Nash Equilibrium Methods Comparison
For each method available for Nash equilibrium finding, this lists various
information about the performance across different game types and starting
locations. “Fraction of Eqa” is the mean fraction of all equilibria found via
that method or starting location. “Weigted Fraction (of Eqa)” is the same,
except each equilibrium is down weighted by the number of methods that found
it, thus a larger weighted fraction indicates that this method found more
unique equilibria. “Time” is the average time in seconds it took to run this
method for every starting location. “Normalized Time” sets the minimum time for
each game type and sets it to one, thus somewhat mitigating the fact that
certain games may be more difficult than others. It also provides an easy
comparison metric to for baseline timing.
Comparisons Between Methods
| Method |
Fraction of Eqa |
Weighted Fraction |
Time (sec) |
Normalized Time |
| Regret Minimization |
0.568731 |
0.173796 |
0.321582 |
9.26718 |
| Multiplicative Weights Bandit |
0.528874 |
0.153923 |
29.4978 |
4398.16 |
| Multiplicative Weights Dist |
0.521398 |
0.0959819 |
17.8935 |
768.361 |
| Multiplicative Weights Stoch |
0.508405 |
0.0873798 |
62.5403 |
7982.75 |
| Fictitious Play |
0.472796 |
0.0594228 |
7.73066 |
576.771 |
| Fictitious Play Long |
0.466721 |
0.0552945 |
179.604 |
22325.4 |
| Replicator Dynamics |
0.438658 |
0.0567802 |
1.56668 |
64.2687 |
| Scarf 30 |
0.432357 |
0.071016 |
16.7026 |
309.018 |
| Scarf 5 |
0.431933 |
0.0708725 |
5.81228 |
144.357 |
| Scarf 1 |
0.43032 |
0.070612 |
2.01623 |
61.1935 |
| Regret Matching |
0.370006 |
0.104922 |
57.9059 |
5180.56 |
Scarf 30
Initial Profile Rates
| Starting Type |
Fraction of Eqa |
Weighted Fraction |
| Uniform |
0.994774 |
0.994774 |
Compared to Other Methods
| Method |
Fraction of Eqa |
Time Ratio |
| Scarf 1 |
1 |
5.04985 |
| Scarf 5 |
1 |
2.14065 |
| Replicator Dynamics |
0.892422 |
4.80821 |
| Multiplicative Weights Bandit |
0.889781 |
0.0702606 |
| Multiplicative Weights Stoch |
0.885552 |
0.0387107 |
| Regret Minimization |
0.879359 |
33.3454 |
| Fictitious Play |
0.878377 |
0.535772 |
| Fictitious Play Long |
0.877982 |
0.0138415 |
| Multiplicative Weights Dist |
0.875912 |
0.402178 |
| Regret Matching |
0.498422 |
0.0596495 |
By Game Type
Shapley hard
| Metric |
Value |
| Fraction of Eqa |
0.5 |
| Weighted Fraction of Eqa |
0.0555556 |
| Time (sec) |
0.000148058 |
| Normalized Time |
1 |
Rbf
| Metric |
Value |
| Fraction of Eqa |
0.331322 |
| Weighted Fraction of Eqa |
0.074747 |
| Time (sec) |
10.8339 |
| Normalized Time |
661.379 |
Chicken
| Metric |
Value |
| Fraction of Eqa |
0.5375 |
| Weighted Fraction of Eqa |
0.0772222 |
| Time (sec) |
0.00523914 |
| Normalized Time |
1 |
Zero sum
| Metric |
Value |
| Fraction of Eqa |
0.312472 |
| Weighted Fraction of Eqa |
0.0581924 |
| Time (sec) |
1.61499 |
| Normalized Time |
120.84 |
Mix
| Metric |
Value |
| Fraction of Eqa |
0.8 |
| Weighted Fraction of Eqa |
0.0954167 |
| Time (sec) |
0.00686474 |
| Normalized Time |
1.24652 |
Sineagg large
| Metric |
Value |
| Fraction of Eqa |
0.5 |
| Weighted Fraction of Eqa |
0.05 |
| Time (sec) |
1.53651 |
| Normalized Time |
67.9854 |
Normagg small
| Metric |
Value |
| Fraction of Eqa |
0.469426 |
| Weighted Fraction of Eqa |
0.0632558 |
| Time (sec) |
40.3947 |
| Normalized Time |
634.09 |
Polyagg large
| Metric |
Value |
| Fraction of Eqa |
0.0178571 |
| Weighted Fraction of Eqa |
0.00595238 |
| Time (sec) |
0.0364892 |
| Normalized Time |
1.00035 |
Local effect
| Metric |
Value |
| Fraction of Eqa |
0.576389 |
| Weighted Fraction of Eqa |
0.106637 |
| Time (sec) |
0.189833 |
| Normalized Time |
6.63798 |
Congestion
| Metric |
Value |
| Fraction of Eqa |
0.453237 |
| Weighted Fraction of Eqa |
0.0686254 |
| Time (sec) |
1.63167 |
| Normalized Time |
84.8835 |
Covariant
| Metric |
Value |
| Fraction of Eqa |
0.155377 |
| Weighted Fraction of Eqa |
0.0352583 |
| Time (sec) |
87.0647 |
| Normalized Time |
2682.6 |
Shapley easy
| Metric |
Value |
| Fraction of Eqa |
0.333333 |
| Weighted Fraction of Eqa |
0.0333333 |
| Time (sec) |
0.000152111 |
| Normalized Time |
1 |
Prisoners
| Metric |
Value |
| Fraction of Eqa |
0.525 |
| Weighted Fraction of Eqa |
0.148674 |
| Time (sec) |
0.00902817 |
| Normalized Time |
1.5135 |
Normagg large
| Metric |
Value |
| Fraction of Eqa |
0 |
| Weighted Fraction of Eqa |
0 |
| Time (sec) |
1800 |
| Normalized Time |
1560.87 |
Sineagg small
| Metric |
Value |
| Fraction of Eqa |
0.239765 |
| Weighted Fraction of Eqa |
0.0375492 |
| Time (sec) |
0.687516 |
| Normalized Time |
4.65944 |
Hard
| Metric |
Value |
| Fraction of Eqa |
0.0588235 |
| Weighted Fraction of Eqa |
0.0196078 |
| Time (sec) |
0.484841 |
| Normalized Time |
31.1158 |
Gambit
| Metric |
Value |
| Fraction of Eqa |
0.0909091 |
| Weighted Fraction of Eqa |
0.030303 |
| Time (sec) |
0.0513456 |
| Normalized Time |
1.61668 |
Polymatrix
| Metric |
Value |
| Fraction of Eqa |
0.349647 |
| Weighted Fraction of Eqa |
0.0554777 |
| Time (sec) |
0.966773 |
| Normalized Time |
33.8824 |
Random
| Metric |
Value |
| Fraction of Eqa |
0.153897 |
| Weighted Fraction of Eqa |
0.0311547 |
| Time (sec) |
0.28003 |
| Normalized Time |
7.51681 |
Polyagg small
| Metric |
Value |
| Fraction of Eqa |
0.732691 |
| Weighted Fraction of Eqa |
0.0718566 |
| Time (sec) |
6.70744 |
| Normalized Time |
124.043 |
Roshambo
| Metric |
Value |
| Fraction of Eqa |
0.464167 |
| Weighted Fraction of Eqa |
0.0838261 |
| Time (sec) |
0.0184558 |
| Normalized Time |
2.28596 |
Shapley normal
| Metric |
Value |
| Fraction of Eqa |
1 |
| Weighted Fraction of Eqa |
0.1 |
| Time (sec) |
0.000151873 |
| Normalized Time |
1 |
Regret Matching
Initial Profile Rates
| Starting Type |
Fraction of Eqa |
Weighted Fraction |
| Random |
0.924928 |
0.64514 |
| Pure |
0.515728 |
0.236737 |
| Biased |
0.10575 |
0.0276221 |
| Role biased |
0.102853 |
0.0250178 |
| Uniform |
0.0637684 |
0.0137799 |
Compared to Other Methods
| Method |
Fraction of Eqa |
Time Ratio |
| Multiplicative Weights Stoch |
0.927526 |
0.648969 |
| Replicator Dynamics |
0.927218 |
80.6078 |
| Fictitious Play Long |
0.926172 |
0.232047 |
| Fictitious Play |
0.926072 |
8.98201 |
| Multiplicative Weights Bandit |
0.9153 |
1.17789 |
| Multiplicative Weights Dist |
0.904918 |
6.74234 |
| Regret Minimization |
0.900291 |
559.022 |
| Scarf 1 |
0.871919 |
84.6587 |
| Scarf 5 |
0.871468 |
35.8871 |
| Scarf 30 |
0.871176 |
16.7646 |
By Game Type
Shapley hard
| Metric |
Value |
| Fraction of Eqa |
1 |
| Weighted Fraction of Eqa |
0.555556 |
| Time (sec) |
33.8172 |
| Normalized Time |
228405 |
Rbf
| Metric |
Value |
| Fraction of Eqa |
0.304548 |
| Weighted Fraction of Eqa |
0.034857 |
| Time (sec) |
127.934 |
| Normalized Time |
7810.02 |
Chicken
| Metric |
Value |
| Fraction of Eqa |
0.0625 |
| Weighted Fraction of Eqa |
0.04 |
| Time (sec) |
3.14211 |
| Normalized Time |
599.737 |
Zero sum
| Metric |
Value |
| Fraction of Eqa |
0.621495 |
| Weighted Fraction of Eqa |
0.391822 |
| Time (sec) |
254.72 |
| Normalized Time |
19059.1 |
Mix
| Metric |
Value |
| Fraction of Eqa |
0 |
| Weighted Fraction of Eqa |
0 |
| Time (sec) |
2.96706 |
| Normalized Time |
538.77 |
Sineagg large
| Metric |
Value |
| Fraction of Eqa |
1 |
| Weighted Fraction of Eqa |
0.1125 |
| Time (sec) |
1.53544 |
| Normalized Time |
67.9379 |
Normagg small
| Metric |
Value |
| Fraction of Eqa |
0.455498 |
| Weighted Fraction of Eqa |
0.0478788 |
| Time (sec) |
4.64639 |
| Normalized Time |
72.936 |
Polyagg large
| Metric |
Value |
| Fraction of Eqa |
0.0357143 |
| Weighted Fraction of Eqa |
0.00478316 |
| Time (sec) |
2.82121 |
| Normalized Time |
77.3436 |
Local effect
| Metric |
Value |
| Fraction of Eqa |
0.145833 |
| Weighted Fraction of Eqa |
0.0148201 |
| Time (sec) |
5.05215 |
| Normalized Time |
176.661 |
Congestion
| Metric |
Value |
| Fraction of Eqa |
0.130333 |
| Weighted Fraction of Eqa |
0.0545 |
| Time (sec) |
23.0914 |
| Normalized Time |
1201.27 |
Covariant
| Metric |
Value |
| Fraction of Eqa |
0.385925 |
| Weighted Fraction of Eqa |
0.224672 |
| Time (sec) |
243.786 |
| Normalized Time |
7511.41 |
Shapley easy
| Metric |
Value |
| Fraction of Eqa |
0.666667 |
| Weighted Fraction of Eqa |
0.366667 |
| Time (sec) |
29.9445 |
| Normalized Time |
196860 |
Prisoners
| Metric |
Value |
| Fraction of Eqa |
0.525 |
| Weighted Fraction of Eqa |
0.0622159 |
| Time (sec) |
0.693169 |
| Normalized Time |
116.204 |
Normagg large
| Metric |
Value |
| Fraction of Eqa |
1 |
| Weighted Fraction of Eqa |
0.142857 |
| Time (sec) |
19.839 |
| Normalized Time |
17.2034 |
Sineagg small
| Metric |
Value |
| Fraction of Eqa |
0.305898 |
| Weighted Fraction of Eqa |
0.0395527 |
| Time (sec) |
5.54689 |
| Normalized Time |
37.5924 |
Hard
| Metric |
Value |
| Fraction of Eqa |
0.0588235 |
| Weighted Fraction of Eqa |
0.0117647 |
| Time (sec) |
78.2362 |
| Normalized Time |
5021 |
Gambit
| Metric |
Value |
| Fraction of Eqa |
0.363636 |
| Weighted Fraction of Eqa |
0.0454545 |
| Time (sec) |
1.52629 |
| Normalized Time |
48.0573 |
Polymatrix
| Metric |
Value |
| Fraction of Eqa |
0.405965 |
| Weighted Fraction of Eqa |
0.0664618 |
| Time (sec) |
39.8172 |
| Normalized Time |
1395.47 |
Random
| Metric |
Value |
| Fraction of Eqa |
0.261022 |
| Weighted Fraction of Eqa |
0.0723834 |
| Time (sec) |
76.8243 |
| Normalized Time |
2062.19 |
Polyagg small
| Metric |
Value |
| Fraction of Eqa |
0.760323 |
| Weighted Fraction of Eqa |
0.0714043 |
| Time (sec) |
2.72771 |
| Normalized Time |
50.4443 |
Roshambo
| Metric |
Value |
| Fraction of Eqa |
0.7075 |
| Weighted Fraction of Eqa |
0.323323 |
| Time (sec) |
33.247 |
| Normalized Time |
4118.02 |
Shapley normal
| Metric |
Value |
| Fraction of Eqa |
1 |
| Weighted Fraction of Eqa |
0.1 |
| Time (sec) |
25.2879 |
| Normalized Time |
166507 |
Multiplicative Weights Dist
Initial Profile Rates
| Starting Type |
Fraction of Eqa |
Weighted Fraction |
| Uniform |
0.945395 |
0.845543 |
| Biased |
0.163391 |
0.0462799 |
| Role biased |
0.160499 |
0.0431626 |
| Random |
0.156742 |
0.0466468 |
| Pure |
0.0609121 |
0.0172996 |
Compared to Other Methods
| Method |
Fraction of Eqa |
Time Ratio |
| Fictitious Play Long |
0.996554 |
0.0344164 |
| Fictitious Play |
0.996255 |
1.33218 |
| Replicator Dynamics |
0.994949 |
11.9555 |
| Multiplicative Weights Stoch |
0.978448 |
0.0962527 |
| Multiplicative Weights Bandit |
0.947091 |
0.1747 |
| Regret Minimization |
0.94645 |
82.9121 |
| Scarf 1 |
0.892497 |
12.5563 |
| Scarf 5 |
0.892045 |
5.32264 |
| Scarf 30 |
0.891754 |
2.48646 |
| Regret Matching |
0.565745 |
0.148316 |
By Game Type
Shapley hard
| Metric |
Value |
| Fraction of Eqa |
0.5 |
| Weighted Fraction of Eqa |
0.0555556 |
| Time (sec) |
2.92369 |
| Normalized Time |
19746.9 |
Rbf
| Metric |
Value |
| Fraction of Eqa |
0.512942 |
| Weighted Fraction of Eqa |
0.160588 |
| Time (sec) |
8.43969 |
| Normalized Time |
515.22 |
Chicken
| Metric |
Value |
| Fraction of Eqa |
0.5375 |
| Weighted Fraction of Eqa |
0.0612103 |
| Time (sec) |
2.60122 |
| Normalized Time |
496.497 |
Zero sum
| Metric |
Value |
| Fraction of Eqa |
0.373656 |
| Weighted Fraction of Eqa |
0.0858021 |
| Time (sec) |
4.23339 |
| Normalized Time |
316.759 |
Mix
| Metric |
Value |
| Fraction of Eqa |
0.8 |
| Weighted Fraction of Eqa |
0.0832143 |
| Time (sec) |
2.59525 |
| Normalized Time |
471.255 |
Sineagg large
| Metric |
Value |
| Fraction of Eqa |
1 |
| Weighted Fraction of Eqa |
0.1125 |
| Time (sec) |
11.4917 |
| Normalized Time |
508.467 |
Normagg small
| Metric |
Value |
| Fraction of Eqa |
0.655801 |
| Weighted Fraction of Eqa |
0.122854 |
| Time (sec) |
32.0664 |
| Normalized Time |
503.358 |
Polyagg large
| Metric |
Value |
| Fraction of Eqa |
0.285714 |
| Weighted Fraction of Eqa |
0.220557 |
| Time (sec) |
1704.14 |
| Normalized Time |
46719.1 |
Local effect
| Metric |
Value |
| Fraction of Eqa |
0.576389 |
| Weighted Fraction of Eqa |
0.0670621 |
| Time (sec) |
15.1364 |
| Normalized Time |
529.282 |
Congestion
| Metric |
Value |
| Fraction of Eqa |
0.529003 |
| Weighted Fraction of Eqa |
0.102581 |
| Time (sec) |
16.2656 |
| Normalized Time |
846.174 |
Covariant
| Metric |
Value |
| Fraction of Eqa |
0.337059 |
| Weighted Fraction of Eqa |
0.167468 |
| Time (sec) |
5.92329 |
| Normalized Time |
182.506 |
Shapley easy
| Metric |
Value |
| Fraction of Eqa |
0.333333 |
| Weighted Fraction of Eqa |
0.0333333 |
| Time (sec) |
2.89428 |
| Normalized Time |
19027.4 |
Prisoners
| Metric |
Value |
| Fraction of Eqa |
0.558333 |
| Weighted Fraction of Eqa |
0.0822159 |
| Time (sec) |
2.58478 |
| Normalized Time |
433.317 |
Normagg large
| Metric |
Value |
| Fraction of Eqa |
1 |
| Weighted Fraction of Eqa |
0.142857 |
| Time (sec) |
69.3236 |
| Normalized Time |
60.1139 |
Sineagg small
| Metric |
Value |
| Fraction of Eqa |
0.437487 |
| Weighted Fraction of Eqa |
0.108665 |
| Time (sec) |
33.4532 |
| Normalized Time |
226.719 |
Hard
| Metric |
Value |
| Fraction of Eqa |
0.235294 |
| Weighted Fraction of Eqa |
0.188235 |
| Time (sec) |
4.93074 |
| Normalized Time |
316.442 |
Gambit
| Metric |
Value |
| Fraction of Eqa |
0.454545 |
| Weighted Fraction of Eqa |
0.0636364 |
| Time (sec) |
5.28843 |
| Normalized Time |
166.513 |
Polymatrix
| Metric |
Value |
| Fraction of Eqa |
0.445319 |
| Weighted Fraction of Eqa |
0.0851808 |
| Time (sec) |
5.67596 |
| Normalized Time |
198.925 |
Random
| Metric |
Value |
| Fraction of Eqa |
0.326832 |
| Weighted Fraction of Eqa |
0.0679519 |
| Time (sec) |
4.73046 |
| Normalized Time |
126.979 |
Polyagg small
| Metric |
Value |
| Fraction of Eqa |
0.796538 |
| Weighted Fraction of Eqa |
0.0885722 |
| Time (sec) |
30.7712 |
| Normalized Time |
569.061 |
Roshambo
| Metric |
Value |
| Fraction of Eqa |
0.380833 |
| Weighted Fraction of Eqa |
0.0529392 |
| Time (sec) |
2.99483 |
| Normalized Time |
370.944 |
Shapley normal
| Metric |
Value |
| Fraction of Eqa |
1 |
| Weighted Fraction of Eqa |
0.1 |
| Time (sec) |
2.88606 |
| Normalized Time |
19003.2 |
Replicator Dynamics
Initial Profile Rates
| Starting Type |
Fraction of Eqa |
Weighted Fraction |
| Uniform |
0.942031 |
0.850344 |
| Biased |
0.143336 |
0.0352437 |
| Role biased |
0.143326 |
0.0355169 |
| Random |
0.142065 |
0.0361951 |
| Pure |
0.0752944 |
0.0219619 |
Compared to Other Methods
| Method |
Fraction of Eqa |
Time Ratio |
| Fictitious Play Long |
0.974041 |
0.00287872 |
| Fictitious Play |
0.973601 |
0.111429 |
| Multiplicative Weights Dist |
0.954491 |
0.0836439 |
| Multiplicative Weights Stoch |
0.954236 |
0.00805094 |
| Multiplicative Weights Bandit |
0.942476 |
0.0146126 |
| Regret Minimization |
0.941664 |
6.93509 |
| Scarf 1 |
0.887903 |
1.05025 |
| Scarf 5 |
0.887451 |
0.445206 |
| Scarf 30 |
0.88716 |
0.207977 |
| Regret Matching |
0.556909 |
0.0124058 |
By Game Type
Shapley hard
| Metric |
Value |
| Fraction of Eqa |
0.5 |
| Weighted Fraction of Eqa |
0.0555556 |
| Time (sec) |
0.0477914 |
| Normalized Time |
322.788 |
Rbf
| Metric |
Value |
| Fraction of Eqa |
0.436103 |
| Weighted Fraction of Eqa |
0.0885598 |
| Time (sec) |
8.00409 |
| Normalized Time |
488.628 |
Chicken
| Metric |
Value |
| Fraction of Eqa |
0.5375 |
| Weighted Fraction of Eqa |
0.0612103 |
| Time (sec) |
0.0729519 |
| Normalized Time |
13.9244 |
Zero sum
| Metric |
Value |
| Fraction of Eqa |
0.120139 |
| Weighted Fraction of Eqa |
0.0142472 |
| Time (sec) |
0.676059 |
| Normalized Time |
50.5854 |
Mix
| Metric |
Value |
| Fraction of Eqa |
0.8 |
| Weighted Fraction of Eqa |
0.0832143 |
| Time (sec) |
0.0424278 |
| Normalized Time |
7.7042 |
Sineagg large
| Metric |
Value |
| Fraction of Eqa |
1 |
| Weighted Fraction of Eqa |
0.1125 |
| Time (sec) |
0.219549 |
| Normalized Time |
9.7143 |
Normagg small
| Metric |
Value |
| Fraction of Eqa |
0.594784 |
| Weighted Fraction of Eqa |
0.0688609 |
| Time (sec) |
1.41217 |
| Normalized Time |
22.1674 |
Polyagg large
| Metric |
Value |
| Fraction of Eqa |
0.0535714 |
| Weighted Fraction of Eqa |
0.00924745 |
| Time (sec) |
46.3598 |
| Normalized Time |
1270.96 |
Local effect
| Metric |
Value |
| Fraction of Eqa |
0.576389 |
| Weighted Fraction of Eqa |
0.0670621 |
| Time (sec) |
0.463779 |
| Normalized Time |
16.2172 |
Congestion
| Metric |
Value |
| Fraction of Eqa |
0.538898 |
| Weighted Fraction of Eqa |
0.116055 |
| Time (sec) |
1.25418 |
| Normalized Time |
65.2456 |
Covariant
| Metric |
Value |
| Fraction of Eqa |
0.161206 |
| Weighted Fraction of Eqa |
0.0185797 |
| Time (sec) |
1.29125 |
| Normalized Time |
39.7854 |
Shapley easy
| Metric |
Value |
| Fraction of Eqa |
0.333333 |
| Weighted Fraction of Eqa |
0.0333333 |
| Time (sec) |
0.0478084 |
| Normalized Time |
314.299 |
Prisoners
| Metric |
Value |
| Fraction of Eqa |
0.541667 |
| Weighted Fraction of Eqa |
0.0655492 |
| Time (sec) |
0.0424702 |
| Normalized Time |
7.11978 |
Normagg large
| Metric |
Value |
| Fraction of Eqa |
1 |
| Weighted Fraction of Eqa |
0.142857 |
| Time (sec) |
1.69171 |
| Normalized Time |
1.46697 |
Sineagg small
| Metric |
Value |
| Fraction of Eqa |
0.365775 |
| Weighted Fraction of Eqa |
0.0514696 |
| Time (sec) |
4.76914 |
| Normalized Time |
32.3215 |
Hard
| Metric |
Value |
| Fraction of Eqa |
0 |
| Weighted Fraction of Eqa |
0 |
| Time (sec) |
7.1365 |
| Normalized Time |
458.002 |
Gambit
| Metric |
Value |
| Fraction of Eqa |
0.454545 |
| Weighted Fraction of Eqa |
0.0636364 |
| Time (sec) |
0.0791451 |
| Normalized Time |
2.49199 |
Polymatrix
| Metric |
Value |
| Fraction of Eqa |
0.351567 |
| Weighted Fraction of Eqa |
0.0363664 |
| Time (sec) |
0.303221 |
| Normalized Time |
10.627 |
Random
| Metric |
Value |
| Fraction of Eqa |
0.29407 |
| Weighted Fraction of Eqa |
0.0453142 |
| Time (sec) |
0.469837 |
| Normalized Time |
12.6118 |
Polyagg small
| Metric |
Value |
| Fraction of Eqa |
0.781501 |
| Weighted Fraction of Eqa |
0.075289 |
| Time (sec) |
0.923911 |
| Normalized Time |
17.0862 |
Roshambo
| Metric |
Value |
| Fraction of Eqa |
0 |
| Weighted Fraction of Eqa |
0 |
| Time (sec) |
0.0532223 |
| Normalized Time |
6.59218 |
Shapley normal
| Metric |
Value |
| Fraction of Eqa |
1 |
| Weighted Fraction of Eqa |
0.1 |
| Time (sec) |
0.0481976 |
| Normalized Time |
317.356 |
Fictitious Play
Initial Profile Rates
| Starting Type |
Fraction of Eqa |
Weighted Fraction |
| Uniform |
0.968887 |
0.844597 |
| Pure |
0.179452 |
0.0399854 |
| Random |
0.177787 |
0.040904 |
| Biased |
0.176497 |
0.0375596 |
| Role biased |
0.172362 |
0.0357947 |
Compared to Other Methods
| Method |
Fraction of Eqa |
Time Ratio |
| Fictitious Play Long |
0.996304 |
0.0258347 |
| Replicator Dynamics |
0.985459 |
8.97436 |
| Multiplicative Weights Stoch |
0.970015 |
0.0722521 |
| Multiplicative Weights Dist |
0.965673 |
0.75065 |
| Regret Minimization |
0.945646 |
62.238 |
| Multiplicative Weights Bandit |
0.94078 |
0.131139 |
| Scarf 1 |
0.890577 |
9.42536 |
| Scarf 5 |
0.890126 |
3.99544 |
| Scarf 30 |
0.889834 |
1.86646 |
| Regret Matching |
0.564136 |
0.111334 |
By Game Type
Shapley hard
| Metric |
Value |
| Fraction of Eqa |
0.5 |
| Weighted Fraction of Eqa |
0.0555556 |
| Time (sec) |
3.42748 |
| Normalized Time |
23149.6 |
Rbf
| Metric |
Value |
| Fraction of Eqa |
0.376006 |
| Weighted Fraction of Eqa |
0.0486926 |
| Time (sec) |
7.0972 |
| Normalized Time |
433.264 |
Chicken
| Metric |
Value |
| Fraction of Eqa |
0.5375 |
| Weighted Fraction of Eqa |
0.0612103 |
| Time (sec) |
3.16318 |
| Normalized Time |
603.759 |
Zero sum
| Metric |
Value |
| Fraction of Eqa |
0.345037 |
| Weighted Fraction of Eqa |
0.0625309 |
| Time (sec) |
4.30669 |
| Normalized Time |
322.244 |
Mix
| Metric |
Value |
| Fraction of Eqa |
0.8 |
| Weighted Fraction of Eqa |
0.0832143 |
| Time (sec) |
3.1759 |
| Normalized Time |
576.692 |
Sineagg large
| Metric |
Value |
| Fraction of Eqa |
1 |
| Weighted Fraction of Eqa |
0.1125 |
| Time (sec) |
15.0365 |
| Normalized Time |
665.315 |
Normagg small
| Metric |
Value |
| Fraction of Eqa |
0.575141 |
| Weighted Fraction of Eqa |
0.0639502 |
| Time (sec) |
10.5182 |
| Normalized Time |
165.108 |
Polyagg large
| Metric |
Value |
| Fraction of Eqa |
0.0357143 |
| Weighted Fraction of Eqa |
0.00478316 |
| Time (sec) |
204.126 |
| Normalized Time |
5596.11 |
Local effect
| Metric |
Value |
| Fraction of Eqa |
0.576389 |
| Weighted Fraction of Eqa |
0.0670621 |
| Time (sec) |
15.187 |
| Normalized Time |
531.051 |
Congestion
| Metric |
Value |
| Fraction of Eqa |
0.453237 |
| Weighted Fraction of Eqa |
0.0506665 |
| Time (sec) |
18.3005 |
| Normalized Time |
952.033 |
Covariant
| Metric |
Value |
| Fraction of Eqa |
0.193276 |
| Weighted Fraction of Eqa |
0.0357827 |
| Time (sec) |
4.168 |
| Normalized Time |
128.422 |
Shapley easy
| Metric |
Value |
| Fraction of Eqa |
0.333333 |
| Weighted Fraction of Eqa |
0.0333333 |
| Time (sec) |
3.4285 |
| Normalized Time |
22539.4 |
Prisoners
| Metric |
Value |
| Fraction of Eqa |
0.525 |
| Weighted Fraction of Eqa |
0.0622159 |
| Time (sec) |
0.289863 |
| Normalized Time |
48.5931 |
Normagg large
| Metric |
Value |
| Fraction of Eqa |
1 |
| Weighted Fraction of Eqa |
0.142857 |
| Time (sec) |
70.5892 |
| Normalized Time |
61.2113 |
Sineagg small
| Metric |
Value |
| Fraction of Eqa |
0.35141 |
| Weighted Fraction of Eqa |
0.0470733 |
| Time (sec) |
16.5122 |
| Normalized Time |
111.907 |
Hard
| Metric |
Value |
| Fraction of Eqa |
0.0588235 |
| Weighted Fraction of Eqa |
0.0117647 |
| Time (sec) |
5.68838 |
| Normalized Time |
365.066 |
Gambit
| Metric |
Value |
| Fraction of Eqa |
0.454545 |
| Weighted Fraction of Eqa |
0.0636364 |
| Time (sec) |
1.13567 |
| Normalized Time |
35.7579 |
Polymatrix
| Metric |
Value |
| Fraction of Eqa |
0.385215 |
| Weighted Fraction of Eqa |
0.0425868 |
| Time (sec) |
1.50801 |
| Normalized Time |
52.8512 |
Random
| Metric |
Value |
| Fraction of Eqa |
0.315737 |
| Weighted Fraction of Eqa |
0.0726808 |
| Time (sec) |
3.85757 |
| Normalized Time |
103.548 |
Polyagg small
| Metric |
Value |
| Fraction of Eqa |
0.774358 |
| Weighted Fraction of Eqa |
0.0788605 |
| Time (sec) |
4.29832 |
| Normalized Time |
79.4902 |
Roshambo
| Metric |
Value |
| Fraction of Eqa |
0.380833 |
| Weighted Fraction of Eqa |
0.0529392 |
| Time (sec) |
3.59557 |
| Normalized Time |
445.351 |
Shapley normal
| Metric |
Value |
| Fraction of Eqa |
1 |
| Weighted Fraction of Eqa |
0.1 |
| Time (sec) |
3.43402 |
| Normalized Time |
22611.2 |
Fictitious Play Long
Initial Profile Rates
| Starting Type |
Fraction of Eqa |
Weighted Fraction |
| Uniform |
0.968629 |
0.84456 |
| Pure |
0.178594 |
0.0403717 |
| Biased |
0.176104 |
0.037672 |
| Random |
0.173102 |
0.0400963 |
| Role biased |
0.172011 |
0.0359403 |
Compared to Other Methods
| Method |
Fraction of Eqa |
Time Ratio |
| Fictitious Play |
0.997874 |
38.7077 |
| Replicator Dynamics |
0.985459 |
347.377 |
| Multiplicative Weights Stoch |
0.969578 |
2.79671 |
| Multiplicative Weights Dist |
0.965337 |
29.0559 |
| Regret Minimization |
0.944251 |
2409.09 |
| Multiplicative Weights Bandit |
0.940954 |
5.07608 |
| Scarf 1 |
0.89096 |
364.834 |
| Scarf 5 |
0.890508 |
154.654 |
| Scarf 30 |
0.890217 |
72.2465 |
| Regret Matching |
0.564378 |
4.30947 |
By Game Type
Shapley hard
| Metric |
Value |
| Fraction of Eqa |
0.5 |
| Weighted Fraction of Eqa |
0.0555556 |
| Time (sec) |
168.961 |
| Normalized Time |
1.14118e+06 |
Rbf
| Metric |
Value |
| Fraction of Eqa |
0.376602 |
| Weighted Fraction of Eqa |
0.0492878 |
| Time (sec) |
173.48 |
| Normalized Time |
10590.5 |
Chicken
| Metric |
Value |
| Fraction of Eqa |
0.5375 |
| Weighted Fraction of Eqa |
0.0612103 |
| Time (sec) |
73.9022 |
| Normalized Time |
14105.8 |
Zero sum
| Metric |
Value |
| Fraction of Eqa |
0.323985 |
| Weighted Fraction of Eqa |
0.0460835 |
| Time (sec) |
134.481 |
| Normalized Time |
10062.4 |
Mix
| Metric |
Value |
| Fraction of Eqa |
0.8 |
| Weighted Fraction of Eqa |
0.0832143 |
| Time (sec) |
87.0887 |
| Normalized Time |
15813.9 |
Sineagg large
| Metric |
Value |
| Fraction of Eqa |
1 |
| Weighted Fraction of Eqa |
0.1125 |
| Time (sec) |
152.084 |
| Normalized Time |
6729.21 |
Normagg small
| Metric |
Value |
| Fraction of Eqa |
0.575141 |
| Weighted Fraction of Eqa |
0.0639502 |
| Time (sec) |
150.813 |
| Normalized Time |
2367.37 |
Polyagg large
| Metric |
Value |
| Fraction of Eqa |
0.0357143 |
| Weighted Fraction of Eqa |
0.00478316 |
| Time (sec) |
313.31 |
| Normalized Time |
8589.39 |
Local effect
| Metric |
Value |
| Fraction of Eqa |
0.576389 |
| Weighted Fraction of Eqa |
0.0670621 |
| Time (sec) |
489.299 |
| Normalized Time |
17109.5 |
Congestion
| Metric |
Value |
| Fraction of Eqa |
0.453237 |
| Weighted Fraction of Eqa |
0.0506665 |
| Time (sec) |
639.256 |
| Normalized Time |
33255.6 |
Covariant
| Metric |
Value |
| Fraction of Eqa |
0.179163 |
| Weighted Fraction of Eqa |
0.0236679 |
| Time (sec) |
104.171 |
| Normalized Time |
3209.66 |
Shapley easy
| Metric |
Value |
| Fraction of Eqa |
0.333333 |
| Weighted Fraction of Eqa |
0.0333333 |
| Time (sec) |
199.545 |
| Normalized Time |
1.31183e+06 |
Prisoners
| Metric |
Value |
| Fraction of Eqa |
0.525 |
| Weighted Fraction of Eqa |
0.0622159 |
| Time (sec) |
0.290636 |
| Normalized Time |
48.7227 |
Normagg large
| Metric |
Value |
| Fraction of Eqa |
1 |
| Weighted Fraction of Eqa |
0.142857 |
| Time (sec) |
1886.26 |
| Normalized Time |
1635.67 |
Sineagg small
| Metric |
Value |
| Fraction of Eqa |
0.34766 |
| Weighted Fraction of Eqa |
0.0433233 |
| Time (sec) |
266.383 |
| Normalized Time |
1805.34 |
Hard
| Metric |
Value |
| Fraction of Eqa |
0.0588235 |
| Weighted Fraction of Eqa |
0.0117647 |
| Time (sec) |
186.185 |
| Normalized Time |
11948.9 |
Gambit
| Metric |
Value |
| Fraction of Eqa |
0.454545 |
| Weighted Fraction of Eqa |
0.0636364 |
| Time (sec) |
25.2319 |
| Normalized Time |
794.46 |
Polymatrix
| Metric |
Value |
| Fraction of Eqa |
0.385215 |
| Weighted Fraction of Eqa |
0.0425868 |
| Time (sec) |
22.0073 |
| Normalized Time |
771.287 |
Random
| Metric |
Value |
| Fraction of Eqa |
0.291578 |
| Weighted Fraction of Eqa |
0.0485217 |
| Time (sec) |
102.072 |
| Normalized Time |
2739.9 |
Polyagg small
| Metric |
Value |
| Fraction of Eqa |
0.774358 |
| Weighted Fraction of Eqa |
0.0788605 |
| Time (sec) |
4.52731 |
| Normalized Time |
83.7249 |
Roshambo
| Metric |
Value |
| Fraction of Eqa |
0.355833 |
| Weighted Fraction of Eqa |
0.0493677 |
| Time (sec) |
182.412 |
| Normalized Time |
22593.7 |
Shapley normal
| Metric |
Value |
| Fraction of Eqa |
1 |
| Weighted Fraction of Eqa |
0.1 |
| Time (sec) |
190.732 |
| Normalized Time |
1.25587e+06 |
Multiplicative Weights Stoch
Initial Profile Rates
| Starting Type |
Fraction of Eqa |
Weighted Fraction |
| Random |
0.159355 |
0.0463557 |
| Biased |
0.157019 |
0.0422164 |
| Role biased |
0.152887 |
0.0409085 |
| Uniform |
0.123094 |
0.0294856 |
| Pure |
0.0740799 |
0.0203774 |
Compared to Other Methods
| Method |
Fraction of Eqa |
Time Ratio |
| Multiplicative Weights Bandit |
0.9531 |
1.81502 |
| Fictitious Play Long |
0.172797 |
0.357563 |
| Fictitious Play |
0.17243 |
13.8404 |
| Replicator Dynamics |
0.168424 |
124.209 |
| Regret Matching |
0.159336 |
1.54091 |
| Multiplicative Weights Dist |
0.146658 |
10.3893 |
| Regret Minimization |
0.128161 |
861.401 |
| Scarf 1 |
0.0885782 |
130.451 |
| Scarf 5 |
0.0881266 |
55.2986 |
| Scarf 30 |
0.087835 |
25.8327 |
By Game Type
Shapley hard
| Metric |
Value |
| Fraction of Eqa |
0 |
| Weighted Fraction of Eqa |
0 |
| Time (sec) |
85.1506 |
| Normalized Time |
575117 |
Rbf
| Metric |
Value |
| Fraction of Eqa |
0.335488 |
| Weighted Fraction of Eqa |
0.0522358 |
| Time (sec) |
197.324 |
| Normalized Time |
12046.1 |
Chicken
| Metric |
Value |
| Fraction of Eqa |
0.8125 |
| Weighted Fraction of Eqa |
0.246419 |
| Time (sec) |
0.739706 |
| Normalized Time |
141.188 |
Zero sum
| Metric |
Value |
| Fraction of Eqa |
0.329394 |
| Weighted Fraction of Eqa |
0.047297 |
| Time (sec) |
197.321 |
| Normalized Time |
14764.3 |
Mix
| Metric |
Value |
| Fraction of Eqa |
0.95 |
| Weighted Fraction of Eqa |
0.214464 |
| Time (sec) |
0.726422 |
| Normalized Time |
131.906 |
Sineagg large
| Metric |
Value |
| Fraction of Eqa |
1 |
| Weighted Fraction of Eqa |
0.1125 |
| Time (sec) |
2.76868 |
| Normalized Time |
122.505 |
Normagg small
| Metric |
Value |
| Fraction of Eqa |
0.593593 |
| Weighted Fraction of Eqa |
0.0710435 |
| Time (sec) |
15.3826 |
| Normalized Time |
241.466 |
Polyagg large
| Metric |
Value |
| Fraction of Eqa |
0.0892857 |
| Weighted Fraction of Eqa |
0.0241284 |
| Time (sec) |
73.1534 |
| Normalized Time |
2005.5 |
Local effect
| Metric |
Value |
| Fraction of Eqa |
0.576389 |
| Weighted Fraction of Eqa |
0.0670621 |
| Time (sec) |
4.79695 |
| Normalized Time |
167.737 |
Congestion
| Metric |
Value |
| Fraction of Eqa |
0.559266 |
| Weighted Fraction of Eqa |
0.119028 |
| Time (sec) |
24.1546 |
| Normalized Time |
1256.58 |
Covariant
| Metric |
Value |
| Fraction of Eqa |
0.221167 |
| Weighted Fraction of Eqa |
0.0503129 |
| Time (sec) |
214.367 |
| Normalized Time |
6604.98 |
Shapley easy
| Metric |
Value |
| Fraction of Eqa |
0.333333 |
| Weighted Fraction of Eqa |
0.0333333 |
| Time (sec) |
27.8162 |
| Normalized Time |
182868 |
Prisoners
| Metric |
Value |
| Fraction of Eqa |
0.541667 |
| Weighted Fraction of Eqa |
0.0655492 |
| Time (sec) |
6.17225 |
| Normalized Time |
1034.73 |
Normagg large
| Metric |
Value |
| Fraction of Eqa |
1 |
| Weighted Fraction of Eqa |
0.142857 |
| Time (sec) |
10.0131 |
| Normalized Time |
8.68284 |
Sineagg small
| Metric |
Value |
| Fraction of Eqa |
0.37734 |
| Weighted Fraction of Eqa |
0.0736149 |
| Time (sec) |
21.801 |
| Normalized Time |
147.75 |
Hard
| Metric |
Value |
| Fraction of Eqa |
0.0588235 |
| Weighted Fraction of Eqa |
0.0117647 |
| Time (sec) |
91.2241 |
| Normalized Time |
5854.53 |
Gambit
| Metric |
Value |
| Fraction of Eqa |
0.363636 |
| Weighted Fraction of Eqa |
0.0454545 |
| Time (sec) |
14.5183 |
| Normalized Time |
457.126 |
Polymatrix
| Metric |
Value |
| Fraction of Eqa |
0.389067 |
| Weighted Fraction of Eqa |
0.0434497 |
| Time (sec) |
69.5512 |
| Normalized Time |
2437.56 |
Random
| Metric |
Value |
| Fraction of Eqa |
0.280575 |
| Weighted Fraction of Eqa |
0.0560627 |
| Time (sec) |
56.8294 |
| Normalized Time |
1525.47 |
Polyagg small
| Metric |
Value |
| Fraction of Eqa |
0.781501 |
| Weighted Fraction of Eqa |
0.075289 |
| Time (sec) |
11.4221 |
| Normalized Time |
211.233 |
Roshambo
| Metric |
Value |
| Fraction of Eqa |
0.380833 |
| Weighted Fraction of Eqa |
0.0529392 |
| Time (sec) |
60.4415 |
| Normalized Time |
7486.36 |
Shapley normal
| Metric |
Value |
| Fraction of Eqa |
1 |
| Weighted Fraction of Eqa |
0.1 |
| Time (sec) |
86.3627 |
| Normalized Time |
568652 |
Scarf 1
Initial Profile Rates
| Starting Type |
Fraction of Eqa |
Weighted Fraction |
| Uniform |
0.99136 |
0.99136 |
Compared to Other Methods
| Method |
Fraction of Eqa |
Time Ratio |
| Scarf 5 |
0.997329 |
0.423903 |
| Scarf 30 |
0.996585 |
0.198026 |
| Replicator Dynamics |
0.891199 |
0.95215 |
| Multiplicative Weights Bandit |
0.887203 |
0.0139134 |
| Multiplicative Weights Stoch |
0.882973 |
0.00766571 |
| Regret Minimization |
0.877238 |
6.60325 |
| Fictitious Play |
0.875705 |
0.106097 |
| Fictitious Play Long |
0.875311 |
0.00274097 |
| Multiplicative Weights Dist |
0.875229 |
0.0796415 |
| Regret Matching |
0.49575 |
0.0118121 |
By Game Type
Shapley hard
| Metric |
Value |
| Fraction of Eqa |
0.5 |
| Weighted Fraction of Eqa |
0.0555556 |
| Time (sec) |
0.000176191 |
| Normalized Time |
1.19002 |
Rbf
| Metric |
Value |
| Fraction of Eqa |
0.318822 |
| Weighted Fraction of Eqa |
0.073497 |
| Time (sec) |
3.64492 |
| Normalized Time |
222.512 |
Chicken
| Metric |
Value |
| Fraction of Eqa |
0.5375 |
| Weighted Fraction of Eqa |
0.0772222 |
| Time (sec) |
0.00536712 |
| Normalized Time |
1.02443 |
Zero sum
| Metric |
Value |
| Fraction of Eqa |
0.312472 |
| Weighted Fraction of Eqa |
0.0581924 |
| Time (sec) |
1.6169 |
| Normalized Time |
120.983 |
Mix
| Metric |
Value |
| Fraction of Eqa |
0.8 |
| Weighted Fraction of Eqa |
0.0954167 |
| Time (sec) |
0.00701667 |
| Normalized Time |
1.27411 |
Sineagg large
| Metric |
Value |
| Fraction of Eqa |
0.5 |
| Weighted Fraction of Eqa |
0.05 |
| Time (sec) |
1.5415 |
| Normalized Time |
68.206 |
Normagg small
| Metric |
Value |
| Fraction of Eqa |
0.46131 |
| Weighted Fraction of Eqa |
0.060965 |
| Time (sec) |
6.22083 |
| Normalized Time |
97.6506 |
Polyagg large
| Metric |
Value |
| Fraction of Eqa |
0.0178571 |
| Weighted Fraction of Eqa |
0.00595238 |
| Time (sec) |
0.0366912 |
| Normalized Time |
1.00589 |
Local effect
| Metric |
Value |
| Fraction of Eqa |
0.576389 |
| Weighted Fraction of Eqa |
0.106637 |
| Time (sec) |
0.189967 |
| Normalized Time |
6.64268 |
Congestion
| Metric |
Value |
| Fraction of Eqa |
0.453237 |
| Weighted Fraction of Eqa |
0.0686254 |
| Time (sec) |
1.63158 |
| Normalized Time |
84.8785 |
Covariant
| Metric |
Value |
| Fraction of Eqa |
0.153815 |
| Weighted Fraction of Eqa |
0.0336958 |
| Time (sec) |
7.43034 |
| Normalized Time |
228.94 |
Shapley easy
| Metric |
Value |
| Fraction of Eqa |
0.333333 |
| Weighted Fraction of Eqa |
0.0333333 |
| Time (sec) |
0.000173092 |
| Normalized Time |
1.13793 |
Prisoners
| Metric |
Value |
| Fraction of Eqa |
0.525 |
| Weighted Fraction of Eqa |
0.148674 |
| Time (sec) |
0.00917681 |
| Normalized Time |
1.53842 |
Normagg large
| Metric |
Value |
| Fraction of Eqa |
0 |
| Weighted Fraction of Eqa |
0 |
| Time (sec) |
60.0021 |
| Normalized Time |
52.0307 |
Sineagg small
| Metric |
Value |
| Fraction of Eqa |
0.239765 |
| Weighted Fraction of Eqa |
0.0375492 |
| Time (sec) |
0.687618 |
| Normalized Time |
4.66013 |
Hard
| Metric |
Value |
| Fraction of Eqa |
0.0588235 |
| Weighted Fraction of Eqa |
0.0196078 |
| Time (sec) |
0.482798 |
| Normalized Time |
30.9847 |
Gambit
| Metric |
Value |
| Fraction of Eqa |
0.0909091 |
| Weighted Fraction of Eqa |
0.030303 |
| Time (sec) |
0.0517638 |
| Normalized Time |
1.62985 |
Polymatrix
| Metric |
Value |
| Fraction of Eqa |
0.349647 |
| Weighted Fraction of Eqa |
0.0554777 |
| Time (sec) |
0.965104 |
| Normalized Time |
33.8239 |
Random
| Metric |
Value |
| Fraction of Eqa |
0.153897 |
| Weighted Fraction of Eqa |
0.0311547 |
| Time (sec) |
0.28036 |
| Normalized Time |
7.52566 |
Polyagg small
| Metric |
Value |
| Fraction of Eqa |
0.725548 |
| Weighted Fraction of Eqa |
0.0711423 |
| Time (sec) |
3.22001 |
| Normalized Time |
59.5486 |
Roshambo
| Metric |
Value |
| Fraction of Eqa |
0.464167 |
| Weighted Fraction of Eqa |
0.0838261 |
| Time (sec) |
0.0186894 |
| Normalized Time |
2.31489 |
Shapley normal
| Metric |
Value |
| Fraction of Eqa |
1 |
| Weighted Fraction of Eqa |
0.1 |
| Time (sec) |
0.000180483 |
| Normalized Time |
1.18838 |
Multiplicative Weights Bandit
Initial Profile Rates
| Starting Type |
Fraction of Eqa |
Weighted Fraction |
| Random |
0.115048 |
0.0483717 |
| Biased |
0.111203 |
0.038484 |
| Role biased |
0.106899 |
0.035918 |
| Uniform |
0.0676234 |
0.0180948 |
| Pure |
0.0614487 |
0.0178271 |
Compared to Other Methods
| Method |
Fraction of Eqa |
Time Ratio |
| Multiplicative Weights Stoch |
0.949729 |
0.550958 |
| Regret Matching |
0.148754 |
0.848975 |
| Replicator Dynamics |
0.14488 |
68.434 |
| Fictitious Play |
0.12992 |
7.6255 |
| Fictitious Play Long |
0.128606 |
0.197002 |
| Multiplicative Weights Dist |
0.112712 |
5.72408 |
| Regret Minimization |
0.108446 |
474.596 |
| Scarf 1 |
0.0709794 |
71.8731 |
| Scarf 5 |
0.0705277 |
30.4673 |
| Scarf 30 |
0.0702361 |
14.2327 |
By Game Type
Shapley hard
| Metric |
Value |
| Fraction of Eqa |
0 |
| Weighted Fraction of Eqa |
0 |
| Time (sec) |
37.7035 |
| Normalized Time |
254653 |
Rbf
| Metric |
Value |
| Fraction of Eqa |
0.433037 |
| Weighted Fraction of Eqa |
0.141674 |
| Time (sec) |
45.2721 |
| Normalized Time |
2763.74 |
Chicken
| Metric |
Value |
| Fraction of Eqa |
0.620833 |
| Weighted Fraction of Eqa |
0.175863 |
| Time (sec) |
5.04106 |
| Normalized Time |
962.191 |
Zero sum
| Metric |
Value |
| Fraction of Eqa |
0.207575 |
| Weighted Fraction of Eqa |
0.0814147 |
| Time (sec) |
61.3869 |
| Normalized Time |
4593.21 |
Mix
| Metric |
Value |
| Fraction of Eqa |
0.8 |
| Weighted Fraction of Eqa |
0.0832143 |
| Time (sec) |
2.10556 |
| Normalized Time |
382.334 |
Sineagg large
| Metric |
Value |
| Fraction of Eqa |
1 |
| Weighted Fraction of Eqa |
0.1125 |
| Time (sec) |
1.31277 |
| Normalized Time |
58.0854 |
Normagg small
| Metric |
Value |
| Fraction of Eqa |
0.611212 |
| Weighted Fraction of Eqa |
0.169466 |
| Time (sec) |
16.1931 |
| Normalized Time |
254.188 |
Polyagg large
| Metric |
Value |
| Fraction of Eqa |
0.125 |
| Weighted Fraction of Eqa |
0.0509141 |
| Time (sec) |
30.0901 |
| Normalized Time |
824.921 |
Local effect
| Metric |
Value |
| Fraction of Eqa |
0.704167 |
| Weighted Fraction of Eqa |
0.167062 |
| Time (sec) |
7.22445 |
| Normalized Time |
252.621 |
Congestion
| Metric |
Value |
| Fraction of Eqa |
0.617381 |
| Weighted Fraction of Eqa |
0.21696 |
| Time (sec) |
20.0386 |
| Normalized Time |
1042.45 |
Covariant
| Metric |
Value |
| Fraction of Eqa |
0.406794 |
| Weighted Fraction of Eqa |
0.246532 |
| Time (sec) |
75.293 |
| Normalized Time |
2319.89 |
Shapley easy
| Metric |
Value |
| Fraction of Eqa |
0.333333 |
| Weighted Fraction of Eqa |
0.333333 |
| Time (sec) |
42.2664 |
| Normalized Time |
277865 |
Prisoners
| Metric |
Value |
| Fraction of Eqa |
0.566667 |
| Weighted Fraction of Eqa |
0.0717992 |
| Time (sec) |
6.16585 |
| Normalized Time |
1033.65 |
Normagg large
| Metric |
Value |
| Fraction of Eqa |
1 |
| Weighted Fraction of Eqa |
0.142857 |
| Time (sec) |
3.99676 |
| Normalized Time |
3.46578 |
Sineagg small
| Metric |
Value |
| Fraction of Eqa |
0.434973 |
| Weighted Fraction of Eqa |
0.152492 |
| Time (sec) |
15.3441 |
| Normalized Time |
103.99 |
Hard
| Metric |
Value |
| Fraction of Eqa |
0.411765 |
| Weighted Fraction of Eqa |
0.411765 |
| Time (sec) |
63.0848 |
| Normalized Time |
4048.62 |
Gambit
| Metric |
Value |
| Fraction of Eqa |
0.363636 |
| Weighted Fraction of Eqa |
0.0454545 |
| Time (sec) |
6.41273 |
| Normalized Time |
201.913 |
Polymatrix
| Metric |
Value |
| Fraction of Eqa |
0.725753 |
| Weighted Fraction of Eqa |
0.354831 |
| Time (sec) |
64.5869 |
| Normalized Time |
2263.57 |
Random
| Metric |
Value |
| Fraction of Eqa |
0.28421 |
| Weighted Fraction of Eqa |
0.108513 |
| Time (sec) |
41.0832 |
| Normalized Time |
1102.79 |
Polyagg small
| Metric |
Value |
| Fraction of Eqa |
0.781501 |
| Weighted Fraction of Eqa |
0.075289 |
| Time (sec) |
12.4333 |
| Normalized Time |
229.932 |
Roshambo
| Metric |
Value |
| Fraction of Eqa |
0.26 |
| Weighted Fraction of Eqa |
0.116534 |
| Time (sec) |
41.2253 |
| Normalized Time |
5106.21 |
Shapley normal
| Metric |
Value |
| Fraction of Eqa |
0 |
| Weighted Fraction of Eqa |
0 |
| Time (sec) |
42.6458 |
| Normalized Time |
280800 |
Regret Minimization
Initial Profile Rates
| Starting Type |
Fraction of Eqa |
Weighted Fraction |
| Biased |
0.913189 |
0.238037 |
| Random |
0.909974 |
0.238086 |
| Role biased |
0.905214 |
0.231136 |
| Uniform |
0.89146 |
0.222137 |
| Pure |
0.105826 |
0.0560536 |
Compared to Other Methods
| Method |
Fraction of Eqa |
Time Ratio |
| Replicator Dynamics |
0.964337 |
0.144194 |
| Fictitious Play |
0.954297 |
0.0160674 |
| Fictitious Play Long |
0.953767 |
0.000415095 |
| Multiplicative Weights Stoch |
0.938871 |
0.0011609 |
| Multiplicative Weights Dist |
0.937148 |
0.012061 |
| Multiplicative Weights Bandit |
0.929212 |
0.00210706 |
| Scarf 1 |
0.879939 |
0.151441 |
| Scarf 5 |
0.879487 |
0.0641962 |
| Scarf 30 |
0.879196 |
0.0299891 |
| Regret Matching |
0.543883 |
0.00178884 |
By Game Type
Shapley hard
| Metric |
Value |
| Fraction of Eqa |
0.5 |
| Weighted Fraction of Eqa |
0.0555556 |
| Time (sec) |
0.0068413 |
| Normalized Time |
46.2069 |
Rbf
| Metric |
Value |
| Fraction of Eqa |
0.520615 |
| Weighted Fraction of Eqa |
0.201114 |
| Time (sec) |
0.0163808 |
| Normalized Time |
1 |
Chicken
| Metric |
Value |
| Fraction of Eqa |
0.5375 |
| Weighted Fraction of Eqa |
0.0612103 |
| Time (sec) |
0.00654014 |
| Normalized Time |
1.24832 |
Zero sum
| Metric |
Value |
| Fraction of Eqa |
0.276208 |
| Weighted Fraction of Eqa |
0.0962257 |
| Time (sec) |
0.0133647 |
| Normalized Time |
1 |
Mix
| Metric |
Value |
| Fraction of Eqa |
0.8 |
| Weighted Fraction of Eqa |
0.0832143 |
| Time (sec) |
0.0055071 |
| Normalized Time |
1 |
Sineagg large
| Metric |
Value |
| Fraction of Eqa |
0.5 |
| Weighted Fraction of Eqa |
0.0625 |
| Time (sec) |
0.0226006 |
| Normalized Time |
1 |
Normagg small
| Metric |
Value |
| Fraction of Eqa |
0.658149 |
| Weighted Fraction of Eqa |
0.205024 |
| Time (sec) |
0.063705 |
| Normalized Time |
1 |
Polyagg large
| Metric |
Value |
| Fraction of Eqa |
0.678571 |
| Weighted Fraction of Eqa |
0.662946 |
| Time (sec) |
82.0281 |
| Normalized Time |
2248.8 |
Local effect
| Metric |
Value |
| Fraction of Eqa |
0.613889 |
| Weighted Fraction of Eqa |
0.162895 |
| Time (sec) |
0.028598 |
| Normalized Time |
1 |
Congestion
| Metric |
Value |
| Fraction of Eqa |
0.456218 |
| Weighted Fraction of Eqa |
0.0836675 |
| Time (sec) |
0.0192225 |
| Normalized Time |
1 |
Covariant
| Metric |
Value |
| Fraction of Eqa |
0.283948 |
| Weighted Fraction of Eqa |
0.130335 |
| Time (sec) |
0.0324554 |
| Normalized Time |
1 |
Shapley easy
| Metric |
Value |
| Fraction of Eqa |
0.333333 |
| Weighted Fraction of Eqa |
0.0333333 |
| Time (sec) |
0.00646641 |
| Normalized Time |
42.5111 |
Prisoners
| Metric |
Value |
| Fraction of Eqa |
0.558333 |
| Weighted Fraction of Eqa |
0.0822159 |
| Time (sec) |
0.0059651 |
| Normalized Time |
1 |
Normagg large
| Metric |
Value |
| Fraction of Eqa |
0 |
| Weighted Fraction of Eqa |
0 |
| Time (sec) |
1.15321 |
| Normalized Time |
1 |
Sineagg small
| Metric |
Value |
| Fraction of Eqa |
0.674401 |
| Weighted Fraction of Eqa |
0.371161 |
| Time (sec) |
0.147553 |
| Normalized Time |
1 |
Hard
| Metric |
Value |
| Fraction of Eqa |
0.294118 |
| Weighted Fraction of Eqa |
0.294118 |
| Time (sec) |
0.0155818 |
| Normalized Time |
1 |
Gambit
| Metric |
Value |
| Fraction of Eqa |
0.909091 |
| Weighted Fraction of Eqa |
0.518182 |
| Time (sec) |
0.0317599 |
| Normalized Time |
1 |
Polymatrix
| Metric |
Value |
| Fraction of Eqa |
0.502937 |
| Weighted Fraction of Eqa |
0.162104 |
| Time (sec) |
0.0285332 |
| Normalized Time |
1 |
Random
| Metric |
Value |
| Fraction of Eqa |
0.64973 |
| Weighted Fraction of Eqa |
0.435108 |
| Time (sec) |
0.0372538 |
| Normalized Time |
1 |
Polyagg small
| Metric |
Value |
| Fraction of Eqa |
0.949546 |
| Weighted Fraction of Eqa |
0.24158 |
| Time (sec) |
0.0540736 |
| Normalized Time |
1 |
Roshambo
| Metric |
Value |
| Fraction of Eqa |
0.4975 |
| Weighted Fraction of Eqa |
0.100479 |
| Time (sec) |
0.00807355 |
| Normalized Time |
1 |
Shapley normal
| Metric |
Value |
| Fraction of Eqa |
1 |
| Weighted Fraction of Eqa |
0.1 |
| Time (sec) |
0.00644888 |
| Normalized Time |
42.4624 |
Scarf 5
Initial Profile Rates
| Starting Type |
Fraction of Eqa |
Weighted Fraction |
| Uniform |
0.994031 |
0.994031 |
Compared to Other Methods
| Method |
Fraction of Eqa |
Time Ratio |
| Scarf 1 |
1 |
2.35903 |
| Scarf 30 |
0.999257 |
0.467148 |
| Replicator Dynamics |
0.89197 |
2.24615 |
| Multiplicative Weights Bandit |
0.88933 |
0.0328221 |
| Multiplicative Weights Stoch |
0.8851 |
0.0180836 |
| Regret Minimization |
0.879302 |
15.5772 |
| Fictitious Play |
0.877925 |
0.250285 |
| Fictitious Play Long |
0.877531 |
0.00646603 |
| Multiplicative Weights Dist |
0.875799 |
0.187877 |
| Regret Matching |
0.49797 |
0.0278652 |
By Game Type
Shapley hard
| Metric |
Value |
| Fraction of Eqa |
0.5 |
| Weighted Fraction of Eqa |
0.0555556 |
| Time (sec) |
0.000164747 |
| Normalized Time |
1.11272 |
Rbf
| Metric |
Value |
| Fraction of Eqa |
0.331322 |
| Weighted Fraction of Eqa |
0.074747 |
| Time (sec) |
10.8419 |
| Normalized Time |
661.87 |
Chicken
| Metric |
Value |
| Fraction of Eqa |
0.5375 |
| Weighted Fraction of Eqa |
0.0772222 |
| Time (sec) |
0.00525931 |
| Normalized Time |
1.00385 |
Zero sum
| Metric |
Value |
| Fraction of Eqa |
0.312472 |
| Weighted Fraction of Eqa |
0.0581924 |
| Time (sec) |
1.6158 |
| Normalized Time |
120.9 |
Mix
| Metric |
Value |
| Fraction of Eqa |
0.8 |
| Weighted Fraction of Eqa |
0.0954167 |
| Time (sec) |
0.00688252 |
| Normalized Time |
1.24975 |
Sineagg large
| Metric |
Value |
| Fraction of Eqa |
0.5 |
| Weighted Fraction of Eqa |
0.05 |
| Time (sec) |
1.53476 |
| Normalized Time |
67.9077 |
Normagg small
| Metric |
Value |
| Fraction of Eqa |
0.464881 |
| Weighted Fraction of Eqa |
0.0627507 |
| Time (sec) |
26.1881 |
| Normalized Time |
411.084 |
Polyagg large
| Metric |
Value |
| Fraction of Eqa |
0.0178571 |
| Weighted Fraction of Eqa |
0.00595238 |
| Time (sec) |
0.0364764 |
| Normalized Time |
1 |
Local effect
| Metric |
Value |
| Fraction of Eqa |
0.576389 |
| Weighted Fraction of Eqa |
0.106637 |
| Time (sec) |
0.189879 |
| Normalized Time |
6.63957 |
Congestion
| Metric |
Value |
| Fraction of Eqa |
0.453237 |
| Weighted Fraction of Eqa |
0.0686254 |
| Time (sec) |
1.63183 |
| Normalized Time |
84.8917 |
Covariant
| Metric |
Value |
| Fraction of Eqa |
0.153815 |
| Weighted Fraction of Eqa |
0.0336958 |
| Time (sec) |
19.4318 |
| Normalized Time |
598.722 |
Shapley easy
| Metric |
Value |
| Fraction of Eqa |
0.333333 |
| Weighted Fraction of Eqa |
0.0333333 |
| Time (sec) |
0.000152349 |
| Normalized Time |
1.00157 |
Prisoners
| Metric |
Value |
| Fraction of Eqa |
0.525 |
| Weighted Fraction of Eqa |
0.148674 |
| Time (sec) |
0.00904394 |
| Normalized Time |
1.51614 |
Normagg large
| Metric |
Value |
| Fraction of Eqa |
0 |
| Weighted Fraction of Eqa |
0 |
| Time (sec) |
300.002 |
| Normalized Time |
260.146 |
Sineagg small
| Metric |
Value |
| Fraction of Eqa |
0.239765 |
| Weighted Fraction of Eqa |
0.0375492 |
| Time (sec) |
0.687357 |
| Normalized Time |
4.65837 |
Hard
| Metric |
Value |
| Fraction of Eqa |
0.0588235 |
| Weighted Fraction of Eqa |
0.0196078 |
| Time (sec) |
0.501494 |
| Normalized Time |
32.1846 |
Gambit
| Metric |
Value |
| Fraction of Eqa |
0.0909091 |
| Weighted Fraction of Eqa |
0.030303 |
| Time (sec) |
0.0513351 |
| Normalized Time |
1.61635 |
Polymatrix
| Metric |
Value |
| Fraction of Eqa |
0.349647 |
| Weighted Fraction of Eqa |
0.0554777 |
| Time (sec) |
0.965855 |
| Normalized Time |
33.8502 |
Random
| Metric |
Value |
| Fraction of Eqa |
0.153897 |
| Weighted Fraction of Eqa |
0.0311547 |
| Time (sec) |
0.2803 |
| Normalized Time |
7.52407 |
Polyagg small
| Metric |
Value |
| Fraction of Eqa |
0.732691 |
| Weighted Fraction of Eqa |
0.0718566 |
| Time (sec) |
6.71802 |
| Normalized Time |
124.238 |
Roshambo
| Metric |
Value |
| Fraction of Eqa |
0.464167 |
| Weighted Fraction of Eqa |
0.0838261 |
| Time (sec) |
0.0185103 |
| Normalized Time |
2.29271 |
Shapley normal
| Metric |
Value |
| Fraction of Eqa |
1 |
| Weighted Fraction of Eqa |
0.1 |
| Time (sec) |
0.000156879 |
| Normalized Time |
1.03297 |


{% extends "sentry/bases/twofactor_settings.html" %} {% load crispy_forms_tags %} {% load i18n %} {% load sentry_helpers %} {% block twofactor_title %}{% trans "Enrollment: " %} {{ auth.name }}{% endblock %} {% block twofactor_body %}
Enter your phone number and then provide the confirmation code we send you to active it.
{% endblock %}


If you want to update the country tool data via a CSV file, go here.
Upload a csv file in which the country names (or country codes) are located under the header name Country or country.
If you select 'OUR WORLD IN DATA NAME' as the output type, the file you get back from the tool will have these standardized names listed on the
newly added column under the header owid_name.
[For a detailed explanation of the tool see here.]


Specify the HTTPS URL of the GitHub Organization / User Account and repository.
GitHub examples:
- https://github.com/jenkinsci/github-branch-source-plugin
- https://github.com/jenkinsci/github-branch-source-plugin.git
GitHub Enterprise examples:
- https://myccompany.github.com/jenkinsci/github-branch-source-plugin
- https://myccompany.github.com/jenkinsci/github-branch-source-plugin.git


extreme knowledge climate events fishers change fish fishing {border-style impacts local px;} #ffffff;} climatic ecological fisheries fishers’ Climate drought fishery years Amazon changes information study commercial cultural flood Fishers’ analysis consensus eventos perceptions social used Ethnobiol level sources water Batista Brazil Central Ethnomed different environmental experience extremos foram identify levels px; research {font-family (Fig. Change Extreme Fig. Fish However, droughts educational events. font-size increase perceived por Brazilian Ecol Fishers Manaus University adaptation communities como conservation data double; event resources scientific traditional use ./s--- Alves Edited Knowledge New Research Silva Traditional applied artisanal based border-color border-width case climáticos conditions dynamics events, familiar floods great identification main management meio populations resource resources. responses solid; studies using valuable {font-style {font-weight ./S- ./j.-...x :–. Amazonian Berkes Biol Conserv Data Federal Fisheries Furthermore, Manaus, Mar Methods Moreover, Press; Sci Soc able aquatic areas bold change. classified consequences courier; development differences dos effects em; entre fishers. frequency freshwater future global high hydrological impactos including influence integrative manuscript. memory natural pt;} quotes) related response results river similar study, sustainable test understanding


I'm Matt, a software developer out of Oklahoma City. I just finished up a Computer Science degree at the University of Central Oklahoma in December of 2015. Currently, I'm focusing on developing full stack web applications, mainly concentrating on learning advanced Javascript and working the MEAN (Mongo, Express, Angular, Node) stack. I'm hoping to join a team where I can focus on producing professional level results for applications of any kind while also continuing to grow my skills as a developer.
I am constantly trying to learn and create new things. I live for the A-HA! moment when everything begins to click. I found my passion in programming knowing that learning one thing can open the possibility of learning countless others.
I try to work hard and I try to work smart. I love tackling problems of any kind, and I've always been the type of person to want to do things in the best way I can. I've always had my own way of doing things, and I crave seeing my visions through to something tangible.
Whether I was playing in bands, playing sports, coding in groups, managing retail teams... I've always loved being a part of a team. Throughout my life, I've learned to successfully play several roles. I am results oriented and nothing makes me happier than seeing my teams exceed expectations.
NBAngular is a website that gathers statistics from NBA.com and presents them in different formats.
Check It OutReel-Info.com is an Angular application that requests movie data from multiple public APIs and displays it in an easy-to-digest format.
Check It OutThis is a quick website I put together to demo my first major iOS application. Legend of You was an experiment in creating a gamified task manager created for an independent study of iOS programming at UCO. I think I'll revisit the idea again soon.
Check It OutThis is a simple calculator application written in JavaScript. It's my answer to the Zipline Challenge on Free Code Camp
Check It OutClassmate is an Android Application developed to satisfy the requirements of UCO's Computer Science Capstone, Software Design and Development. I worked with a 9 member team over the 2015 Fall semester to create this application. It was developed to benefit the classroom by connecting students and professors. You can checkout my contributions HERE.
Check It OutinQUIZITive is a quick scaffolding for creating multiple choice quizzes for the iPad. I created it to help me quickly customize quizzes while studying for school and interviews.
Check It OutAnother application inpsired by a challenge on Free Code Camp.
Check It OutThis application takes the current time and produces a hex value. That value is updated as the background color. I remember seeing this done a while ago, and it stuck in my mind as something that is simple yet beautiful. One afternoon, I decided I'd recreate it.
Check It OutThis website is the first dollar plaque you see hung up behind the counter at your favorite local restaurant. It was the first website I ever published and I think still does a great job representing a Norman, OK landmark.
Check It OutA C++ AI Tic Tac Toe Client that competed against other programs created by other teams in UCO's Spring 2015 Software Engineering course. The project provided experience in Software Design and teamwork.
An e-commerce site designed for a fake independent record label. The site was created for the University of Central Oklahoma's Advanced Visual Programming course. The project introduced development with Microsoft's ASP.NET. The website required major website functionality including user authorization, role access, and basic CRUD functions for purchasing items that are tracked by a SQL database.
A clone of the classic Brick Breaker game created in Java. The project introduced Object Oriented Programming fundamentals, GUI development with Java's Swing library, and a basic introduction to game mechanics.


Architectural & CommercialBuilding Solutions.
Residential Solutions
Post Frame &Agricultural Solutions.
Recreational Vehicle &Transportation Solutions.


My name is Kanmanidevi Ranganathan
And I'm Senior Business Analyst.
Energetic and high performing professional with 9+ years of strong experience in Business Analysis in various domains.
Proven ability in interpreting and implementing complex business requirements.
Excellent communicator with innovative problem solving skills.


Last updated: 2021-09-15
Checks: 7 0
Knit directory: myTidyTuesday/
This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
The command set.seed(20210907)
was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.
The results in this page were generated with repository version 58a0374. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.
Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish
or wflow_git_commit
). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:
Ignored files:
Ignored: .Rhistory
Ignored: .Rproj.user/
Ignored: catboost_info/
Ignored: data/2021-09-08/
Ignored: data/acs_poverty.rds
Ignored: data/fmhpi.rds
Ignored: data/grainstocks.rds
Ignored: data/hike_data.rds
Ignored: data/us_states.rds
Ignored: data/us_states_hexgrid.geojson
Ignored: data/weatherstats_toronto_daily.csv
Untracked files:
Untracked: code/work list batch targets.R
Untracked: figure/
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were made to the R Markdown (analysis/GDPRfines.Rmd
) and HTML (docs/GDPRfines.html
) files. If you’ve configured a remote Git repository (see ?wflow_git_remote
), click on the hyperlinks in the table below to view the files as they were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 58a0374 | opus1993 | 2021-09-15 | remove the superfluous raw data table |
| html | 764c09b | opus1993 | 2021-09-15 | Build site. |
| Rmd | 92bc445 | opus1993 | 2021-09-15 | add tweet attribution and new viridis color scheme |
| Rmd | 02009d5 | opus1993 | 2021-09-15 | add tweet attribution and new viridis color scheme |
This week’s #TidyTuesday
dataset is on EU GDPR violations.
In addition, R version 4.0.0 Arbor Day was just released. I am re-installing packages as-required while going through projects like this one.
The R Studio team recently launched tidymodels.org
, a new central location with resources and documentation for tidymodels packages. Check out the official blog post for more details.
Julia Silge published a great blog post with another screencast demonstrating how to use tidymodels
. She includes a good video for folks getting started with tidymodels
.
Our modeling goal here is to understand what kind of GDPR violations are associated with higher fines in the #TidyTuesday dataset for this week. Before we start, what are the most common GDPR articles actually about? Roughly speaking:
Let’s get started by looking at the data on violations.
gdpr_raw <- readr::read_tsv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-04-21/gdpr_violations.tsv")
How are the fines distributed?
gdpr_raw %>%
ggplot(aes(price + 1)) +
geom_histogram(fill = "midnightblue", alpha = 0.7, bins = 40) +
scale_x_log10(labels = scales::dollar_format(prefix = "€")) +
labs(
title = "EU General Data Protection Regulation 2016/679 (GDPR) Fines",
subtitle = "Scraped from https://www.privacyaffairs.com/gdpr-fines/",
x = "GDPR fine (EUR)", y = "Number of GDPR violations",
caption = "@Jim_Gruman | #TidyTuesday"
)
Some of the violations were fined zero EUR. Let’s make a one-article-per-row version of this dataset.
gdpr_tidy <- gdpr_raw %>%
transmute(id,
price,
country = name,
article_violated,
articles = str_extract_all(article_violated, "Art.[:digit:]+|Art. [:digit:]+")
) %>%
mutate(total_articles = map_int(articles, length)) %>%
unnest(articles) %>%
add_count(articles) %>%
filter(n > 10) %>%
select(-n)
gdpr_tidy %>%
head() %>%
knitr::kable("html") %>%
kableExtra::kable_styling(
bootstrap_options = c("striped", "condensed"),
full_width = F, fixed_thead = T
)
| id | price | country | article_violated | articles | total_articles |
|---|---|---|---|---|---|
| 2 | 2500 | Romania | Art. 12 GDPR|Art. 13 GDPR|Art. 5 (1) c) GDPR|Art. 6 GDPR | Art. 13 | 4 |
| 2 | 2500 | Romania | Art. 12 GDPR|Art. 13 GDPR|Art. 5 (1) c) GDPR|Art. 6 GDPR | Art. 5 | 4 |
| 2 | 2500 | Romania | Art. 12 GDPR|Art. 13 GDPR|Art. 5 (1) c) GDPR|Art. 6 GDPR | Art. 6 | 4 |
| 3 | 60000 | Spain | Art. 5 GDPR|Art. 6 GDPR | Art. 5 | 2 |
| 3 | 60000 | Spain | Art. 5 GDPR|Art. 6 GDPR | Art. 6 | 2 |
| 5 | 150000 | Romania | Art. 32 GDPR | Art. 32 | 1 |
How are the fines distributed by article?
gdpr_tidy %>%
mutate(
articles = str_replace_all(articles, "Art. ", "Article "),
articles = fct_reorder(articles, price)
) %>%
ggplot(aes(articles, price + 1, color = articles, fill = articles)) +
geom_boxplot(alpha = 0.2, outlier.colour = NA, show.legend = FALSE) +
geom_quasirandom(show.legend = FALSE) +
scale_y_log10(labels = scales::dollar_format(prefix = "€")) +
labs(
x = NULL, y = "GDPR fine (EUR)",
title = "GDPR Fines Levied, by Article",
subtitle = "For 250 violations in 25 countries",
caption = "@Jim_Gruman | #TidyTuesday"
)
Now let’s create a dataset for predictive modeling.
gdpr_violations <- gdpr_tidy %>%
mutate(value = 1) %>%
select(-article_violated) %>%
pivot_wider(
names_from = articles, values_from = value,
values_fn = list(value = max), values_fill = list(value = 0)
) %>%
janitor::clean_names()
gdpr_violations %>%
head() %>%
knitr::kable("html") %>%
kableExtra::kable_styling(
bootstrap_options = c("striped", "condensed"),
full_width = F, fixed_thead = T
)
| id | price | country | total_articles | art_13 | art_5 | art_6 | art_32 | art_15 |
|---|---|---|---|---|---|---|---|---|
| 2 | 2500 | Romania | 4 | 1 | 1 | 1 | 0 | 0 |
| 3 | 60000 | Spain | 2 | 0 | 1 | 1 | 0 | 0 |
| 5 | 150000 | Romania | 1 | 0 | 0 | 0 | 1 | 0 |
| 6 | 20000 | Romania | 2 | 0 | 0 | 0 | 1 | 0 |
| 7 | 200000 | Greece | 2 | 0 | 1 | 0 | 0 | 0 |
| 9 | 30000 | Spain | 2 | 0 | 1 | 1 | 0 | 0 |
Let’s preprocess our data to get it ready for modeling.
gdpr_rec <- recipe(price ~ ., data = gdpr_violations) %>%
update_role(id, new_role = "id") %>%
step_log(price, base = 10, offset = 1, skip = TRUE) %>%
step_other(country, other = "Other") %>%
step_dummy(all_nominal_predictors()) %>%
step_zv(all_predictors())
gdpr_prep <- prep(gdpr_rec)
gdpr_prep
Data Recipe
Inputs:
role #variables
id 1
outcome 1
predictor 7
Training data contained 219 data points and no missing data.
Operations:
Log transformation on price [trained]
Collapsing factor levels for country [trained]
Dummy variables from country [trained]
Zero variance filter removed no terms [trained]
Let’s walk through the steps in this recipe.
recipe()
what our model is going to be (using a formula here) and what data we are using.id
, since this variable is not a predictor or outcome but I would like to keep it in the data for convenience.price
, the amount of the fine)."Other"
category.Before using prep()
these steps have been defined but not actually run or implemented. The prep()
function is where everything gets evaluated.
Now it’s time to specify our model. I am using a workflow()
in this example for convenience; these are objects that can help you manage modeling pipelines more easily, with pieces that fit together like Lego blocks. This workflow()
contains both the recipe and the model (a straightforward Ordinary Least Squares linear regression).
gdpr_wf <- workflow() %>%
add_recipe(gdpr_rec) %>%
add_model(linear_reg() %>%
set_engine("lm"))
gdpr_wf
== Workflow ====================================================================
Preprocessor: Recipe
Model: linear_reg()
-- Preprocessor ----------------------------------------------------------------
4 Recipe Steps
* step_log()
* step_other()
* step_dummy()
* step_zv()
-- Model -----------------------------------------------------------------------
Linear Regression Model Specification (regression)
Computational engine: lm
You can fit()
a workflow, much like you can fit a model, and then you can pull out the fit object and tidy()
it to work with the estimates of the linear coefficients.
gdpr_fit <- gdpr_wf %>%
fit(data = gdpr_violations)
extract_fit_engine(gdpr_fit) %>%
tidy() %>%
arrange(desc(estimate))
# A tibble: 13 x 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 3.77 0.409 9.21 3.82e-17
2 total_articles 0.480 0.166 2.90 4.20e- 3
3 country_Spain 0.430 0.364 1.18 2.40e- 1
4 country_Other 0.234 0.355 0.660 5.10e- 1
5 country_Germany 0.0597 0.419 0.143 8.87e- 1
6 art_32 -0.153 0.315 -0.487 6.27e- 1
7 country_Hungary -0.155 0.479 -0.324 7.46e- 1
8 country_Romania -0.346 0.433 -0.799 4.25e- 1
9 art_5 -0.419 0.283 -1.48 1.40e- 1
10 art_6 -0.560 0.295 -1.90 5.91e- 2
11 country_Czech.Republic -0.650 0.467 -1.39 1.66e- 1
12 art_13 -0.763 0.407 -1.87 6.27e- 2
13 art_15 -1.57 0.465 -3.37 8.97e- 4
GDPR violations of more than one article have higher fines.
Lots of those coefficients have big p-values (for example, all the countries) but I think the best way to understand these results will be to visualize some predictions. You can predict on new data in tidymodels with either a model or a workflow()
.
Let’s create some example new data that we are interested in.
new_gdpr <- crossing(
country = "Other",
art_5 = 0:1,
art_6 = 0:1,
art_13 = 0:1,
art_15 = 0:1,
art_32 = 0:1
) %>%
mutate(
id = row_number(),
total_articles = art_5 + art_6 + art_13 + art_15 + art_32
)
new_gdpr %>%
head() %>%
knitr::kable("html") %>%
kableExtra::kable_styling(
bootstrap_options = c("striped", "condensed"),
full_width = F, fixed_thead = T
)
| country | art_5 | art_6 | art_13 | art_15 | art_32 | id | total_articles |
|---|---|---|---|---|---|---|---|
| Other | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
| Other | 0 | 0 | 0 | 0 | 1 | 2 | 1 |
| Other | 0 | 0 | 0 | 1 | 0 | 3 | 1 |
| Other | 0 | 0 | 0 | 1 | 1 | 4 | 2 |
| Other | 0 | 0 | 1 | 0 | 0 | 5 | 1 |
| Other | 0 | 0 | 1 | 0 | 1 | 6 | 2 |
Let’s find both the mean predictions and the confidence intervals.
mean_pred <- predict(gdpr_fit,
new_data = new_gdpr
)
conf_int_pred <- predict(gdpr_fit,
new_data = new_gdpr,
type = "conf_int"
)
gdpr_res <- new_gdpr %>%
bind_cols(mean_pred) %>%
bind_cols(conf_int_pred)
gdpr_res %>%
head() %>%
knitr::kable("html") %>%
kableExtra::kable_styling(
bootstrap_options = c("striped", "condensed"),
full_width = F, fixed_thead = T
)
| country | art_5 | art_6 | art_13 | art_15 | art_32 | id | total_articles | .pred | .pred_lower | .pred_upper |
|---|---|---|---|---|---|---|---|---|---|---|
| Other | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 4.000446 | 3.410428 | 4.590464 |
| Other | 0 | 0 | 0 | 0 | 1 | 2 | 1 | 4.326841 | 3.922444 | 4.731237 |
| Other | 0 | 0 | 0 | 1 | 0 | 3 | 1 | 2.912359 | 2.245405 | 3.579314 |
| Other | 0 | 0 | 0 | 1 | 1 | 4 | 2 | 3.238753 | 2.407347 | 4.070160 |
| Other | 0 | 0 | 1 | 0 | 0 | 5 | 1 | 3.717506 | 2.992813 | 4.442199 |
| Other | 0 | 0 | 1 | 0 | 1 | 6 | 2 | 4.043900 | 3.336772 | 4.751029 |
There are lots of things we can do wtih these results! For example, what are the predicted GDPR fines for violations of each article type (violating only one article)?
gdpr_res %>%
filter(total_articles == 1) %>%
pivot_longer(art_5:art_32) %>%
filter(value > 0) %>%
mutate(
name = str_replace_all(name, "art_", "Article "),
name = fct_reorder(name, .pred)
) %>%
ggplot(aes(name, 10^.pred, color = name)) +
geom_point(size = 3.5) +
geom_errorbar(aes(
ymin = 10^.pred_lower,
ymax = 10^.pred_upper
),
width = 0.2, alpha = 0.7
) +
labs(
x = NULL, y = "Increase in fine (EUR)",
title = "Predicted Fine for each Type of GDPR Article Violation",
subtitle = "Modeling based on 250 violations in 25 countries",
caption = "@Jim_Gruman | #TidyTuesday"
) +
scale_y_log10(labels = scales::dollar_format(prefix = "€", accuracy = 1))
We can see here that violations such as data breaches have higher fines on average than violations about rights of access.
David Sjoberg built this incredible chart
tweetrmd::include_tweet("https://twitter.com/davsjob/status/1256293020791685123")
😱 I thought it might be a neat idea to combine a map, rank and a bar chart in one plot for #TidyTuesday. It was messy but fun.
— David Sjöberg (@davsjob) May 1, 2020
Code: https://t.co/IouFXdvYjf #rstats #ggplot #dataviz pic.twitter.com/wyHsUGpwKC
gdpr_df <- gdpr_raw %>%
group_by(name) %>%
summarise(
price = sum(price),
.groups = "drop"
)
sdf <- rnaturalearthdata::countries50 %>%
st_as_sf() %>%
st_make_valid() %>%
st_crop(xmin = -24, xmax = 31, ymin = 33, ymax = 73) %>%
filter(admin %in% gdpr_df$name) %>%
left_join(gdpr_df, by = c("admin" = "name")) %>%
mutate(
price_cap = price / pop_est,
admin = case_when(
admin == "United Kingdom" ~ "UK",
admin == "Czech Republic" ~ "Czech",
TRUE ~ admin
)
)
ranking <- st_geometry(sdf) %>%
st_point_on_surface() %>%
st_coordinates() %>%
as_tibble() %>%
bind_cols(tibble(
fine_cap = BBmisc::normalize(rank(sdf$price_cap), range = c(40.12161, 66.12161), method = "range"),
country = sdf$admin,
xend = 60,
x_axis_start = xend + 10,
fine_cap_x = BBmisc::normalize(sdf$price_cap, range = c(first(x_axis_start), 100), method = "range"),
val_txt = paste0(format(sdf$price_cap, digits = 0, nsmall = 2)),
val_txt2 = if_else(country == "Austria", paste0(val_txt, "€ per capita"), val_txt)
))
sdf <- sdf %>%
bind_cols(ranking %>% select(fine_cap))
ggplot() +
geom_sf(
data = sdf,
size = .3,
fill = "transparent",
color = "gray17"
) +
# Sigmoid from country to start of barchart
ggbump::geom_sigmoid(
data = ranking,
aes(
x = X,
y = Y,
xend = x_axis_start - .2,
yend = fine_cap,
group = country,
color = fine_cap
),
alpha = .6,
smooth = 10,
size = 1
) +
# Line from xstart to value
geom_segment(
data = ranking,
aes(
x = x_axis_start,
y = fine_cap,
xend = fine_cap_x,
yend = fine_cap,
color = fine_cap
),
alpha = .6,
size = 1,
lineend = "round"
) +
# Y axis - black line
geom_segment(
data = ranking,
aes(
x = x_axis_start,
y = 40,
xend = x_axis_start,
yend = 67
),
alpha = .6,
size = 1.3,
color = "black"
) +
# dot on centroid of country in map
geom_point(
data = ranking,
aes(x = X, y = Y, color = fine_cap),
size = 2
) +
# Country text
geom_text(
data = ranking,
aes(
x = x_axis_start - .5,
y = fine_cap,
label = country,
color = fine_cap
),
hjust = 1,
size = 2.5,
nudge_y = .5
) +
# Value text
geom_text(
data = ranking,
aes(
x = fine_cap_x,
y = fine_cap,
label = val_txt2,
color = fine_cap
),
hjust = 0,
size = 2,
nudge_x = .4
) +
coord_sf(clip = "off") +
scale_fill_viridis_c(option = "H") +
scale_color_viridis_c(option = "H") +
theme_void() +
labs(
title = "GDPR fines per capita",
subtitle = str_wrap(
"The General Data Protection Regulation (EU) 2016/679 (GDPR) is a regulation in EU law on data protection and privacy in the European Union (EU) and the European Economic Area (EEA).",
100
),
caption = "Source: TidyTuesday & Wikipedia"
) +
theme(
plot.margin = unit(c(0.5, 1, 0.5, 0.5), "cm"),
legend.position = "none",
plot.background = element_rect(fill = "black"),
plot.caption = element_text(color = "white"),
plot.title = element_text(
color = "white",
size = 16,
family = "Helvetica",
face = "bold"
),
plot.subtitle = element_text(color = "white", size = 8)
)
sessionInfo()
R version 4.1.1 (2021-08-10)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19043)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.1252
[2] LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] rvest_1.0.1 ggtext_0.1.1 sf_1.0-2
[4] rnaturalearth_0.1.0 ggbeeswarm_0.6.0 yardstick_0.0.8
[7] workflowsets_0.1.0 workflows_0.2.3 tune_0.1.6
[10] rsample_0.1.0 recipes_0.1.16 parsnip_<IP_ADDRESS>0
[13] modeldata_0.1.1 infer_1.0.0 dials_<IP_ADDRESS>00
[16] scales_1.1.1 broom_0.7.9 tidymodels_0.1.3
[19] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7
[22] purrr_0.3.4 readr_2.0.1 tidyr_1.1.3
[25] tibble_3.1.4 ggplot2_3.3.5 tidyverse_1.3.1
[28] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] utf8_1.2.2 R.utils_2.10.1 tidyselect_1.1.1
[4] grid_4.1.1 pROC_1.18.0 munsell_0.5.0
[7] codetools_0.2-18 ragg_1.1.3 units_0.7-2
[10] future_1.22.1 withr_2.4.2 colorspace_2.0-2
[13] ggbump_0.1.0 highr_0.9 knitr_1.34
[16] rstudioapi_0.13 wk_0.5.0 Rttf2pt1_1.3.9
[19] listenv_0.8.0 labeling_0.4.2 git2r_0.28.0
[22] rnaturalearthdata_0.1.0 bit64_4.0.5 DiceDesign_1.9
[25] farver_2.1.0 rprojroot_2.0.2 parallelly_1.28.1
[28] vctrs_0.3.8 generics_0.1.0 ipred_0.9-11
[31] xfun_0.25 R6_2.5.1 lhs_1.1.3
[34] cachem_1.0.6 assertthat_0.2.1 promises_<IP_ADDRESS>
[37] vroom_1.5.4 nnet_7.3-16 beeswarm_0.4.0
[40] gtable_0.3.0 globals_0.14.0 timeDate_3043.102
[43] rlang_0.4.11 BBmisc_1.11 systemfonts_1.0.2
[46] splines_4.1.1 extrafontdb_1.0 checkmate_2.0.0
[49] s2_1.0.6 yaml_2.2.1 modelr_0.1.8
[52] backports_1.2.1 httpuv_1.6.2 gridtext_0.1.4
[55] extrafont_0.17 tools_4.1.1 lava_1.6.10
[58] ellipsis_0.3.2 kableExtra_1.3.4 jquerylib_0.1.4
[61] proxy_0.4-26 Rcpp_1.0.7 plyr_1.8.6
[64] classInt_0.4-3 rpart_4.1-15 viridis_0.6.1
[67] haven_2.4.3 hrbrthemes_0.8.0 fs_1.5.0
[70] here_1.0.1 furrr_0.2.3 magrittr_2.0.1
[73] reprex_2.0.1 GPfit_1.0-8 whisker_0.4
[76] R.cache_0.15.0 hms_1.1.0 evaluate_0.14
[79] readxl_1.3.1 gridExtra_2.3 compiler_4.1.1
[82] KernSmooth_2.23-20 crayon_1.4.1 R.oo_1.24.0
[85] htmltools_0.5.2 later_1.3.0 tzdb_0.1.2
[88] lubridate_1.7.10 DBI_1.1.1 dbplyr_2.1.1
[91] MASS_7.3-54 Matrix_1.3-4 cli_3.0.1
[94] R.methodsS3_1.8.1 parallel_4.1.1 gower_0.2.2
[97] pkgconfig_2.0.3 tweetrmd_0.0.9 sp_1.4-5
[100] xml2_1.3.2 foreach_1.5.1 svglite_2.0.0
[103] vipor_0.4.5 bslib_0.3.0 hardhat_0.1.6
[106] webshot_0.5.2 prodlim_2019.11.13 snakecase_0.11.0
[109] digest_0.6.27 janitor_2.1.0 rmarkdown_2.10
[112] cellranger_1.1.0 gdtools_0.2.3 curl_4.3.2
[115] lifecycle_1.0.0 jsonlite_1.7.2 viridisLite_0.4.0
[118] fansi_0.5.0 pillar_1.6.2 lattice_0.20-44
[121] fastmap_1.1.0 httr_1.4.2 survival_3.2-11
[124] glue_1.4.2 conflicted_1.0.4 iterators_1.0.13
[127] bit_4.0.4 class_7.3-19 stringi_1.7.4
[130] sass_0.4.0 rematch2_2.1.2 textshaping_0.3.5
[133] styler_1.5.1 e1071_1.7-8 future.apply_1.8.1


UT MASA Officers 2012/2013
President
Name: Alex Dehe Ng
Major: Computer Science
Class: 2014
Email<EMAIL_ADDRESS>Secretary
Name: Syairah Husna
Major: Economics
Class: 2015
Email<EMAIL_ADDRESS>Treasurer
Name: Yingle Choo
Major: Accounting
Class: 2014
Email<EMAIL_ADDRESS>Social Coordinator
Name: Jasson Khai Shin Jong
Major: Chemical Engineering
Class: 2015
Email<EMAIL_ADDRESS>Webmaster
Name: Kim Yu Ng
Major: Computer Science
Class: 2015
Email<EMAIL_ADDRESS>Disclaimer:
Web browser requirement:
IE 9 and above
Firefox 18 and above
Chrome 24 and above
Graphic Design
Dreamweaver
Gimp
Hosting
Google Drive
and....
This website was created by WebMaster of UT MASA


Frequently Asked Questions
How is my cost calculated?
Where do profits go?
Where are meats collected from?


## Automated Testing - Mocking Irini Koutaki @DevStaff
Why using test doubles?
* Test isolation, * Stand in for objects which do not yet exist * Exploratory design
Example code (PHP - Mockery)
Mocking Frameworks
* Mockery (PHP) * AspectMock (PHP) * Phake (PHP) * Spock (Java, Groovy)
* Mockito (Java) * JMockit (Java) * NSubstitute (.Net) * Rhino Mocks (.Net)
Bibliography: 1. ['The Art of Mocking', Gil Zilberfeld & Dror Helper](http://www.methodsandtools.com/archive/archive.php?id=122) 2. [github.com/testdouble/contributing-tests/wiki](https://github.com/testdouble/contributing-tests/wiki/Don't-mock-what-you-don't-own) 3. [martinfowler.com/articles/mocksArentStubs.html](https://martinfowler.com/articles/mocksArentStubs.html) 4. [blog.tremblay.pro/2017/09/mocks.html](http://blog.tremblay.pro/2017/09/mocks.html) 5. [www.infoq.com/articles/stubbing-mocking-service-virtualization-differences/](https://www.infoq.com/articles/stubbing-mocking-service-virtualization-differences/) 6. [laracasts.com](https://laracasts.com)
Slides: [irinikp.com/mocking-presentation](https://irinikp.com/mocking-presentation) Code: [github.com/irinikp/mocking-demo](https://github.com/irinikp/mocking-demo)


District PopulationsRegional PopulationsHealth FacilityMorbidity & MortalityData Reporting Rate of HMIS Forms By Ownership for Tanzania Data Reporting Rate of HMIS Forms For RegionsData Reporting Rate of HMIS Forms By Type for TanzaniaReporting Rate OPD and IPDReporting Rate ANC and L&DReporting Rate of PNC and Family PlanningReporting Rate of Drugs and Child HealthReporting Rate of HMIS Forms By TypeReporting Rate of HMIS Forms By OwnershipReporting Rate of NACP FormsReporting Rate of NACP HIV Care and TreatmentReporting Rate of NACP Home based Care (HUWANYU)Reporting Rate of NACP HIV Testing and Councelling (HTC)NACP Sexually Transmitted Infections (STI)Reporting Rate of NMCP FormsReporting Rate of NMCP National Malaria Control ProgrammeReporting Rate of Wagonjwa wa Nje (OPD)Reporting Rate of NTLPReporting Rate of MB Leprosy patients treatment outcomeReporting Rate of PB Leprosy patients treatment outcome


"x" "1" "VON DER HUDE,W, SEELBACH,A, BASLER,A, EPOXIDES: COMPARISON OF THEINDUCTION OF SOS REPAIR IN ESCHERICHIA COLI PQ37 AND THE BACTERIAL MUTAGENICITYIN THE AMES TEST, MUTAT. RES. 231(2):205-18, 1990"


Beginning Summer 2015, all financial aid awards will be disbursed on your IVC MyCashCard. Verification of identity through the USA Patriot Act is not required. All students, including undocumented (AB540) students will be eligible for the card.
After receiving your card, you may access your IVC MyCashCard account by visiting www.moneynetworkedu.com. This is a great way to manage your account and alerts.
Cardholders:
Money Network has announced a new Loyalty and Rewards Program! Through this program, Cardholders can earn cash back on everyday purchases made with their Money Network® Account. The program has over 16,000 participating national, regional, local and online retailers across a wide breadth of categories, with new retailers being constantly added. There is no fee to participate in this program.
HOW IT WORKS (see www.moneynetwork.com/loyaltyedu):
If you’d like to try out the program, just complete the following steps to register:
After 3 days, when you log into moneynetworkedu.com again, you will be able to view the list of participating retailers and begin earning rewards. Under Services, click on “Rewards and Offers” and that will redirect you to continue to the Loyalty site.
1For other card activity, fees may apply.
*Only upgraded cards may be reloaded with funds. Upgrading is free. Requires one-time verification of your identity.


All you need to start working with this cache is datasource.cache.initialize() in your datasource controller/service code.
Base data retrieving method datasource.get() has not to be changed. We don't touch its implementation. Nothing changes.
You may disable/enable cache via datasource.cache.isEnabled property. Toggle-link demonstrates this ability.


Kuang Hui Fong - Senior Frontend Developer
<EMAIL_ADDRESS>Github: https://github.com/roadmanfong
Summary
A frontend engineer. For the past 8 years, I've be developing web and app with react and react native. Always enjoy working with people and being engaged in problem solving.
Technical Skills
- React/React Native/Electron/Redux/Typescript/Django/Python
- Git/Svn/Webpack/Grunt/Yulp/Mocha/Jasmine
- NodeJS/Express.js/MySql/RethinkDB
Experience
AP Leader
Sept. 2017 - Present
- Defined feature specifications with team and customer.
- Collaborated with UX designer to build user-friendly interface.
- Developed app for device with Bluetooth and NFC
- Setup CI for linting, text, build, deploy and notify status.
- Created component and tools library to reduced development cost.
Senior Engineer
Jan. 2016 - Aug. 2017
- Designed the GUI flow and developed features, maintained 2 main products.
- Developed internal collaboration tool side project.
Senior Engineer
Mar. 2012 - Dec. 2015
- Lead javascript techniques programming and survey the latest(up-to date) approach(method, knowledge) of Javascript and HTML.
- Establish coding convention and introduced modularized code and BBD unit testing.
- Cooperate with design, backend, iOS and Android teams as frontend developer and develop excellent communication skill.
Projects
Open Source
- React Cropper A react library for cropping image. Weekly download about 50k, Github 1.4k stars.
- Zustand Persist A persist middleware for Zustand.js. Inspiring by redux-persist. Works on both React and React Native.
- Co-working space CRM system.
- Resident can book the meeting room or apply a special event on website or mobile app.
- Admin can manage membership, client's bill, payment.
- App provided strong security on login and other sensitive actions.
- Support Wallet Connect protocol.
- Integrated with CoolWallet S via Bluetooth.
- Crypto currency App for private Ethereum Blockchain Service.
- Management System Web for Admin.
- Pain management. Track the pain level and medicine usage
- An Electron desktop App for our cold wallet production, Kelvin Wallet. Support Bitcoin and Ethereum
Chelpis Co.: Other Side projects
- Implemented rn sdk of CoolWalletS
- E-learning: Online video education platform for non-chinese speaker to learn chinese.
- iFancy: admin web for heat map camera, hot-spot analysis.
- Elderly share notes: bronze medal of TCA open-data contest. Use block chain to store share notes.
A10Networks: Thunder
- The web interface provides plenty of forms to help user change the configuration and visual tool for managing network status.
- A geolocation based App. User have to be in certain location to see unlocked message.
- A Hybrid HTML 5 app available iOS & Android. Develop with Cordova.js. We made several Cordova plugins to achieve our need. And do unit test in each plugin to ensure stability.
- Cooperate with LevelUp.com, a mobile payment in US and integrate their API so user can pay the money and get discount bonus.
Education
National Central University, Master of Communication Engineering
2010
National Cheng Chi University, Bachelor of Mathematical Sciences
2008
Certificates
- TOEIC 905 2011 Aug
- JLPT N3 2014


This example searches for jQuery locally at ../node_modules/jquery/dist/jquery.min.js
, but
jQuery was not found there. Please eather install jQuery (via npm or manually)
or modify this HTML file and change the src attribute of the script tag loading jQuery
to specify a different local path or a CDN URL.
This example searches for jQuery.scrollTo locally at ../node_modules/jquery.scrollto/jquery.scrollTo.min.js
, but
it was not found there. jQuery.scrollTo is an optional dependency:
The show/HideSection() plug-ins will still work, but auto-scrolling to a certain position in the viewport
will not be animated (smooth scrolling) without the scrollTo plug-in, but instead the
viewport will instantly "jump" to the target position.
This example searches for jQuery.scrollTo locally at ../node_modules/jquery-folding-arrow-icon/dist/jquery-folding-arrow-icon-min.js
, but
it was not found there. Folding-arrow-icon is an optional dependency:
It's not necessary to use the showOrHideSection plug-in, but both plug-ins can optionally
be combined. However, this demo page is to demonstrate this combination, i.e. this page won't work
as intended without this additional plug-in!
showOrHideSection()
function.showOrHideSection
are the parent list items themselves,
that are fitted with the folding arrow icons.ul.folding-arrows svg path {
stroke: none;
fill: #aaa;
}
ul.dashes svg.dash line {
stroke: #aaa;
stroke-width: 3px;
}
$("ul.folding-arrows:not(.dashes) > li").prependFoldingArrowIcon();
$("ul.dashes > li").prependFoldingArrowIcon({preset: "dash"});
//setup showOrHide options:
$("ul.folding-arrows:not(.dashes) > li > ul").each(function() {
var me = $(this);
var li = me.parent();
me.setupShowOrHideSection({
heading: li,
toggle: "a",
restrictSelectorsToChildren: li,
scroll: true,
onToggle: function(section, heading) {
//For Internet Explorer and older Edge Browser versions that don't support
//CSS transitions for inline SVG:
heading.transformFoldingArrowIcon();
//If you don't care for icon transformations on those older browsers, you
//may leave the onToggle option.
//See folding-arrow-icon's documentation for details.
}
});
}).hideSection(); //initially collapse all sub-lists
//Add click handlers to links
$("ul.folding-arrows:not(.dashes) > li > a[href='#!']").click(function() {
$(this).next("ul").showOrHideSection();
return false;
});


Mount Everest
Reach the highest point on earth or choose to live out your dream by standing at the foot of Mt. Everest (8,848 m). The Everest region in Nepal is more than just climbing and trekking, it is a life changing experience and some see it as a journey close to achieving Nirvana. Located in the north eastern province of Nepal, this region is in a world of its own with vast glaciers, icefalls, the highest mountains, deep valleys, precarious settlements, and hardy people challenging the harshest conditions thrown at them by nature in the thin air of high altitude.
Passing through legendary Sherpa villages, the trek is a mix of deeply cultural and spiritual experiences and physical challenges that test your strength and endurance. Buddhist lamas, monks and nuns led by Rinpoches (reincarnate at mas) serve the predominantly Sherpa communities from their gompas (monasteries). The journey to Everest or Everest Base Camp, begins with a dramatic flight from Kathmandu to Lukla, after which you hike up the Everest region to reach your destination in the Himalayas. However, for die-hard lovers of trekking, there is another switchback starting from Jiri through the mid-hills of Solu, an ethnically diverse section of the trek rich in flora.
Lifetime Experience
Taking a flight is a time-saver, while trekking from Jiri gives you the opportunity to follow in the footsteps of Tenzing and Hillary before their conquest of Everest. With more time to interact with the charming people along the route, you get to see the rich flora and fauna on the long trail. You get the added benefit of natural acclimatization as you go up and down the cliffs and valleys.
Things to do in while you are in Everest region
- Mountaineering
- Meet The People
- Mountain Viewing
- Trekking
- Bird Watching
- Sky Diving


An Android bottom navigation with customizable tabs.
Use the Touch button on the component to get information about the selected tab.
A boolean that is true when the tab bar is displayed.
A number representing the relative distance from the bottom of the screen. Useful for offsetting the position of the bottom navigation bar when a system navigation bar is present.
The opacity of the tab bar.
A boolean that determines if labels should be only shown on the selected tab or on all tabs.
A number representing the number of items in the bottom navigation.
The index of the tab to switch to, starting at 0.
A pulse that changes the currently selected tab.
The background color of the bottom navigation.
The color of the icons of the unselected tabs.
The color of the labels of the unselected tabs.
The color of the icon of the selected tab.
The color of the label of the selected tab.
A boolean that determines if a pressed indicator is shown on tabs when tapped.
A boolean that determines if the pressed indicator color is automatically generated based on the background and tab icon colors.
The color of the pressed indicator if Use Auto Color is not set to true.
An index that represents the currently selected tab, starting at 0.
A text string that represents the label of the currently selected tab.
A pulse that represents when any tab is tapped.
A pulse that represents when the first tab is tapped.
A pulse that represents when the second tab is tapped.
A pulse that represents when the third tab is tapped.
A pulse that represents when the fourth tab is tapped.
A pulse that represents when the fifth tab is tapped.
A pulse that represents when the sixth tab is tapped.


{% extends 'index.html' %} {%block main_content%}
There are no posts for {{month}}. Maybe you should write one?


Distribution Envelope
The Distribution Envelope is the transport wrapper used to carry ITK content.
The following table documents all the elements and attributes of the Distribution Envelope and the restrictions placed on them for use with ITK correspondence.
| Name | Cardinality | Data Type | Description |
|---|---|---|---|
| header | 1..1 | Distribution envelope header. | |
| @Service | 1..1 | URI | The service under which this transmission is sent. This should be populated with a service name taken from the service listing in the "ITK Implementation" page of the Domain Message Specification. |
| @trackingid | 1..1 | UUID | A unique identifier for this transmission. This is a DCE UUID generated by the sender that is used as a tracking identifier for the transmission. |
| addresslist | 0..1 | A list of recipient addresses, which indicate the end-to-end business destination of the distribution. | |
| address | 1..* | A business delivery address URI. | |
| @type | 0..1 | String | The format of address used. (default= "2.16.840.1.1138<IP_ADDRESS>.<IP_ADDRESS>", which indicates an ITK address format). Other addressing formats are supported, but these are generally used by local agreement. For sending an inter-organisational transmission, the default ITK address format should be used. |
| @uri | 1..1 | URI | The actual business delivery address for this transmission. Further addressing guidance is given in the latest version of the "Interoperability Toolkit Addressing and Routing Requirements". |
| auditidentity | 0..1 | An auditable reference for the sender. Examples could include an ITK format audit identity, a spine smart-card authentication etc. This attribute is used by middleware to audit the sending of transmissions. | |
| id | 1..4 | Up to 4 levels of identity are allowed to identify the sender. For example the 1st identity could be a person, 2nd their role, and 3rd the responsible organisation. | |
| @type | 0..1 | String | The format of audit identity used. (default= "2.16.840.1.1138<IP_ADDRESS>.<IP_ADDRESS>", which indicates an ITK identity format). Other audit identity formats are supported, but these are generally used by local agreement. For sending an inter-organisational transmission, the default ITK identity format should be used. |
| @uri | 1..1 | URI | The actual audit identification. |
| manifest | 1..1 | Technical details of each payload. It is mandatory that each payload has a Manifest entry in the distribution envelope. | |
| @count | 1..1 | Integer | A count of the number of payloads being described. This must match the payloads.count attribute. |
| manifestitem | 1..* | There must be one manifestitem per payload. | |
| @id | 1..1 | XS:IDREF | The id of the payload being described. This must match the payload.id attribute. The recommended format for id is to use a DCE UUID prefixed with "uuid_". For example id="uuid_A570ED3C-3D67-11E2-9389-A28C6188709B". |
| @mimetype | 1..1 | String | The mime type of the payload. The mime-type should be set to "text/xml". |
| @profileid | 1..1 | URI | The identification of a description of the versionable artefacts of a
payload. Not all payloads will have a profileId - for example an image may not
have any versionable artefacts. For more structured payloads such as a CDA
document, this will document versionable payload artefacts such as vocabularies
and templates. The profileId should be populated with a profile from this specification. |
| @metadata | 0..1 | Boolean | A flag to indicate whether the payload being described is the metadata content payload (default="false"). Metadata will be in an IHE conformant format. |
| @compressed | 0..1 | Boolean | A flag to indicate whether the payload is compressed (default="false"). The only supported compression routine is gZip. |
| @base64 | 0..1 | Boolean | A flag to indicate whether the payload is in base64 format (default="false"). |
| @encrypted | 0..1 | Boolean | A flag to indicate whether the payload is encrypted (default="false"). |
| senderAddress | 0..1 | The sender's address. This provides an address for acknowledgements. | |
| @type | 0..1 | String | The format of address used. (default= "2.16.840.1.1138<IP_ADDRESS>.<IP_ADDRESS>", which indicates an ITK address format). Other addressing formats are supported, but these are generally used by local agreement. For sending an inter-organisational transmission, the default ITK address format should be used. |
| @uri | 1..1 | URI | The actual delivery address for the acknowledgement. This is the return address for infrastructural acknowledgements for example. |
| handlingSpecifications | 0..1 | An extensible list of handling requirements - such as send business ACK, interaction IDs etc. This list is expected to grow over time. Each specification and the values it can take will be documented outside this document. | |
| spec | 1..* | A set of key / value pair to represent a handling specification. | |
| @key | 1..1 | URI | Specification Key (such as send business ACK). For example, to request a Business Acknowledgement "urn:nhs:itk:ns:201005:ackrequested". |
| @value | 1..1 | String | Value for the key (such as "true"). |
| payloads | 1..1 | The payloads. A variety of content types can be carried, as described by the manifest. | |
| @count | 1..1 | Integer | A count of the number of payloads (must match manifest.count). |
| payload | 1..* | The actual payloads. | |
| @id | 1..1 | XS:ID | The unique identifier of a payload (must match manifestItem.id). |
| @filename | 0..1 | String | The file name under which the extracted payload should be saved. The filename has been excluded since files are not permitted. |


Emma Signup Form Helper
Use with react-emma-signup-form components. Must have this Chrome extension installed and enabled.
Signup Form URL
Submit
Form Action URL
Form Elements
Instructions
- Use 'name' attributes for form elements. Other attributes, such as 'id', can be your own.
- Pass number from hidden 'group' input fields as props, shown here.
- Ignore any other hidden input fields.


{% extends "./base.html" %} {% block title %}{% endblock %} {% block css %} {% endblock %} {% block content %}
{{ bio_desc }}
I'm basically the greatest thing ever. Any given day, you can find me crushing code, eating kale chips, slamming four lokos, foraging in my jean shorts, or participating in the latest pinterest lo-fi vegan master cleanse.


[t]Dear {$toname|escape},[/t]
[t]Related to:[/t] {$candidate_name}
{if $clooksSuccess == "true"}
[t]Candidate has succesfully finished Clooks interview[/t]
{else}
{if $code == "NOTAPPROVED"}
[t]Candidate has stopped Clooks interview[/t]
{else}
[t]Candidate has unsuccesfully finished Clooks interview[/t]
{/if}
{/if}
[t]You can open candidate interview by clicking[/t] [t]here[/t]
{/if}[t]OTYS INTERACTIVE CLERQ[/t]


Foxsemicon sees revenue rebound
by Julian Ho, Taipei; Jessie Shen, DIGITIMESSemiconductor equipment maker Foxsemicon Integrated Technology, an affiliate of the Foxconn Group, has seen its monthly revenues return to an on-year growth track with the company's sales outlook turning more optimistic.
Foxsemicon has reported consolidated revenues increased 2.2% from a year ago to NT$683 million (US$22.4 million) in October 2019, ending a 11-month streak of on-year decreases.
Foxsemicon's cumulative 2019 revenues through October totaled NT$5.71 billion, down 29.1% from a year earlier. Foxsemicon generated earnings per share of NT$2.10 in the third quarter, the highest level for 2019. EPS for the first three quarters of this year came to NT$4.54.
Market observers have expressed optimism about Foxsemicon's sales outlook for the rest of this year and 2020, citing a rebound in orders particularly OEM orders placed by Applied Materials.
Applied Materials has disclosed impressive results for its fourth quarter ended October 27, 2019. "Applied Materials' fourth quarter results reflect a healthy uptick in demand for semiconductor equipment, combined with strong execution across the company," said company president and CEO Gary Dickerson. "The semiconductor industry is increasingly adopting a new playbook for improving chip performance, power, area and cost, and we are investing in unique solutions to enable our customers' success in the AI-Big Data era."
Applied Materials announced revenues for fiscal 2019 slid 13% on year to US$14.61 billion. On a non-GAAP adjusted basis, the company reported gross margin of 44% and EPS of US$3.04 for the year.


Logged In to Salesforce via OAuth
This page confirms the successfull OAuth login for instance
Query Contacts Example
Publish Event
INSTANCE URL:
ACCESS TOKEN:
REFRESH TOKEN:


Germany: Luftansa arbitration with cabin crew union falls apart
Lufthansa and trade union UFO remain at odds over pay for some 21,000 workers, which lead to a two-day strike earlier this month. The two sides had agreed to arbitration, but negotiations to that end have broken down.
Talks between Lufthansa and the cabin crew trade union UFO over entering comprehensive arbitration have fallen apart, both sides said in a statement Tuesday evening.
Last week, Lufthansa and UFO agreed to enter arbitration to resolve ongoing wage disputes, though neither side had yet named their respective arbitrators. The two sides have been at odds over pay for some 21,000 staff members as well as the union's legal status.
However, Lufthansa withdrew its approval for arbitration because UFO did not agree to completely renounce strikes at four Lufthansa subsidiaries. Meanwhile, a UFO spokesman said Lufthansa was trying to "blackmail" the trade union.
Lufthansa insisted that it would continue to the arbitration process with the UFO, though the German airline said the sticking point was now the so-called small mediation, which concerns wage issues that spurred the union to call for its most recent work stoppage.
Wage dispute
Earlier this month, UFO organized a 48-hour strike which impacted four Lufthansa subsidiaries — Germanwings, Eurowings Germany, Lufthansa City Line and SunExpress Germany. Lufthansa was forced to cancel 1,500 flights, which affected 200,000 passengers.
The collective bargaining issues that led to the UFO strike included more expenses and allowances for flight attendants as well as opportunities for seasonal workers to switch to regular employment.
Lufthansa CEO Carsten Spohr had tried to ban the strike in court, but did not succeed. On the first day of the strike, he offered negotiations for comprehensive arbitration.
dv/dr (AFP, dpa)