Strategic planning using neural network-based, Delphi workshop software
Ray Wyatt
Department of Geography and Environmental Studies, The University of Melbourne,
Parkville, 3052, AUSTRALIA (r.wyatt@geography.unimelb.edu.au)
Interms of achieving clean
water for Yarra River
In terms of achieving clean
water for Yarra River
Os ee
1 In terms of achieving clean
water for Yarra River
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Training Data : criterion scores and overall scores | 7 ‘Overall
User :- Likelihood | Effectiveness | Improvability | Restrictiveness | Ease | Responsiveness | Speed| Dependence | Morality! Score
wat O33) 033 | 033 0.17 O33 025 083] 038 | 033 | 0.92
wat 0.08 | 0.08; 0S 05 0.08 0.67 0.08 [0.58 |
wat 0.58 ose | 0.17 0.33 0.58 0.08 0.58 [0.08 |
wyad 0 0.25 | 0.58 0.58 0.58 0.08 | 0.58
wya2, 0.48 O58 | 0.08 0.08 0.08 0.58] 0.08
wa O58 | O47; 0.38 0.33 0.33 0.33! 0.33
pent 0.33 033; 033) 025 0.33) 0.25 O5
pent 058 | 05 | 0.33 O5 0.33! 0.58 025 1
pent 0.08! 017 F033 0.25 033! O17 0.25 |
pend 0.08 7 0.08 025} 025033! 0B 05
pen? 0.58 058] 0S oss Tos! 058 017 T
pen2 033 7033 [025 Ot? ~—S«Oi7 0S 0331
One User's Training Data : criterion scores and overall scores i it i \_ Overall
User | Uketthood | Effectiveness | improvabllty ” Resirictiveness | Ease | Responclvenees | Speed! Dependence | Moraity Score
wat O33) 0.33 O33 Ot? —«*t 33 025 1033; 003 | Oss) 032 |
_wyat 0,080.08 O51 O05 0.08 067 008, 058 | 058 | 0.42
wal 0.581 0.58 0.17 0.33 10.58 0.08 0.58! 0.08. 0.08 0.26
lcorrel with >| -0.99 O89 +099 063-0997 1.00 0.98; 0.99 0.99 |
Joverail score t 7 ; | —+ - +
Initially Assumed Correlations between criterion scores and overall scores
GroupiCiuster | Likelihood | Effectiveness | improvability | Restrictiveness | Ease | Responsiveness Speed! Dependence | Morality
arts group o 1 oO | 05 . O85 1-051 O68 05705 Os
commarcagroup| O05 0 1 OS: 05 10S Os 0 05 0
‘engineering goup| 0.5 | 05 | 0 ' 08 1-05 0 0 05 05
science group os: 0 7 05 05 105 25 0 05 0
‘luster 1 os | 05 ; 0S 0 To 0 sl 05 [05 a
cluster 2 5 | 05 T 05 05 1 05 0 oo 05
cluster 3 of os Os OS [os Os Ca) 0 |
cluster 4 os 70. 7 OO | 0 [0.5 05 05105 os
cluster 5 Os ot os "as [0 5 Os 0 05 j
cluster 6 0 OS. Os 0 05 Os 0 5 Os i
cluster 7 05 a) 05 Os! 0 5,05; 0
cluster 8 0 0 05 05105" 05 Oo 05 + 05
cluster 9 5 Os ) 0 05 05 0505 0
[Arts Group's Training Data (part) : criterion scores and overall scores|
Teer "| tiketinooa | Effectiveness | wprovality | Restrctvenccs | ave | Responsiveness | Speed | Dependence | Morally
pret 0.25 0.33 025 i 0.17 0.25 0.33 {0.08 0.33 0,33
pret 0.08 0.33 0.17 0.58 0.5 0.08 i 0,67 0.33 0.33
pret 0.67 0.33 0.58 0.25 0.25 0.58 10.25 0.33 0.33
jam2 05 0.17 OS 0.17 0.42 05 1 O47 0.42 0.58
jam2 O17 0.33 0.17 0.67 oO 0.17 0.5 0.25 tC)
jam2 0.33 Os 0.33 0.17 0.58 0.33 0.33 0.33 0.42
corel 043 [5 KX OE 05 0341 0.18 | 045
All Groups’ Correlations between oriterion scores and overall score =
within Likelihood | Effectiveness | tmprovabliity | Restrictiveness | Ease | Responciveness | Speed | Dependence | Morality
‘arts group_[ 0.43 21 0.38 0,09 O35 05 oss | O18 | 049
commerce grp | _ 0.74 0.85 0.27 051 0.46 0.74 0.82 | 0.52 0.74
engineer. grp | _-0.34 0.41 0.57 0.85 0.66 0.83 0.59 | 0.8 0.37
science group | __ 4 0.89 09 0.24 os 0.98 0.43 09 0.43
[All Groups’ Correlations (> 0.85 or < - 0,5) between criterion scores and overall score :-
wihin =| Unellhood | Ettectiveness | tnprovabllty | Restrictivenese | Ease | Recponciveness | Speed | Dependence | Moralty
‘arts group
‘commerce orp 0.85 O52
engineer. orp O85 0.66 O58 _|
Science group | __+ 099 09 058 og
."
1 z a
os
06 f—
04
02 e :
Of a
02 a
04 t= | = 1D ependence
06 Ts Be | Morality
Hi 08 -
i :
|All Clusters’ Correlations between Criterion Scores and Overall Score |
within :- | Likelihood | Effectiveness | improvability | Restrictiveness| Ease | Responsiveness! Speed Dependence ; Morality|
cluster 7 st 05 0 5 05; 0 1-05 105 a
cluster 1 O72 | 04 1 “0.58 0.65: O92 [035 [0.88 O57
cluster 2 0.79 Os i 0.98 0.98 0.07 a) 0.99 “6t
cluster 3 0.99 0.45 i 0.3 09 0.89 i 1 0.45 10.99
cluster 4 0.15 065 0.58 0.6 | 0.87 O53 0.86 0.68
‘cluster § O11 [ 04 i 0.91 Toay 0.36 0.44 0.98 0.78
cluster 6 0.16 O58 | i 0.82 0.39 | O71 0.63 “0.4 0.81
cluster 8 0.45 oer [022 0st! 0.35 0.73 052 0.45
cluster 8 0.08 07 i i ost 0.97, 0.39 0.65, 0.07
7
i I
|All Clusters’ Correlations (> 0.9 or < - 0.5) between Criterion Scores and Overall Score
within :-_| Likelihood
[Effectiveness | improvability | Restrictiveness| Ease! Responsiveness| Speed | Dependence ! Morality
cluster 1 [002 jose 0.82 i H i
cluster 2 i _ 098 [0.98 | i [oss
cluster 3 0.99 i 1 09 | ae ee 19.99
cluster 4 I 1 0.53 | i
cluster 5 i i _ 0.91 ! Hi i ‘t98
cluster 6 1 7 i i id
cluster 8, i i . T a
cluster 9 T T aa 1 T 0.97, l T |
- n — 1 : |
| |B Likelihood |
ml i Effectiveness HH
= [Dimprovebity 4
i
Li |
4 |
Lj |
|Group/Cluster Members’ Characteristics | [Error @
iGriCluster| Age | Sex [Child[Educ.[Occup.| Field | Problem] Role __| §00 Ep. | Strategy Biases|
‘Ants _[21-30|Female| 0 | Ter. | Other | biogeog, | scallops] other | 0.206 (one)
[21-30|Female| 0 | Ter. | Other | geography | scallops| solver
21-30] Male | 0 |Ter.| Prof. [| ecology | water [bystander]
[21-30] Male |_0 | Ter. | Prof. | ecology | water | bystander|
[31-40] Mate | O | Ter. | Prof. | geography { scallops| solver
[47-50| Male | 2 | Ter. | Prof. | ecology | scallops| solver
[57-60] Male [2 | Ter. | Prof. | computing |_water_| bystander:
j61-70| Male | 2 | Ter. | Prof. [boundaries| scallops| solver.
(Commerc}41-50| Male | O | Ter. | Prof | drawing | water |bystander| 0.152 | Effective
f41-50| Male |_O | Ter. | Prof. | ecology | water | bystander! hot Dependent
fat-So|_Male | O | Ter.| Prof. | ecology |scaliops| ns. |
fEngineer.|21-30[Female| O- | ter. | Other| biogeog. [water | other | 0.148 | Restaclive
[21-30|Female| 0 | Ter. | Other | geography | water |" solver not Easy
[31-40] Male | 0 | Ter. | Prof. | landuse | water | other not fast
[31-40|_Male-| 0 | Ter. | Prof. [geography| water | solver
r41-50| Male | 2 | Ter. | Prof.| ecology | water | solver
[51-60)_Male | 2 | Ter. | Prof. | computing | scallops | bystander:
Science [31-40|_Male | 0 | Ter. | Prof.| landuse | scallops! other
Cluster 1]21-30|Female; 0, ter, Other | geography |<callops: solver | 0.080 | Improvable
[31-40] Male | 0 } Ter. | Prof. | landuse |scallops| other 1 Responsive
i not Restrictive
Cluster’2|21-30| Male | 0 | Ter. | Prof. | ecology | water_| bystander]
Cluster 321-30] Female| 0 | Ter. | Other | biogeog. | scallops! other_|
Cluster 4[27-30|Female| 0 | Ter. | Other | biogeog. | water | other | 0.028 | notFast
[31-40| Male [0 | Ter. | Prof. | landuse | water | other |
[41-50| Male | 2 | Ter. | Prof. | ecology | water | solver
51-60] Male | 2 | Ter. | Prof. | computing | scallops | bystander|
[61-70| Male | 2 | Ter. | Prof_|boundaries| scallops| solver
Cluster 5421-30 Female| 0 | Ter. | Other | geography| water | solver
Cluster 6]21-30| Male | 0 | Ter. | Prof.| ecology | scallops | bystander| 0.017 (none)
[31-40| Male | 0 | Ter. | Prof. | geography | scallops| solver
[41-50] Male | 0 | Ter. | Prof. | ecology |scaliops; ns. | i
44-50| Male | 2 | Ter. | Prof. | ecology | scallops] solver | {
[S160] Male | 2 | Ter_| Prof. | computing | water | bystander! i
Cluster 6]47-50| Male | 0 | Ter. | Prof. | drawing | water |bystander| 0.485 | not Dependent
f41-S0|_ Male | 0 | Ter. | Prof. | ecology | water | bystander!
‘Cluster 5]31-40| Male | 0 | Ter. | Prof_| geography | water i solver |
ALL tt I H 10.454]
{Problem Addressers' Correlations between Criterion Scores and Overall Score i
within :- | | Likelihood! Effectiveness Improvability | Restrictiveness| Ease Responsiveness! Speed| Dependence | Morality
water [i 008 | 0.26 055 0.51 [0.44 0.65) O15 | 0.33 0.40
scallops[i 0.67 10.32 0.38 0.10 037 0.69 0.34 0.26 0.47
