Defining developmental problems for policy intervention
or
Building reference mode in 20 steps over 5 learning cycles
Khalid Saeed
Social Science and Policy Studies Department
Worcester Polytechnic Institute
Worcester, MA 01609, USA
Ph: 508-831-5563, Fax: 508-831-5896, email: saeed@wpi.edu
Jan 2001
Copyright 2000, Khalid Saeed
Prepared for
19" Intemational Conference of the System Dynamics Society
Atlanta Georgia, USA July 23-27, 2001
Defining developmental problems for policy intervention,
or building reference mode in 20 steps over 5 learning cycles!
Khalid Saeed
Social Science and Policy Studies Department
Worcester Polytechnic Institute
Worcester, MA 01609, USA
March 15, 2000
Abstract
Developmental problems are invariably perceived as existing conditions, which must be
alleviated. This often removes a policy from the factors that created the problem in the first
instance. System dynamics method requires that a problem must be viewed as an internal
behavioral tendency found in a system so its causes can be determined before a corrective action
is initiated. A pattern representing internal dynamics of a system, called a reference mode, must
be constructed before developing a model that serves as an apparatus to create a policy design for
system change. Such a problem definition process is also appropriate for understanding
developmental problems such as food shortage, poverty and insurgence, so their causes rather
than only symptoms are addressed by a developmental policy. A reference mode is, however,
different from a precise time history in that it represents a pattern incorporating only a slice of
the history and it requires several learning cycles to construct a reference mode from time
history. A learning process based on a well-known model of experiential learning is used to
describe the construction of a reference mode, which is illustrated at length by revisiting the
problem of food shortage.
Key words: _ sustainable development, policy intervention, policy design system dynamics,
modeling, reference mode, problem definition, experiential leaming, planning.
' This paper emerged out of my efforts to teach system dynamics at WPI. I appreciate the
encouragement of my colleagues and students, whose penetrating questions led to the growth of
the problem definition process from one learning cycle to two, then to three and finally to four. I
particularly appreciate the feedback from my colleague Isa Bar-On who constantly urged me not
to skip implicit steps in the process.
Introduction
Developmental problems are often perceived as pre-existing conditions, which must be
alleviated. For example, food shortage, poverty, poor social services and human resources
development infrastructure, technological backwardness, low productivity, resource depletion,
environmental degradation and poor governance are often defined as developmental problems. In
all such cases, the starting point for a policy search is the acceptance of a snapshot of the existing
conditions. A developmental policy is then constructed as a well-intended measure that should
improve existing conditions. Experience shows, however, that policies implemented with such a
perspective not only perform variedly, they also create unexpected results. This happens because
the causes leading to the existing conditions and their future projections are not adequately
understood. The well-intentioned policies addressing problem symptoms only create ad hoc
changes, which are often overcome by the system’s reactions.
Development planning must adopt a problem solving approach in a mathematical sense if it is to
achieve reliable performance. In this approach, a problem must be defined as an intemal
behavioral tendency found in a system and not as a snapshot of existing conditions. This
behavioral tendency may represent a set of patterns, a series of trends or a set of existing
conditions that appear resistant to policy interventions. In other words, an existing condition by
itself must not be seen as a basis for problem definition. The complex pattern of changes implicit
in the time paths preceding an existing condition would be, on the other hand, a basis for
defining a problem.
The solution to a recognized problem should also be a solution in a mathematical sense, which is
analogous to creating an understanding of the underlying causes of a delineated pattern. A
development policy should then be conceived as a change in the decision rules that would
change a problematic pattern to an acceptable one. Such a problem solving approach can be
implemented with advantage using system dynamics modeling process that entails building and
experimenting with computer models of problematic patterns, provided of course a succinct
representation of such pattems has first been constructed. Called a reference mode in system
dynamics, such a representation is based on historical information and is often described in a
graphical form. It is, however, quite different from point by point description of historical trends
in that it may represent only a few selected organized pattems embodied in the complex and
seemingly unorganized profile of the trends. It also encompasses both past and inferred future
patterns.
Developmental policy based on recognition of existing conditions
Table 1 collects the various developmental problems and the broad policies implemented to
alleviate them that one comes across in the economic development literature, although not
presented as in the table. However, if one reviews the timeframes of problems and policies, the
organization of the table appears to be quite cogent.
The initially perceived problems indeed were hunger, poverty and insurgence that created threats
to human security. Since these problems were taken as given, the natural response to alleviate
them was to facilitate intensive agriculture so more food could be produced, to foster economic
growth so aggregate income could be increased and to strengthen internal security and defense
infrastructure so insurgent groups could be suppressed.
The common denominator of those policies was that they attributed the existing conditions to
outside factors, as if they came to be as acts of fate. They also assumed that the system is static
and not self-regulating. Thus, it was expected that directly attacking symptoms would help
alleviate them. Attacking symptoms without knowing how these were created of course also
required powerful intervention by an outside hand and entailed an effort to strengthen
government infrastructure, which in fact displaced some of the development effort. The
subsequently experienced problems were many, but in most instances, these included a
continuation of the existing problems [Saeed 1996, 1998].
Table 1 Developmental problems, policies implemented to address them and
subsequent problems
Initially Policies implemented Subsequently experienced
perceived problems
problems
Food security + Intensive agriculture + Land degradation
- land development + Depletion of water aquifers
- irmigation + Vulnerability to crop failure
- fertilizer application + Population growth
- use of new seeds + Continuing/increased
vulnerability to food shortage
Poverty + Economic growth + Low productivity
- capital formation + Indebtedness
- sectoral development - Natural resources depletion
- technology transfer + Environmental degradation
- external trade + _Continuing/increased poverty
Insurgence and |- Spending on intemal + Poor social services
threats to peace security and defense + Poor economic infrastructure
infrastructure + Authoritarian governance
Limiting civil rights - Continuing insurgence/increased
threats to peace
Thus, food shortages have continued but are now accompanied also by land degradation,
depletion of water aquifers, a threat of large-scale crop failure due to a reduction in crop
diversity and a tremendous growth in population. Poverty and income differentials between rich
and poor have in fact shown a steady rise, which is also accompanied by unprecedented debt
burdens and extensive depletion of natural resources and degradation of environment. Insurgence
and threats to peace have intensified together with burgeoning expenditures on intemal security
and defense, which has stifled development of social services and human resources and have
created authoritarian governments with little commitment to public welfare.
The subsequent problems experienced are also more complex than the initial problems and have
lately drawn concerns at the global level, but whether an outside hand at the global level would
alleviate them is questionable. This is evident from the failure to formulate and enforce global
public policy in spite of active participation by national govemments, global agencies like the
UN, the World Bank, the World Trade Organization, and advocacy networks sometimes referred
to as the Civil Society. This failure can largely be attributed to the lack of a clear understanding
about the roles of the actors who precipitated those problems and those whose motivations must
be influenced to turn the tide.
The following sections of this paper describe a learning process entailed in creating a reference
mode for system dynamics modeling, which can greatly help discern developmental problems.
This learning process is illustrated by revisiting the problem of food shortage. Further, the
problems of poverty and intemal security are redefined as manifestations of internal trends of the
system rather than as acts of fate.
Reference mode construction as a learning process for defining developmental problems
Notwithstanding the assertion that the definition of a developmental problem should depend on
historical trends and not on a snapshot of existing conditions, it must be understood that
historical trends in their unfettered form cannot adequately describe a problem, although they
might portray it a shade better than a snapshot. A succinct problem description is created in
system dynamics by constructing a reference mode, which is a fabric of trends representing a
complex pattern rather than a collection of historical time series. It may contain variables
actually existing in historical data as well as those summarizing qualitative information from a
related body of knowledge, or those conceming policy options to be explored or all three types.
Historical data is only a starting point for constructing a reference mode, which is an abstract
concept that must be developed very carefully from the historical data, qualitative information
and the inferred future patterns they points to.
At the outset, while both historical behavior and a reference mode can be expressed in either
quantitative or descriptive terms, a reference mode is essentially a qualitative and intuitive
concept since it represents a pattern rather than a precise description of a series of events. A
reference mode also subsumes history, extended experience and a future inferred from projecting
the inter-related past trends. It can be seen with the mind's eye as an integrated fabric, although it
can be represented on paper only as isolated tendencies. A reference mode will also not contain
random noise normally found in historical trends, as this noise lies outside of the deterministic
processes underlying our understanding of the system behavior. Finally, a reference mode is an
integrated fabric that can only be visualized in the abstract, although it can only be represented in
a graphical form on a two-dimensional block. Fortunately, we have an immense experience of
visualizing such a fabric due to the constant demands made on our perceptions to convert limited
perceptual images of reality into more comprehensive mental images. For example, a two-
dimensional vision frame that our eyes construct can be perceived as a three- dimensional mental
image by our mind [A bbot 1987].
An experiential learning framework for constructing a reference mode
I have pointed out in Saeed (1998), that the system dynamics modeling process is best
implemented using an experiential leaming framework originally proposed by Kolb (1984).
Kolb’s model of experiential learning, originally proposed in an organizational leaming context,
draws on the faculties of observation, concrete thinking, experimentation and reflection [Kolb
1984, Hunsacker and Alessandra 1980, Kolb, et. al. 1979, Kolb 1974]. It requires that an abstract
concept be developed through a learning approach calling upon all four faculties as illustrated in
Figure 1.
RULE BASED THINKING AND
DEDUCTIONS
(thinking)
w
#
in
a
1)
5
EXPERIMENTATION ACTIVE a PASSIVE Ons Ron ane
(doing) _ \ watching)
andl g
a
cocnitive DOMAIN. | & PHYSICAL DOMAIN
@
ABSTRACT
CONCEPTUALIZATION
(feeling)
y
LEARNT CONCEPT
Figure 1 Kolb’s model of experiential learning
Four basic faculties drive Kolb’s learning cycle: watching, thinking, doing and feeling. For the
learning process to be effective, watching must result in careful observation of facts, leading to
disceming organized patterns. These patterns then must drive rule based thinking, which should
create a concrete experience of reality. The implications of the concrete experience must be
tested through experimentation conducted mentally or with physical and mathematical apparatus.
Finally, this experimentation must be translated into abstract concepts and generalizations
through a cognitive process driven at the outset by feeling, which would, in tum, create further
organization for careful observation thus invoking another leaming cycle. Successive learning
cycles lead to the refining of the leamed concept, which is the outcome of the learning process.
The learning faculties, according to Kolb’s model, reside in two basic human functions, physical
and cognitive - each integrated along two primary dimensions, which are also illustrated in
Figure 1. The first dimension, conceming the physical functions is passive - active. The second,
concerning the cognitive functions is concrete - abstract. Thus, the faculty of watching is a
passive physical function, thinking a concrete cognitive function, doing an active physical
function and feeling an abstract cognitive function. Since the mental construction of reality and
its interpretation must filter unwanted information, each faculty must be guided by certain
organizing principles to affect learning. Additionally, the leamer is required to shift constantly
between dissimilar abilities to create opportunities for resolving the anomalies, which would
appear among the constnucts of each ability. This learning process lends itself with great ease to
the construction of a reference mode.
The time horizon of reference mode depends on the purpose of the model, but it would
invariably be longer than the historical information it is based on, as it would include also
information about the inferred future. The development of a reference mode requires integration
of four abstract concepts:
1) Delineation of a preliminary system boundary.
2) Aggregation of variables in the preliminary system boundary and selection of a subset of
these to determine a preliminary model boundary.
3) Insertion of missing variables (usually stocks) in the preliminary model boundary to obtain
the model boundary.
4) Determination of a further extended model boundary incorporating also the policy related
variables (usually flows) connected to the stocks contained in the model boundary.
5) Projecting past trends of variables in the model boundary into future to create a fabric of
inter-related patterns that constitutes a reference mode. Policy variables may not be
extrapolated at this stage since they are included largely to create a decision space for further
experimentation.
This is accomplished through implementation of an undocumented process an experienced
modeler follows. This process entails twenty steps built around five learning cycles as described
below:
First learning cycle: Delineation of a preliminary system boundary
The first learning cycle that delineates a preliminary system boundary is described in Figure 2.
3
graph various components of
decomposed patterns in each
set and collect related patterns
2
decompose each set of complex
patterns into simpler parts
learning cycle 1
outcome: preliminary
system boundary
1
examine multiple sets of
complex historical time series or
qualitative descriptions
representing problem history
from all sets into subgroups
4
Select a subgroup of patterns
representing the behavior of
interest and discard remaining
subgroups.
PRELIMINARY SYSTEM
BOUNDARY
Figure 2 Delineation of a preliminary system boundary
One must begin by 1) carefully examining problem history manifest in information both
quantitative and qualitative residing in the complex time series and event descriptions as well as
in the multiple manifestations of the problem behavior in different periods and in different
places. 2) This is followed by decomposition of observed complex pattems into simpler parts.
This can be accomplished either by visual examination or by using a formal decomposition
process like the Fourier series analysis. 3) Next, a round of experimental graphing creates simple
multiple patterns representing slices of the complex behavior one has set out to model. Related
components of pattems from various times and places should be grouped together.
Figure 2a illustrates how complex time series from different times and places might be
decomposed and their simpler components regrouped (Saeed 1998). 4) A careful examination of
the various groups of decomposed graphs helps delineate the system boundary in terms of the
variables that must be considered to describe the discemed patterns. Normally, the groups
collected in columns in Fig 2a should be a part of the delineated system boundary, those in the
rows can often be viewed as separate systems.
simultaneous modes
composite decomposed
sapout peyeredas Aydeiioe pue sum
_
| LE be
group 1 group 2
Figure 2a Decomposing complex time series from different times and places and
regrouping their simpler parts.
Second learning cycle: Determination of a preliminary model boundary
The model is an abstract version of the system. The variables incorporated into it would
invariably be different from those in data. Some of the variables in the data will have to be
aggregated to create appropriate model variables. Some of these aggregations might even create
abstract intangible variables markedly different from those in the data. Some of the variables in
the data might even be ignored if these are only tangentially related to the policy agenda the
model is built to address [Saeed 1992]. This is illustrated in Figure 2 b.
The second learning cycle begins with 1) a careful examination of the selected group of patterns
in the preliminary system boundary keeping in view the model purpose and the time horizon of
interest. 2) This examination leads to combining disaggregate variables into aggregate categories
and defining abstract variables not present in data. 3) A second round of graphing past trends
addresses model variables as differentiated from the first round that concemed decomposing
system variables and drawing trends for the decomposed parts. 4) Finally, the drawn trends are
reviewed as a multi-dimensional fabric to delineate preliminary model boundary.
6
combine disaggregate variables
to get to a desired level of
aggregation apropriate for the
model purpose. this step might
create abstract variables not
present in original data
7 learning cycle 2 5
graph inferred historical outcome: preliminary model examine selected group of
behavior of aggregated and boundary patterns in the preliminary
abstract variables system boundary
8
assemble graphed historical
patterns into a fabric of model
variables
PRELIMINARY MODEL
BOUNDARY
Figure 2b —_ Second learning cycle leading to the determination of a preliminary model
boundary
Third learning cycle: Extending model boundary to include STOCK variables missing in data
Not all variables in a model are captured by historical information, qualitative as well as
quantitative. Often stocks that are widely distributed or hard to discern would not enter data. For
example, the amount of capital stock in an economy, the amount of in situ resources, the level of
fertility of soil, the quality of life and the amount of dissidence in a community, cannot be
captured by data, although the flows related to them, respectively, production, extraction,
fertilizer application and nitrogen uptake by a crop, and violent events would often be recorded.
Fortunately, it is possible to discern the behavior of stocks through integration of related flows.
Rules for this are well documented in mathematics and articulated in the system dynamics
10
context in Sterman (2000). The leaming cycle leading to inclusion of these stocks in model
boundary is illustrated in Figure 2c.
10
infer behavior of STOCKS
missing in the data
9
examine selected group of
patterns in the preliminary
model boundary
11 learning cycle 3
graph behavior of additional outcome:model boundary
variables conceived
12
assemble historical patterns in
decomposed data and inferred
variables into a fabric of model
variables
|
MODEL BOUNDARY
Figure 2c —_‘ Third learning cycle leading to the a more complete model boundary.
The identification of such stocks must start with 1) a careful review of the trends in the
preliminary model boundary in an effort to recognize flows whose sources or sinks are missing,
but these cannot be believed to be unlimited or infinitely flexible. 2) This is followed by
application of principles of integration to infer the pattems in the behavior of the missing stocks.
3) The inferred behavior of the newly constituted variables is drawn along side the already drawn
patterns for variables in the preliminary model boundary; and 4) a fabric of variables so created
is viewed as a more complete system boundary.
11
Fourth learning cycle: Further extending model boundary to include FLOWS representing
policy variables
While a model that replicates history can be constructed without building an adequate policy
space in it, such a model is often not useful in terms of exploring an operational means for
system improvement. Experimentation with such a model would often lead to normative
statements about what should be done, not what can be done, to improve system behavior. To
create an adequate policy space in the model, structure representing policy decisions must be
included although information about the behavior of policy-related variables may not exist in the
historical data. For example, if potential policies concem resource management, flows connected
to the related stocks that are influenced by such policies should be included. If they concem
delivery of certain services, the way these services are determined and how they affect existing
flows or create new flows in the model should be included. If taxation and expenditure by
government are possible policy instruments, the process of their determination and how they
would impact other flows in the model or create new flows should be included. Construction of
past patterns of behavior for the policy variables would often require going through an additional
learning cycle illustrated in Figure 2d.
14
identify any policy-related
FLOWS missing in the
delineated model boundary
learning cycle 4
15 outcome: extended model 13
graph historical behavior of boundary with policy carefully examine variables in
policy-related flows space the delineated model boundary
16
review the extended fabric of
variables now also including
policy-related flows
EXTENDED MODEL
BOUNDARY
INCORPORATING POLICY
SPACE
Figure 2d Extending model boundary to include policy space in it.
12
This cycle begins with 1) a careful examination of the past behavior of the variables in the
delineated model boundary. This is followed by 2) inferring behavior of policy-related flows or
influences on flows for which one might often have to delve into the empirical or the theoretical
premises of the various policy threads. Next, 3) an attempt is made to graph the past patterns of
behavior for the policy-related flows which when 4) combined with the already drawn past
trends for the system variables create an integrated fabric of past trends representing a more
complete model boundary.
Fifth learning cycle: extending past trends into the future to create a reference mode
The fifth cycle is illustrated in Figure 2e.
18
make intelligent projections for
the future behavior of model
variables in the extended
system boundary
19
graph inferred future trends for
the variables in the extended
model boundary
Figure 2e
learning cycle 5
outcome: reference mode
17
examine past behavior of
variables in the extended model
boundary
20
review the past and inferred
future trends of model and
policy variables as a fabric and
make sure there are no logical
inconsistencies in the fabric
!
REFERENCE MODE
13
Extrapolating past trends into the future to obtain a reference mode
It begins by 1) carefully examining the past behavior of system variables paying special attention
to their phase relationships and relative positions. 2) Next, these trends are intelligently projected
into the future keeping in view the progression of the whole fabric instead of concentrating on
one trend at a time. This process might often bring to fore any tuming points in system behavior
that would appear if current policies continue to be practiced. Policy-related flows may often be
assumed to continue unchanged or with unchanged decision rules. 3) A third round of
experimental graphing extrapolated trends creates essential components of a reference mode; and
4) the graphed trends representing past behavior of the system variables, policy variables and
their inferred future viewed as a fabric finally define the reference mode representing a succinct
description of a developmental problem.
Above problem articulation process is illustrated with an extensive treatment of the food security
problem in the next section.
An illustration of reference mode construction for the food security problem:
This section illustrates how historical data would be used to redefine the first developmental
problem - food security - listed in table 1.
Historical data from selected 14 countries is a basis for determining the demand for food, food
production and land use pattems in the Asia and Pacific region covering past as well as inferred
future behavior following the five leaming cycles described in the last section.
Some 300 time-series, covering past three decades and representing fourteen selected countries
in the Asia and Pacific region, are constructed from published UN sources to serve as a data-base
for the this exercise [Saeed and Acharya 1995]. There are many missing cells in the data. There
are also differences in units and definitions of data categories and national policies across
countries. Last, but not least, there are great differences across countries in terms of size and the
volume of activity represented in the data, hence any aggregation can lead to domination of
aggregate data by one or more larger countries.
The selected countries are divided into three categories based on per capita income; each
category is expected to characterize a different manifestation of the problem pattern. Australia,
Japan, Korea and Singapore are placed in category (A), representing relatively high levels of
income. Malaysia, Thailand, Philippines, and Indonesia are placed in category (B), representing
middle levels of income. China, India, Nepal, Pakistan, Sri Lanka, and Vietnam are placed in
category (C), representing relatively low levels of income. This classification covers the variety
of the countries in the Asia and Pacific region well, in terms of geographic location, form of
goverment and economic conditions.
The individual differences between the data elements, in this case the country-specific time
series, allow the data to be viewed as a sample representing the region it was drawn from. The
countries in the various categories of the sample are not viewed as special cases, but as multiple
manifestations of the behavior of the agricultural system of the region. A reference mode is
constructed from above data following the process outlined in the last section. The specific steps
taken are described below:
14
1. Determining preliminary system boundary
The historical data is divided into two broad categories respectively representing the growth of
consumption base and the condition of renewable agricultural resources. Time series plots for the
various categories of countries are prepared for Population, GDP and GDP per capita to examine
growth in the consumption base. The use of agricultural resources is examined through Per
Capita Food Production Index, Fertilizer and Pesticide A pplication, Cultivable Land, and Area
under Forests.
Following observations are made regarding each category covering steps 1 and 2 in first leaming
cycle of Figure 2:
a) Growth of the Consumption Base
Figures 3: a, b and c show population and GDP growth in the three categories of countries
selected for the analysis. Considerable population growth is shown over the three decades
covered by the data in all categories, although growth is much higher in the low-income
countries. GDP growth is the highest in the middle-income countries, while growth rates in the
high- and low- income countries are comparable. Consequently, as shown in Figures 4: a, b and
c, GDP per capita has grown at comparable rates in the high- and medium- income countries due
to moderate population growth in the former and high economic growth in the latter. However,
high population growth rates and moderate economic growth have led to stagnation in GDP per
capita in the low-income countries.
According to the projections made by the United Nations, shown in Figures 5: a, b and c,
tremendous growth has also occurred in urban populations across board and the high growth rate
is expected to continue, although these rates are projected to taper off in the high-income
countries. On the other hand, rural populations have shown stagnating or declining trends in the
higher income countries that may be expected to decline further in the future. At the same time,
due to the overall momentum of population growth, rural population has risen significantly in the
medium- and low- income countries, but is expected to taper off and begin to decline over the
second decade of the twenty-first century.
As also shown in Figures 5: a, b and c, the total population is expected to continue to rise in all
countries well into the twenty-first century, although the rates of projected population growth are
negatively correlated with the levels of income. Thus, the lower income countries experience
higher and continued rates of total population growth and urbanization [UNHS 1987].
Urbanization, however, encroaches on prime agricultural land, thus reducing overall land
productivity. It also creates concentrated demand on critical natural resources like water, clean
air, etc., whose depletion would also lower agricultural productivity. In all cases, there is growth
in the consumption base originating from two sources, growth of population and urbanization
and expansion in economic activity.
15
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18
b) Condition of Renewable Agricultural Resources
Renewable resources considered include agricultural land and forests, which have traditionally
met the food, fuel and timber needs of society. Figures 6: a, b and c show past trends in food
production per capita and agricultural land per capita in the countries of the three designated
categories of the sample. The food production index is not comparable across selected countries
due to differences in the criteria used for calculating the base figures, but represents only an
internal measure of the changes in food availability in each country. Some autonomous jumps
also appear in the data since it has been constructed from many sources, which although mostly
published by the UN, contains some inconsistencies in the definitions used to represent the
various categories of data. For constructing a reference mode, however, long-term patterns of
trends rather than numerical values of the time series are to be compared across the countries of
the sample. Hence, the above problems could be tolerated.
It is observed that food production per capita exhibits a rising trend in all cases in spite of
considerable population growth, while agricultural land per capita shows a declining trend,
except in Australia, where it has been possible to maintain it at a steady level. This indicates that
increases in food production have been obtained largely through increasing the intensity of
cultivation and application of chemical fertilizers and pesticides. Indeed, as indicated in Figures
7: a, b, and c, fertilizer application has drastically increased in all countries of the sample over
the past three decades. The application of pesticides also seems to have increased in the countries
where data is available. The pesticides data, however, is inconsistent since in some cases it
refers only to DDT while in others it covers all pesticides.
Irrespective of the increases in yield, the absolute quantity of cultivable land has not increased
much in most of the countries of the sample, except in Australia, where it has been possible to
commission large tracts of unused land. This is shown in Figures 8: a, b, andc. It is observed
that, in general, where cultivable land did increase, it was at the cost of the forest area, which is
already very small in the countries with a stagnant level of land under agriculture. Some jumps
again appear in the plotted data, due to the changes in the definitions of the forest area and
agricultural land categories used.
Unfortunately, deforestation not only reduces valuable timber and fuel wood resources, it is also
known to cause soil erosion, water loss, flooding or drought, desertification and silting of
irrigation reservoirs, depending on the particular function of a forest in the complex organic
relationships existing in the ecological system [Bowonder 1986]. In spite of this knowledge,
about half of the area under forests in the developing countries was cleared between 1900 and
1965. At current rates of deforestation, the rest is likely to disappear in 50 years [UN-ESCAP
1986].
Excessive use of land resources has also been known to depreciate soil quality. Soil degradation
has occurred in the countries of the sample and elsewhere because of erosion, chemical
deterioration, loss of texture, water logging and salinity, all resulting from efforts to intensify
agricultural activity [Bowonder 1981]. Given the over-taxing of land resources, the per capita
food production index may be expected to decline in the future across the board. Declining
trends have already appeared in Nepal and Bangladesh, as shown in Figure 9.
19
a) High Income
INDEX OF PER CAPITA FOOD PRODUCTION
(1979 -1981 = 100)
i ‘SINGAPORE
val
ae
120 NL s
100 a abe of
“asTRAUR, phos
2 wee OF cen
Countries
AGRICULTURAL LAND PER CAPITA
(HA/PERSON)
a Seer ee
we ee
- oe RS TRALIA
o Ot petite eee meee eee KOREA
60 ® ‘JAPAN
NV 01k
sete
«0 SINGAPORE pene een y
ee ea! ae
1006-08
oles we eae : int 1 erase
feco 064008 to72 wre 10008 OF eee 068 72” 076 ~«OBOSC«NOOASCw
TIME TIME
Source: Source:
FAQ 1980: FAO Quartely Bulletin of Statistica 1989, Vol.2. New York.
UN (1970-1986): Statistical Yearbook for Asia & the Pacific (1970-1088). NY.
UN (1970-1988): statistical Yearbook for Asia & the Pacific (1970-1988). NY.
UN 1979: Demographic Yearbook 1978. NY.
b) Medium Income Countries
INDEX OF PER CAPITA FOOD PRODUCTION
(1979 - 1981 = 100)
160 a onsen
MALAYSIA |
Epi
Papal
athens
a ~ INDONESIA
“0 4 MALAYSIA
> PHILUPINES
=) “© THAILAND
[ae carer ona TO er ro
yoeoe64 wea. 1078 was 1088
TIME,
Source:
FAO 1989: FAO Quartely Bulletin of Statistics 1989. VoL2. New York.
UN (1970-1088): Stalistical Yearbook for Asia & the Pacitic (1970-1988). NY.
AGRICULTURAL LAND PER CAPITA
(HA/PERSON)
[—naonean wnt > emma —= Tan
06) =
THAILAND
de . ee eae
* kL MALAYSIA
oat Ss
| ge 4 PHILLJPINES
eo po HPS
02: ete ( Vi
|
. |
os :
eo iass eee wre noo wee ae
TIME
Source
UN (1970-1088): Statistical Yearbook for
Asia & the Pacific (1970-1088). NY
c) Low Income Countries
INDEX OF PER CAPITA FOOD PRODUCTION
(1979 - 1981
100)
40
20
pled
Wweo 19040872
TIME
Souree:
FAO 1989: FAO uartely Bulletin of Statistics 1989. Vol2. New York
UN (1970-1988: Statistical Yearbook for Asia & the Pacific (1970-1988).NY
Figure 6
AGRICULTURAL LAND PER CAPITA
(HA/PERSON)
++ INDIA,
“LT PAKISTAN,
TvIETNAM
CHINA
Source:
UN (1970-1088); Statistical Yearbook for
Asia & the Pacific (1970-1088). NY.
Food Production per capita and agricultural land per capita
20
a) High Income Countries
N FERTILIZER APPLICATION
(THOUSANDS TONS/YEAR)
“JAPAN “KOREA —* i
1000
100
10
’ \va eee SINGARORE, 2-*
pyle rea ee ee i
fee ieee wee er? wore wo te0a 08
TIME
source:
UN (1970-1988): Statistical Yearbook for
‘Asia & the Pacitic (1970-1088). NY.
INSECTICIDE APPLICATION
(TONS/YEAR)
100000 —-
AKAN”
KonEA+
ae a ae
“ ¥
cep. AUSTRALIA
‘eo tera wore too 1000000
TIME
Source:
UN (1870-1988) Statistical Yearbook for
Note:
3960-1971: DDT & Related Compound
Asia & the Pacific (1970-1988). NY.
1072 onward: Other Insecticide
b) Medium Income Countries
N FERTILIZER APPLICATION
(THOUSANDS TONS/YEAR)
10000,
INDONESIA. -—-
: eat
of de
tae -
Big THAILAND: =~ INDONESIA
4 ow + MALAYSIA
+ pmu.irimes
pane marrereren
feo woe we
Source
UN (1070-1988): Statistical Yearbook for
Asia & the Pacitic (1970-1088). NY.
c)
N FERTILIZER APPLICATION
(THOUSANDS TONS /YEAR)
CHINA. INDIA
“2 PAKISTANA ~
NEPAL
VIETNAM
SRILANKA +
100000
CHINA
TE INDIA
RAKISTAN
atts VIETNAM
KA
1 -
are aro ee
theo foes te 727 98O oan toe
TIME
Source:
UN (1970-1988). Stat
‘Asia & the Pacitic (1
Figure 7
ical Yearbook for
188). NY.
Source.
UN (1970-1988) Statistical Yearbook for
Asia & the Pacific (1970-1988). NY.
Fertilizer and Pesticide application
INSECTICIDE APPLICATION
(TONS/YEAR)
—
[[== woowsie]
|| Teaco |
ssl
74
Ll /
i vinta,
: a,
wai
Note:
1960-1071: DOT & Related Compound
1972 onward: Other insecticide
UN (1970-1988) Statistical Yearbook tor
Asia & the Pacific (1970-1988). NY.
Low Income Countries
INSECTICIDE APPLICATION
(TONS/YEAR)
Note:
1960-1971; DDT & Related Compound
1972 onward: Other insecticide
21
a) High Income Countries
AGRICULTURAL LAND
{THOUSANDS HECTARES)
f a
(oe Se eee ee
FOREST AREA
(THOUSANDS HECTARES)
ee ee
100000 — - - ~ 1000000 en
je oo oe SO AURERAUAY Oooo oe RO AUSTRALIA
100000} Poe O eo gee eeeuoe
z a mr, .
* VOO00 fg pt tt HE pent tented
f 1000
100
t 100
eee SINGAPORE
vf Ra. OE ae wa OF eee SNQArORE
were woo wes 1900 co” ‘ea ees wre wre too” tea we
TIME
Source: source
UN (1970-1988): Statistical Yearbook for UN (1970-1988): Statistical Yearbook for
Asia & the Pacitic (1070-1088). NY. Asia & the Pacific (1970-1988). NY
b) Medium Income Countries
AGRICULTURAL LAND FOREST AREA
(THOUSANDS HECTARES) (THOUSANDS HECTARES)
bad aie — — INDONESIA = MALAYSIA
INDONESIA PHILLIPINES = THAILAND
20|-| + MALAYSIA a ~ 7
1 2 PRILLIPINES [yen ae _ _
Bg. | & THatano ~ [ = -
‘ 12h INDONESIA
s ee PHILLIPINES 4 x bh 100)
Ser ee po ERR | |
H x } \ 8
® « eet ted x sd 2 ool
5 ete MARAYSIA, poe 3 yaar ren
| 20) pauupives- PSS
Py rea aren Te ETOP PON ee ce a ted
1960 964 1968 1972 976 1980 1984 1988 1960 964 wos wre ‘676 0 wes
TIME TIME
Source: Source
UN (1970-1968): Statistical Yearbook for UN (1970-1908): Statistical Yearbook for
Asia A the Pacitic (1970-1088). NY. Asia & the Pacific (1970-1988). NY.
c) Low Income Countries
AGRICULTURAL LAND
(THOUSANDS HECTARES)
4000000
(=m Sa Sea]
||P PAKISTAN GRILANKA “VIETNAM |
FSS SSE WDIA
HINA
PAKISTAN |
se Op CEO O GF OA6-00 |
t0000) VIETNAM
. Paar Oa = acacia ea
\ JEPAL,
eee eres o rrr pir
fooght
"960 1064 «106819721076 1980 tabs 1088
TIME
Source:
UN (1970-1088). Statistical Yearbook for
‘Asia & the Pacitic (1970-1988). NY.
Figure 8
FOREST AREA
(THOUSANDS HECTARES)
sy sere
|
| ~
t VIETNAM 6 cence en
10000 oe
mise
UN (1970-1988). Stata
Asia & the Pacific (1970-1988). NY.
The competition between cultivable and forest land
22
INDEX OF PER CAPITA FOOD PRODUCTION
% (4979 - 1981 = 100)
140}
1205 Nepal
100}
Bangladesh
sot
so}
sof
sok ——— Nepal
—+— Bangladesh
°
4960 1964 1968 1972 1976 1980 1984 1988
Year
SOURCE:
UN: Stat. Yb. for Asia & the Pacific. N.Y.. Various issues.
Figure 9 Declining food per capita trends in Nepal and Bangladesh
Figure 10a shows the various patterns observed in the data representing the growth of demand
manifest in population (rural and urban categories are aggregated together), GDP and GDP per
Capita, and the condition of the renewable agricultural resources manifest in Food production per
capita, Agricultural land per capita and Forest area. Labels H, M and L pertain respectively to
high, medium and low income countries. Complex patterns are decomposed into trends and
periodicities superimposed on them. Trends and periodicities are drawn separately and placed in
different groups for determination of a system boundary appropriate for addressing the food
security problem.
The next step is to determine the system boundary. Key variables in this boundary concern both
growth of demand and condition of renewable resources necessary for food production. Thus,
trends representing Population, GDP, GDP per capita, Food per capita index, Agricultural land
and Forest area define system boundary. Since short term fluctuations are not of concer to the
long term problem of food security, the cyclical components of these trends are discarded.
2. Delineation of a preliminary model boundary
A model boundary will depend on the purpose of the model, the time horizon of interest and the
policy agenda the model will address. It need not contain all the variables in the system boundary
delineated above while some of the system variables might be aggregated into fewer categories.
For example the source for demand for food can be limited to population if growth in income is
not of interest. Food per capita, land per capita being determined by population, food and land,
need not be a part of the minimum set of variables in the model boundary. Urban and rural
population need not be separated (in fact, these were already aggregated together in the previous
23
step). Thus, preliminary model boundary will consist of population, food production, agricultural
land and forests as shown in Figure 10b.
==
Population
Population
M
H
i GOP
H
M
GDP/Capita u GDP/Capita
Now Now
TIME TIME
H
Food Production/Capita u
L
] Very Poor
Agyicutual Land/Capita
Food Production’
Ff Capita
M
L
Forest ea
NOW. Now
TIME TIME
Figure 10a: | Decomposition of data into patterns for delineating system boundary
24
Population
Food Praduction
Agricutural Land
Pe | aod
NOW
TIME
Figure 10b _ Delineation of a preliminary model boundary
3. Identification of missing stocks to develop a more complete model boundary
Many stocks pertinent to the purpose of the model may not exist in data, although information
about these will be available in the experience domain. Thus, agricultural land when consumed is
converted into wastelands. Production depends on harvest biomass, which, in tun, depends on
soil nitrogen, humus, and non-harvest biomass. The behavior of these additional stocks shown in
Figure 10c can be inferred from the stocks and flows existing in the data.
4, Addition of policy related flows to the model boundary
A model constructed with variables included so far would be useful for explaining the problem
history identifying entry points for policy intervention, but it may not issue any operational
policy instruments available within the organizational contexts it addresses. However, if flows
representing policy are identified and later connected to the stocks they must regulate, policies
can be constructed in terms of new and modified decision rules through policy experimentation.
Hence, policy related flows must be identified at the outset when a model is built. Policy related
flows in our example include irrigation, use of high yield technology, fertilizer application, land
rehabilitation. The trends in these, as inferred from qualitative descriptions, are shown in Figure
10d.
25
—————-
Non Harvest Biomass
Harvest Biomass
Now
TIME
Figure 10c Determination of stock variables missing in the preliminary model boundary
26
Inigation
High Yield Seed Technology
Fettiize: Application
Land Rehebiltation
Now
TIME
Figure 10d Determination of policy-related flows missing in data
5. Extrapolation of past trends into an inferred future to obtain reference mode
A system dynamics model has a time horizon that is often much longer than the historical
information used for describing the past. In many instances, historical information inadequately
describes the pattem the model must replicate. In all such instances, the delineated historical
trends must be extended into the future. In our example, a threat to food security is not indicated
in historical information but appears when historical trends are extrapolated into future. It is
evident that food production cannot grow in the face of shrinking agricultural land, increasing
wastelands, declining forests, and decreasing soil nutrients and humus. This is shown in Figure
10e, which illustrates the reference mode for a model that should be built and experimented with
to explore robust policies for creating food security.
It should be noted while the patterns in Figure 10e are carefully digested as a fabric that the
trends in the data taken from a geographically, economically and politically diverse set of
countries show increases in agricultural production in all cases - clearly a private gain whether
pursued by individuals or collectives. It is also evident that the increases in production have been
achieved in the first instance by making an intensive use of land resources viewed as capital
inputs rather than as an environmental system. It is also quite evident that expansion in
agricultural land has been achieved by consuming forests - another environmental system which
is important to the maintenance of agricultural land as a sustainable resource, but which is
27
viewed by individuals and collectives involved with agriculture as an unused endowment [Saeed
1992a].
a, eadisn
i con
Production
Agricutural
Land
| tt
| —-- Forests
ie eae ae SalNieoee
Soll Biomass
an Non Harvest Biomass
Now
Harvest Biomass
TIME
Figure 10e: Extrapolation of past trends into future to obtain reference mode
The projections obtained from digesting the patterns contained in the reference mode indicate an
impending tragedy of the commons in which food production per capita will overshoot and
decline, followed by a similar trend in population. Land under forests and soil fertility will
decline to a low stagnant level and land under cultivation rises to a high stagnant level. It is also
evident that land use management and soil rehabilitation are important parts of policy agenda
that must be explored by the model.
28
6. Reference modes for poverty and internal security problems
Reference modes for describing internal trends towards poverty and insurgence and threats to
security can likewise be constructed. Since a historical record of variables describing poverty is
difficult to obtain, the intemal trends towards poverty can be understood by examining the past
efforts to alleviate poverty and the system tendency to restore the original patterns. The patterns
shown in Figure 11 and 12 have been constructed following above learning process, but using
pertinent information relating to poverty and political instability. Figure 11 illustrates how real
wage rate has stagnated after an initial increase following the commencement of developmental
effort while GDP rose and the production by formal firms replaced self-employment.
developmental interventions
production by formal sector
|
real wage rate
Figure 11 A reference mode constructed to describe the problem of widespread poverty
Figure 12 shows how insurgence and internal security and defense budgets rose concomitantly
with national income when developmental interventions occurred. Some scholars even suggested
on the basis of an observed high correlation between growth of income and growth of internal
security and defense expenditure that the latter was a means to achieve the former. Subsequent
experience showed, however, that economic growth could not be sustained along with increasing
spending on defense and intemal security and all three graphs must tum down although with a
specific phase relationship with respect to one another as also shown in Figure 12.
29
developmental interventions internal security and
defense expenditure
insurgence
income
Figure 12 —_A reference mode constructed to describe the problem of insurgence
7. Current developmental issues
As the global economic system becomes highly integrated, concomitant methodological
advancements have no doubt also greatly increased our ability to understand the increasingly
complex problems of sustainability. However, both formulation of developmental policy and its
implementation now involve actors and institutions operating beyond national boundaries, which
makes it exceedingly difficult to converge on shared perceptions of both problems and the roles
and responsibilities of the various local and global organizations. This has led to much
controversy. Table 2 lists the key actors involved in the formulation of development policy and
their performance expectations from the various local and global organizations they are trying to
influence.
These key actors consist of global agencies like the World Bank, the World Trade Organization,
the various developmental agencies of the United Nations and European Union and the various
regional strategic and trade alliances. They also include national governments, the public and a
relatively new actor - the non-government advocacy organizations often referred to as the civil
society. These actors bring different goals and different mental models about the sources of
problems to the formulation of national and global policies.
30
Table 2 Key actors and systems they try to influence in present day developmental
agenda
System performance expectations of various actors
Systems Global agencies | National Civil society Public
government
Global economy | Grow Change in Support special | Create
structure interests opportunities
National Maintain Grow Support special | Deliver welfare
economy interests
Production Compete Generate tax Support special | Provide
units revenue and interests meaningful
foreign exchange employment
Natural Sustain global Support national | Be nurtured Support living
resource system _| interests growth standard
The various systems whose performance is influenced include the global economy, the national
economies, the production units and the natural resource system, albeit with different
expectations. Thus, the global agencies would like the global economy to grow but without any
structural change. This means that the national economies must maintain their existing position
in the global system, while production units compete to deliver a larger output. Of course, the
resource system must accommodate the aggregate performance expectations without disrupting
the current consumption structure.
The national governments require the national economies to grow and to accommodate this the
global economy to change in structure. They expect the production units to generate enough tax
revenue and foreign exchange so burgeoning national security and defense needs are met and
national debts are serviced. They also expect to exploit their natural resources, which includes
logging tropical rain forests and burdening pristine resort environments as much as possible to
accommodate growth.
The civil society organizations expect all global, national and local production systems to
accommodate the various special agendas they attempt to articulate while the natural resource
system is preserved and nurtured. Finally, the public expects that the global economy will create
new opportunities for them, the national economy will deliver welfare, the production units
would provide meaningful employment and the natural resource system would support an
improvement in their standard of living.
Unfortunately, this variety of expectations combined with an even larger variety of perceptions
about how the different systems function has led mainly to controversies and anomalies instead
of generating any effective policies. In particular, global public policy that mainly concems trade
and environmental agenda has been difficult to formulate and implement due to disagreements
31
among the proponents of free trade, economic efficiency, responsibility for environmental cost
and fair trade.
An attempt to carefully define problems through construction of reference modes, building
system dynamics models around defined reference modes and experimenting with the models to
design developmental interventions can deliver effective public policy for implementation both
at local and global levels. It can also help the actors involved in policy formulation to converge
to a shared view of the developmental problems through the learning process involved in
problem definition and modeling processes.
8. Conclusion
Developmental problems can be succinctly defined by constructing a reference mode
representing the patterns of behavior preceding an observed problematic condition. I have
attempted in this paper to define the characteristics of a reference mode and how it is
distinguished from historical data, both qualitative and quantitative. A reference mode is an
abstract concept subsuming past as well as inferred future behavior. It can best be visualized as a
fabric collecting several patterns as well as the phase relationships existing between them. It may
contain concrete as well as abstract variables that are different from the data it is based on. It
may also represent only a slice of the complex time history it emulates and may thus look very
different from the history itself. A policy design based on experimentation with a model that is
closely tied to a reference mode has promise to be effective.
The process of constructing a reference mode and building a model based on it can be as
important as the model itself. This process assists people to identify their assumptions and test
their beliefs and assertions. In this way, it generates dialogue between system participants. Since
the model can represent different insights and points of view in an objective fashion, it provides
a relatively neutral language and framework to help reveal critical issues in a shared framework.
Implemented over the course of a negotiation, the system dynamics modeling process is
invaluable for creating a shared vision leading towards a resolution that is based on the logic of
the problem rather than on the adversarial views articulated during the negotiation. A logic-based
mitigation process elicits greater commitment and cooperation on the part of parties than an
adversarial process based on different mental models of problems.
A system created in a given developmental context can be studied by translating known and
inferred experiential information into a model and experimenting with it prior to formulating the
terms of a policy. Developing intemational policy accords requires dealing with complex
relationships and diverse perspectives that create unforeseen future system behavior if one relies
only on the often limited mental models of policy makers. Bargaining based on existing power
structures often results in terms that are unfair and that create unresolvable future conflicts and
security threats. Experimentation with a system dynamics model, on the other hand, allows all
parties to develop a common perception of the problem and recognize future implications of
decisions, which should help in the design of robust accords and reliable system performance.
32
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