr)

SINV

= ASR‘SupCap/AvglnvPeriod

SocCapAI

= FraSOA‘(CapitalPooI/DelayCapA)

[% Social Overhead Allocation (1000 Dollars/Yr)

SupCapPerProd

= SupCap‘PeriodDays

SuplnvPar

= GRAPH(SUSR,0.2,0.1,[0,0.083333,0.2504583,0.666'Min:0;Max:0.7'])

cm Fraction

1% Superstructure investment parameter. The variable is defined by means of a graphical function. The assumption of
how large fraction of superstructure use rate as a function of the investment decision is entered in the model by
means of a graphical curve. The user can simply change the shpae.

Suplan

= SuplnvPar‘SupCap/AvglnvPeriod

Eh Superstructure equivalent investment rate is determined by superstructure investment parameter times
superstructure capacity divided by average investment time period

Suplanate

= SuplnvPar‘(SupCap/AvglnvPeriod)

SupServiceLev

= SupUnit/SupCap

SUSR

= DELAYINF(UseRate,6)

[Q Superstructure use rate goes through an average expontential time delay and responses to investment parameter.

TarWage

= TRWIINAWAGE

1% Target Wage (Dollars/Person/Day)

Taxlncome

= PersonlncomeTax+ProductTax+RealPropertyTax

1% Tax Income of Government (1000 Dollars/Yr)

TEE

= DELAYINF(TEEIDTED)

1% Tertairy Education Expansion (1000 Students/Yr)

'TEEF

= BTE-METS

@ Teratiry Education Expansion Fund (1000 Dollars/Yr)
TEEI

= TEEFIPTSFC

[a Teratiry Education Expansion, Indicated (1000 Students/Yr)
TEEMP

= Tertiary‘ASPTEDP

1% Tertiary Education Employment (1000 Persons)
TotaIRWorkPop

= RChildI3+RAduIt+ROld

TotaIWorkPop

= ChiPopl3+AduPop+OldPop

TotEmployment

= AgriEmp+8usEmp+MinEmp+ServiceEmp

Total Employment (1000 Persons)

TMPOP

= RPOP+UPOP

[735 Total Population (1000 Persons)

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182

TotWage

= Angage+BusWage+MinWage+SerWage

Total Wages (1000 OolIars/Person/Yr)

TransWage

= RelativeWage‘Averageray‘NWGTR‘TranspotEmp
[Q Wages in Transportation (1000 Dollars er)

TRW

= IF(TarWage>Ml\Nage,TRWt ,TRWZ)

[3 Target Relative ,Wage

TRWI

= TRWA+(TRWB‘AveWage)+(TRWC‘SLUR)

Target Relative Wage 1

TRWZ

= MNVage/NAWAGE

Traget Relative Wage 2

TSDO

= Tertiary‘TSDORN

[Q Tertiary Student Drop-out (1000 Students)

UCenLaVaIue

= IF(UrbanPopDensity>=DesirUubanPopD.UCenReaLV,UCenterLVN)
Urban Center Land Value (Dollars/Acre)

UCenReaLV

= UCenterLVN'(UrbanPopDensity/DesirUubanPopD)
Urban Center Land Value, Acture (Dollars/Acre)
UDeathRate

= UAdead+UCDead+UOAdDie

[3 Urban Population Death Rate

UDTSE -

= NSLUSS‘ULREM'FSSGTEN'UPerCaplncEM

Demand for Tertiary School Education, Urban (1000 Students/Yr)
UEmpMig

= IF(SLUR>=CLUR1, UEPRMI, UEPRMZ)

[Q Unemployment Ratio-Migration Multiplier

UEPRMI

= GRAPH(SLUR,0.02.0.06,[0.25.0.7,1.2,1.5'Min:0;Max:2'j)
Unemployment Ratio-Migration Multiplier 1

UEPRMZ

= IF(SLUR>=CLUR2, 0, UEPRM3)

E—b Unemployment Ratio-Migration Multiplier 2

UEPRM3 '

= GRAPH(SLUR,-0.2,0.06,[-l .2,-0.8,-0.5,-0.2'Min:-1.5;Max:0'1)
UHBedIncI

= MIN(UHBIRD,UHBIRP)

Urban Hospital Bed Increase Ratio, Indicated (Bed/Yr)
UHBIRD

= ((DUHBPR'UPOP-UHospiBed)IUHDRT)'(MedicaIPersomeIDM)
E Urban Hospital Bed Increase Rate, Desired (Bred/Yr)
UHBIRP

= UrbanHBCapRatio'HealBuAI/CPUHB

13 Urban Hospital Bed Increase Rate, Possible (Bed/Yr)
UHBPR

= UHospiBed/UPOP

[Ea Urban Hospital Bed Population Ratio (Bed/1000 PersonsO
ULR

= ULiteracyP/UPOP

[3 Urban Literacy Rate

ULREM

= GRAPH(ULR,0,0.2,[0.407095.098,099,1"Min:0;Max:1'])
Urban Literacy Rate-Education Muitipl1er

UPerCapIn

= (BusWage+Schage)/UPOP

[3 Urban Per Capita Income (Dollars/Person/Yr)

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00000

183

UPerCaplncEM

= GRAPH(ULR_500,500,[0.9,0.98099,0.995,l'Min:0.9;Max:l"])

1% Urban Per Capita Income-Education Multiplier

UPOP

= UAduIl+UChiId+UOId

Urban Population (1000 Persons)

UPrimDrop

= UPrimS‘UPrimDropR

Urban Primary School Drop out (1000 Students)

UPrimExpan

= DELAYINF(UPSEI,DPSD)

Urban Primary School Expansion (1000 Students IYr)

UPSEI

= FundPExpa/PPSFC

Urban Primary School Expansion, Indicated (1000 Students/Yr)

UrbanAreaInc

= LandVaIueMuI'UrbanAreaIncN

Urban Area Increase (Acre)

UrbanLandVaIue -

= UCenLaValue°(EXP(-Distance'UrbanRadius))

Urban Land Value (Dollars/Acre)

UrbanPopDensity

= UPOP/UrbanLand

Urban Population Density (1000 Person/Acre)

UrbanRadius

= SQRT(UrbanLand/314.16)

[Ea Urban Radius (Mile)

UseRate

= AveUserRatio'Vrsitors/SupCapPerProd

USSDO

= USecond‘USSDORN

@ Urban Secondary School Drop out (1000 Students)

USSE

= DELAYINF(USSEI,DSSD)

[Q Urban Secondary School Expansion (1000 Students IYr)

USSEF

= BUSS-MEUSS

@ Urban Secondary School Expansion Fund (1000 DollarsIYr)

USSEI

= USSEF/PSSFC

[i=3 Urban Primary School Expansion, Indicated (1000 Students/Yr)

Visitors '

= MIN(InfraCapPeriod,Potenvisit)

The visitors can be expressed by the comparison of infrastructure capacity and potential visitors. In other words, if
the infrastructure capacity is avialable for the potential visitors, the potential visitors will be the visitors.

AdjustTIme

= 1

The manpower gaps' average adjust time.

AgriEmp

= 150

@ Employment in Agriculture ( 1000 Persons)

AIDRU '

= 0.75

1% Acceptable Income Difference between Urban and Rural

ASLMP

= 35

Average Service Life of Medical Personnel (Yr)

ASPEDP

= 24

Average Students per Primary Education Personnel (Students/Person)

000000

00,000000000000

184

ASPTEDP

= I5

[6% Average Students per Tertiary Education Personnel (Students/Person)
AttVlsitor

= 560000
Averageray

= 260

[3 Average Numbers of Working Days (Days/Y r)
AveUserRatio

= .2

[Q Average pr0portion of visitors using the superstructure
AngecPer

= 60

@ Average resources decfine time period
AvglnvPeriod

= 12

Average Investment Rate. Investment does not always occur as immediately response to-infrastructure or

superstructure lacking.

CapAddRatio

= 0.06

[=9 Capital Add Normal in Business Services (1IYr)
CapAddRatio__1

= 0.05

® Capital Add Normal in Household Services (ler)
CapDisHRatio

= 0.04

@ Capital Discard Ratio in Business Services (1/Yr)
CapDisRatio '

= 0.04

[a Capital Discard Ratio in Business Services (1er)
CapRequrFPP

= 7500

[3 Capital Requirement Per Family Planning Personnel (1000 Dollars/Person)
ChFactor

= 0.01

E31) Change in Capital Output Factor Normal in Business Services
ChFactorH

= 0.04

12:) Change in Capital Output Factor Normal in Household Services
CLOFRBS

= 0.01

[2 Change in Labor-Output Factor Ratio in Business Service (1 Nr)
CLOFRHS

= 0.01

Change Labor Output Factor Ratio in Household Services
CLURI

= 0.02

@ Critical Local Unemployment Ratio 1

CLUR2

= -0.02

Critical Local Unemployment Ratio 2

CostPerRHospBed ~.

= 5

Cost Per Urban Hospital Bed (1000 Dollars/Bed)

CostPrim

= 70

Per Primary Student Maintenance Cost (Dollars/Student)
CPUHB

= 7

Cost Per Urban Hospital Bed (1000 Dollars/Bed)

000000000

000000000000

185

CriticaIPoint

= 12

[E5 The critical time period of overuse the resources
DAP

= 50

Duration of Adult Period (Yrs)

DCP

= 15

Duration of Childhood Period (Yrs)

DdlayULAI

= 2

Delay of Urban Land Area Increasing (Yr)
DelayCapA

= 0.5

Delay on Capital Alocation (Yr)

DelayRMAI

= 1.5

[Q Delay of Rural Mmicipality Area Increasing (Yr)
DelCap

= 2

[Eh Delay in Capital Add Realization In Business Services (Yrs)
DelCapH

Delay in Capital Add Realization in Household Services (Yrs)
DepTime

= 120
cm Years

@5 Average depreciation time period.
DesirFPPerPopRatio

= 0.0005

E—h Desired Family Planning Personnel Population Ratio
DesirRResDen

= 0.1 .

1% Desired Rural Residential Density (1000 Persons/Acre)
DesirRuraIHBPopRatio

= 1.35

@ Desired Urban Hopital Bed Population Ratio (Bed/1000 Persons)
DesirUubanPopD

= 0.2

Desired Urban Population Density (1000 Persoancre)
DevPeriod

Resources developing time period.

Distance

= 2.5

Distance Constant Specifing the Land Value Change
DMF 1 88

= 0.2

Demand Factor in Business Service

DMF 1 HS

[Q Demand Factor 1 in Household Service

OMFZBS

= 0.3

[3 Demand Factor in Business Service

DMFZHS ‘

= 0.06

[2 Demand Factor 2 in Household Service

DMPTBS

= 2

DMPTHou

= 2

000000000000000000000

186

OPSD

= 15

E11 Delay in Primary School Development (Yr)

DSE

= 4

Duration of Secondary Education (Yr)

0880

= 2

[E Delay in Secondary School Development (Yr)
OTE

= 4

Duration of Teratiry Education (Yr)

DTED

= 3

Delays in Tertiary Education Development (Yr)
DUHBPR

= 1.35 '
Desired Urban Hopital Bed Population Ratio (Bedlt 000 Persons)
DurPrim '
= 6

[3 Duration of Primary Education (Yr)

ECStand

= 0.5

EduBudgAF

= 0.75

E Education Development Budget Allocation Fraction
ExtAidIiti

= 6680

E5 External Aid, Initial (1000 Dollars/Yr)
FamiPIaPerTraTlme

= 0.5

12 Family Planning Personnel Training Time (Yr)
FamPlanPerSerTlme

= 35

[Q1 Famfly Planning Personnel Service Time (Yr)

F BTE

= 0.3

[2 Fraction of Tertiary Education Budget Allocation
F BUPS

= 0.4

Fraction of Budget for Urban Primary School
FIamPIanBAF

= 0.1

Family Planning Alocation Fraction

FMSCVG

= 0.03

Fraction of Medical Students in College and Vocational Graduates
FPEBA

= .38

Fraction of Primary Education Budget Allocation
FPPRealizationTlme ._

= 2

Family Planning Personnel Realization Tlrne (yr)
FPSSEN

= 0.75

E5 Fraction Primary Student to Secondary Education Normal
FracBugUSec

= 0.4

[a Fraction of Budget for Urban Secondary School
FracInIraAIIocation

= 0.4

E5 Fraction of Infrastructure Allocation

 

 

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187

FracLandRA

= 0.5

[3 Fraction of Land Retorm Allocation

FraSOA

= 0.35

[3 Fraction of Social Overhead Allocation

FSEBA

= .32

Fraction of Secondary Education Budget Allocation
FSSGTEN

= 0.75

HeallhBudgetAF

= 0.15

Health Budget Allocation Fraction

lRMlR

= -0.01

lRMlU

= -.015

Inter-Regional Migration index. Urban (Personlt 000Person)
LandT ax

= 200

Tax on land (1000 Dollars)

LFRI

= .495

Labor Force Participated Initial

LoanRRliti

= 6500

[a Loan Receive Raté. Initial (1000 Dollars/Yr)
LoanTime

= 10

Loan Time (Yr)

LUnempRTlme

= 2

Q Local Unemployment Ratio Adjustment Time (Yrs)
MinEmp

= 12

Employment in Mining (1000 Persons)

MiWage

= 8

Minmum Wage (Dollars/PersonlDay)

MPBR

= 0.003

lib Medical Personnel to Hospital Bed Ratio (1000 Persons IBed)
MR

= 1

Dummay variable

NAWAGE

= 8

National Average Wage (DoflarslPerson/Yr)
NWageED

= 12

Normal Wages in Education ‘(1000 Dollars/Yr)
NWageF

= 15

Normal Wage in Health and Family Planning (Dollars/PersonlDay)
NWBS

= 9

[a Normal Wage in Household Services (Dollars/Person/Yr)
NWGAG

= 4.5

E3) Normal Wages in Agriculture (1000 Dollars / Yr)

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188

NWGG

= 12

[a Normal Wages in Government (1000 Dollars/Yr)

NWGM

= 9

[Q Normal Wages in Mining ( 1000 Dollars er)

NWGP

= 6

Normal Wages in National Park (1000 Dollars/Yr)

NWGTR

= 9

(3;; Normal Wages ln Transportation (1000 DolarsIYr)

NWHS

= 6

Normal Wage in Household Services (Dollars/PsesorVDay)

PerGovEmpExp

= 3000

@ Per Government Employment Expendature (Dollars/Person)

PeriodDays

= 30

The numbers of day in period.

PerManRes

= .18

Average responsibe for maintenance the recreation resource per manpower.

PersonlncomeTaxRate

= 0.3 -

[Q Personal Income Tax Rate

PollNorm ‘

= 10000000

PollStandard

= 200

E5 Pollution Standard

PPSFC

= 500

Per Primary Student Facility Cost (DollarsIStudents)

PPSMC

= 100

Ej Per Secondary Student Maintenance Cost (Dollars/Student)

ProductTax

= 500

Products Tax (1000 Dollars/Yr)

PSSFC

= 600

Q Per Secondary Student Facility Cost (Dollars/Person)

PTSFC

= 6000

E5 Per Tertairy Student Facility Cost (DolarsIShldentL

PTSMC

= 500

[3 Per Tertairy Student Maintenance Cost (Dollars/Student)

QOLStand

= 1

E5 Quality of Life Standard

RADeadl

= 0.0043

Eh Death Rate of Rural Adult. Normal (ler)

RatioTechMan

= l

The lraction ol the technology manpower. who is used lor developing the potential recreation resources is
represented by parameter. It demonstrates that not only this variable is not able to be controled by decision
maker, but also it includes the idea ol an execution plan.

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189

RBirthRate
= 0.06

RCDeath

= 0.0034

[3 Death Rate of Rural Children, Normal (1/Yr)
RecoveTime

= 24

if the overuse the resources, the average RECOVER TIME to back to the original resources.
ResCap

= 1000

The carrying capacity per unit resource.
RHCDelay

= 2

Rural Hospital Construction Delay (Yr)
RLFPRI

= 0.54

RLPt

= 21

Rural Literate Population (1000 Persons)
RODealh

= 0.044

Death Rate or Rural Old Adult, Normal (1m)
RPSOORN

= 0.07

1% Rural Primary Student Drop-out Rate
RSSDORN

= 0.1

Rural Secondary Student Drop-out Rate
RUMI

= .01

RualoUrban Migration Index (Person/1000 Person)
RuralHBCapRat

= 0.7

Urban Hospital Bed, Capital Ratio

RuralHDRT

= 5

Urban Hospital development Realization Time (Yr)
RWAT

= 5

Relative Wage Adjustment Time

SRQAG

= 10

Service Requirementln Agriculture (1000 Dollars/Yr)
SRQMl

= 5

[ED Service Requirement in Mining (1000 Dollars/Yr)
TaxHous

= 500

Tax on Housing (1000 Dollars)

TranspotEmp

= 500

E Employment in Transportation (1000 Persons)
TRWA

= 1.1

E5 Target Relative Wage Factor A

TRWB

= 0.0001

Target Relative Wage Factor B

TRWC

= -.2

E1; Taget Relative Wage C

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I90

TSDORN

= 0.1

Tertiary Student Drop-out rate

UAdeath

= 0.0043

[a Death Rate of Urban Adult, Normal (ter)
UBirthRate

= 0.058

UCDeath

= 0.0033

Death Rate of Urban Children. Normal (1er)
UCenterLVN

= 600

[3 Urban Center Land Value. Normal (Dollars/Acre)
UHCDelay

= 2

Urban Hospital Construction Delay (Yr)
UHDRT

= 5

Urban Hospital development Realization Time (Yr)
UOdeath

= 0.0435

Death Rate of Urban Old Adult (1IYr)
UPrimDropR

= 0.05

Urban Primary Student Drop-out Rate
UrbanAreaIncN

= 100 '

1% Urban Area Increase. Normal (Acre)
UrbanHBCapRatio

= 0.3 .

Urban Hospital Bed. Capital Ratio
USSDORN

= _ 0.08

E) Urban Secondary Student Drop-out Rate
start = 0.00000

stop = 100.00000

dt = 1.00000

method = Euler (tixed step)

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