System Dynamics Model on Harmonious Development of
Chinese Science & Technology, Education and Economy
under High Technology
Xu Qingrui Fan Baoqun
School of Management, Zhejiang University (Yu Quan), Hangzhou, P.R.China
School of Management, Zhejiang University ,YuGu Road 20, Hangzhou, 310027
Telephone: 0086(571)7951886, 7993120 Fax:0086(571)7951358
E-mail: sba_xugr @ema.zju.edu.cn, or xqr@sba.zju.edu.cn
Abstract Studies on Harmonious development of Science & Technology, Education
and Economy(STEE) are hotspots in the world under Knowledge Economy and
Globalization era whose base is High Technology. However there are very few
researches on harmonious development of Chinese Science & Technology, Education
and Economy(CSTEE). Thus, this paper tries to study CSTEE system with System
Dynamics Method, model it and test some combined patterns and policies. After
Modeling and analyzing the CSTEE system, this paper also gives suggestions on
Science & Technology, and Education input strategies in China based on better
harmonious development of CSTEE.
Keywords Harmonious Development of Chinese Science and Technology,
Education and Economy; System Dynamics Modeling; Science & Technology and
Education input Strategies;
1 Introduction
System Dynamics Method(SDM) is a kind of effective way to describe internal
structure, interaction and evolutionary behaviors of complex systems(Forrester, 1980).
Compared with other analysis tools, System Dynamics Method have two obvious
advantages in analyzing non-liner complex systems and making decisions. First is that
SDM is helpful to solve problems from systemic exterior(exogenous variable) to
interior(endogenetic variable). The other is that SDM makes it easy and feasible to
evaluate feasible scheme because SDM can show all possible results with system
emulation and overcome traditional methods’ non-intuitionisty. Many scholars study
complex systems with SDM, such as Roberts(1979) and Senge(1990) and so on.
However there are very few researches on the complex systems--harmonious
development of CSTEE with SDM. Therefore this paper tries to build system dynamics
model of harmonious development of CSTEE with SDM in order to analyze and review
issues of Chinese S&T and education input strategies. This paper will analyze mainly
following four questions:
i) Dynamic change of Chinese R&D input and R&D input/GDP;
ii) Change on internal structure of R&D input
iii) Dynamic change of Chinese education input(EI) and EI/GDP
iv) Internal structural change of education input.
2 Model structure, causal relationship and flow map of CSTEE under
high-technology
In system thinking, harmonious development of CSTEE must consider internal
structures and interactive relationship among S&T, education and economy systems
and have to consider the match relationship among them(see figure 1). According to
figure 1, there are there subsystems: S&T subsystem, education subsystem and
economy subsystem.. A brief causal effect loops about three subsystems is show as
figure 2. Based on figure 1 and figure 2, we build a system dynamics model including
three subsystems and more than 700 variables. As the space is limited, we omit material
about stocks and flows.
Fig. 1 Model structure for harmonious development of CSTEE
Harmonious development
economy S&T education
economic BR PE
—s — |
structure AR ED ME HE
Fig.2 Brief causal relationship of STEE
fiducation
Gon) amie
Power output
input
_———
3 Testing System Dynamics Model of CSTEE
To system dynamics model, rationality and validity are basic precondition for model
effective emulation. Next this paper discusses the rationality and validity of CSTEE
model with two key indicators: R&D expenditure and education input in China.
i) by comparing model emulation result with actual value of R&D expenditure, we
can find the model is very good in being close to actual situation. (see table 1 and figure
3).
Table 1 Comparison on CSTEE model emulation and actual value of Chinese R&D
expenditure unit: 100million yuan
Year 1988 | 1989 | 1990 | 1991 | 1992 | 1993 | 1994 1995 _| 1996
AV 89.5 | 112.3 | 125.4 | 142.3 | 169 196 222.2 | 286 332
MV 90.4 | 114.92 | 122.5 | 143.1 | 166.0 | 197.9 | 224.75 | 281.5 | 332.2
E% 1 2.34 2:3. 0.56 1,72 0.98 1.24 1.56 | 0.05
Note: AV: actual value; MV: model value; E: error
Figure 3 R&D expenditure comparison figure 4 education input comparison
330
305
280
255
230
205
180
155
130
105
80
1988 1989 1990 1991 1992 1993 1994 1995 1996
1981 1984 1987 19981993
ii) same situation is found in education input. (see table 2 and figure 4).
Table 2 Comparison on CSTEE model emulation and actual value of Chinese
education expenditure unit: 100million yuan
Year | 1987 | 1988 | 1989 | 1990 | 1991 | 1992 | 1993 | 1994 | 1995
AV [294 | 357 | 412 | 462 [532 | 622 | 755 | 1019 | 1194
MV _ | 295.5 | 354.9 | 415 | 468.5 | 537 | 631 | 757_| 986.3 | 1189
E 0.5 0.6 0.7 | 14 0.9 1.45 |0.27 {3.2 |04
Note:See table 1
Therefore, the system dynamics modeling on harmonious development of CSTEE is
effective and feasible.
4 Policy test and analysis
The growth patterns for R&D input and education input may be analyzed with four
types:
Pattern 1) investment growth with steady ratio to GDP;
Pattern 2) investment growth with regular rate
Pattern 3) exponent growth
Pattern 4) —_ logarithmic growth
Based on above four pattern, we can get 16 kinds of combined growth patterns with
matching different education input pattern with different R&D expenditure pattern (see
table 3). In order to evaluate different schemes, we choose three indicators: GDP (unit:
100 billion yuan), S&T personal (unit: 10 thousands) and Total factor productivity( TFP,
unit: %). Respectively Emulating the 16 combined pattern, the results are shown as
figure 5 to figure 12, and table 4 to table 11.
Table 3 _16 kinds combined patterns of R&D input and education input
R&D input pattern Pattern | Pattern 2 Pattern 3 Pattern 4
education input pattern
Pattern 1 Py Pir Pi3 Pig
Pattern 2 Pai Poo Pos Pog
Pattern 3 P3; P32 P33 P34
Pattern 4 Py Py Pas Pag
For pattern 1 of education input(belongs to typical conservative and negative input
strategy), the ratio of education input to GDP keeps same value each year, i.e. education
expenditure/GDP=2.5%, then let R&D input patterns change from pattern | to pattern 4.
The results of emulation are shown as figure 5 and table 4.
Fig.5 combined patterns’ results under pattern 1 of education input
= cpp combined pattern P,, — op
Combined pattern Pu
{50'c- - stp] 140 p--~
100 ies
80 30
60 ee
40 40
20 20
0 ‘ ‘ i ; j Fs
1997 2007 2017 2027 2037 2047 1997 2007 2017 2027 2037 2047
combined pattern Pi —= = 'GDP| combined pattern Puy = = 'GDPI
200 200
150 150
100 100
50 50
0 rs cor. re
1997 2007 2017 2027 2037 2047 1997 2007 2017 2027 2037 2047
Table 4 Model emulation results of combined patterns Pi1-Pi4
1997 2007 2017 2027 2037 2047
Pi GDP. 753 87.5 88 82.5 71.2 68
STP 83.4 101 97.5 72 67 Si?
TEP 29.6 37 36 28 25 23
Pio GDP. 75.3 97 98 84 i) 71
° STP 83.44 M1 7 100 86 80
TEP 29.6 44 48 35 26 23
Pis GDP. 75.3 93 153 145 108 93
° STP 83.44 135 142 120 99 90
TEP 29.6 62 50 38 32 35
Puy GDP 75.3 88 130 152 150 123
STP 83.44 113 137 153 155 140
TEP 29.6 52 58 55 45 42
Pattern 2 to 4 of education input belongs to active input strategy. The results of model
emulation are analyzed and shown as figure 5 to 7 and table 5 to 7.
Fig.5 combined patterns’ results under pattern 2 of education input
combined pattern P2, combined pattern P2,
200 p--
0 j
41997 2007 2017 2027 2037 2047 1997 2007 2017 2027 2037 2047
combined pattern P,,
combined pattern P.;
1997 2007 2017 2027 2037 2047 1997 2007 2017 2027 2037 2047
Table 5 Model emulation results of combined patterns from P2; to P24
1997 2007 2017 2027 2037 2047
Po GDP 753 150 175 163 147 133
STP 83.44 130 162 170 157 135
TEP 29.6 42 58 60 47 42
Pos GDP. 753 108 162 202 204 175
STP 83.44 122 155 175 165 142
TEP 29.6 50 65 70 70 42
Ps GDP. 75.3 123 162 198 213 198
° STP 83.44 us 150 177 190 152
TEP 29.6 46 63 73 69 54
Pog GDP. 75.3 146 187 206 204 194
STP 83.44 170 158 170 173 167
TEP 29.6 44 65 80 Ta: 80
Fig.6 combined patterns’ results under pattern 3 of education input
combined pattern P,, =—— cpp} combined pattern P, DE
350
300
250
0 f . . . j
1997 2007 2017 2027 2037 2047 1997 2007 2017 2027 2037 2047
combined pattern P,, combined pattern P,,
250 p----
200
150
100
50
0
1997 2007 2017 2027 2037 2047 1997 2007 2017 2027 2037 2047
Table 6 Model emulation results of combined patterns from P3; to P34
1997 | 2007 | 2017 | 2027 | 2037 | 2047
Ps, GDP. 75.3 119 198 213 180 160
STP 83.44 | 175 183 150 138 140
TFP 29.6 56 69 60 52 38
Px GDP 75.3 102 228 295 268 260
. STP 83.44 | 117 208 248 238 233
TFP 29.6 35 82 88 82 70
Pas GDP 75.3 181 173 158 180 150
~ STP 83.44 | 198 215 170 156 123
TFP 29.6 42 75 70 60 52
Py GDP. 15:3 133 250 273 245 240
STP 83.44 | 102 144 190 196 202
TFP 29.6 40 53 63 73 80
Fig.7 combined patterns’ results under pattern 4 of education input
combined pattern P,, — oP combined pattern P, —= ‘cpp
250
200
1997 2007 2017 2027 2037 2047 1997 2007 2017 2027 2037 2047
combined pattern P,, == cpp| combined pattern Py, — ‘GDP
; 500 p----
250
200
1997 2007 2017 2027 2037 2047 1997 2007 2017 2027 2037 2047
Table 7 Model emulation results of combined patterns from P4; to Pag
1997 | 2007 | 2017 | 2027 | 2037 | 2047
Pa GDP 753 92 154 177 194 165
STP 83.44 | 106 144 196 181 181
TEP 29.6 42 58 73 65 38
Py GDP 753 113 181 238 | 227 175
STP 83.44 | 138 168 185 168 188
TEP 29.6 60 68 65 65 63
Pixs GDP 153 98 135 192 177_| 200
STP 83.44 | 135 169 167 190 154
TEP 29.6 40 63 71 65 52
Pas GDP 75.3 127 193 360 390 392
STP 83.44 | 90 220 327 340 323
TEP 29.6 33 46 63 80 80
Table 8 collection of different patterns results and evaluation of feasibility
1997 | 2007 | 2017 | 2027 | 2037 | 2047 Feasibility
Py GDP | 75.3 | 87.5 | 88 | 82.5 | 68 | 71.2
STP | 83.4 | 101 | 97.5 | 72 57 67 =
TFP | 29.6 | 37 36 28 23 25
Pp GDP | 75.3 | 97 98 84 HA 2B
STP | 83.4 | 111 | 117 | 100 | 80 86 oa
TFP | 29.6 | 44 48 35 23 26
Ps GDP | 75.3 | 93 | 153 | 145 | 93 | 108
. STP | 83.4 | 135 | 142 | 120 | 90 99 _
TFP | 29.6 | 62 50 38 35 32
Pis GDP | 75.3 | 88 | 130 | 152 | 123 | 150
STP | 83.4 | 113 | 137 | 153 | 140 | 155 +
TFP | 29.6 | 52 58 55 42 45
Py GDP | 75.3 | 150 | 175 | 163 | 133 | 147
. STP | 83.4 | 130 | 162 | 170 | 135 | 157 =
TFP | 29.6 | 42 58 60 42 47
Ps» GDP | 75.3 | 108 | 162 | 202 | 175 | 204
. STP | 83.4 | 122 | 155 | 175 | 142 | 165 =P
TFP | 29.6 | 50 65 70 42 70
P, GDP | 75.3 | 123 | 162 | 198 | 198 | 213
“ STP_| 83.4 | 115 | 150 | 177 | 152 | 190 =
TFP | 29.6 | 46 63 BB 54 69
Pos GDP | 75.3 | 146 | 187 | 206 | 194 | 204
. STP | 83.4 | 170 | 158 | 170 | 167 | 173 “eb
TFP | 29.6 | 44 65 80 80 77
Py GDP | 75.3 | 119 | 198 | 213 | 160 | 180
. STP | 83.4 | 175 | 183 | 150 | 140 | 138 =
TEP | 29.6 | 56 69 60 38 52
Py GDP | 75.3 | 102 | 228 | 295 | 260 | 268
° STP | 83.4 | 117 | 208 | 248 | 233 | 238 +++
TFP | 29.6 | 35 82 88 70 82
Table 8 continued
GDP | 75.3 | 181 | 173 | 158 | 150 | 180
Ps stp [8341198 | 215 | 170 | 123 | 156 =
Trp | 296 | 42 | 75 | 70 | 52 | 60
p,, | GDP | 75.3 | 133 [250 [273 | 240 | 245
; STP | 83.4 | 102 | 144 | 190 | 202 | 196 +4+4++
Trp | 296| 40 | 53 | 63 | 80 | 73
p, [SDP | 75.3 [92 [154 [177 | 165 [194
STP | 83.4 | 106 | 144 | 196 | 181 | 181 +
Trp | 296 | 42 | 58 | 73 | 38 | 65
py | GbP | 75.3 | 113 | 181 | 238 | 175 | 227
STP | 83.4 | 138 | 168 | 185 | 188 | 168 a
Trp | 296| 60 | 68 | 65 | 63 | 65
p, | SbP | 75.3 [98 [135 | 192 [200 [177
> [stp [93.4 | 135 | 169 | 167 | 154 | 190 _
Trp | 296| 40 | 63 | 77 | 52 | 65
p, [LSbP | 753 | 127 | 193 | 360 [392 [390
STP | 83.4 | 90 | 220 | 327 | 323 | 340 +++++
Trp | 296 | 33 | 46 | 63 | 80 | 80
Note:— stands for low feasibility; + stands for high feasibility.
According to table 8, it is obvious that feasibility of combined patterns P24, P32, P34 and
P44 is high. Above patterns mean different input combination. (see table 9)
Table 9 special meanings of four feasible patterns
Combined Input mode
pattern
Pog Investment growth as regular rate of education input and logarithmic
growth of R&D input
P32 Exponent growth of education input and growth as regular rate of R&D
input
P34 Exponent growth of education input and logarithmic growth of R&D
input
Pag Logarithmic growth of education input and logarithmic growth of
R&D input
Based on above four feasible combined patterns, this paper also emulates and analyzes
the tendency of internal structural change of different patterns. The emulation results
are shown as table 10 to table 13.
Speaking to combined pattern P 4, the possible trends of internal structural change of
education input are that the ratio of primary education input may decline from 53
percent of education input in 1996 to 50 percent of 2050, middle education input keep
at 29~30 percent of total education expenditure, and higher education input increase
from 17 percent of 1996 to the max value 20% about 2020, then last to 2050 or so. The
allocation strategies of R&D input may be that the ration of Basic Research increases
from 5% to max value 9% of 2020, then declines slightly and last 7.5 % to 2050;
Applied Research expenditure may decline from 30 percent of 1996 to 25 percent of
2050; and Experiment Development may increase from 64 percent of 1996 to 68
percent of 2050 or so.
Table 10 Emulation results of internal structural change of combined pattern P24
Year 1997 _| 2002 | 2007 2012 2017 2022 2027 2032 2037 2047
EI/GDP 2.5 3.75, 5 6.25 1) 8.75, 10 11.25 12.5 13.75
R&D/GDP_| 0.496 | 1.119 | 1.663 1.971 2.073 1.974 1.794 1.697 1.649 1.610
PE 53 50.24 | 49.63 49.25 49.45 49.83 50.00 50.08 50.07 50.18
ME 30 30.58 | 30.51 30.60 30.47 30.30 30.10 29.86 29.80 | 29.73
HE 17 19.17 | 19.85 20.13 20.07 19.85 19.88 20.05 20.11 20.07
BR 5.999 | 6.918 | 8.137 8.983 9.110 8.927 8.703 8.350 7.945 7.580
AR 30.20 | 29.37 | 28.40 27.52 26.47 25.45 24.82 24.37 24.35 24.51
ED 63.79 | 63.70 | 63.45 63.48 64.41 65.61 66.46 67.27 67.69 67.90
Note: El: education input; R&D: R&D expenditure; PE: ratio of primary education
expenditure; ME: ratio of middle education expenditure; HE: ratio of higher education
expenditure; BR: basic research; AR: applied research; ED: experiment development.
To education and R&D structural change of combined pattern P32 and P34, detailed
strategies are shown in table 11 and table 12.
Table 11 Emulation results of internal structural change of combined pattern P32
Year 1997 | 2002 | 2007 2012 2017 2022 2027 2032 2037 2047
EV/GDP 25: 2.36 | 2.227 2.164 | 2.133 | 2.390 2.948 | 3.883 5.125 6.033
R&D/GDP | 0.496 | 0.74 | 0.992 1.24 1.488 | 1.736 1.984 | 2.231 2.48 2.728
PE 53 47.7_| 47.33 46.44 | 44.44 | 43.86 44.14 | 42.84 45.46 47.23
ME 30 33.1 32.25 29.76 | 29.13 | 27.77 27.43 | 27.82 25.12 24.27
HE 17 19.11 | 20.40 23.78 | 26.41 | 28.35 28.42 | 29.33 29.40 28.49
BR 5.999 | 6.91 8.193 9.551 10.82 | 12.32 14.46 17.39 17.62 17.44
AR 30.20 | 29.3 | 28.58 28.60 | 29.06 | 29.85 30.85 [ 31.70 35.61 33.69
ED 63.79 | 63.7_ | 63.21 61.84 | 60.10 | 57.81 54.68 | 50.90 46.75 42.86
Note : see table 10.
Table 12 Emulation results of internal structural change of combined pattern P34
Year 1997 2002 2007__| 2012 2017 2022 | 2027 _| 2032 | 2037 _| 2047
EV/GDP. 2.5 2.360 2.227 | 2.164 | 2.133 | 2.390 | 2.948 | 3.883 | 5.125 | 6.033
R&D/GDP 0.496 1.119 1.663 | 1.971 | 2.073 1.974 | 1.794 | 1.697 | 1.649 | 1.610
PE 53. 51.49 50.59 | 46.41 | 42.44 | 42.69 | 41.11 | 39.48 | 40.15 | 42.72
ME 30 29.38 28.57 | 29.79 | 31.13 | 28.95 | 27.43 | 26.81 | 25.06 | 23.80
HE 17 19.11 20.83 | 23.78 | 26.41 | 28.35 | 31.45 | 33.70 | 34.77 | 33.47
BR 5.999 6.898 8.116 | 9.515 | 10.98 | 12.86 | 15.41 | 18.41 | 22.19 | 26.57
AR 30.20 29.57 29.27 | 29.56 | 30.20 | 31.61 | 33.49 | 35.33 | 36.82 | 37.92
ED 63.79 63.53 62.61 | 60.91 | 58.81 | 55.51 | 51.08 | 46.25 | 40.97 | 35.50
Note : see table 10.
To combined pattern P44, the possible trends of internal structural change of education
input are:(see table 13)
a) the ratio of primary education input may decline from 53 percent of education input
in 1996 to min value 40 percent of year 2010, then return to 46 percent of year 2050
or So;
b) middle education input would keep at 29~30 percent of total education expenditure;
c) higher education input increases from 17 percent of 1996 to the max value 29%
about 2020, then declines slightly to 26 % of year 2050 or so.
The allocation strategies of R&D input may be:
a) the ration of Basic Research increases from 5% to max value 19% of 2030, then
declines slightly and last 17 % to year 2050 or so;
b) Applied Research expenditure may keep as 29~30 percent of total R&D expenditure;
c) Experiment Development may decline from 64 percent of 1996 to 53 percent of year
2050 or so.
Table 13 Emulation results of internal structural change of combined pattern P44
Year 1997 2002 | 2007 _| 2012 2017 2022 2027 2032 2037 2047
EI/GDP. 2.5 5.35 15.8 15.55 15.37 15.24 14.69 14.46 14.48 14.57
R&D/GDP | 0.496 | 1.119 | 1.66 | 1.971 2.073 1.974 1.794 1.697 1.649 1.610
PE 53 43.7 | 40.1 39.33 42.13 45.33 47.17 48.21 48.59 46.2
ME 30.0 30 31.9 | 31.62 29.41 27.04 25.99 25.38 25.65 30
HE 17 23.8 | 26.2 | 27.90 29.04 28.45 27.61 26.83 26.40 25.74
BR 5.999 | 6.75 | 7.57__| 8.855 10.61 13.76 17.03 18.89 19.14 18.31
AR 30.20 | 30.6 | 31.2 | 30.72 29.57 28.04 26.92 26.72 27.88 29.82
ED 63.79 | 62.5 | 61.2 | 60.42 59.80 58.19 56.04 54.37 52.97 51.85
Note : see table 10.
Compared with GDP and the result shown as table 10 to table 13, this paper gives an
sketch map of four different feasible patterns. (see fig. 8) Different areas of Fig 8
represent the respective scope of four feasible patterns.
Fig. 8 Sketch map of P32 to P44
A R&DIGDP(%)
27
16
GDP
10 240
0s
EVGDP(%)
—
25 6.0 13.75 15.8
In order to ensure Chinese economic growth fastest and total factor productivity growth
biggest before half of next century, the combined pattern Py, is the best choice, i.e.
logarithmic growth of education and R&D input. This pattern is also very fit to the
situation of China. Because the scale of R&D and education input in China was too low
in last several decades and did not keep up with the pace of economic development.
Therefore, Chinese R&D and education input should grow with a fast rate. In
short-term, the emphasis of Chinese R&D input should be the firms which pursue the
work of Applied Research and Experiment Development so that firms can become the
main body of technology innovation. In long-term, China have to enhance the area of
Basic Research by investing vigorously R&D funds in Basic Research and building the
Universities and Colleges as base of Basic Research and high technology under
knowledge era of 21“ Century.
For allocation of education input, in short-term, the emphasis of education investment
should be primary and middle education. And in long-term, China must develop
actively higher education in order to provide the base of person with ability for
scientific and technological development and intensive knowledge competition of 21“
Century. Moreover from the result of pattern P44, we can find that the growth of
Chinese education input is earlier than growth of R&D input about ten years.( see fig. 9)
According to pattern P44, the ratio of education input will reach max value 15 % in year
2005~2007, but the max value of R&D input in year 2015~2017.
Fig. 9 The Sketch map of education input growth ahead of R&D input
10 years gap
1996 2003 2010 2017 2024 «2031 «2038-2050
EV/GDP
R&D/GDP
5 concluding remarks
The conclusions by our research are:
(1) In order to ensure Chinese economic growth fastest and total factor
productivity growth biggest before half of next century, the combined pattern
P44 is the best choice, i.e. logarithmic growth of education and R&D input. The
year 2007 will be dividing line for logarithmic growth of education input. The
mode of education expenditure growth should adopt high-level logarithmic
growth before year 2007 and the ratio of education input will reach the max
value 15 % in 2007. The mode of education expenditure growth should adopt
low-level logarithmic growth after year 2007 and basically keep same level
during 2020 to 2050. Year 2017 will be the dividing line of R&D input. The
mode of R&D expenditure should adopt high-level logarithmic growth before
year 2017 and reach the max value 1.6 percent of total R&D expenditure.
(2) The growth rate of education input will be faster than that of R&D input
during year 2000 to year 2010.
(3) In order to ensure economic development with faster rate, better performance
and better relationship among S&T, education and economy in the future 50
years, it is necessary for structure of R&D and education to adjust as following:
a) During year 2000 to 2025, the ratio of Basic Research should increase from
6.6% of year 1997 to 15 percent of year 2025, Applied Research keep in 30
% or so and Experiment Development decline from 63.4% of year 1997 to
55% 0:
f year 2025.
O50 the rath
with big scope,
d Research keep
experiment ie to 53~55% of
development
applied research
bi
asit resea
1997 2000 2025, 2040 2050
c) During year 2000 to 2020, the ratio of higher education expenditure should
increase from 17% of year 1997 to 28% of year 2020, middle education
increase to 31 % of year 2020 and primary education decline from 53% of
year 1997 to 41% of year 2020.
d) Near 2050, the ratio of higher education expenditure will reach 26% of total
education input or so, middle education keep at the level of 28~30%, and the
primary education decline to 46% or so of year 2050.
100
90
80
70
60 middle education
50
higher edudation
primary education
1997 2000 2025 2040 2050
e) The time education input grows to max value may be earlier 10 years than
the time of R&D input.
Main References
Qingrui XU, Junjie Li et al(1988). Science-Technology, Education and Economy System
Dynamic Model, Proceedings of ICSSE’88, International Academic Publishers, Beijing.
Qingrui XU(1986). R&D management. High Education Publishers, Beijing.
Qingrui XU, Jin Chen (1991). Resource Allocation on the Technological Innovation,
Proceedings of International Conference of System Dynamics Society, Bangkok.
Qingrui XU(1990). Managing Technological Innovation, Zhejiang University Press,
Hangzhou.
Roberts,E.B.(1979).Managerial Applications of System Dynamics, The MIT Press,Cambridge.
Senge,P.(1990).The fifth Discipline: The Art and Practice of the Learning Organization,
Century Business: London.
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