2.
I. ................................................................................................................. 3
II. .............................................................................. 3
...................................................................................................... 14
......................................................................................................... 18
................................................................................... 19
AHP..................................................................................... 20
.......................................................... 38
......................................................................... 47
2
3. I.
1.
-
2.
- -
3.
1
.
4.
I I.
1.
2.
3.
,
1
Merriam-Webster
3
4. .
4.
= =1 ,
i- - i-
=1 =1
= -
= - ,
| |
=1 -
{ ; }
min, max, opt
-
1)
5.
4
5. the Analytic Hierarchy Process, AHP
AHP
.
2
PEST-
:
(P)
(E)
(S)
(T)
P)
2
Williams, Teresa The Business Approach to Training. Vermont: Gower Publishing Ltd. 1997
5
6. 1)
Institutions -
The Global Competitiveness Report 2009 Economic
F
3
.
2)
3)
-
4
.
-
,
)
4)
(E)
,
.
1)
2)
3
http://www.transparency.org.ru/
4
http://www.oecd.org
6
7. 3)
.
5
.
4)
(S)
:
1)
.
2)
(Global Entrepreneurship Monitor, GEM)
(T)
-
1)
5
Global Entrepreneurship Monitor, GEM)
www.hse.ru
7
8. Infrastructure - The Global Competitiveness
Report, World Economic Forum.
2)
Forbes.
PEST- .
,
AHP
n-
11 1
1
n
1
1
n 1
8
9. 1
= , i, j
i-‐ j-
n(n-‐1)/2 .
6
eigenvector)
= ,
A
eigenvalue).
6
Saaty, Thomas L. A Scaling Method for Priorities in Hierarchical Structures. // Journal of Mathematical Psychology, issue 15,
volume 3. 1977
9
10. ( ),
I - n*n ,
( )
= =0
n
eigenvalues).
n
n
n n
=
=1
=1 =1
-
10
11. n
A-‐
- A (eigenvector).
=
complex)
(real) (imaginary)
+
1 2 = 2 1 =0
Hermitian
MathCad, MathLab, Stata
- R,
11
12. nosym<-read.table("nosym.txt", header=TRUE)
lds<-( as.real( (eigen(nosym, symmetric=FALSE)
$vectors)[,1]) )/
sum ( as.real( (eigen(nosym, symmetric=FALSE)
$vectors)[,1]) )
lambda<-as.real(eigen(nosym,
symmetric=FALSE)$values[1])
nosym
lds
lambda
nosym
lds
lambda
1)
2)
3)
4)
5)
Compatibility Index)
=
1
12
13. CI
RCI Random CI)
(CI/RCI)
Consistency Ratio CR)
=
CR . CR=0
CR=1 CI=RCI
CR>20
AHP)
AHP
13
14. P -
1 - +
- -
- -
2 - +
- - - -
2.1- - -
3 - +
- - - - 5-
4 - +
- - - - -
5 - 2- - - -
-
6 -
-
-
7 - +
- -
8 - +
- - -
-
9 - -
(%)
14
15. 10 - -
-
- - -
11 -
12 -
13 -
-
-
- -
- - -
14 - -
-
- - -
15 - +
-
-
-
16 - - -
- - -
-
-
16 - - - -
-
2,4-
17 +
E -
-
18 opt
15
16.
opt
opt
opt
opt
opt
opt
opt
opt
19 - opt
20 - +
7- -
21 - -
22 +
23 +
24 +
25 +
S -
26 +
27 +
28 +
29 - +
30 - +
31 - +
16
17. 32 +
T -
33 +
34 - - - -
- -
35 +
-
- - -
36 -
37 -
38 +
39 - - -
40 - -
41 - -
-
- -
4- - -
17
22. 2.
.
.
1
2
3
4
. .
.
. 1
. 1
1
. 1
1.
1
2
3
4
22
23.
1
1
1
1
2.
1
2
3
4
-‐
.
. 1
1
1
-‐ 1
3.
1
2
23
24. .
. 1
1
4.
1
2
1
1
7
1.
1 -
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7
24
25. 2
-
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1.4- - - -
3 : -
1- 2- - - -
4 -
- - - - -
1 2 3 4
1 1
2 1
3 1
4 1
2.
1 -
- - -
-
2
3 - -
-
- -
1 2 3
25
26. 1 1
2 1
3 1
3.
1 . -
- -
- -
2 -
3 -
- -
- -
4
5
6
1 2 3 4 5 6
1 1
2 1
26
27.
3 1
4 1
5 1
6 1
4.
1 - -
-
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-
-
-
2- -
2 . -
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3 -
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5
1 2 3 4 5
1 1
27
28.
2 1
3 1
4 1
5 1
2
1.
1 -
*
2 -
*
1 2
1 1
2 1
2.
1 -
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2 -
28
29. 3
1 2 3
1 1
2 1
3 1
3.
1
2
1 2
1 1
2 1
4.
3
1.
29
30. 1
2
3
-‐
1 2 3
1 1
2 1
3 1
2.
1 -
2 -
3 -
4
4
1.
1
2 -
- - -
-
3
30
31. 4 - -
- -
5
6
1 2 3 4 5 6
1 1
2 1
3 1
4 1
5 1
6 1
2.
1 - -
2 -
3 -
-
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5- -
31
33. 1. ?
1
2
-‐
1 2
1 1
2 1
33
34. 2.
?
1)
1
2
3
4
1 2 3 4
34
35. 1 1
2 1
3 1
4 1
3. ?
1
2
-‐
1 2
1 1
35
36. 2 1
4. ?
1
2
?
3
1 2 3
1 1
2 1
36
37. 3 1
5. ?
1
2
?
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1 1
2 1
37
40. 2. 12)
13)
___________________( 14)
15)
16)
1)
17)
2)
3)
4)
5)
6)
7)
8)
9)
10)
11)
40
43. 9
10
?
11
12
_______________________(%)
13
9
-
9
43
45. 18
_______________________
19
-
)?
-
20
____________%
-
45
46. 7-
1-
21
____________________
-
22
46
50. 7-
4-
1-
5
- ______________
50
