This shows the results of the peer evaluation process. Portfolios are ranked according
to the percent of revenues available in each market that were earned by each portfolio.
The Skills column shows the computer skill rating you reported in the skills assessment
task that I assigned the first week of this course. .
Note: The results shown here may differ from those shown in last weeks task
because last week's task included all submissions, including those in the pending,
revise and accepted states. This table only includes submissions that were actually
accepted.
| # |
Portfolio |
TEB |
% |
S |
Market |
Deposits |
|
1
|
SaraRBartos
|
36.00
|
16.00
|
4
|
3=225.00
|
5 4 4 5 0 5 4 0 5 0 0 4 |
|
2
|
MichaelRMcCaffery
|
40.00
|
14.55
|
3
|
4=275.00
|
6 10 0 0 3 3 0 4 4 0 5 3 2 |
|
3
|
JohnAndre
|
29.00
|
12.89
|
5
|
3=225.00
|
3 0 4 4 0 4 4 0 3 4 0 3 |
|
4
|
MaryANademin
|
35.00
|
12.73
|
4
|
2=275.00
|
3 2 8 2 0 4 3 3 2 0 4 4 |
|
5
|
RonaldESappenfield
|
27.00
|
12.00
|
0
|
3=225.00
|
2 4 2 2 0 3 5 0 0 4 0 5 |
|
6
|
JonathonPerrelli
|
32.00
|
11.64
|
5
|
2=275.00
|
3 3 6 2 0 0 3 2 4 4 2 3 |
|
7
|
JohnGPayneJr
|
31.00
|
11.27
|
5
|
4=275.00
|
0 5 0 4 3 3 0 4 2 0 1 6 3 |
|
8
|
KatrinaJJoseph
|
25.00
|
11.11
|
6
|
3=225.00
|
3 4 0 4 0 2 3 0 3 3 0 3 |
|
9
|
CarolIvory
|
36.00
|
11.08
|
3
|
1=325.00
|
3 4 3 3 0 4 2 3 4 3 3 2 2 |
|
10
|
ElaineGreene
|
30.00
|
10.91
|
7
|
2=275.00
|
2 2 0 2 0 4 4 2 2 2 5 5 |
|
11
|
MarkRDuke
|
30.00
|
10.91
|
8
|
4=275.00
|
2 0 0 10 3 2 7 2 2 0 2 0 0 |
|
12
|
DonnaMSink
|
29.00
|
10.55
|
4
|
2=275.00
|
4 0 2 2 0 3 3 3 1 4 4 3 |
|
13
|
NutnichaSanguanraksa
|
29.00
|
10.55
|
3
|
2=275.00
|
3 2 1 4 0 3 4 5 1 4 2 0 |
|
14
|
BethAlwin
|
32.00
|
9.85
|
6
|
1=325.00
|
3 0 4 2 5 3 0 2 3 3 3 1 3 |
|
15
|
RobertLanier
|
22.00
|
9.78
|
6
|
3=225.00
|
2 3 2 1 0 3 3 0 2 4 0 2 |
|
16
|
DariaDenissova
|
31.00
|
9.54
|
2
|
1=325.00
|
3 2 2 3 2 3 5 0 2 2 3 2 2 |
|
17
|
DanielSullivan
|
26.00
|
9.45
|
5
|
2=275.00
|
0 2 1 2 0 3 0 4 5 4 2 3 |
|
18
|
StevenLaFroth
|
21.00
|
9.33
|
7
|
3=225.00
|
5 3 2 2 0 1 1 0 3 4 0 0 |
|
19
|
MattPayne
|
25.00
|
9.09
|
5
|
4=275.00
|
2 2 0 3 3 2 3 2 2 0 1 2 3 |
|
20
|
BethMcCoy
|
28.00
|
8.62
|
6
|
1=325.00
|
4 4 0 2 3 2 0 2 2 3 2 1 3 |
|
21
|
DavidSchatzman
|
28.00
|
8.62
|
7
|
1=325.00
|
4 2 3 2 2 2 0 3 1 0 3 3 3 |
|
22
|
MaxineDavis
|
19.00
|
8.44
|
2
|
3=225.00
|
2 2 3 1 0 0 3 0 3 3 0 2 |
|
23
|
ChienFuChen
|
27.00
|
8.31
|
4
|
1=325.00
|
2 1 2 1 3 0 2 2 2 2 2 7 1 |
|
24
|
ChristopherFeola
|
27.00
|
8.31
|
9
|
1=325.00
|
2 1 2 3 1 3 0 2 4 1 1 4 3 |
|
25
|
JulianneMRuddy
|
22.00
|
8.00
|
1
|
2=275.00
|
2 4 1 3 0 1 2 0 2 4 2 1 |
|
26
|
StevenGrange
|
21.00
|
7.64
|
6
|
4=275.00
|
4 1 0 2 2 4 0 3 0 0 2 1 2 |
|
27
|
WilliamCPurdy
|
21.00
|
7.64
|
6
|
4=275.00
|
1 1 0 0 1 1 8 2 1 0 4 0 2 |
|
28
|
HyeunHeeKim
|
17.00
|
7.56
|
0
|
3=225.00
|
0 2 2 2 0 2 2 0 2 3 0 2 |
|
29
|
GeorgeBennett
|
23.00
|
7.08
|
8
|
1=325.00
|
3 1 1 2 1 3 5 2 1 1 0 1 2 |
|
30
|
RoseJohnson
|
19.00
|
6.91
|
5
|
4=275.00
|
3 1 0 3 0 2 0 2 5 0 1 0 2 |
|
31
|
GregoryPBalzer
|
22.00
|
6.77
|
5
|
1=325.00
|
0 3 2 1 3 2 2 2 1 2 3 0 1 |
|
32
|
NGALIEUBERTIN
|
22.00
|
6.77
|
1
|
1=325.00
|
0 2 2 1 2 2 5 2 1 2 2 1 0 |
|
33
|
JosephJDolan
|
18.00
|
6.55
|
5
|
2=275.00
|
3 3 1 3 0 1 0 1 1 1 4 0 |
|
34
|
StephenLNegri
|
18.00
|
6.55
|
3
|
4=275.00
|
1 1 0 1 2 1 4 0 3 0 1 1 3 |
|
35
|
WongsiriRianrungroj
|
18.00
|
6.55
|
7
|
4=275.00
|
1 2 0 0 2 3 0 2 2 0 4 2 0 |
|
36
|
AliKheder
|
20.00
|
6.15
|
3
|
1=325.00
|
0 2 1 2 2 1 2 2 2 2 1 1 2 |
|
37
|
CarinaLynch
|
19.00
|
5.85
|
4
|
1=325.00
|
1 2 2 0 1 0 2 2 2 3 1 1 2 |
|
38
|
MarkDFlannery
|
13.00
|
5.78
|
4
|
3=225.00
|
2 1 2 3 0 2 0 0 2 0 0 1 |
|
39
|
GaryCMalloy
|
15.00
|
5.45
|
2
|
2=275.00
|
2 1 1 0 0 1 0 3 4 0 0 3 |
|
40
|
KevinBLefton
|
15.00
|
5.45
|
2
|
2=275.00
|
2 2 1 2 0 3 2 0 0 0 0 3 |
|
41
|
SharonMSouthard
|
15.00
|
5.45
|
8
|
4=275.00
|
1 0 0 0 2 2 0 1 1 0 2 4 2 |
|
42
|
JenniferLittle
|
14.00
|
5.09
|
3
|
2=275.00
|
0 2 2 2 0 2 2 0 2 2 0 0 |
|
43
|
ScottPerksy
|
14.00
|
5.09
|
4
|
4=275.00
|
2 0 0 0 2 0 0 3 3 0 1 0 3 |
|
44
|
TheresaWerner
|
14.00
|
5.09
|
3
|
4=275.00
|
0 1 0 2 1 1 3 0 0 0 1 4 1 |
|
45
|
KevinParra
|
10.00
|
4.44
|
6
|
3=225.00
|
1 2 2 0 0 2 0 0 1 0 0 2 |
|
46
|
MelindaSPajak
|
10.00
|
3.64
|
7
|
2=275.00
|
1 2 1 1 0 0 2 2 1 0 0 0 |
|
47
|
ThomasEPorter
|
9.00
|
3.27
|
7
|
4=275.00
|
2 1 0 0 1 1 0 0 0 0 0 2 2 |
|
48
|
DavidSSuh
|
10.00
|
3.08
|
5
|
1=325.00
|
0 1 1 3 0 0 0 1 0 1 1 1 1 |
|
49
|
PrakittayaChangsila
|
6.00
|
2.67
|
4
|
3=225.00
|
0 0 2 1 0 1 0 0 1 0 0 1 |
|
50
|
SeanRussell
|
0.00
|
0.00
|
5
|
3=225.00
|
0 0 0 0 0 0 0 0 0 0 0 0 |
Legend
I sorted the results by the % of Market column, instead of the raw "TEB earned"
column, to adjust for the different number of people that actively participated in
each market and thus the number of TEB that was available to be earned in each market.
This presents this table as a histogram. The rows show the percentage of money
in each market earned by individuals. The graphics show how many earned each percentage.
This task typically generates classical Gaussian (Bell Curve) distributions that
are very similar to the wealth distributions of real market economies. A few gets
most of the wealth, some get nothing, and there's a huge middle class in between.