RESEARCH ON THE EVOLUTION MECHANISM
OF ECOSYSTEM OF CYBER SOCIETY
BASED ON THE HAKEN MODEL
Xiaolan Guan and Zhenji Zhang
School of Economics and Management, Beijing Jiaotong University, Beijing, 100044, P.R. China
Keywords: Ecosystem of cyber society, Self-organization, Haken model, Order parameter, Evolution mechanism.
Abstract: The cyber-society is a new social form derived from the emergence of computer and Internet technology
which serve as the representatives of the information technology. It is similar to the ecological system, and its
internal structure is hierarchical and composed according to a certain structure, and self-organization is an
important mechanism of its evolution. Based on the theory of self-organization and by the cross-disciplinary
analysis and research, this paper analyzes the self-organization conditions of Ecosystem of cyber-society,
constructs the evolution model based on the Haken model, and then analyzes the functions of competition and
coordination between the system elements and the process that the order parameter dominates that they bring.
Finally, it selects the Internet development measurement data of Beijing, Shanghai, Shanxi, Tianjin, and other
27 provinces and cities in 2006 and 2009 as samples and analyzes the evolution process or China Ecosystem
of cyber-society. The result reflects that technology innovation is the dominant order parameter in the
evolution of Ecosystem of cyber-society, and a key factor to the development process of China Ecosystem of
cyber-society and then the paper puts forward some ideas and suggestions to the development of China
Ecosystem of cyber-society.
1 INTRODUCTION
As an integral whole that constituted by the
interactions between various elements, the ecosystem
of cyber society is an economic and social area that
comes into being naturally as the development of the
ecosystem of cyber society to a certain stage, and it is
the product of interaction between people in the
society. During the evolution process of the
ecosystem of cyber society, when one element
changes, the other elements will change accordingly
and thus form a new order (Shi, 2007). The process
and result of interrelate and interact between various
elements and subsystems in the absence of the
specific intervention from the outside world, which
create self-organization, self-creation and
self-evolution, and thus make the ecosystem of cyber
society develop from disordered to ordered structures
is a self-organizing process. The various elements
that interact at different levels with different
structures play a role in promoting the evolution of
the whole ecosystem of cyber society from different
perspectives and different aspects coordinately. If we
can identify the key behaviors that affect the system
evolution and development at one stage, namely the
order parameter, then it is no doubt very significant to
both of the managers or participants of the ecosystem
of cyber society.
Based on the theory of self-organization and by
the cross-disciplinary analysis and research, this
paper analyzes the self-organization conditions of
ecosystem of cyber-society, constructs the evolution
model based on the Haken model, and then analyzes
the functions of competition and coordination
between the system elements and the process that the
order parameter dominates that they bring, in order to
give some guidelines to the rational development of
ecosystem of cyber society, and also guarantee its
healthy, orderly and rapid development.
124
Guan X. and Zhang Z..
RESEARCH ON THE EVOLUTION MECHANISM OF ECOSYSTEM OF CYBER SOCIETY BASED ON THE HAKEN MODEL.
DOI: 10.5220/0003484001240131
In Proceedings of the 13th International Conference on Enterprise Information Systems (ICEIS-2011), pages 124-131
ISBN: 978-989-8425-56-0
Copyright
c
 2011 SCITEPRESS (Science and Technology Publications, Lda.)

2 ANALYSIS ON THE
CONDITIONS OF
SELF-ORGANIZATION OF THE
ECOSYSTEM OF CYBER
SOCIETY
According to the theory of dissipative structure, the
system must meet the following four conditions to
generate the phenomenon of self-organization and
form a dissipative structure: open and open to a
certain extent, far from equilibrium, nonlinear
interaction and fluctuations.
(1) Open and open to a certain extent
The ecosystem of cyber society is a system that
constructed over the Internet. Open is one of the most
fundamental characteristics of Internet, and the
Internet is built on the basis of free and open.
Meanwhile, as to the ecosystem of cyber society that
depends on both of the technology and economics, it
is placed in a larger system of human society, and the
exchange of material, energy and information with
the external environment is a necessary condition of
its survival and development. The Internet is an
image of the real world, so what happens in the real
world will appear in the virtual space. There is no
information on the Internet itself, and the information
comes from the real society. If people simply try to
get information from the Internet without making the
production, processing and transmission of
information, then there will be energy (information)
imbalances within the system and lead to the
destruction of ecosystem of cyber society.
(2) Far from equilibrium
The ecosystem of cyber society is not a system
that is isolated and in a quiescent state, it contacts
with the outside world closely. The entire system
changes with time, and it shows non-uniform and
diverse characteristics with different degrees within
the system, and the distribution and development of
its network groups are non-equilibrium. For example,
shown as Figure 1, there is a clear imbalance between
the regional distributions of Internet users in China.
At the same time, although the Internet resources are
abundant and diversified, their distributions are still
uneven. Shown as Figure 2, there is also a clear
imbalance between the regional distributions of
domain names in China. As the human social
activities in the ecosystem of cyber society are
unpredictable, creative, and changeable over time, the
information content that they create is also dynamic
and unpredictable. Therefore, the entire ecosystem of
cyber society is unstable, and far from equilibrium.
(3) Nonlinear interaction
The change of various elements within the
ecosystem of cyber society is complex, and there are
interlinks and interactions between the various
elements. When the environment influences one or
several elements of the ecosystem of cyber society,
the ecosystem of cyber society will not take the
change behavior of this element as the result,
however, this element will influences the other
elements, and other elements also counterproductive
to the other elements, then the ecosystem of cyber
Figure 1: The number of Internet users in different
provinces of China in 2009.
Figure 2: The number of domain names in different
provinces of China in 2009.
society will show some changes in each other. The
changes of the elements can not only cause the
quantitative changes of the system, but also can
cause the changes of morphology, structure and
function of the system, and this kind of changes from
quantity to quality is the result of the nonlinear
effects (
Xu, 2009). The fundamental reason why the
ecosystem of cyber society is so colorful, and the life
is so diversified is due to the nonlinear interactions
during the evolution of ecosystem of cyber society.
(4) Fluctuations
The evolution process of the ecosystem of cyber
society from a lower degree of organization to a
higher degree of organization is the process of level
upgrading, and it needs a cluster size at a certain
critical point that can lead to qualitative change. And
RESEARCH ON THE EVOLUTION MECHANISM OF ECOSYSTEM OF CYBER SOCIETY BASED ON THE
HAKEN MODEL
125

at the critical point of its evolution, the ‘fluctuation’
plays an important role in triggering, such as the
breakthrough of network technology, the emergence
of new species and so on. Fluctuations can lead to a
non-equilibrium process in obtaining material,
energy and information between various populations
of ecosystem of cyber society. Almost all the species
have ‘equal rights’ in competition at first, but then
some populations have a greater advantage on
accessing to ‘resources’ due to the intrinsic random
fluctuations inside or outside, while others lost their
edge. So the differences increase, and the unbalance
exacerbates. The amplification of the fluctuations
near the critical point will also further exacerbate
this process, and then make the ecosystem of cyber
society can not maintain its original structure as
avalanche, thus result in a new ordered structure.
3 MODEL CONSTRUCTION OF
THE SELF-ORGANIZATION
EVOLUTION OF ECOSYSTEM
OF CYBER SOCIETY BASED
ON THE HAKEN MODEL
As a complex giant system, the state of the ecosystem
of cyber society has to be described by using multiple
variables, and it also has to depend on the state
variables of the system to analyze its evolution of
self-organization (
Wei, 2006; Chen and Zhong, 2005).
These state variables change over time according to
their characteristics, and they can be divided into fast
time-varying variables and slow time-varying
variables. According to the Servo Principle of Haken,
we can know that when the system changes, the
evolution process and characteristics of the system
are determined mainly by the slow variables. The
evolution of the system is dominated by the slow
variables, and the fast variables are servitude by the
slow variables. Therefore, we can distinguish fast and
slow variables by calculating, find the linear
instability point, eliminate fast variables, and then
obtain the order parameter equation which can be
used to reveal the formation and evolution process of
self-organization of the ecosystem of cyber society
with ordered structure.
3.1 Model Assumptions
We will give the following assumptions before
constructing the model.
Assumption I: The self-growth rate of each
element of the ecosystem of cyber society is
i
λ
;
Assumption II: The development and evolution
of the ecosystem of cyber society is related to its
self-accumulation
(
)
tq , and the higher the
self-accumulation is, the faster the development is;
Assumption III: There are relationships of
cooperation and competition between the various
elements of the ecosystem of cyber society, and the
interaction coefficients are
α
β …、
;
Assumption IV: There is mathematical sense of
continuity with the development and evolution of the
ecosystem of cyber society.
3.2 Model Construction
According to the Haken model, the interactions
between different variables within the ecosystem of
cyber society that make the system evolution process
occurred can be described in a mathematical form,
shown as equation (1).
*
1,..., 1, 1,...,
, 1,2,...,
iii ij
jiin
qq qqi n λα
=−+
=− + =
∑
(1)
Here,
i
q is a state variable,
α
and
i
λ
are the
control parameters. And
α
represents the intensity of
competition and cooperation between different
variables. If
α
is positive, then there will be
inhibition between
i
q and other variables. Otherwise,
if
α
is negative, then there will be synergistic effect
between these variables.
We can get the quantitative relationship between
these variables through solving equation (1), and
then identify the system order parameter.
We will only take two variables as an example to
solve the equation. For ease of application, discrete
the above equation into the following.
(
)
(
)
(
)()()
( ) ( ) () ()
11112
2
2221
11
11
qt qt qtq t
qt qt q t
λα
λβ
+=− −
⎧
⎪
⎨
+=− +
⎪
⎩
(2)
First put the original values of
1
q and
2
q into
equation (2) and execute the regression analysis to
get the control parameters values of
α
、
β
、
1
λ
、
2
λ
.
Then put them into (1) which reflects the interaction
between
i
q and
2
q , and let
*
1
0q = to get the solution
using the method of adiabatic approximation.
2
21
2
qq
β
λ
≈
(3)
We can see from (3) that
1
q decides
2
q , that is to
say that the latter changes with the former, indicating
that
1
q is the order parameter of the system. Then put
ICEIS 2011 - 13th International Conference on Enterprise Information Systems
126

(3) into (1) and get the order parameter equation.
*3
111 1
2
qq q
αβ
λ
λ
=− −
(4)
And then get the potential function by calculating
its opposite number.
24
11 1
2
0.5
4
qq
αβ
νλ
λ
=+
(5)
We can determine the convex and concave
character of the potential function
ν
by its second
derivative, and then describe it graphically. The
structural characteristics of the potential function can
reflect the evolution mechanisms of the ecosystem of
cyber society intuitively, that is, when the state
variables and control parameters change, the system
potential function also changes, and the original
stable state comes into an unstable state.
We can get the stationary solution of the order
parameter by equation (4), and there are two cases
shown as follows.
① If
1
0
λ
> , then equation (4) has only one
unique stable solution
1
0q = , and its potential
function is shown as Figure 3 (Put the simulated data
into Matlab6.5, assuming
2
1
λ
= , and 0.01
α
β = ).
② If
1
0
λ
< ,then equation (4) has three solutions,
1
1
0q = ，
2
12
1
q
λλ
α
β
=−
， and
3
12
1
q
λλ
α
β
=− −
。
Among them, the first solution is unstable, but the
other two solutions are stable, shown as Figure 4.
They show that the system can come into a new
stable state through mutation. The change of
1
q can
affect the changes of the entire system.
4 ANALYSES ON THE
SELF-ORGANIZATION
EVOLUTION MECHANISM OF
THE CHINA ECOSYSTEM OF
CYBER SOCIETY
4.1 Variable Selection and Data
Collection
The key to the research on the formation and
evolution of ecosystem of cyber society is to analyze
the development process of its various components
after they come into being, and summarize the nature
of the evolution of ecosystem of cyber society from
its complex forms. There are two viewpoints in the
development promotion of ecosystem of cyber, one is
that we should increase the investment on the
Internet-based resources to improve the resource
environment of ecosystem of cyber society, that is to
say that we should focus on the investment on
construction practices, including domain names, IP
addresses, websites and other network
infrastructures; while the other one is that we should
develop the productivity of the ecosystem of cyber
society and improve the information productivity of
the information resources through technology
innovation, that is, we should focus on the behaviour
of technology innovation, such as innovation of
network communication, web technology, network
security, online payment and so on. In essence, both
of these viewpoints are all aim to improve the
ecosystem of cyber society, so we don’t think that
they are contradictory. If we can identify the key
behaviours that affect the evolution of the system at
one stage,
Figure 3: The potential function when
1
0
λ
> .
Figure 4: The potential function when
1
0
λ
< .
namely the order parameter, then it is no doubt very
significant to both of the managers or participants of
the ecosystem of cyber society.
Based on the above two viewpoints, this paper
will mainly select the following two variables as an
example to study.
①
e
R ——Internet-based resources index;
②
e
P ——The number of dynamic web pages
per Internet user has. Here, we take the number of
dynamic web pages per Internet user has as the
RESEARCH ON THE EVOLUTION MECHANISM OF ECOSYSTEM OF CYBER SOCIETY BASED ON THE
HAKEN MODEL
127

information productivity of the ecosystem of cyber
society, and also as a symptom of technology
innovation. This variable can be used as the state
representative of the production of information
resources, but it is not the only one state variable.
As to the previous two selected variables, in need
of special note here is that in view of the research on
the ecosystem of cyber society is still in an early
stage, there are very few relevant studies about its
evolution mechanism, and there is not a mature
variable system that has been recognized, so the
hypothesis we make here is only a theory hypothesis
to the ecosystem of cyber society. We have done a
lot of data analysis and spreadsheet work in the
selection of these variables to make sure that these
two variables meet the requirements of Haken model
and basically reflect the nature of the ecosystem of
cyber society. And we also learn from the variables
selection criteria in the other researches about
system evolution based on the Haken model.
Due to the model computing needs, this paper
selectes the Internet development measurement data
of Beijing, Shanghai, Shaanxi, Tianjin, and other 27
provinces and cities in China in 2006 and 2009 as a
sample for quantitative empirical research.
4.2 Model Calculation
We can get the values of
e
R by using the calculation
formula of the Internet-based resources index in the
‘27
th
China Internet Development Status Survey
Report’ that released by the China Internet Network
Information Centre, shown as Table 1.
The index value of each basic indicator = the
current number per Internet user has / the number
per Internet user has in the base period
1
*100
Internet-based resources index = 0.3005 × IP
Address Index+ 0.2435 ×
 Domain Index + 0.2727 ×
Website Index + 0.1833 × International Bandwidth
Index
2
Identify the fast variable and slow variable.
Assume that
e
R is
1
q , and
e
P is
2
q first, shown as
equation (6), and then verify the variable hypotheses.
( ) ( ) () () ()
()( )() ()
1
2
2
11
11
eeee
eee
Rt Rt PtRt
Pt Pt R t
λα
λβ
+=− −⎧
⎪
⎨
+=− +
⎪
⎩
(6)
 
1 This paper selects the average from December 2005 to June
2007 of China as base of data.
2 The International bandwidth is the ability that one country can
connect to other countries or regions. In the model we build in this
paper, as we select the provincial and local networks as an
application, and there is no disaggregated statistics, so we use the
national data during the actual calculations instead, that is, 98.0 in
2006, and 133.7 in 2009.
Execute the regression analysis with the
calculated
e
R and
e
P using the linear process of SPSS
13.0 based on the multivariate regression model, and
use Enter variable analysis.
We can get the following equation from the
above running result.
()
()
()
()
() ()
()
()
()
()
()
()
()
*
2.178 4.956 2.280
*
2
5.232 4.773 0.618
1 0.443 0.008 33.387
1 1.352 0.001 1.756
eeee
eee
Rt Rt PtRt
Pt Pt R t
⎧
+= + +
⎪
⎨
+= + +
⎪
⎩
(7)
Analyze the running results of SPSS 13.0, and
we can see that there is higher degree of model fit no
matter to
① or ② in (7) ( R approaches 1), and the
significance level of
F
test is 0.00. The regression
results are very good. The figures in parentheses are
the
t test value (The same below).This equation can
better reflect the relationship between different
variables, and the results are reliable.
Then get the values of control parameters.
0.008
α
=
− , 0.001
β
=
,
1
0.557
λ
= ,
2
0.352
λ
=−
Here,
12
λ
λ >
, indicating that the change
of
e
R is faster than the change of
e
P , so
e
P should be
the order parameter that changes slowly. The
assumption we made above is incorrect, we should
take
e
P as
1
q ,
e
R as
2
q , and then re-establish the
equation shown as (8) below.
(
)
(
)
(
)()()
( ) ( ) () ()
1
2
2
11
11
eeee
eee
Pt Pt PtRt
Rt Rt P t
λα
λβ
+=− −⎧
⎪
⎨
+=− +
⎪
⎩
(8)
Execute the regression analysis to (8), and then
get the following equation.
(
)
()
(
)
()
(
)
(
)
()
()
()
()
()
()
()
3.387 4.178 1.950
2
2.452 4.821 2.176
1 1.146 0.004 0.146
1 0.490 0.024 32.655
eeee
eee
Pt Pt PtR t
Rt Rt P t
⎧
+= + +
⎪
⎨
+= + +
⎪
⎩
(9)
Analyze the running results of SPSS, and we can
see that there is higher degree of model fit to (9)
(
R approaches 1), and the significance level of
F
test
is 0.00. The regression results are very good. So this
equation can better reflect the relationship between
different variables, and the results are reliable.
Meanwhile, analysis on the parameter value of
t test
shows that the square of
e
P or the product of
e
P and
e
R
in 2006 has a certain influence to
e
P or
e
R in 2009.
The result is
consistent with the hypothesis we made
above that takes
e
P as the slow variable, so the result
here has a certain interpretation of meaning.
Then we can get the control parameters values.
0.004
α
=
− , 0.024
β
=
,
1
0.146
λ
=− ,
2
0.510
λ
=
ICEIS 2011 - 13th International Conference on Enterprise Information Systems
128

At this point,
12
λ
λ < , so
e
P is the order parameter
and
e
R changes with
e
P .
α
and
β
reflect the results of
interaction between these two factors. Thus, we can
get the differential equations that reflect the
interaction between
e
R and
e
P , shown as (10).
() ( )
()
()
() ( )
()
() ()
()
()( )
()
()
()
()
()
*
1.950
4.178
3.387
2
4.821 2.176
2.452
1 1 0.146 0.004 0.146
1 * 1 0.510 0.024 32.655
eeee
eee
Pt Pt PtR t
Rt Rt P t
⎧
+ = −− −− +
⎪
⎪
⎨
+=− + +
⎪
⎪
⎩
(10)
Let
()
*
10
e
Pt+=
, then we can get the solution of
the equation.
()
222
2
0.024
1 0.0470588P
0.510
ee e e
Rt P P
β
λ
+≈ = ≈
(11)
We can see from (11) that
e
P determines
e
R , that
is to say the latter changes with
the former,
indicating that
e
P is the order parameter of the system.
Put (11) into (10) and get the order parameter
equation.
*3 43
1
2
0.416 1.8823529*10
ee e e e
P
PP P P
αβ
λ
λ
−
=− − = +
(12)
And then get the potential function by calculating
its opposite number.
242 54
1
2
0.5 0.073 4.70588*10
4
eee e
PPP P
αβ
νλ
λ
−
=+ =−−
(13)
Let
0
e
d
dP
ν
=
, put the calculated values of
α
、
β
、
1
λ
、
2
λ
into (13) and get the stable solution of the
order parameter equation as follows. The system will
generate a new ordered structure at the stable
solution obtained.
1
=27.85
e
P
，
1
=-27.85
e
P
We can determine the convex and concave
character of the potential function by its second
derivative
()
2
2
e
d
dP
ν
.
Table 1: The values of
e
R
and
e
P of 31 provinces and cities in China in 2006 and 2009.
Provinces
Var ia b l e (
e
R ) Variable (
e
P )
Variable calculation
2006 2009 2006 2009
*
ee
RR *
ee
RP *
ee
PP
1 Beijing 386.84 542.98 115.61 311.72 149641.5 44723.62 13366.62
2 Zhejiang 105.24 149.08 20.05 65.08 11076.45 2110.16 402.00
3 Guangdong 101.07 100.93 13.39 39.70 10215.59 1353.48 179.32
4 Shandong 63.97 91.42 9.14 14.14 4092.07 584.73 83.55
5 Fujian 121.11 120.00 19.79 48.76 14666.67 2396.18 391.48
6 Shanghai 185.10 236.39 64.73 131.16 34260.70 11981.69 4190.25
7 Liaoning 86.11 66.78 8.33 13.86 7415.13 717.22 69.37
8 Hunan 67.03 95.79 10.02 11.84 4492.61 671.80 100.46
9 Chongqing 89.98 79.10 31.16 19.19 8096.04 2804.09 971.21
10 Tianjin 87.15 93.28 19.24 49.95 7594.36 1676.67 370.17
11 Sichuan 63.55 69.19 10.28 27.26 4038.73 653.38 105.70
12 Jiangsu 97.69 82.20 11.62 41.68 9543.06 1134.82 134.95
13 Gansu 51.06 39.92 8.96 4.62 2607.30 457.29 80.20
14 Henan 78.64 55.96 12.22 27.59 6183.73 961.19 149.40
15 Hebei 67.23 67.78 6.49 15.51 4519.29 436.26 42.11
16 Jiangxi 69.04 75.39 10.10 21.77 4766.87 697.08 101.94
17 Yunnan 55.10 47.35 4.19 6.70 3035.95 230.61 17.52
18 Hubei 69.57 73.26 14.09 26.37 4840.45 980.57 198.64
19 Shaanxi 56.34 97.32 5.25 16.03 3173.85 295.96 27.60
20 Qinghai 54.59 49.43 1.05 1.68 2979.89 57.15 1.10
21 Guangxi 51.22 60.51 5.79 13.99 2623.00 296.29 33.47
22 Anhui 71.16 61.38 13.02 25.62 5063.94 926.61 169.55
23 Heilongjiang 60.95 86.00 6.03 14.98 3714.70 367.67 36.39
24 Jilin 75.74 64.36 5.13 8.06 5736.58 388.18 26.27
25 Hainan 80.19 87.59 2.55 45.50 6430.37 204.80 6.52
26 Neimenggu 61.13 52.49 1.38 4.81 3737.12 84.57 1.91
27 Xinjiang 61.99 42.21 6.75 0.49 3842.59 420.74 45.53
28 Guizhou 63.69 49.46 2.42 6.15 4055.98 154.76 5.84
29 Shanxi 47.98 51.93 2.56 4.04 2302.49 123.92 6.57
30 Ningxia 139.88 63.33 3.56 14.87 19566.07 498.90 12.65
31 Xizang 73.62 27.89 0.69 0.06 5420.50 51.01 0.48
RESEARCH ON THE EVOLUTION MECHANISM OF ECOSYSTEM OF CYBER SOCIETY BASED ON THE
HAKEN MODEL
129

()
2
32
2
0.146 5.647 *10
e
e
d
P
dP
ν
−
=− +
(14)
Put the stable solution into (14), we can
get
()
2
2
d
4.23394 0
e
dP
ν
=>
, which indicates that
the potential function has a minimum at these two
points of
27.85
e
P =± . And then use Matlab 6.5 to
simulate data and get the potential function curve of
self-organization evolution of China ecosystem of
cyber society, shown as Figure 5. The structural
characteristics of the potential function reflect the
evolution mechanism of China ecosystem of cyber
society, that is, when the state parameters
e
R and
e
P ,
and the control parameters
α
、
β
、
1
λ
、
2
λ
change, the
potential function of the system will change
correspongdlly, and change from the original stable
state to an unstable state.
4.3 Analysis and Implications
The state of the potential function depends on the
state variables that reflect the behavior of the system
(
e
R and
e
P in this paper), and the control parameters
that reflect the impact of the environment on the
system (
α
,
β
,
1
λ
and
2
λ
in this paper). When the
state variables and control parameters change, the
potential function of the system also changes, and
the original stable state changes into an unstable
state. As to the ecosystem of cyber society, certain
structure determines the limit of its development, so
when approaching the limit, the system would be
difficult to adapt the original structure. However, the
development of Internet-based resource, progress of
the science (information) technology will enable the
ecosystem of cyber society to upgrade its structure,
and then the values of
α
,
β
,
1
λ
and
2
λ
in the equation
will change and thus form a new structure, which
can accommodates a higher limit of development.
The system operates in a new potential function and
comes into a stable state of higher level. This is the
formation and evolution of the ecosystem of cyber
society, and the continuous development is the
process of self-organization of the system which is
such a complex super-cycle with self-catalytic.
Through the above analysis, we can clearly
reveal the formation and evolution mechanism of the
ecosystem of cyber society, identify accurately the
decisive factors, and then provide development
Figure 5: The potential function curve of China ecosystem
of cyber society evolution.
direction for the cyber society. From the potential
function curve of China ecosystem of cyber society
evolution, we can see that in the appropriate control
variables, there will be non-zero interactions
between the variable
e
R which is the representative
of Internet-based resources index and the
variable
e
P which is the representative of technology
innovation within the China ecosystem of cyber
society, and thus form the stable solution of the
system. Here, the order parameter of the system is
technology innovation, which is the decisive factor
during the development process of China ecosystem
of cyber society, and in turn to promote the
development direction.
We can get the following implications.
(1) It can be seen from Figure 5 that at the
critical point during the evolution process of China
ecosystem of cyber society, the critical point that
dominates the evolution of system is technology
innovation. When the system generates a new
ordered structure, there will be a new stable
solution
27.85
e
P
=
± . But from the values of
e
P shown in Table1, we can see that the majority of
China sub-ecosystems of cyber society has not
reached the critical state. Therefore, we should pay
more attention to the important role of technology
innovation, and take practical measures to increase
the research of network, investment on the
information technology, especially emphasis on the
constructive role of technology innovation and their
amplification role.
(2) The behaviours that each control parameter in
the model reflects are as follows.
①
α
is negative, reflecting that the construction
of Internet-based resources will promote the
technology innovation. It indicates that the
transformation of computer equipments and
improvement of resources are very important during
the process of social development. There are
ICEIS 2011 - 13th International Conference on Enterprise Information Systems
130

synergistic benefits between the investment of
Internet-based resources and technology innovation.
②
β
is positive, reflecting that the technology
innovation will drive the growth of Internet-based
resources. It indicates that these two variables
promote each other and thus create synergy. The
system will achieve good cycle of sustainable
development if they increase together.
③
1
λ
is negative, indicating that the China
ecosystem of cyber society has established a positive
feedback mechanism of technology innovation
growing within the system. The larger the absolute
value is, the faster the growth will be.
④
2
λ
is positive, indicating that there is a
negative feedback mechanism of Internet-based
resources index decreasing within the ecosystem of
cyber society, that is to say, as the innovation and
development of technology, the Internet-based
resources index is beginning to show a downward
trend to decline after an initial growth phase.
However, the study of this paper shows that the
Internet-based resources and technology innovation
should be the indexes of mutual promotion and
coordinated development. The opposite result we got
reflects the drawbacks during the formation and
development of China ecosystem of cyber society.
The construction investment on the
Internet-based resources is the material basis of
technology innovation, and the technology
innovation is also an important means to increase the
Internet-based resources index. Both of them are the
important prerequisites and fundamental guarantee
for the development of China ecosystem of cyber
society. The Internet-based resources and technology
innovation promote each other and thus create
synergy. The micro-fluctuations amplify and change
to giant fluctuations under the effect of nonlinear
mechanism, and the entire system comes into a
virtuous cycle and thus reaches a new state.
5 CONCLUSIONS
Based on the theory of self-organization and by the
cross-disciplinary analysis and research, this paper
analyzes the self-organization conditions of
Ecosystem of cyber-society, constructs the evolution
model based on the Haken model, and then analyzes
the functions of competition and coordination
between the system elements and the process that the
order parameter dominates that they bring. Finally, it
selects the Internet development measurement data of
Beijing, Shanghai, Shanxi, Tianjin, and other 27
provinces and cities in 2006 and 2009 as samples and
analyzes the evolution process or China Ecosystem of
cyber-society. The result reflects that technology
innovation is the dominant order parameter in the
evolution of Ecosystem of cyber-society, and a key
factor to the development process of China
Ecosystem of cyber-society and then the paper puts
forward some ideas and suggestions to the
development of China Ecosystem of cyber-society.
ACKNOWLEDGEMENTS
This work is supported by the Fundamental Research
Funds for the Central Universities (No.
2009YJS035).
REFERENCES
Cunshangkanghui. Ubiquitous network society [J].
Financial sector, No. 12, 2006, pp22-25.
Fang Wei. Research on the evolution mechanism of
self-organization of high-tech industry [D]. Wuhan
University of technology [D], 2006.
Haimei Shi. Research on the theory and evolution model
of ecosystem of cyber society [D]. Beijing Jiaotong
University, 2007.
Hong Zhang. The network society - new forms of society
reconstructed by information technology [J].
Contemporary world, No. 7, 2008, pp42-44.
Jianing Xu. Study on the Mechanism of Internet
Information Self-organization Based on Web 2.0 [J].
Journal of Intelligence, Vol. 28, No. 6, June 2009,
pp139-142.
Lei Zhang. Analysis of the nature and its development
process and trend of the network society [J]. Journal
of Tianjin Administration Institute, Vol. 10, No. 4, July
2008, pp23-26.
Li Guo, Jingle Su. Study on the Evolvement Mechanism
of Industrial Ecosystem Based on Haken Model [J],
China Soft Science, No. 5, 2005, pp156-160.
Quanfeng Zhu. When the opportunity will reappear and
seize the Internet today. Computer World, No. 45,
2006, pp1-2.
Xinan Zhao. Research on the evolution mechanism of
competitive activity based on Haken model [J].
Science and Technology Management Research, No.
12, 2008, pp132-140.
Yongda Zhu, Tao Zhang. Evolution mechanism and
optimum control of regional industrial system [J].
Journal of management science in China, Vol. 4, No. 3,
June 2006, pp73-78.
Yu Chen, Jiajia Zhong. Introduction to Systems Science
and Method [M]. China Remin University Press, 2005.
RESEARCH ON THE EVOLUTION MECHANISM OF ECOSYSTEM OF CYBER SOCIETY BASED ON THE
HAKEN MODEL
131