tag:blogger.com,1999:blog-120891922024-03-07T18:18:13.552-08:00Technology HyperbolesSelf-conscious exaggerations about computing gghttp://www.blogger.com/profile/10478964622265922809noreply@blogger.comBlogger32125tag:blogger.com,1999:blog-12089192.post-59294780496749332392020-04-19T23:23:00.004-07:002020-04-19T23:31:22.287-07:00<a href="https://sydneycare.ai/questions-and-answers.html">The most trustable questions & answers for covid-19 super-easy to search and navigate</a>
gghttp://www.blogger.com/profile/10478964622265922809noreply@blogger.com0tag:blogger.com,1999:blog-12089192.post-84403140161642725062015-11-05T11:10:00.001-08:002015-11-05T11:58:00.496-08:00PippoProxy reached ~3000 downloads <div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg7m8h7WOVZBEJIBYxzaZm8N0qaf8TfxiqwCIOS0R7bnn0I8SqLsMGfFMamacthvWoxZ0ot6JAWxn1TWWU3jLrv6hMQts0YT9nnkjgW_M9qwQeJS-xbjIc0t8QU5n_zARlCxdGq/s1600/Screen+Shot+2015-11-05+at+10.50.25+AM.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"><img border="0" height="201" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg7m8h7WOVZBEJIBYxzaZm8N0qaf8TfxiqwCIOS0R7bnn0I8SqLsMGfFMamacthvWoxZ0ot6JAWxn1TWWU3jLrv6hMQts0YT9nnkjgW_M9qwQeJS-xbjIc0t8QU5n_zARlCxdGq/s640/Screen+Shot+2015-11-05+at+10.50.25+AM.png" width="640" /></a><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg7m8h7WOVZBEJIBYxzaZm8N0qaf8TfxiqwCIOS0R7bnn0I8SqLsMGfFMamacthvWoxZ0ot6JAWxn1TWWU3jLrv6hMQts0YT9nnkjgW_M9qwQeJS-xbjIc0t8QU5n_zARlCxdGq/s1600/Screen+Shot+2015-11-05+at+10.50.25+AM.png" imageanchor="1" style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;"></a></div>
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Ten years ago <a href="http://www.javaworld.com/article/2071848/build-ci-sdlc/boost-tomcat-performance-for-static-content.html">PippoProxy</a> was born and, although from that moment ~6 Java versions have been released, I'm very proud of this well crafted piece of code. The <a href="http://sourceforge.net/projects/pippoproxy/files/CheSpettacolo/1.0/stats/timeline?dates=2005-01-30+to+2015-11-05">SourceForge release</a> (at the time GitHub wasn't yet born) was called <i>CheSpettacolo </i>in honor to <a href="https://www.facebook.com/Valentino-Rossi-Che-Spettacolo-322465854459385/">Valentino Rossi</a> the Italian motorcycle racer and many times MotoGP world champion who, at that time, was winning one of first MotoGP. I was senior software architect at the <a href="http://www.virgilio.it/">N.1 Italian web portal</a> and I was working on search engines and text mining projects.... </div>
gghttp://www.blogger.com/profile/10478964622265922809noreply@blogger.com0tag:blogger.com,1999:blog-12089192.post-33092246738035491982015-08-19T17:58:00.001-07:002015-08-19T20:45:49.181-07:00Collateral effects of a bad resampling procedure <b>Resampling technique</b><br />
To reduce the influence of randomness introduced by data split, 8-fold cross-validation has been repeated 3 times, i.e. <b>3-times 4-fold cross validation</b>.<br />
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<b>Data transformations, models and meta-models </b><br />
Measured cross-validation score and Kaggle Public LB score in a wide range of configurations:<br />
<ul>
<li><b>scaling</b> numerical numerical predictors or without scaling;</li>
<li><b>removing high-correlated predictors</b> or without removing them;</li>
<li><b>clustering</b> (train/test) data (2/4/8/14 clusters configurations; 2 different predictors for clustering) or without clustering data; </li>
<li><b>stacking</b>, i.e. training (k+1)-level learners to combine predictions of k-level learners or meta-learners, or without stacking (in case of stacking, 2 and 3 architectural layers used);</li>
<li><b>using a wide range of algorithms</b> (knn, cubist, xgbTree, enet, pls, gbm). </li>
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The idea is that if I am making some mistake in the process whose effect is creating a difference between cross-validation score and Kaggle Public LB score, then such difference should have a proper variance. In this case it would not be correct talking of bias. </div>
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<b>Observations </b></div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiVJMERXhbGJXIdUgUJbppurwP5q_e21zWqYMW0G1e6pp3jeV0RrwM68ClNHz-0Sqo4oBNzqeR6drUdsRVGMqRmg_LhaQvnALYkd6SQmuxC1_F_Hl2hbx1JXkDo0QHdZQAyDl2W/s1600/kbias.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="271" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiVJMERXhbGJXIdUgUJbppurwP5q_e21zWqYMW0G1e6pp3jeV0RrwM68ClNHz-0Sqo4oBNzqeR6drUdsRVGMqRmg_LhaQvnALYkd6SQmuxC1_F_Hl2hbx1JXkDo0QHdZQAyDl2W/s400/kbias.jpg" width="400" /></a></div>
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<b>t-test </b><br />
On the choice of the t-test (<a href="https://en.wikipedia.org/wiki/Paired_difference_test">paired difference test</a>) for this problem, please see <a href="http://web.cs.iastate.edu/~honavar/dietterich98approximate.pdf">[Dietterich, 1998]</a>. Assuming the null hypothesis as the means of cross-validation score and Kaggle Public LB score are equal., it results that difference in means with 95 percent confidence lies in the interval [-0.05392342, -0.02951592]. So,there is a bias.<br />
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<b>Why such a bias</b><br />
<span style="background-color: #fcfcfc; color: #444444; font-family: 'Open Sans', 'Helvetica Neue', Helvetica, Arial, sans-serif; font-size: 16px; line-height: 20.3636360168457px;">AS a tube_id has 3.4 +/- 2.9 different prices, if in a cross-validation training holdout set I have instances of tube_id occurring also in the related cross-validation test holdout set (maybe related to different quantities or quote dates) I am training my learner with a train set probably more correlated with the related test set than the whole train set is correlated with the whole test set (both public and private). And also, probably the related effect is more evident for low rmsle scores than for high rmlse scores.</span><b> </b><br />
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<br />gghttp://www.blogger.com/profile/10478964622265922809noreply@blogger.com0tag:blogger.com,1999:blog-12089192.post-82686743286833087662015-07-24T13:51:00.001-07:002015-07-24T14:38:58.583-07:00Technical features correlation vs Cost - Kaggle's Caterpillar Tube Pricing<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgwhuGXPi3wtR_MrBmpoYTMPM_5v80H_aiD-tVb6pCFgThXBgKUbtJfAnVz-x9R6_XKuJOKj2LxhyIl54RBqsWHdLgQEr3fyyJZKieLEKVOEC8ASq-UIibTn7UHcABCgsVIm7A6/s1600/DiamterVsCost.jpeg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="219" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgwhuGXPi3wtR_MrBmpoYTMPM_5v80H_aiD-tVb6pCFgThXBgKUbtJfAnVz-x9R6_XKuJOKj2LxhyIl54RBqsWHdLgQEr3fyyJZKieLEKVOEC8ASq-UIibTn7UHcABCgsVIm7A6/s320/DiamterVsCost.jpeg" width="320" /></a></div>
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Wondering if for higher levels of quantity tube technical features are less correlated to the selling price. This could be pretty expectable as the more the quantity the more the discount Caterpillar buyers are likely to ask.</div>
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<a href="https://www.kaggle.com/ginotesei/caterpillar-tube-pricing/plot-predictors-correlation-vs-cost" style="color: #2699c7; font-weight: bold; text-decoration: none;">Here</a> I uploaded a script showing this fact for tube diameter (<a href="https://www.kaggle.com/c/caterpillar-tube-pricing/data">here</a> you find data). Output plot is reported here below. As you can see the slope of the linear model becomes flatter for higher levels of quantity.</div>
gghttp://www.blogger.com/profile/10478964622265922809noreply@blogger.com0tag:blogger.com,1999:blog-12089192.post-38964230745550689142015-06-25T11:45:00.003-07:002015-06-25T12:35:14.473-07:00My machine learning open source project fast-furious released on github <br />
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<b>My <a href="http://github.com/gtesei/fast-furious">machine learning open source project fast-furious</a> released on github. </b></div>
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<b>Check it out!! </b></div>
gghttp://www.blogger.com/profile/10478964622265922809noreply@blogger.com0tag:blogger.com,1999:blog-12089192.post-1100549975838589772015-04-27T19:49:00.005-07:002015-04-27T19:49:59.541-07:00Very proud of my 8th place / 504 teams - Kaggle's American Epilepsy Society Seizure Prediction Challenge5 months have passed since the Kaggle's <i>American Epilepsy Society Seizure Prediction Challenge</i> finished and Isaac (my Kaggle's alias) placed <a href="http://www.kaggle.com/c/seizure-prediction/leaderboard">8th</a>. <div>
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gghttp://www.blogger.com/profile/10478964622265922809noreply@blogger.com0tag:blogger.com,1999:blog-12089192.post-35529061210873796092014-11-03T12:37:00.000-08:002014-11-03T13:55:29.042-08:00How to paint a Van Gogh with R Caret ... and suicide immediately after!!There's no yet the function <i>paint(as.van.gogh(..),..)</i> , but it's already possibile to get a beautiful paint in Van Gogh style training no less than 150 models (perhaps, after 20/30 hours computing) with the same sampling algorithm and painting resampling results across models where each line corresponds to a common cross-validation holdout (aka <i>parallelplot). </i><br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEimeh9EyVmRmNnxI9jYu6CW5aNs5IUj1xxmS51TiPHt9pocx_82IWMhkzyagXTVg-Ry_0r7awjlNZL7f8Twga5Q_g18AUd9RkmwvtvTcXS_UvQi_Kjw_VBFmGjGRdSPIaQaub-Y/s1600/parallelplot.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEimeh9EyVmRmNnxI9jYu6CW5aNs5IUj1xxmS51TiPHt9pocx_82IWMhkzyagXTVg-Ry_0r7awjlNZL7f8Twga5Q_g18AUd9RkmwvtvTcXS_UvQi_Kjw_VBFmGjGRdSPIaQaub-Y/s1600/parallelplot.png" height="320" width="320" /></a></div>
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Why doing that? ... that's another story ... anyway, I find the use of red a bit excessive, so we can sell it as a Van Gogh of earlier years. Very important, there're no correlation with the problem, as results don't change.<br />
And what about a Matisse? ...same information can be presented with <i>dotplot ... </i>and results don't delude.<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgCedYF4oO5cxcY4vBl1soWyXcSYmzbuA2BPD7M3W24mOBID1BsjlFHSSybPWox2lSX7hgO5WMFtGMAggzw3qnzGN6nVH8FdmjY8U-gLhdQYHlaw4nqL679IX18RX70FEQwLrxo/s1600/dotplot.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgCedYF4oO5cxcY4vBl1soWyXcSYmzbuA2BPD7M3W24mOBID1BsjlFHSSybPWox2lSX7hgO5WMFtGMAggzw3qnzGN6nVH8FdmjY8U-gLhdQYHlaw4nqL679IX18RX70FEQwLrxo/s1600/dotplot.png" height="320" width="320" /></a></div>
<br />gghttp://www.blogger.com/profile/10478964622265922809noreply@blogger.com0tag:blogger.com,1999:blog-12089192.post-14226258876718978862014-10-11T20:55:00.000-07:002014-10-12T12:37:56.270-07:00Comparing Octave based SVMs vs caret SVMs (accuracy + fitting time)In this <a href="http://rpubs.com/Isaac/caret_reg">post</a> <strong style="color: #333333; font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif; font-size: 14px; line-height: 20px;">caret R </strong><span style="background-color: white; color: #333333; font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif; font-size: 14px; line-height: 20px;">package</span><span style="background-color: white; color: #333333; font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif; font-size: 14px; line-height: 20px;"> regression models has been compared, where t</span><span style="background-color: white; color: #333333; font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif; font-size: 14px; line-height: 20px;">he</span><span style="background-color: white; color: #333333; font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif; font-size: 14px; line-height: 20px;"> </span><strong style="color: #333333; font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif; font-size: 14px; line-height: 20px;">solubility</strong><span style="background-color: white; color: #333333; font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif; font-size: 14px; line-height: 20px;"> </span><span style="background-color: white; color: #333333; font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif; font-size: 14px; line-height: 20px;">data can be obtained from the</span><span style="background-color: white; color: #333333; font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif; font-size: 14px; line-height: 20px;"> </span><strong style="color: #333333; font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif; font-size: 14px; line-height: 20px;">AppliedPredictiveModeling R</strong><span style="background-color: white; color: #333333; font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif; font-size: 14px; line-height: 20px;"> </span><span style="background-color: white; color: #333333; font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif; font-size: 14px; line-height: 20px;">package and where </span><br />
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<li>Models fitting on train set > 15 minutes has been discarded.</li>
<li>Accuracy measure: <strong>RMSE</strong> (Root Mean Squared Error)</li>
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From this, the top performing models are Support Vector Machines with and without Box–Cox transformations. Linear Regression / Partial Least Squares / Elastic Net with and without Box–Cox transformations are middle performing. Bagged trees / Conditional Inference Tree / CART showed modest results.</div>
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<b>SVMs with Box–Cox transformations performs on test set as 0.60797 RMSE</b> while without Box–Cox transformations as 0.61259.<br />
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Let's start Octave session with <b><span class="n" style="box-sizing: border-box; font-family: Consolas, 'Liberation Mono', Menlo, Courier, monospace; font-size: 12px; line-height: 16.7999992370605px; white-space: pre;">Regularized</span><span style="font-family: Consolas, 'Liberation Mono', Menlo, Courier, monospace; font-size: 12px; line-height: 16.7999992370605px; white-space: pre;"> </span><span class="n" style="box-sizing: border-box; font-family: Consolas, 'Liberation Mono', Menlo, Courier, monospace; font-size: 12px; line-height: 16.7999992370605px; white-space: pre;">Polynomial</span><span style="font-family: Consolas, 'Liberation Mono', Menlo, Courier, monospace; font-size: 12px; line-height: 16.7999992370605px; white-space: pre;"> </span><span class="n" style="box-sizing: border-box; font-family: Consolas, 'Liberation Mono', Menlo, Courier, monospace; font-size: 12px; line-height: 16.7999992370605px; white-space: pre;">Regression</span></b><span style="font-family: Consolas, 'Liberation Mono', Menlo, Courier, monospace; font-size: 12px; line-height: 16.7999992370605px; white-space: pre;"> </span>where we got performances pretty similar to caret <span style="background-color: transparent; color: inherit; font-size: 12px; white-space: pre-wrap;">Elastic Net</span>. We got <b>0.71 RMSE</b> on test set with a <b>10 polynomial degree and lambda 0.003</b>. From the validation curve we can see the model is under fitting. </div>
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Let's focus on<b> SVMs</b> (fom <b>libsvm</b> package).<br />
<b><span class="n" style="box-sizing: border-box; font-family: Consolas, 'Liberation Mono', Menlo, Courier, monospace; font-size: 12px; line-height: 16.7999992370605px; white-space: pre;">epsilon</span><span class="o" style="box-sizing: border-box; font-family: Consolas, 'Liberation Mono', Menlo, Courier, monospace; font-size: 12px; line-height: 16.7999992370605px; white-space: pre;">-</span></b><span class="n" style="box-sizing: border-box; font-family: Consolas, 'Liberation Mono', Menlo, Courier, monospace; font-size: 12px; line-height: 16.7999992370605px; white-space: pre;"><b>SVR</b></span> performs as <b>0.59466 RMSE </b>on test set with C = 13, gamma = 0.001536 and epsilon = 0. <br />
Time to fit on train set: 9 secs. </div>
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<b><span class="n" style="box-sizing: border-box; font-family: Consolas, 'Liberation Mono', Menlo, Courier, monospace; font-size: 12px; line-height: 16.7999992370605px; white-space: pre;">nu</span><span class="o" style="box-sizing: border-box; font-family: Consolas, 'Liberation Mono', Menlo, Courier, monospace; font-size: 12px; line-height: 16.7999992370605px; white-space: pre;">-</span></b><span class="n" style="box-sizing: border-box; font-family: Consolas, 'Liberation Mono', Menlo, Courier, monospace; font-size: 12px; line-height: 16.7999992370605px; white-space: pre;"><b>SVR</b></span> performs as <b>0.594129 RMSE </b>on test set with C = 13, gamma = 0.001466 and nu = 0.85 </div>
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Time to fit on train set: 8 secs.</div>
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg3u2FhRlir2qiuupoF1pCkHQIqnYdd8F1yFn256Sq2t24Q24HL0-KMioWmxk6gOdfQNjZwIhoghnQ1fhvCE1b4Jwb4Nq6LpegsTOt-H9fMiVHUfkj_5CmJ0Auy8UYgy2kfMfqS/s1600/nu_svm.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg3u2FhRlir2qiuupoF1pCkHQIqnYdd8F1yFn256Sq2t24Q24HL0-KMioWmxk6gOdfQNjZwIhoghnQ1fhvCE1b4Jwb4Nq6LpegsTOt-H9fMiVHUfkj_5CmJ0Auy8UYgy2kfMfqS/s1600/nu_svm.jpg" height="240" width="320" /></a></div>
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<span style="color: #333333; font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif; font-size: 14px; line-height: 20px;">So, <b>Octave based SVMs have similar accuracy performances of caret SVMs</b> (0.59 vs 0.60 RMSE) on this data set (perhaps, a bit better), but <b>they are much more fast in training (9 secs vs </b></span><span style="color: #333333; font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif; font-size: 12px; line-height: 20px; white-space: pre-wrap;"><b>424 secs</b></span><span style="color: #333333; font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif; font-size: 14px; line-height: 20px;"><b>)</b>. </span><span style="color: #333333; font-family: Helvetica Neue, Helvetica, Arial, sans-serif;"><span style="font-size: 14px; line-height: 20px;">In my experience, same considerations holds for memory consumption, but I'm not going to prove it here. </span></span><br />
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<span style="font-size: 14px;">Let's go back to our </span><strong style="font-size: 14px;">on-line learning</strong><span style="font-size: 14px;"> </span><span style="font-size: 14px;">applications. In that shipping service website where user comes, specifies origin and destination, you offer to ship their package for some asking price, and users sometimes choose to use your shipping service (y = 1) , sometimes not (y = 0). Features x captures properties of user, of origin/destination and asking price. We want to learn p(y = 1 | x) to optimize price.</span></div>
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<span style="font-size: 14px;">Clearly, based on above example, Octave seems a much more performant and scalable choice than R. For instance, our <b>application architecture </b>can be made of </span></div>
<ul>
<li><span style="color: #333333; font-family: Helvetica Neue, Helvetica, Arial, sans-serif;"><span style="font-size: 14px; line-height: 20px;">presentation tier: bootstap js + JSP </span></span></li>
<li><span style="color: #333333; font-family: Helvetica Neue, Helvetica, Arial, sans-serif;"><span style="font-size: 14px; line-height: 20px;">application tier: <b>Octave</b> (Machine Learning) + Java (backoffice, monitoring tools, etc.)</span></span></li>
<li><span style="color: #333333; font-family: Helvetica Neue, Helvetica, Arial, sans-serif;"><span style="font-size: 14px; line-height: 20px;">data tier: MongoDB or MySql </span></span></li>
</ul>
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<br />
This is a hybrid choice, good for all seasons. It's the aggregation of 2 "pure architectures":</div>
<ul>
<li><span style="font-size: 14px;"><span style="color: #333333; font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif; line-height: 20px;">bootstap</span> + <b style="color: #333333; font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif; line-height: 20px;">Octave</b> + <span style="color: #333333; font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif; line-height: 20px;">MongoDB</span> </span></li>
<li><span style="font-size: 14px;"><span style="color: #333333; font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif; line-height: 20px;">JSP</span> + <span style="color: #333333; font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif; line-height: 20px;">Java</span> + <b style="color: #333333; font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif; line-height: 20px;">Octave </b>+ <span style="color: #333333; font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif; line-height: 20px;">MySql</span> </span></li>
</ul>
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<span style="font-size: 14px;">For both of them, the question is: is there any interface (open source?) JavaScript 2 Octave / Java 2 Octave / MySql 2 Octave / MongoDB 2 Octave? Are they stable enough for production? What about the community behind them? </span></div>
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gghttp://www.blogger.com/profile/10478964622265922809noreply@blogger.com0tag:blogger.com,1999:blog-12089192.post-3361692083397166992014-09-28T18:56:00.000-07:002014-10-02T19:29:20.132-07:00Comparing R caret models in action … and in practice: does model accuracy always matter more than scalability? and how much this is about models instead of implementations?The post with code and plots is published on <a href="http://rpubs.com/Isaac/caret_reg">RPubs</a>.<br />
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<span style="background-color: white;">Here I report just</span><span style="background-color: white; color: #333333; font-family: 'Helvetica Neue', Helvetica, Arial, sans-serif; font-size: 14px; line-height: 20px;"> parallel-coordinate plot for the resampling results across the models. Each line corresponds to a common cross-validation holdout.</span><br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgf0ekN2WIYqovuv6E_RJ5-74LB9S6BIB-KWCZedW9BsGXcXgmEfHu5-oaktWjG4IyfTMJV73-m8yQYeNt2pTaI5azQMVuZJ1kBJAyqPfGCKDSeMj1_oibzvtrLx83Z9k0mSD00/s1600/Rplot.jpeg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgf0ekN2WIYqovuv6E_RJ5-74LB9S6BIB-KWCZedW9BsGXcXgmEfHu5-oaktWjG4IyfTMJV73-m8yQYeNt2pTaI5azQMVuZJ1kBJAyqPfGCKDSeMj1_oibzvtrLx83Z9k0mSD00/s1600/Rplot.jpeg" height="320" width="282" /></a></div>
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<div class="p1">
<span class="s1">Is this </span>a zero-sum game? As for bias and variance, it seems there’s a clear trade-off between accuracy and scalability. On the other hand, continuing the metaphor, as for machine learning problems I need to check there’s no additional noises in addition to bias, variance and irreducible errors, so here it's necessary to check that such a loss of scalability for top performer models is intrinsically bound to the problem and not to the implementation.</div>
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<div class="p1">
<span class="s1">Is it possible to improve RMSE performances of linear regressors (that is middle performing in this contest) with an <b>octave</b> based model? Similarly, is it possible to build a nu-SVR based model that improves caret SVM RMSE performance fitting on the training set in less than a minute?</span></div>
<div class="p1">
<span class="s1"> … <i>stay tuned</i> …</span></div>
gghttp://www.blogger.com/profile/10478964622265922809noreply@blogger.com0tag:blogger.com,1999:blog-12089192.post-62390702059737989672014-04-05T07:28:00.000-07:002014-04-05T11:23:10.080-07:00[R] Comparing time series forecasting models in actionI preferred to publish on <a href="http://rpubs.com/Isaac/Comparing_time_series_forecasting_models">RPubs</a> ...<br />
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<br />gghttp://www.blogger.com/profile/10478964622265922809noreply@blogger.com0tag:blogger.com,1999:blog-12089192.post-72619926424914314712013-11-19T04:59:00.000-08:002013-11-19T04:59:08.048-08:00An example of exploratory analysis in R (lattice package)<!-- saved from url=(0014)about:internet -->
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<body>
<h2>
Introduction </h2>
<h3>
Data set</h3>
The data consists of a sample of <strong>2,500 peer-to-peer loans (= number of observations/samples)</strong> issued through the <a href="https://www.lendingclub.com/"><strong>Lending Club</strong></a>. The interest rate of these loans is determined by the Lending Club on the basis of characteristics of the person asking for the loan such as their employment history, credit history, and credit worthiness scores. Such a data set (loansData.csv) is stored in working directory. <br />
Let's load the data set and do some convenient operations aimed to traslate fake factor variables (e.g. Debt.To.Income.Ratio) into numeric variables. <br />
<pre><code class="r">data <- read.csv("loansData.csv")
data$MyInterest.Rate <- as.numeric(sub("%", "", data$Interest.Rate))/100
data$MyDebt.To.Income.Ratio <- as.numeric(sub("%", "", data$Debt.To.Income.Ratio))/100
data$MyDebt.To.Income.Ratio <- as.numeric(sub("%", "", data$Debt.To.Income.Ratio))/100
doMean <- function(x) {
ret <- vector("numeric", length = length(x))
for (i in 1:length(x)) {
ret[i] <- (as.numeric(substr(x[i], 1, 3)) + as.numeric(substr(x[i],
5, 7)))/2
}
ret
}
data$FICO.Range.mean <- doMean(data$FICO.Range)
</code></pre>
<h3>
Purpose of analysis</h3>
The purpose of analysis is to identify and quantify associations between the interest rate of the loan and the other variables in the data set. In particular, considering whether any of these variables have an important association with interest rate after taking into account the applicant's FICO score. <br />
<h2>
Methods and Results</h2>
<h3>
Bivariate analysis</h3>
Let's start considering the association between <strong>Interest Rate</strong> and <strong>FICO range</strong>. <br />
<pre><code class="r">## par(mfrow=c(1,2))
plot(data$MyInterest.Rate, data$FICO.Range, pch = 19, col = "blue", cex = 0.5,
main = "Fig. 1 - The association between Interest Rate and FICO score range",
xlab = "FICO score range", ylab = "Interest rate")
</code></pre>
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" /> <br />
<pre><code class="r">boxplot(data$MyInterest.Rate ~ data$FICO.Range, col = terrain.colors(length(data$FICO.Range),
alpha = 0.8), varwidth = TRUE, main = "Fig. 2 - The association between Interest Rate and FICO score range",
xlab = "FICO range score", ylab = "Interest rate")
</code></pre>
<img alt="plot of chunk unnamed-chunk-3" 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" /> <br />
As showed by Fig. 1 and Fig. 2, <strong>Interest rate</strong> (quantitative response variable) seems negatively associated with <strong>FICO score range</strong> (categorical explanatory variable). In order to prove these variable are significantly (confidence level 95%) associated applying Pearson correlation, we consider the variable <strong>FICO score range mean</strong> (quantitative) instead of <strong>FICO score range</strong> (categorical).<br />
<pre><code class="r">cor.test(data$MyInterest.Rate, data$FICO.Range.mean, method = "pearson", conf.level = 0.95)
</code></pre>
<pre><code>##
## Pearson's product-moment correlation
##
## data: data$MyInterest.Rate and data$FICO.Range.mean
## t = -50.26, df = 2498, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.7281 -0.6891
## sample estimates:
## cor
## -0.7091
</code></pre>
Hence, we can conclude <strong>Interest rate</strong> is negatively associated with <strong>FICO score range mean</strong> (p-value < .0001).
Moreover, if we know the <strong>FICO score range mean</strong>, we can predict <strong>50,2%</strong> (Adjusted R-squared: 0.5026) of the variability we will see in <strong>Interest rate</strong>. <br />
<pre><code class="r">lm1 <- lm(data$MyInterest.Rate ~ data$FICO.Range.mean)
summary(lm1)
</code></pre>
<pre><code>##
## Call:
## lm(formula = data$MyInterest.Rate ~ data$FICO.Range.mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.07988 -0.02136 -0.00455 0.01837 0.10195
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.29e-01 1.19e-02 61.2 <2e-16 ***
## data$FICO.Range.mean -8.46e-04 1.68e-05 -50.3 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0295 on 2498 degrees of freedom
## Multiple R-squared: 0.503, Adjusted R-squared: 0.503
## F-statistic: 2.53e+03 on 1 and 2498 DF, p-value: <2e-16
</code></pre>
Are there other features statistically correlated to Interest rate explaining more variability than FICO range? <br />
<pre><code class="r">features = dim(data)[2]
pValue <- rep(NA, features)
r2 <- rep(NA, features)
for (i in 1:features) {
lm1sum <- summary(lm(data$MyInterest.Rate ~ data[, i]))
pValue[i] <- lm1sum$coeff[2, 4]
r2[i] <- lm1sum$adj.r.squared
}
data.frame(names(data), pValue, r2)
</code></pre>
<pre><code>## names.data. pValue r2
## 1 Amount.Requested 1.545e-65 0.1101018
## 2 Amount.Funded.By.Investors 1.326e-67 0.1134749
## 3 Interest.Rate 0.000e+00 1.0000000
## 4 Loan.Length 1.772e-109 0.1791880
## 5 Loan.Purpose 1.654e-03 0.0322881
## 6 Debt.To.Income.Ratio 8.447e-01 0.0472510
## 7 State 1.575e-02 0.0036624
## 8 Home.Ownership 2.026e-01 0.0062175
## 9 Monthly.Income 5.396e-01 -0.0002497
## 10 FICO.Range 8.741e-01 0.5382865
## 11 Open.CREDIT.Lines 6.169e-06 0.0077580
## 12 Revolving.CREDIT.Balance 2.246e-03 0.0033351
## 13 Inquiries.in.the.Last.6.Months 1.216e-16 0.0267187
## 14 Employment.Length 3.751e-01 -0.0001105
## 15 MyInterest.Rate 0.000e+00 1.0000000
## 16 MyDebt.To.Income.Ratio 2.733e-18 0.0296123
## 17 FICO.Range.mean 0.000e+00 0.5026398
</code></pre>
We found that several features are statistically correlated (confidence level 95%) to Interest rate but <strong>FICO range mean</strong> can <strong>predict its variability better than other variables</strong>. After FICO range, the features statistically correlated to Interest rate that predict best its variability are <br />
<ol>
<li><strong>Loan.Length</strong> (18%)</li>
<li><strong>Amount.Funded.By.Investors</strong> (11%)</li>
<li><strong>Amount.Requested</strong> (11%)</li>
</ol>
<br />
<h3>
Potential moderators</h3>
Such a negative association holds also <strong>for each loan purpose</strong> / <strong>with and without home ownsership</strong> / <strong>for each US state</strong> or do these variables moderate the association between Interest rate and FICO score? <br />
<pre><code class="r">library(lattice)
xyplot(data$MyInterest.Rate ~ data$FICO.Range.mean | data$Loan.Purpose, panel = function(x,
y, ...) {
panel.xyplot(x, y, ...)
lm1 <- lm(y ~ x)
lm1sum <- summary(lm1)
r2 <- lm1sum$adj.r.squared
p <- lm1sum$coefficients[2, 4]
panel.abline(lm1)
panel.text(labels = bquote(italic(R)^2 == .(format(r2, digits = 3))), x = 780,
y = 0.15)
panel.text(labels = bquote(italic(p) == .(format(p, digits = 3))), x = 770,
y = 0.2)
}, data = data, as.table = TRUE, xlab = "FICO range score mean", ylab = "Interest rate",
main = "Fig. 3 - Interest rate vs. FICO range (mean) score for each loan purpose")
</code></pre>
<img alt="plot of chunk unnamed-chunk-7" 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" /> <br />
Looking at Fig. 3, we find that FICO scores <strong>explains better the variability of interest rate</strong> in case of loan for <strong>education</strong> (explain 75% variability, p < 0.001), <strong>vacation</strong> (explain 71% variability, p < 0.001), <strong>medical</strong> (explain 66% variability, p < 0.001), <strong>car</strong> (explain 61% variability, p < 0.001) and <strong>house</strong> (explain 60% variability, p < 0.001). <br />
<pre><code class="r">xyplot(data$MyInterest.Rate ~ data$FICO.Range.mean | data$Home.Ownership[data$Home.Ownership !=
"NONE"], data = data, as.table = TRUE, panel = function(x, y, ...) {
panel.xyplot(x, y, ...)
lm1 <- lm(y ~ x)
lm1sum <- summary(lm1)
r2 <- lm1sum$adj.r.squared
p <- lm1sum$coefficients[2, 4]
panel.abline(lm1)
panel.text(labels = bquote(italic(R)^2 == .(format(r2, digits = 3))), x = 780,
y = 0.15)
panel.text(labels = bquote(italic(p) == .(format(p, digits = 3))), x = 770,
y = 0.2)
}, xlab = "FICO range score", ylab = "Interest rate", main = "Fig. 4 - Interest rate vs. FICO range with and without home ownership")
</code></pre>
<img alt="plot of chunk unnamed-chunk-8" 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" /> <br />
<pre><code class="r">
xyplot(data$MyInterest.Rate ~ data$FICO.Range.mean | data$State[data$State !=
"MS" & data$State != "MD" & data$State != "IA"], data = data, as.table = TRUE,
panel = function(x, y, ...) {
panel.xyplot(x, y, ...)
lm1 <- lm(y ~ x)
lm1sum <- summary(lm1)
r2 <- lm1sum$adj.r.squared
p <- lm1sum$coefficients[2, 4]
panel.abline(lm1)
if (p > 0.001) {
panel.text(labels = bquote(italic(p) == .(format(p, digits = 3))),
x = 770, y = 0.2)
}
panel.text(labels = bquote(italic(R)^2 == .(format(r2, digits = 3))),
x = 780, y = 0.15)
}, xlab = "FICO range score", ylab = "Interest rate", main = "Fig. 5 - Interest rate vs. FICO range score for each US state")
</code></pre>
<img alt="plot of chunk unnamed-chunk-8" 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TE9zR9MNOFuDbldo2AuuCLlu2evuyWg+xVxZtNPN+6L79FgHVBgjZRuD/nJ0BOv/3OB3nJJniSbzPfvIvHgT39QHihmXY+/bX83Wck5AM1FR3aOp3vSMU4BrlfAuGZMpKUCAi7eA2fawut4F6eebTT5d12vj4emrMeH8uny3NZL4nt3pVthR8N9hHPTGQ3W/K4dirTD09tDcF8oIv72sem3btiOevyF/mPkL/R4m6M9QBxd2pl3DnyPv/hIGuKjFnpyHHAyetqsVUDrULeaWxI3HBsVE/cIpi2HeGVSGJse2T8L/dI3bDLHO+LP74byGZzuPQ9wk4T0+AiXEW+nt+tvHyuBGVZ+e8h3DcKfzglNt3tgORO+5z4s+LNzPAIxUEi4Q4+3k7Gd43HYOyIFtHFCAW0RH1cqJi2gc6gLKGEroFRAwFF/bFsS8boV6po8vXXmepAgHTHcrlFpjzc40+nu2aHC9gjzR9e+A84QqOeaqHz2jqAGnmUlCDjo8THOhIhdUIapouBP/yG00INbboqXAYh2/e0jee8hDChpj4c4b7kMRtJgewWEmnHeYpy8d4fglt/js527yqNX7m68bvtPj3FYIqtKN9PqdYmCOf6c4gzgUNie5Md7V0vhNs+p2zKk63i3fa0dqmy0D7jzEs6m6AI2XSn4Ss+okT+STcr7ES6m/vCpBd50HfM4KaD76AeQDArcGlD9AhKHEU1XJpmSBHe+ZOKTnbv6o1dYOwpXqsB9+l4R56c9xOD0yQnWXidEeF20DOfyccc8SVSyDTIy5/q4vLkcAWf6aCIuBAr3Wu1uYK18GU767d56ZZb9dW1g56766FWIMrnXXbvtXsB5tsUQ8fDBIvXQwaUE5iGmwJR1hwxxy2hvpkS8pzvPY32byYkPnsnYlBO/0p276qNXUTRxUPlt99ypxAIiZJx4+1SOGiQ+RCXyEDNgxHFoM9LfOG8TD9OX+wQhYk8e2iA+7kZJ9ax256766BUEg//FrEWPu5WdYo/BWg33J+o8xGh7z0YNEK9CHwyzUt7a8kCzTk9uoAZ8LdBkrEjCqQ9NuHtfm89WvHPXnuN99CkD+RBUJiJ0m3DrjdmwJdynrbxdIZ6SVf7owpxboz5ivjw0+Wsk2UQI4dSiIGNM7aGzle/cVbN6GyAUu0BY4qWcpNEhQ4gx4l0nqr1d8RclZEIoSl0FWGIeb7mTTysST2h3jTuUrt38fJd3+cEaiJ8GLrXKOPNo43xtZp/T6vFmIGT3eED57MJ1N0F30svAMOwWOn243VDt8ZinS+W35KJ5sVI6/79K+94O4mvzULi6gQudQojsc5o9Xqp8+7brjy4l7WMvHeLLXT6M940hm1yNv4n0WxNV4nGwSBd27QK2jLeB415UHr3i+8Lr2zjfEbMitnt8KZto4yRN7WHm7RCvatwL8mntHh9lvhziwyLEtax822JVyZ17sYRTr4AZJvHZhXUc9iRejpZglSdeJtuA1Tk3M+XStWaSlozr9uJ40u5WqPSLkGgiXNVyzr1YyqlXFtPBhQEx6cMMf8VL+v7cD5WOpA0g9kDCfZic2BWTNDU2TiTEr2gDx71Y0qlXYuw7r7Q4HFzpGp4/HGn6yQhc7vNCv/+E0DEKeP4KoDX1ePOn5NGr2bbElkt8MscXHr1a95Gta8btTKAr28DxWX3y6NW6y7dm3M4EuvZ1/LrLt2bczgS6BcQ7bRh7Grs/Zp1TPo9zL8oiI2VlCwv4eFy2zAZoz6kf048Lilf18+PLf25B2v6+efLZ5XafV29jsyovQViBUz6PG1ehcoAJenXf+hqEx4b04zZEvKnNrSfeyL0QKRVO+TxubcS70IywzJB+3OVmiP+jf/hgF4jXrA9KvXjc2ogPgCH9uMsNEf91S5Nva4h/+4vfVBTZauXzuJaAXH2On6BXF2S9hvTj0GNTKm0VxL/5aX003BriTc/9wQ70eE3h7/1mTD/uclM9XrfP7Sdej/OT5niNW/Mcj02UrR93uTHi3/7i19tO/I5k9WbNqP8Z1I/bGPGXLrUv4raA+OWs48u6bE3ix/TqEPKtr4f14xYjvhFmj/iqkuY2EM+yXcHtTKAz8cvF7Uygayd+ti0xykpPJnIZxL/juJ0JlOK6MpELuSo0tBH0grj00bVV+duZQCkdDZnIZbhaCLwgLn9okelvlIeFA1XVb3WtxJ+1rkzkYq4WAi9M/CCFCQ988E4S35OJXMzVQuBl9PgR6j0P4fsMQ8BhW3RomujPWlcmcjFXC4Ej3EDdxMTzO1M0UoTvu7CBw7ZJ4rsykYu5WghMcSPD4YLE0+9ojQFHbZND/YpdLQRuEp8pFer1qFmImmUpQBbq8WqgG7YKmMWJp/g9FXdXQnxNwvF80vJq64jPlArNOWBm0tI/SM+VMpw/0uUxnuMHyEgLSFzlccIZ38+JzBa5ntXaWhWaSzgaQS9zVO/LnevxVqgwqZFMxQhSVPPD+ws9vvQ1+pq/+Mtuw4GeRa6yOOGkXvtb1ELdKxb1voBsCUd30GF7566obLIZ4lHMJhcqLOiWffQrE7r5kfR4Sj/Hnx8m2MyjQwiUusriVHCKX6Ko6CeX+HuxbX8sCccK8V2ZyMTVmC28c2egJaHCXKnw4ok9T9j8iIknh1D0e7xQOKvAaM+aJMAhBirl44aiIp7baYgnLS00UD7xLAlHO9Sb3nzxpLFzl59cFLuaZIsM9TpMIlQYOKg1bPOjUp+sOR6/xSzx5DhO7gUFhEAjV70eL7wzMrt0AwXyaM3UHRrtxi+yHt+ViYyKNs0Wm+NFECosHn2Lc/ynrn1/ak8v9UfVxOy3uQd/GVIxiYdAJQ00n+PhUHac48OhOlIpRpDgz1JPaqYu4aiU12Vp7NxtXY83PwQKFZLOlykVmnTeVARm9Sl/qr9Rgv5IO6G9vgF0PRAUFaOGksfpiLe/CeKP0M+rGO0zSDgGyxza4/gf2oG/tXO33Dm+Aebr4538SIBQoamVC8g+M6XCKydnaE7lt5dGkl7SCAc+Um0GMdfySAU4xhxvnKKiomteFxVFxXwdH0fKrxghvIRj8FeUcDQ/TkXhfnxDJjJyNc2K2R1bH8+0SJKov8fEyUQAUPu7o3jEK6oA+N4jqwHRdyjE82ivmy8ASA491b/wcXp8J4FtpeBgkXqmPp6besgnMHEyPVTQ6f+R61I+yTqeIu8fq842HgSalpIrABj5G8Bd3XwJJ5WP4UZs4aE+p35EAJD+xsNBNdJTw3+eMJ0lbdEGjh/ur+7geSb9QKNSsgtI/WkvVz9njTAQfTjhaLBCezKRBcioQQ4jnDs3peQ5ZNhjhl336Hh1h3tfygbuHHIBQZdyAoCHN2XoHJZQe5y7+RPIssV79bcJTkqyui7pzpGyCkYBIdBrSrzXnTv8BHt+rWKuKPESarRVMbBbT8+578tEQnmKf+1avIFjtEjM51uVtNuJkF8sk3ZOdeCU3aowU6+OsI6zR/I/dMH6JN7LsoGOn98Tffz8gb5agj9aSEu81YEDXCQVmsm5Gd25EM7jL8zoa7dmXaCewzxQL5Ang7+X1qEixGcVo+lXAWhqVIiLDzNFRSKs53brofOkGzi5TGTEYOFvbAtgIYx+pjw0CXYymsXrz9Sp/pu+/lDk55HG0iuuxpFfCbJs4tDIssG6DFbKh/oTD24SWbaIB9fRLU76MSLxR3XnvBlZOiqsF+aTLNCrT4hD50/ceJTdGcwqJuDwbZ3H5sN0Iqz37EUkyNeXiXS2rOXcuZ10jfZKyiDdcfqVKwfFPTTX37t1JtLbGLHYku/xOGM6HThxyyfNSPxBKstG/UnQGPEHzZe24jLdOWP3H7pc7UzR+/rFQIngoInT/HuPahrUKiYWDnSp/GmeQ2VbeNGU1JeJdLYs4lHOzW2IljffYdc9wnlZNqe4c12SZfNLWdwGNf+gLBscVw4OdWvIZdki4tEhMBFUoTJc0J0zHqCAZzA2FfXjIFA983p3wZ87svhxo2JiNSlU8oMcqrRpD7v1sRhRVyYy427cCFjPUqaMcfbi6iLf1CZEGFk2p1cXbqSkOGMBB7O814Hz50mC76ZeXYqTfuooyLlhj5cQqBfWI89wlQKNJEaDXl1Y2LleX7grEWW9VljPMhj3euIPd+uLGzg1mUjlK3uyBbDtWI+//QAFeor6cbDrTnGPdW6nL35gf9Fc1PTqKIG2wqkOnAg96Y4Rc5WyJucW68cJ2uNlXXdOQqC61z2ABUVTP+7q0+Dw8XdeOH/KNTXUqytVTDTHO2E93DrSNVPRnYvEcwdsScQHebVMaofozp1kI5qekM0wessNTZHMVizLljQYFenARfs5jzXuHpVzo8tjaXTnIhyefC3runPSBWrmCRMonB0u8b5LHiiRvY716qLtnKxiSFaPNXrqt9lJJ/M4v1u/OeLPcXkh87O/oz3mZNlpNYWNXp3/SFHAgbwaDdbldl4HjvAgr54m+nGU+PM7NtV+irhAfO7PJ03SaBHbkEyu6Bo2CKWZ33JgUkAV9OpoE80qhmrE+hrNayYS1nO79WzKvC1rjgfDSokn+nTibzptvEnfkrHFSgGyjPMyDzmuvKOWxYLbCPZNAW2mE2jqsPEsQJuN+oMv7Z27dTx6hRWziD4es3ypRkh0K6yCc2/iRThY1PdsCwUkH44ktnGuSnwOSXQwRomvV01j525Nj16F8tTEBjlOa+/HxGfmZZd7I0zMvqg/xdMOVBZENMqBxtHiOoTchmH5s1cUL2ns3K3rQQxSjrLYIMsppz5j5v2tMK4/fARSliTMmYE6MBdHA8VxScY3iThs9Px1ZSL5roZiMIbs+wSIPcf7K4XKO25hyqXUx+M9G2fZK8wP7UDpRflVDeKx04c/DRLfU2nqykQOuKqGUP4z6T+xkELXaYTMu2GBQLj1Eib8sBdLxp7cjSQ9XuLyrJ4cFEIlF/NUk0hiqZyMNfrn+KMflN3ZaO3cre3Rq0CfLVTh9guLeJUpsDJ6Lu30JOMuEp8keaKUHTSGJhUFh2NbE5eMMgJzPo6/xHfS1No7d2v7QgVUCqazDInRBIm/tBq2u8TdG6A9XkrcFpcM4lWEyiaJLvHEBx3b6sTTxUh4RKPvL/PNFU2q2QrnePefa9zs+qTXZeJ6OYEJ5VGV1nu8X1pFfd7uAnF7IBROBbLp2FYZ6gstdArxECO5eFuId0ZqJRnwB5yK6i/UQ7Jewudp2/6weUq/zvYisYxAZboap2NbjXjMK6AJKMkTHKxY6BblDZyKTOQUV2PgUIXJgD/itIUL9e4HTd+DaYJQn1rIxO5mJTpQ9AONp6awuKsT716ENicEb4QpW0nurCsTOc2V91jewKlYMisNNuzCK/zceGYgmXo0CXb9+eaixNjQG09hFiKqOH+xH5mghfL9ZeZKWdzAyWUiI+Cwq0lgmJUKDZTjpjmC5p78XgAfR5O0BXogumXgkrRwuj8DL27g1GQip7siPmfbDqPE92Qil0H8uo9sXTNuZwJtzPGrefRq3eVbM25nAi1v4FRkImfit8/hzpxeXYjBHqn/9vMPLt88rSm9FMsHuD/83Bzi/+WA4IBXC6jKBtZEjKwnE+Ko4CAiR4QKAqihHdjCXV6+EhUBh60h3si9OHkSvpoU4r757m+qMiNt4quygRXcd//N7y7f/LlBjwoOInKIeA9qaAe2cLYy3zwtRrolxFuZHycyU+5KLZzp7V+O8IBVWJcNrOH+nW5if2lVfkYFBwE5RHwA1bUDW7g3PwFBlBJuK4j/reXv7ed1xcEWTjeWX9eUy5rE12UDa7h/+Ozyfxthp3HBQUCOER9AVe3AFs4pOFZkvbaB+O9+H7R2Xte0gcvEe9yrykTWIb4uG1jD/eMP3/7yH62g26DgICLHiA+gqnZgCwchFpvoVhD/5OMwEZkJm1c+gqsrdLWIb8gG1nC//U//59/bGaaKrBEPyDHiCaimHdjCOeK3uMebsVPz7eLk6/ghbirxDdnAKu7v/7MhYIrgICCHiCegmnZgC7f9c/zXNpu3Wf1YcgergGnEN2QDq7jXv2cUTacIDjrkIPEEVNEObOGs6tx2Z/UuszPr+JE5HnHDxNtzHf+wIRtYJd7+/7dTBAf9/9mBJqCydmAL5xQct3cdz7Fdwe1MoGsnfrYtsSUT35Oum9pqdgW3M4EumfiudN0OlG8h3M4EumTiG9J1k0eYrtNtwu1MoBPoaBLfk64j4Pzhs4lOtwm3DIfbWjHMHl+Wrgvg2rcOx52uGDfEQ+qQDd6BimHN8TXpuh0oX4Ibi5MCpRwAJw7ZjW1LiO9K1+0i8dOGXnzwfgrx8TehmbgxW/s63r+Si0xlE0bQIVgUp1IDkS5OPPk62RhuyDZG/OQRdAwc4abVZ/xdNTbQf01n2OHUQIds5Rs4yZewdo/40R4v0NdgUoi40nevW7gBHwviBjZw8q/Y7yDxY5MSfidGjk1liBtjXW0N8Q0Fs3THYN2rpDGfEfFqYKwXKhx5MNZg8FgHQK56qF/pBk5+jFsczoEAACAASURBVM7UvbsMxawZEQ+hbCT6C9+G53hDYDhmYaTBmH+kHyuYTWf7enxZwUwoNYn6FMMdtYUiQ+gA0s/UtgcODPcC/k1O2ODj/NehJWtRsCXEdxXMoGFPmAKtha8NKzbxOPTKIeJLCpWckGGGF9hjGTESXKiaYeLH6nP1GzjFrB5PrIZQOfJq/sAYQh9blk2QQ1b0fwflw30SnJ9yPe2gV8dwaPXO1FABEUfmeHmgcUzio57E9zdiy1jHu4NOcDh87xFHJg2lXpU/hogvrxaOKeL7CygkH/TqOA5VOJFQDenOUZ079dWj+z2H3p8iKeFW6s5Zg7Nc8BwpdZ8lkwa9ngyAbHk1ONwIp1AuLtal1f4edHCEiOj4Jn6gcNgm7NoO4EjT3lLdOWOSHhYmr7gdyR3/7Cc0vrxafDbZIR+Hp2HZfw/v+Oh7QAXn8LtLBwI1ryFOln4c2TYIDXtUyI9lrJ27mnRdlC3D6XLq8JNuXuKdYnpgPuLqJnu1IyXp9IelAztrOBFOkT282c8PabKFRyCWTwitOFR2onc9noXDcZAQP1DAAWNl9TXpupCdS+mFA1P9uIKQH3EqAKcU6MeVcLCuuKDEg+DgkUpl2XLcadQBAWcCvoN/lMU4Y+JvA67lMBUOVKRiGIGC/DiMLeBQgUBeCQcChxdLJr4hXRdtFZmYHutWobn4MO87BSE/4vTaFvPgZn63m+AMFcfq6hmtz8ffeeEEANOROsMR3TmlQKjw4kPvDl9kcVJJTBAOtIG2C5gKB3qBw2wHJy8gKmLYxMAAsWaiTadM4FBXzMp27srSdaTHW6FPeVhwXhDyE/RdM/wegqK3LOJ0Cf/kZ7pKH9ItYic4ePXMS4hj1WS4oMeHgTqhQqxPTDG8iJEXKryRFBCECqsFzIQD9bVXTnDw2YtsDygLlKhQaRatQys7g2EWcJb8WL2Kacydu7J0Henx95083t2z7AMLQn7kmvOHtl7uniW1EqlsXX/xd89j4UDUbBF3sb34dpPhgrSMqUCnHS6sJlEENHJDh7FQIW7gGAa9wCHNR5NAM+FAfdX5HXPlPXGm0jt1WaCkYiTuVBk1ooT4i490M6QCh2Qo5Btvjq9I15GehPJ4cP5sWXAQhfyIU9DxwwMeizjdAq8z4p2Q313cCrouCAcCzuvxWcK8UKFLoILD+xrnFEu9UGEgXnmcdGvQlqJiKKAkQoUuwysKDkKgRDFSBqVC9xsBnt/SuBtUifFqycR3pesIGOTxbOGq+njuwqjHG8FBp/+XzoGRHl9GvLxtFA5Ns4QmU+jxKc5eQgUHiz1ehgJR4k+cPynCzj1E2xIORMHBMxDKggV9pYAXpMfLkweOfIkjhccR4p3A4feWTTwfjPJ4sFshioKD1oyQX4RD/b9UYjTgTiG9pcmrzu0UCPkJl5ohMMfRken6Cyc4iHsPQTjQTMYHGpcT73JJL1RoqSgGWhAOfPzFIxOn/TBDXrFiskBtEmoc6hqFLSBSQJ1rXdwkFbpJ3TmUx8NtKtLpC0J+Ec4Mh06LOFYmIHp8LpMNkq3KCPmduRkwdlbCRfViHCrX5RWcXAvz5zUIFcogVEgKaAUHVaQVqiDapnDgwUfmSiNUSPZvK4FGczUW0DZQQR8kuH7+vgyKiivp8T0FswB2qRbo6rn3/JtNPT6r0exwKjnLOeBgCUMJdkqFVwbo9khUFRfp8RmcCgKAuOskAec6PQoV0gIiTvn2orCZtoQDnXj1oc4VZZArk5VAz9NApXXo+kR4giSr0PMlE99VMEvASdbK1I9TuI2qkAl2sB4IG161rT+aG3gYbDwJLwPXd+i7XXawfcOhhIYF59O3NgojfzizQ1cSuVJLNU6ODWzgtB69wlDjSswIrjmF4slklugGS+rf3+1r4yTGGUzQW29Nh34vRXZqulhw54xNfNi2hdhI0pyGu9oNnMKjV/H1GfHR06akxAUC8RrbfdnBYkrhfZUJzIh3MN+dhIiA1RYqfXbdYb74rj8Qvz6mZT1ehbqz78MTJSppPKvdwCk+ehWZzAey8MhFi3gcfaHjc5Q0Pc67kyJtdyUcrqhkZIJ8VCNQALtxohxjJVA/l0lRaZ1ZoNF/krbvVRPfVTCrlC/ueCJMqnXisV1j864oaeYwRapAllXuSiNF1NKSqb4daGgywgfOCxQHmEqQKc7P8SruNeEuTm1OZdnYBk41q48tbpGCaOt1cDI08RL1HeLdCMzjgUwunnrMM1Wb+IzB3GMl0DCgcZJJPwLFMbnt3Hhs3dw6PraQQblfY8WdnlMsUp7LdImvzBLFESYQj4z4ntRpodLnoaPEK4WrguIH1ysmIV7JOMnfLuJ9sFlJO07DfEY/roxLk7Jiwl3y5zM77Icw4CtJiKwE6kfg2uqsNcc7R37I6QYaXEYjoqTfatgW4n0GpfxgNU0YMUliR0eKvj9Chxv1BYt4BSNaTY24MZfB0kUkE3c3UPqfz6Mmqjt1IT3pOh4ReHG4eiDOKInl+cvXg5wRBtfo0QTcSUaqg3bDYVizTiY+QjTkzprGyupr0nXcITu9fGynIax/uMQ3NXeLUN8TVTQ4dRzKgaQiRIm7fxy5bAKr5RNMubOBEFvSdRO2ipSaOjJx9ePCNJhcyvJHkroBh7J4UQvnU4rCXk6rwTQ2icWqdu5q0nXjDQ2EEafBhnBTJU0nOMyvYjocSEZGI1gI0pWuE7NtiS2X+K503c6c8TsRtzOBrmznriJdtzP1MhG3M4GufR2/7vKtGbczgS5E/PVzcfefn6UrtmHiUSDvt5/byacoRVXU1fMyfK9rsCbOnf/PlnqJPI4I3QWHQQmQ5RCBTmzrm6LSVhtXl2JchPjr50f2yeCz1tUc4oOO35gQI8rwWe2GL/kNBnATiA/Cf0PEo8NQUJZDD3xl1EVelSVGWriGFOMixOu0XRPP+GJfj3iv4zdGPMrwOY2QithSAzdOPBH+GyE+OPQFZTkMQN3Z3/5iQKwH46xLMS7e408X7/Fex2+QeJDhe13XKWziJhAfhP+GiPcOfUFZDj3QkF4e6du4hhTjonO8EEO8V4hHHb9R4p0MX000sIdzmkRl/bga8V74b4x4cBgKynIY/OlhvjzSd3B1KcZtyOq9QN4w8U6Gr9IX+rgJPd4L/40Rjw59QVkOg79vfvBP5ZG+g2tU6ALEu9l9GXM8COSNE29l+IbneMBNIh6F/waJR4dYUJbD4E+PE7Uhux3nCoi3XxAVo2N9JU4QyBsm3iWwg1k94iYRb5D/U7e0WmLRcYgFZTkkQoOvRmTLCW6FPX4QXI7TyymOEu9IH1vHI24a8QZZFz/uOaxqDlYJdCWsSO72casgfortzMbWRNzOBLoQ8e4rmzeH5vjZtsQoKz21wIT4q2fmHD3/7c3BVjNmu4LbXKC1Ry4Y/rpqgQnEPGJj/8/zlDe0iXFuM25jgVYesqrjCB0NtcBiiLpl6P+TEwxY/gYuLjldoGGvA7c54ssP77L8ddUCU4jm/Dy94d71N62Ekxv2JG8xbqhGN0e8UlPj7KoF5pBhE4Af7LgbIx6/B7E2h1Nx/tseE/x11QITyKR1vPtvlL9NEU+/irIWh5NxcpD5PKtvqAUmkFr21/XXOpGgF2f4TsgYbtCQeBhAZXC9WodTcf7bXSv0F3q827QdW8e7f3Piw2mr8Ap+XDyxJwfGPR6/DToQbGoNf6ZAEfHhS2t9p8MO3TGjyyF+ao9fGSQDZ18MCKetwiv4YU51M6cHhvL5fxYKtuXv9O4ZOfw4HGbC6lCjDuG0oKtoyGYVLfInR5nfCPHnP36e3tqJT3fVr+CHPRvr5BicknMCmaXM/TlUzZ89QlC/jJM78MgmfqCA6uTG3+h/LwQp4cgsuBTiezKRBcioWfCJ/twkfSic9ehPtzQ/YuJDR2T7OwF/MGDX/EGPD8kd+f4Sf46nDvsFvLDEh9XOlOVjRDy7R4B1ZSJzyICFrSKbFJqD1oqnu8Kr8Adzql8g3g+8fOKdv4fKO7zT8Aczb1af3M4EDqU0ZzTzCmiPuxPKCxVOmavTOPsVU9+5y08uilxNfBBDuWM8zYnitHT1DnH19Cgqn//CN2tM9P7u34Ev3qtmjzf5hNm5EkR+LCySGfw7h9IcNxj9vdPjcbkznJ7HxBN4J9DGzl1+VlkEmfgghoLDTE/eV6J8ums8BZrjnZWKFCNH53jjT+qRlx4gXc0poB/CRlNMPF3btR1KW0BVPIY2KWCU1QufT7CJTwXyQttpBtrYuWP2+DEzYLsXfBOPCHDRhVNa4RX8AN6VwG1+HOUHiDf+7t18GdVD1Z/r8fbET9Q7iyuVQfy5LtxpIkxRc6jCUA/wcFYux8IUEY/2vcbT2rlb7hwfwCc/Mue3SvhNuiPUo9NdwzLX3fKHrJ6czGtLxREO1BjnLyHs//1r8S8K/oxmi/lQ6IAiqs2DJ/Zw+jbx6JBe1SigStbxKC9rvXCFA0UyEOqKedRZ8rZ37jpZvU5/T0GYgWsCm5VrpG73dkQ4kKR38j0WzkWYtv+OT89DqE4l33t0/076OVWH1OeIACA9bp6NS1ooBMomnmdhqLern8HbsvEMIeFAmAHhQGTB/MLCXX2SK0p3fQZ/nnrTgazgYG+oB4eSTgt8AUB6ZiavgCROb/cfdHa5FiL+k5dmk2uUeG/SnwrC1seTRCZCsvT44IfwbQWtjQ1bxJLU6OHPu0ul4JCuNwcFB/EFXzhQShqnPLzT2wkqbuDU1AJTyKl+/3x4qEeDOhSsLBHW8dbwnHJ52L9N4Ddi8GRH7AQdn4T4IDF79Ul/jUyHbJ9hjQkOuoP3DM4KB7KSNKgX5P2mHCG+qxaYQwYse/QKdPWOREtXD+6Z4LaWLaCT1XvfCvPdkVWcIJItth+ZIRQl+Vo+jSZftHWODk/qOn4+dQlDNpb4/aYzUCq8Ji0tKuDDqKllQoXxOh4cSqfESEItKDiWNnBytUBqi2/ZOrOCgzqlzT8v3NDAHVTlp3bQehEmbY5m0QynV2ZERsx2JGGxqjQ7JZp8lHgiACijzZFcqJAW0AoVKo3LBA4LSoUnocHIx18YoUIMkhKfCRXGxOtAdac3gSYdvq3g2FcLdBYgUw5GCC+dMqymKGOe7G3APRNc8uM4ZqQ7HhEaXM0GHNzboXJuEkZt/fKqEHOiyefr084otjM9ewSO/Hif6f/F0p36bSNweMMLHBac5YqKOrxPJAgO4jgvM5zfcKHEE2VEMIY/xVALdOYhkw5GCC/PUXAwG0OI4CD0+GsRtlLuW+mVe7dCvo1ZVCxbFqt6uen6/h0DKn2zO8aeUuLd0ugeiosELnKBwyBRaQvoDj/M84KWoiIUUIeA+WgYLzKhwrjHg8IhzfL6/hRDLdAZzerHD0YIL1FwEP5aFg6EyenU116Q1YMiwr7vnRjnNsiIHp8j/l68yVz2afZN/BQhg0Oo0cd1oUL9IyvgrTM4Y5VsUGeKikQui/hz1BUFB0GokCSFMgX6mpEdBce+WmDC3aSDEYg3JziY/llFDdvtoJoGCs1XBv0/Gc6Oznu8u7cTiSZLwBYf8U5GC1qfwSFq0UAsmf6f/hEVEGXSguhGyVk8J+lfHyhpcSJdi2d3emLibz9QXhkRe7z938WtaH5LFBy5Fs3xwwcjBDAIDuLfizc0oKkSGTGd+qDgoK1/QTqE0XeHOZfs8QeT5n6p8YkLwrrIYZSjeeFAu1jGHu906CP9v1O6bEFlRPd5gvbcXFHx00D8tRMcfCBhaV6+0/NpTjwNVISh6Y4L9CCoaMcKjmxbUlaPgoNn+aeS+xdwl5S8fWCV/05vYZMO6mO6io4k4BzviXauPLhl1AZPb52FOz65T9vYCPEXt1xOcYZjDKR4ZvBO9P/SAoIzV0Ds9ZkzM7wS3XJzb88LFcbKFJlQYUS8rRkcDKOZ/rGumHt2JXo/V3BkG53jp4Mht7t6epy9Q/Xx4J5JjJNKBqHCcHa0DDi8txPp6klphKiV1fLzah8y92k0+Qjx5yAA+ABqEbQDjffDVP8vLaDGYQEjgTxwdtNKK8eKijpFM/+iv/iQ80yokG71utyOKiN6u7JJ0cWHBX/GejKRCUOTH68efit900+0QH5FYivB+bUtdNiaEFGMww4eDGWli1vhtVLY4JLsggac4fDNTteMW3Z0exYFSv0WCI24sXPXvS079nh1tnPXLEPvPc8FVFBNQi4mXqloheR1npv+ZG4CFWb5hcAN6jygCi7QV/nEHFeME9qob8GIq+/cdR/EmGID3br5lu/sMusZESHxHI87fT4rKK6yc3++sfgbpmFDiVkG74ZQz+rxI3VWbqI+6FrFdGUi20VjWRUs/a26YlcqdwhFK98/oEyJLPjD6+FnebxPcOloT+/3hZrtF5AGGv2tgPOdFcWSekMTQVHqERXDGzt3/R6/2JZtHK/7L1nvtnDx4AVkZPB6K/U7QkVNtxKOVqeCtRZOOpwpuUQ9wyGmr5yhKVRL1OkVwkkIBNOViUwgC27ZpvFivbGJx2oh43cqq8cgniwLOnFGtOPtWj7xUc1nLosOlR9ravVSzw1S6pO5qb1z187qF9yyjcNVOAWyiYcKlz5jg+ypOpVVPNrLWDyE71W4LTE/g/KJD04Z6kc0tah90bToL6T30Dv8wysKE/tF1vELbtmmsWL3483xynf4mPhEtZk18toL+zykYz3WqGTN8SnxicvqEIONDdeQiZUDTfp9eDIrZETVOKsWzfHTt2yLVu0zJeIV1rYkL1SsMTtQPobAoSc89Kh0rGi3tKTX9mTLfJOyAzZzKFS0hYaXAttor4FWbXXLuVKXbeCinkb/RCp0KFiGfBy0MZzaw1KE6TDttEzZMsxCmcRnqxCXIoYtrNIcH150jzub+BWqhkGXHcbl14tJuC4PCfExaIJDj2YQjxN9DK9CSOKBswVJFIpZfU0mMnI1+hWq/s4dhlsqzfgwsyK9umgMTUCTHAK6Q7zvtBm4hyPzkySpb3HnLpeJLLqadJNmti0xQkpXJjIhfortzFGvE3E7E2hx564mE5kSf26bztgZOOsu35pxOxNoY47vP3P3tPDuTPyO4CgrPZnIlPglnVe/0vKtGbczgS60jj8ZOs20TvwrPWF81tb16uLefl7W1WvhrBhRUYuopjtnAEYqwqmhsIHB1YgSg3bzFOIbUmLwOOe1rEa0CPGTzrkrxPBKF+nN04+Hiae4siJNH+d+4+EcwHL+uqYUUiMeXQ0R/+bpZy6+Me0Vj3PxPi3WzNp37gph/gT4HiSe4mq893Aj/vzQ8tnl21/87U8rylct4qu6JBUcNrRBtSWPA69ladLNE+9kfXSxBokguN/WeO/gpvR4K9v5g999yW9pl1N7/NvPXWyDiooe55yVp6XpxPudu0WHetAL/PKzQQHAgPvu90VNc7Dvb2Dq9HPmq48vayqHrTleuxqb4y+d6tGgoqLHOWdlbdJt6PG2WBN6vMc9+dhM2dx6ify9rqhJNVSoDLaaFDZ6vHU1SPylHWEGFRUDbiU9fmnEL2OOr8pzdf29GsnqITRLRGWsbwz1r0azetdEv/xsdI5HnHNWnii2gHhbIZOyeoorT9V93MgyEEN7ZRiojL8N4o2r8azesD4lq8fkbnuzerdKnbKOfx2t4/lJWuzvSWmWaBH/9pf1EbSd1T+p6IvWcGY9bueiCet4M8evaB2/NOIZtiu4nQl02cT3pOtWeJ9xtiFbLvFd6bqpw8UycKVnFZftb6MFXDGuBWlI101uaF2nTFz58eQl+9sFAh1unI4m8T3pup0jfmSYSBy+azhmjy9L1y0lzgEyFiV+CJQ4HLIdwLHm+Jp03VLmasUnY9E5fgPEj40xW0J8V7puk8RP8UcfUF6Lw9Gmti3Erwq8qR4ffUVn1OGIzcQzcOuf45dAPFdwcGnE1xy6U3B3kvgJuPx7a0ycwm/PVA8/KQNT4woOLm2Orzp0p+CufOcu+RJWCuaWcoHlqnMD/7Fx8JMSz2o5FmgEB2/FJesIDp4v1LILAodVh7DqWu3OXX62AumB9odikpGeFzWQawmFxE+c4/13pjjBWuDJjRdO9ioYS35MTdg3MDVj/N1+P36jKrDmTsFd7c4d/TXaKpLJfwyn0VH3XJgNVvgez6/VeIqQ0Rexu0CzUy3NeXx8wUE/NCmlRuK0Ldse2GmOTOYoHMIpuKvducvPT0Li6Vc5OeUUkePBXAsPpvOdl4nzKzmJ/PPmePPVBEs8+Tujx+PXoIeyEVsx4gyIZziEH6vducvPT3LgUP+0BXSdTsh8AeLPe+NCfQeUZLDgthh71ujt92X5rNyG4KCYRvzFR0KqkyNZOZw3meM/XQXxXem6lHiFX+JnOUX11eH9FHIUiOIR753RDk8uqEVgHDpFxfgZ1I7g4AUW0DvjlQ/9CZEIHNYdnqSnbHNtbOcuzeqjJBtG4AthVRw4TqPzQLh6fNaP8ASqA66OX3QmqJTEX7X9CKJw6JtHX3DQa6F4wcGogPWGbnDWX0qLUeUpOnSn4K5/He8PHw37Iu89us/X41Ohxvk6foa2oGgxJHBIer3B9ZYjdqg4Tjb8xgQHldctQ1xjmBLh+OSYGKai4oAtvIGDx/CQyhnQ44vOI+QLFVrqffWxdfwUHlvrwvWCgy3iUXCQXsUvIJ6wSwNtEx8WTvHIwFJUHDDWBk5Nui6aq/1oT3T1KoMa3dgKr/k6fsA8JGsjOn5hjnA6fkBBa44H8shkPRAolNB9OuDaxMe/+Ot4iooDxkruatJ1mNwR8QzQ1XNWK2JWPmM8HT9KkeuKI/p/eHCMoVDHGXbymkAZxjV+oMEknNAJOJoK93FwJVNRccBYy7lcuo7uGLgpF3T19Mon6PG5is2F/GiH4IgGOl29U3/Ok60PUDi0woEYR4IDf9ekxwcBwNsoOIhM1AUHXYNhB0q2bIM/xJHOUBAOpB8rUXOw7dOoOK5uA6csXUeWc49t8zz4MFof20IWhPzIUG+1XoSTWIk7Qq7jF62M7YaaJMfd4n+5vxPfcRUIDgqr4xcnbU3BQakef2F6/UXhu4VZoEF2UX/+9Xce2RHjpf8kJL4jHCidyGFPUdGcZHK6qh5fk65D4pWVx9NBOnk8EryUJSE/QvyVHXDNCtBnv2CZrl5MvBXy0w5FSnzmD7V6HNYpHArxSIbu7lKFpuAg6P9dmSQnaaHFQLH8VolRKh1nsr/dFQ6URo/JrNTaiop2725lc3xFuo4MoVZCxWilqHRPrCDkR+rT7YWe0mVPhgNdPT/Hu6XDHXP96S1/RCT+l/lDdS5H/H0rSgMKhzjFm1dtwUEjLmN77lmWuZQDhZpR507g0CgxYlYpK7hYRkxfZbCqePZgsoW7qg2cmnQdqReQx8M0ryz+h0J+xCmIBmKOXtbjc7p6cZIGAoDurHm/vXmn4M/r/9nIiMKhnaK+XcVRwcHgD1QwigXMAlXe3xkkay2hwkQ4ELH+r0Wfqxjq+WCdwhzLII8X9YiCkB9xevLA4pRTdqfATMcvDvb2A2VUvewwEY2iTX+RwiEeUe/cNgUHpRUcNP1W+tsvvEBPHhjVMkhKhYwKqCeepnAgqjjmaRvxqdv1jzdHvNFM9vJ4wvfcO9EcH4T8/Cdca5wyOn6wLicdItPxi4K9/o4RAHz+wM0QQmBvKPgL+n/aIsFBo0jtlTtLcYYC2pulj58fwxqmoW4YB+qECp8fu1FeU0gKqGdwkizmwoFExdG+UfW59KGeDZYHHxkFv9OgFeqnM13L78tEyC84Na3e6vGF7QDlL011/EhuoC7s4Hl6F/qrDLINRMcP/J0Q3IGVGrwXKxzC4N0SHDywIeruF40x5UDpkE2UGLFuFKSHVuHwDsGlwoFUxTHp9MGnVWpe+c5d9dGrRB7PLED9XXOdutzCRUlKPOj/PX3gFgeC5D6JaGCiq+f22c0EB+mev9dHcOAvwiWB4oCvZI6LAzX/GhyOMeEzs0BPU5w7PRLqw586bRQOZa2ACTZlKlJxvLtk4rvSdel+Q1gbSbIvnt/9TnGYcbvhvn4TsxqshdeLUniHLuWwuSXLtAoQlx+e+hxWCRSXA0GNKEU2CWxs0Kx256766BUYsh73pS7xvieYV25vdDhYxA7gSLTYRuGvHKB7VwTfnEBD5RS1sqq4/tur3bmrPnoVTEYGMgy+SzOc+pFwMFjgonJB8c+klcJtGJWx2KtKuofQd+jv9MjSWfVcf1Nwg5CudF1OPGnVrpThKRSGUwceHtGIg4H6lLSdktvLiuQZxTkieqxQFFUqay2N3MAWotC++wROazCDkK50XYF4VwlkdC8NazUCFZkD+cHCrIv1ycXRRkrv20kZ+nAOzIeFoFLZdoh9HV4pVWCeQWDxkpXv3HW+UIGZWmjXKkqDOk597clSb6jifLqlasN9fcolXRAHfMoih3ilUg3zCvHBITjPGymHwFLT3tg6PjYZOhMMcCJ+nKRDvJQyFhps4xLiC4NoPc6YeBVWFSPEkzy94TBC+UGGFWhifidgEDfB1SA4zvD8ECwZOPtD+YdeWP4Id6o4iPbiRFIk3V+pAJM53rsQXSIoyk+G8ZVMNjLV7JVt4FQUzKoVqhR2nNBDSNV04oR9gELHaqLC3MJSmvSoZAk/UeFwDBdGqWlyZ4wRhvEJNesqmA0RT8rYIx7ybDVEfOw8urSFK/beIYeTcDKscac0tCRlXtVyLlcwa+8Y+BUOGRZhzcSKE0bgbD3AKx/MLcM4ChDTgCO40OKmNLR41buqDZyaglkVHDLXzJ/oOI0+ZVLuQz1NwFH0OHAablJDW7CAjfe6CmZiti2x5RLfVTDbmaNeJ+J2JtCVbeBUFMx2pl4m4nYm0LWv49ddvjXjdibQbSDeKn0I8fv/zZzI/mVZY6QqtnRppIgcnntevZN6MDITg/JxAPzjf1v3VwIaJUx+rQAAEYNJREFULYvX5th4o21Qc1l0SJC1g+6b/v7QlzTHbQHxl8ihjrgmv9Ii/utRqZdXH8M/o/JxHlj1VwIawCvjqeWyGiggB4gnKB9wjtsm4r/5wT8VFZMq9TKZeNO6jDbTuHwcAMeI16i3v/i1+afhshYoIgeIpygMOMdtE/GunXLLN514UxWmSobl4xA4Rvybn3z95qf/9MuvjQpS1WVZYiQgB4gnKB9wjtsq4r95MiAcOJ14M/i9+niCfBwCx4g3Pe+HRhTqhw2XtRaKyAHiE3/FkX67iLcDVPmC5RLv5pRx+Tg/GQ0Rf/nqs1efXb42E27dZaWFeuQA8QRVnz23injdFypJfVllyybI7aG3MmT/8h/dSD8oHwfAUeJff/D3esT94d//puGyohgZkAPEB5QPOMdtEfF2Dhwo3+WXH5glVnPorQ3ZNukdlo/zWcgY8W/+3OZYf/x1w2XZYUCOVExA1dOmLSLeUTggHKiZF07OfZh4I0w5QT7uEhUtx4h3ooa6mTZcVoYmRA4RH1CXdQnO7SC+a7uC25lA1078bFti6ybe/Sg+GLkKp4gbdTi5kIsGuhFcTyZyMVcEXPoqCQc31d+4w70ivisTuZArOsJMJn7qSDETn+EIHQ2ZyGW4IuCpxE9vMKPAd5948rorE7mYKwrOH4Bn4aZPEZVn27u4YdtJ4nsykYu5ouCJPXCh3GAIvCjx0ZPCq3S41Dm+JhO5mCsKlqNdfvHVgBzyuSDxMvlvdQ6nyjTnWX1DJrIAmeaPfk9ySr2Qr7Lw5dyaJ2e0/VFjOZxO/HiPmMbHZtbx9HtyvIqJnJIv3HTk3CLiR7p8tZA8h5OJx+9Es01MJGRTxPtvf44Qj/JqhPiOnNt1TDy/P0X+qHUcXiVz0ugcP4X4SYy0N3CKCh7L6vFDo70AeTV1chQR3xF3CodC0TGGEyf1R63j8GJq7SxC/KLdt6sWmENGPNENHMo6iwszogmzo2QP9CqdKlqUc/M8QFvhE6+snBv44+vHRcSPTC6hasYmeQFoBLNx9Q2c/AAb6mqiCVQjiq2PU+ZMtzM8yc3biIAjjjOsOM1/n+b++j2enmkW/HXL6OfqwSWr8D+lHIE2NnDyI6tyyLBB+Uhn5+2sGNDFR0LI20eSHnjclXNLtGVH5vgLO9yboZ6vH3cVCPTEs1q3n6snEm9eJF/b5+EUQy0whwybLZ+XJeGP9QZ3flMqJ68WLu/IuZ0LFQvgjMRp5NxS+bieQyigosQrDpturhbTiZfCzWUDQxpaVy0whwwbTEkyHew5uJMfCSOpoaKiteXcMPcJxDN7hPP3NMhEDDmE7hfKxiQegCMLApK9htGeWaHeemqBBciowTAYd/qDsqMEp5ujdFgVhrSe5iDmPmRmscKBj/pTLhyiS4rLEDj0BAZ9Q+e0V0aR/LQwrjIiXI5Dm62mTrAb28ChzH/16P6dPs7MO447EVp1T8oPV0m0tWh/ieJv1Z996fsPQzeQzrnRXPbeI1agoaUZWA+kKPFKUTXNnqri5nbuok5//4Fbb1XZoOtx159wvduW8gu1SLqg9nfMG3ktlrSzrm4gAcIxhn6vmBVoeOki5SsqWog7ew0mlrUKDrLAMOYG5dbDO5jh1eiIyqcwY1NdKT+ytgoCl+rw5718khKvvHBVXzcwaqFOzgL+xwzUv7aRHg4qKipobCbeAcHBnkxkATJqLjVz9Y9ybiDzRIjP9NwuUuJBQi5fdyTQqF4EODz8JBlbMjm3mHjUq2s4M1JuLyM5MBjVUEPOaGdFTmuBKi+sJ+Vh3aH2ZMmJN4xwY+aoIDgYY4tZfU0m0tnCO3dAvJVzk0bODYT8PPGZnpuOGcvmxgsj3yGETqNTWb0UGnq80WR7pDu9dmj80XmlqFcXTOOUcSbrzqy+j9nlPvYHcrrGLYyuifSBygI2DlQZTRNdMepCV0yjdDpQS1ZEvNWdU2Z9YSu1hS3u3OUykdSW0ONtrdh+Lqx+HE25ZUHPjWjuwNxg4xQia5oplG6jOR04J3QXrepzGTgqTeIqRRZGv0TK7dkL/Qc8ZBHKdGgPabQ7e+GNVqDK6NW91GN2WzvOb7hEbNiKMXnpjRfZrJlgKzt3ZZlIZ0uZ4yXozsl78W6Oiu+8JDpwFmyTszvmU+7lAhsp1Cd3SoFDcio60qBB8vBGUQbOmMuOT0W2p1HYvv1VQrz2KVBgxgXv3q4FahM6I8iEipjBzMhBHP4qH+qV018R6vTWWUZ8gq3s3JVlIp0tJatXQc4NmHiMYksy13MLQ72rO+lUn+7imb5hSzWFCg8iDh3xQYXqXH/Q1bNHqiQDp1Bi6u4ZDIwlZ3aov/Hi4smxJ95RTzTkbCF7gRogYoD5oI+n7ocbQk+OnfJQwoaL1Z/tWta6M9jyHF+RiaTcTTSyjx3k3EinV3GPBz23KE5z1ckD0HdKw0mhnniTMz0gQncwudj/XXyExFtL9epQyy0reiLl9sULqzsHpZC+kKd3nXiKX4LGPT4K1OaSIMinoj5vXh6kQ0wWkq4Yo1mHew8kkWkpTfZkIp0tiXgq52ZKGOTc7LhzYDflTzFHjZNXk25J2MlTkaRbKgVH5vhrL3Tn9nNQsM48zWGUnXwVpXp1QcvN/rnkzJg5wvVTLzjoSLaFvH7+wEmW6VHm271Anc6dvvABDPeS9PjDME9/WiRel9HctD7Gei7fXfp0BRKjTLAuz4Ht7FTB0Xf66+dHRnfOMWGH+mdxDnNgx857t2DWJdNhKgVHN1w/Ig4lasKjv3tUzi0qJNVyU9HMm0i52acY4nW1L6QCCRu/T1UP1GQcttvaFhrmatsGjkhmXhjqL5yYnx4LXTKlAjzBboh45fXjUM6N7m3DkvMg6Lmdx05jeTWzxRIydCNZJ70UnIhAkUMifZPJwClRcUZdpVJu7oYNjdPqGFufuJ+qcKcl1ayjFWOrQDipSZTDdtjoDpG9cVioGHlltO5g/RSijbHtDZxV3aQBk9RQeYzsqXOdyuheSgTNcOQOeXqjrxpn4qq55xcXMPwvxFjeoIwd4mgk8VZt9eHEUqAuexH4Od04u2qBdVcME8lUBhH6AV76RwnCeMh06m5QpJt7ZZwfV6TrhLVoG66aj7KXCuj3zyPl+p5D7OlKDBPv/mvWWWMDZ2UPYpAIZUQ9KsUWqqbt1LUSmsHXcVKSllVU9Gr6c7B6KOlbtJ1JSR8K6TkMzV9MIz7cGOngumqBdVdsS8AysUile9hp4ZHDIvG+pdE5Iq6gjr8gS5u9k/8pOFRK1oBF4hEVayf3/CFOYmrYi7OrFthwxbVKD/RMBHnZIacIYMiWSdg/8pkE3nrzFeSG115BKjVadwjtWbDlssJYkd6WbuMU7fMc4rtqgS1XTOsR79b0UFpSys5Qr3xOmFzY6oBAPNEFDx/WL2QlM6z33HhY6+MUThBQrvIs2CZe1tpn4bZsQy2w5YppraEXHrn1t9r9dNx16kqHNPaClTEVSnn9v9D3+4WsyPwyiJelzKJCoPTPVWB2P0J8rZVVcU1bMvHZPA/Pkfi2znBKiY+vLec+UWOzf/EPSrGJV+VKbc/x0vdfBg6w2HJcdi8Tny2cW7mUr9gC4pWvGk+J/9ZFmLzbHxsPgx3NHWhUZJJxGLqzxiok3VdrOSRbE+iSq6Hq0NDxBZ94dFtd1m0H8SoiXrmtSiww32mok7ZeHWHcQ8hUz/fH1iZFN7SlcXAEHeaIUeKL15R37ioykX1XXatujEjc4QLq/YJpgPiCo/bGD+k+0uX3g8TnF/aHJj97dZMRgqLZPZv41nhZzOprMpEMV3Ur7dzlcfrZUMVbjuNOOXJudNiMB2Cev8KkMBJoLxmJHGFFsJZzLU/wl9LOXS4TOe6KHUHjWnLxFKcGP4qboOM30HMbUDaOL4LbvCzawOnJRA6GWAxgti0xSnxPJnIZxK/vzN3ZuNaViXQ2E//OWU8m0tlM/J7asomn+njhgP2Z+K2zZRNPVfW+WxNfmYnfvC2beKrH92VNjmgmfvO2bOKpHt+bP6sIkM3Eb96WTTzVx7t8JSpyYMuKfrbJtvSsnujj1cWWlhX9bJNt6cQTfbyZ+C22pRNP9fFm4rfXlk58pI83E7+1Nu/c7alt5u7csqKfbbI1OehJ161RUGG2JVuLg6503YRnI7pOZ1uPtThoSNeFIVvEzzzxnM7j/catSXxPus49CFf7esc0p7Otx5g9vixdJ/y/M/G7Zqw5viZdNxO/u9bP6hvSdfgQ6/AcP3b5bCuwpTxeXfqQTMrPndd6Oud122HLea4+/5RMys+f15odfjTbJmxsAyf5EhaCz38sUi2/oswPvPVsJn7zNrCBk5+tgOCTGy9EouVXPBnWtZvThzPxm7eBDRz6a7Tn7s6LjbX8Mik/f16rfj1v4GzeBjZw8vOTAGyP3Ugm7rqUn24IM+ebt4ENnPz8JADbjnxyJMonw+Icj+e16jlhJn7zNrKBU5vj7V6u/o9+Viblh+e1mnlhJn7zNraBU87qjZZfkH4BibSSlJ+5yPT6mfjN2xLW8XAIOP7O0HObid+8LYF4MvG7P7Dl1WbbnC35RAzzl76e20z85o21c1eTriuARfnk1AGns63FWFl9TbqOglE0UKTyarmQ3xQljdmWbKx1fC5dlz0t61qFSeLTTyRCfkby4+4ZLPZn9jdqzJ27snQdAbsWUpJSSoT8Pnl59cnM+eaNuXNXlq4jYNDxK+h6hc1b6PFUcHC2TRlvjq9I1xEw6vjB34vaeDDbn+eSkrOt3Vg7dzXpOgIOOn7pp5LbNd97YeaLucdvgy1pHU91/MwbRW086PvzHL8NtiTiIx2/+GPD7Rro8USWfbaN2dgGTuXRq0w0ME74qZCf+YR5Hb8FNrCBU3/0avSDZ+I3bwMbONVHr4Y/ed7A2bwNbOBUH70a/uiZ883bwAZO9dGrkjWlYGbiN28jGzgDc3xNnqnvdLb12NgGTjWrTw10V4R7Pag0OdsabMkPYqCh4I4oyCnOxG+DrYh41Bqbid9WWxXxyqtPzcRvpfF27irSdRwCxTzHb6XxvjRZka5jEVh8MG+2TRtrHZ9L140cVFh4Fnfeudu4sXbuatJ188mWu2sDPd78qSBdJyb8MtvGbWCOr0jXzcTvpPF27lrSdTPxO2mL8zETv5M2E7+nNhO/pzbzsac2E7+nNhO/pzYTv6c2E7+nNhO/pzYTv6c2nfhT+51I93BG9BjmqZc1SN+ZbXtsMvFUkix78Pr6r88q78y2JTaZ+NO/Mj3ePZyRfdXiqxe1d2bbEptMvD3W+Ng9nJF+ucqMBuV3ZtsWm97jj/GxjPOjtF9/BdN6/s5s22ILzfG6x9uHM5KZ3J1fX3pntq2xhbL6I3w4I87dL/7U/Ft6Z7atsXkdv6c2E7+nNhO/pzYTv6c2E7+nNhO/pzYTv6c2E7+nNhO/pzYTv6c2E7+nNhO/p/bOEX/xxD75ZU/IP0fRDPM0WE8Fcc/s3SPeaiLYH0boWJ1o5s05+ldPC1/x3mN7h4l3eijXz4/doyCghPUnP7v50owKRtv6V3YcuHoq/uAvXqDosbIDhZXHe2qPB7B/trBwxTtg7zDx/gkQqoV+8eTYKuTo9y+eHNm3TvQP8zwwCmmYdqJfmev12yf2IgMLV7wL9u4R/8Rq4RjikSR4cWKJd+3CsGtemoHhEyuiaVoDEc/DK82f9B+sHLq/4l2wd4943+OdTo5C4qHH27dPzGAOxJvLDPFPBZ7fqNuOfuHg5iL9riU+XPEO2DtMfHmOtwweW8bjHh/1ZTO6Zz3+nentxt5h4klWf/MlZvXAtlHEwpdhjvdnfJn/zAih34Y53nyqv+JdsHeZ+LCOP/frePt3vaz/g58dI/F6CP9XX7yAJN7YSSGr/9D/4R2xd474SYYp3x7ZTLwRxXx3OjLbZuL31Gbi99Rm4vfUZuL31Gbi99Rm4vfUZuL31Gbi99Rm4vfUZuL31P4/L8rvdC8ezuoAAAAASUVORK5CYII=" /> <br />
As showed, such a statistically significant negative association is confirmed also <strong>for each loan purpose</strong> / <strong>with and without home ownsership</strong> / <strong>for each US state</strong>. So, <strong>these variables don't moderate the association between Interest rate and FICO score</strong>. <br />
Just a note regarding analisys by state: in some cases there'are not enough observations to estimate coefficents (MS,MD,IA), while in some other cases there're enough observations to calculate coefficients but we have p > 0.001. For instance, in case of SD there're just 4 obs. <br />
<h3>
Variables associated with interest rate at the same level of applicant's FICO score</h3>
<pre><code class="r">data$FICO.Range.cut <- equal.count(data$FICO.Range.mean, 15)
xyplot(data$MyInterest.Rate ~ data$Monthly.Income | data$FICO.Range.cut, data = data,
as.table = TRUE, panel = function(x, y, ...) {
panel.xyplot(x, y, ...)
## panel.loess(x,y)
lm1 <- lm(y ~ x)
lm1sum <- summary(lm1)
r2 <- lm1sum$adj.r.squared
p <- lm1sum$coefficients[2, 4]
panel.abline(lm1)
# panel.text(labels=x,x,y)
panel.text(labels = bquote(italic(R)^2 == .(format(r2, digits = 3))),
x = 65000, y = 0.15)
panel.text(labels = bquote(italic(p) == .(format(p, digits = 3))), x = 65000,
y = 0.2)
}, xlab = "Montly Income", ylab = "Interest rate", main = "Fig. 6 - Interest rate vs. Montly Income in different FICO score levels")
</code></pre>
<img alt="plot of chunk unnamed-chunk-9" 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" /> <br />
<pre><code class="r">
xyplot(data$MyInterest.Rate ~ data$Open.CREDIT.Lines | data$FICO.Range.cut,
data = data, as.table = TRUE, panel = function(x, y, ...) {
panel.xyplot(x, y, ...)
## panel.loess(x,y)
lm1 <- lm(y ~ x)
lm1sum <- summary(lm1)
r2 <- lm1sum$adj.r.squared
p <- lm1sum$coefficients[2, 4]
panel.abline(lm1)
# panel.text(labels=x,x,y)
panel.text(labels = bquote(italic(R)^2 == .(format(r2, digits = 3))),
x = 30, y = 0.15)
panel.text(labels = bquote(italic(p) == .(format(p, digits = 3))), x = 30,
y = 0.2)
}, xlab = "Credit lines", ylab = "Interest rate", main = "Fig. 7 - Interest rate vs. Credit lines in different FICO score levels")
</code></pre>
<img alt="plot of chunk unnamed-chunk-9" 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" /> <br />
<pre><code class="r">
xyplot(data$MyInterest.Rate ~ data$MyDebt.To.Income.Ratio | data$FICO.Range.cut,
data = data, as.table = TRUE, panel = function(x, y, ...) {
panel.xyplot(x, y, ...)
## panel.loess(x,y)
lm1 <- lm(y ~ x)
lm1sum <- summary(lm1)
r2 <- lm1sum$adj.r.squared
p <- lm1sum$coefficients[2, 4]
panel.abline(lm1)
# panel.text(labels=x,x,y)
panel.text(labels = bquote(italic(R)^2 == .(format(r2, digits = 3))),
x = 0.2, y = 0.15)
panel.text(labels = bquote(italic(p) == .(format(p, digits = 3))), x = 0.2,
y = 0.2)
}, xlab = "Debt To Income Ratio", ylab = "Interest rate", main = "Fig. 8 - Interest rate vs. Debt To Income Ratio in diff. FICO score levels")
</code></pre>
<img alt="plot of chunk unnamed-chunk-9" 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" /> <br />
<pre><code class="r">
histogram(~data$MyInterest.Rate | data$FICO.Range.cut, data = data, xlab = "Interest rate",
main = "Fig. 9 - Interest rate distribution across diff. FICO score levels")
</code></pre>
<img alt="plot of chunk unnamed-chunk-9" 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" /> <br />
<h3>
Missing data or other unusual features</h3>
There are 7 missing values in the provided data set. <br />
<pre><code class="r">sum(is.na(data))
</code></pre>
<pre><code>## [1] 7
</code></pre>
Regarding unusual features the list could be pretty long. Let's mention just the FICO score that is provided as factor variable and it's by grouped by range and it's not provided as numeric variable. <br />
<h3>
Potential confounders</h3>
Credit scores are designed to measure the risk of default by taking into account various factors in a person's financial history. Although the exact formulas for calculating credit scores are secret, FICO has disclosed the following components: <br />
<ul>
<li>(30%) <strong>Credit utilization</strong>: the ratio of current revolving debt (such as credit card balances) to the total available revolving credit or credit. This components is probably correlated to <strong>Debt.To.Income.Ratio</strong></li>
<li>(15%) <strong>Length of credit history</strong>. This components is probably correlated to <strong>Loan.Length</strong></li>
<li>(10%) <strong>Types of credit used</strong>. This components is probably correlated to <strong>Loan.Purpose</strong></li>
<li>(10%) <strong>Recent searches for credit</strong>. This components is probably correlated to <strong>Inquiries.in.the.Last.6.Months</strong></li>
</ul>
<strong>All these hypotesis are true except the one regarding</strong> the correlation between <strong>Loan.Length</strong> and FICO score. <br />
<pre><code class="r">summary(lm(data$FICO.Range.mean ~ data$MyDebt.To.Income.Ratio))
</code></pre>
<pre><code>##
## Call:
## lm(formula = data$FICO.Range.mean ~ data$MyDebt.To.Income.Ratio)
##
## Residuals:
## Min 1Q Median 3Q Max
## -79.14 -26.61 -5.82 21.53 116.52
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 723.56 1.56 464.0 <2e-16 ***
## data$MyDebt.To.Income.Ratio -101.92 9.11 -11.2 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 34.2 on 2498 degrees of freedom
## Multiple R-squared: 0.0477, Adjusted R-squared: 0.0473
## F-statistic: 125 on 1 and 2498 DF, p-value: <2e-16
</code></pre>
<pre><code class="r">anova(lm(data$FICO.Range.mean ~ data$Loan.Length))
</code></pre>
<pre><code>## Analysis of Variance Table
##
## Response: data$FICO.Range.mean
## Df Sum Sq Mean Sq F value Pr(>F)
## data$Loan.Length 1 459 459 0.37 0.54
## Residuals 2498 3066619 1228
</code></pre>
<pre><code class="r">anova(lm(data$FICO.Range.mean ~ data$Loan.Purpose))
</code></pre>
<pre><code>## Analysis of Variance Table
##
## Response: data$FICO.Range.mean
## Df Sum Sq Mean Sq F value Pr(>F)
## data$Loan.Purpose 13 179846 13834 11.9 <2e-16 ***
## Residuals 2486 2887233 1161
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
</code></pre>
<pre><code class="r">summary(lm(data$FICO.Range.mean ~ data$Inquiries.in.the.Last.6.Months))
</code></pre>
<pre><code>##
## Call:
## lm(formula = data$FICO.Range.mean ~ data$Inquiries.in.the.Last.6.Months)
##
## Residuals:
## Min 1Q Median 3Q Max
## -68.23 -28.23 -7.99 21.77 121.77
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 710.233 0.866 820.15 <2e-16
## data$Inquiries.in.the.Last.6.Months -2.620 0.567 -4.62 4e-06
##
## (Intercept) ***
## data$Inquiries.in.the.Last.6.Months ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 34.9 on 2496 degrees of freedom
## (2 observations deleted due to missingness)
## Multiple R-squared: 0.00849, Adjusted R-squared: 0.0081
## F-statistic: 21.4 on 1 and 2496 DF, p-value: 3.95e-06
</code></pre>
<h3>
A more powerful linear model</h3>
Let's build a multiple variable regression model with FICO range and other features statistically correlated to Interest rate that predict best its variability<br />
<ol>
<li><strong>FICO Range mean</strong> (50%)</li>
<li><strong>Loan.Length</strong> (18%)</li>
<li><strong>Amount.Funded.By.Investors</strong> (11%)</li>
<li><strong>Amount.Requested</strong> (11%)</li>
</ol>
<pre><code class="r">lm1sum <- summary(lm(data$MyInterest.Rate ~ data$FICO.Range.mean + data$Amount.Requested +
data$Amount.Funded.By.Investors + data$Loan.Length))
lm1sum
</code></pre>
<pre><code>##
## Call:
## lm(formula = data$MyInterest.Rate ~ data$FICO.Range.mean + data$Amount.Requested +
## data$Amount.Funded.By.Investors + data$Loan.Length)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.09763 -0.01453 -0.00135 0.01271 0.10275
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.26e-01 8.53e-03 85.01 < 2e-16 ***
## data$FICO.Range.mean -8.75e-04 1.21e-05 -72.40 < 2e-16 ***
## data$Amount.Requested 6.69e-07 2.23e-07 3.00 0.00270 **
## data$Amount.Funded.By.Investors 7.44e-07 2.24e-07 3.33 0.00088 ***
## data$Loan.Length60 months 3.28e-02 1.12e-03 29.32 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0211 on 2495 degrees of freedom
## Multiple R-squared: 0.746, Adjusted R-squared: 0.746
## F-statistic: 1.83e+03 on 4 and 2495 DF, p-value: <2e-16
</code></pre>
As we can see, this model is <strong>statistically correlated to Interest rate</strong> (p-value < .01).
Moreover, with this model we can predict <strong>74,6%</strong> (Adjusted R-squared: 0.746) of the variability we will see in <strong>Interest rate</strong>. <br />
<h2>
Conclusion </h2>
We found that the <strong>features statistically correlated to Interest rate that predict best its variability</strong> are <br />
<ol>
<li><strong>FICO Range mean</strong> (50%)</li>
<li><strong>Loan.Length</strong> (18%)</li>
<li><strong>Amount.Funded.By.Investors</strong> (11%)</li>
<li><strong>Amount.Requested</strong> (11%)</li>
</ol>
Moreover, there're no moderators between <strong>Interest rate</strong> and <strong>FICO range</strong> , i.e. it is confirmed also <strong>for each loan purpose</strong> / <strong>with and without home ownsership</strong> / <strong>for each US state</strong> (=these variables don't moderate the association between Interest rate and FICO score).<br />
Finally, it's possible to build more powerful linear models with multiple features. As reference, we built one with the above 4 features. We found that it is <strong>statistically correlated to Interest rate</strong> and that we can predict the <strong>74,6% of its variability</strong>. <br />
</body>
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gghttp://www.blogger.com/profile/10478964622265922809noreply@blogger.com6tag:blogger.com,1999:blog-12089192.post-90837423265097353262013-06-24T01:03:00.001-07:002013-06-24T01:03:11.811-07:00An example of data visualization with Tableau<div class="separator" style="clear: both; text-align: center;">
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<br />
I was able to build such a "<i>custom dashboard</i>" in about 1,5 hours since downloading the product.<br />
UI very simple and intuitive. A one-to-one replacement for Excel in quick business data visualization. gghttp://www.blogger.com/profile/10478964622265922809noreply@blogger.com0tag:blogger.com,1999:blog-12089192.post-43677982395106860862013-06-20T06:09:00.001-07:002013-06-20T06:34:38.461-07:00An example of chi-square analysis with SAS: "The association between drinking quantity and alcohol abuse or dependence among middle adult drinkers with and without major depression"<br />
<b>
</b>
<br />
<h2>
<b>1. Introduction</b></h2>
<b>
</b>Alcohol misuse by adults is an important public health concern with
significant consequences such as the detriment of the drinker’s health,
personal relationships, and social standing. On the other hand, small
amounts of alcohol can provide some health benefits and a drink once in a
while is a socially accepted custom. It follows that it is not easy to
predict behaviors such as alcohol abuse or alcohol dependence except
when the above serious consequences emerge. Furthermore, adults
experiencing depression seem to be more likely to drink than those
without depression. This may be because those with depression drink to
soothe the painful condition caused by the depression itself or,
alternately, because they are sensitive to alcohol abuse or dependence
at low level of drinking quantity compared to individual without
depression.
<b></b><br />
<h2>
<b>
1.1 Research Questions</b></h2>
<b>
</b>
<br />
<ol><b>
<li><span style="font-weight: normal;">Is drinking level associated with the experience of alcohol abuse or dependence. </span></li>
<span style="font-weight: normal;">
</span>
<li><b><span style="font-weight: normal;">Is the association between drinking and alcohol abuse or dependence similar for individuals with and without major depression.</span></b></li>
</b></ol>
<b>
</b>
<br />
<h2>
<b>
2 Methods</b></h2>
<b>
</b>
<br />
<h2>
<b>
2.1 Sample</b></h2>
<b>
</b>
<br />
<div class="Unindented">
<ul><b>
<li><span style="font-weight: normal;">Middle Adults (age 35 to 59) who reported daily or nearly daily
drinked in the past year (n=1498) were drawn from the first wave of the
National Epidemiologic Study of Alcohol and Related Conditions (NESARC).</span></li>
<span style="font-weight: normal;">
</span>
<li><span style="font-weight: normal;">NESARC is a nationally representative sample of non-institutionalized adults in the U.S.</span></li>
</b></ul>
</div>
<b>
</b>
<br />
<h2>
<b>
2.2 Measures</b></h2>
<b>
</b>
<br />
<ul><b>
<li><span style="font-weight: normal;">Major depression was assessed using the NIAAA, Alcohol Use and Associated Disabilities Interview Schedule - DSM-IV (AUDADIS-IV)</span></li>
<span style="font-weight: normal;">
</span>
<li><span style="font-weight: normal;">A diagnosis of DSM-IV alcohol abuse requires that a person show a
maladaptive pattern of alcohol use leading to clinically significant
impairment or distress; it requires at least one of four specified abuse
criteria.</span></li>
<span style="font-weight: normal;">
</span>
<li><span style="font-weight: normal;">A diagnosis of DSM-IV alcohol dependence requires that a person meet at least three of seven specified dependence criteria.</span></li>
<span style="font-weight: normal;">
</span>
<li><span style="font-weight: normal;">Current drinking was evaluated through quantity (“<i>How many drinks did you USUALLY have on days when you drank during the last 12 months?</i>”)</span></li>
</b></ul>
<b>
</b>
<br />
<h2>
<b>
3 Results</b></h2>
<b>
</b>
<br />
<h2>
<b>
3.1 Univariate</b></h2>
<b>
</b>
<br />
<div class="Unindented">
<ul><b>
<li><span style="font-weight: normal;">Daily, or nearly daily, middle adult drinkers, drinked an average of 3.3 drinks per day (s.d. 2.5)</span></li>
<span style="font-weight: normal;">
</span>
<li><span style="font-weight: normal;">A total of 28% of daily, or nearly daily, middle adult drinkers met criteria for DSM-IV alcohol abuse or dependence</span></li>
<span style="font-weight: normal;">
</span>
<li><span style="font-weight: normal;">A total of 20.2% met criteria for major depression at some point in their life</span></li>
</b></ul>
</div>
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</b>
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<b><br /></b></div>
<b>
</b>
<h2>
<b>
3.2 Bivariate</b></h2>
<b>
<div class="Unindented">
<ul>
<li><span style="font-weight: normal;">Chi-square analysis showed that among daily, or nearly daily, middle adult drinkers the number of drinks (categorical explanatory) is positively and significantly associated with past year DSM-IV alcohol abuse or dependence (categorical response).
In fact, corresponding to 1 drink of any alcohol usually consumed
(USQUAN=1) we have a total of 5.9% of daily, or nearly daily, middle
adult drinkers meeting the criteria for alcohol abuse or dependence; for
2 drinks (USQUAN=2) we have a total of 16.8%; for 3 drinks (USQUAN=3)
we have a total of 35.5%; for 8 drinks (USQUAN=8) we have a total of
57.8%. X2 = 256.6, 3 df, p < .0001. That is the more drinks daily, or
nearly daily, drinkers drink, the more they are likely to be affected
by alcohol abuse or dependence.</span></li>
<span style="font-weight: normal;">
</span>
<li><span style="font-weight: normal;">Moreover, Bonferroni method showed that among daily, or
nearly daily, middle adult drinkers the number of drinks is positively
and significantly associated with past year DSM-IV alcohol abuse or
dependence for each pair of drinking quantity levels with an overall (or
family-wise) 95% confidence level, i.e. with an overall type I error
rate of 5% (6 comparisons , Bonferroni Adjustment = 0.05 / 6 = 0.008).</span></li>
<span style="font-weight: normal;">
</span>
<li><span style="font-weight: normal;">Daily, or nearly daily, adult drinkers with major depression were significantly more likely to meet the criteria for alcohol abuse or dependence (42%) than those without major depression (24.4%) X2= 37.1, 1 df, p < .0001.</span></li>
</ul>
</div>
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<b> The following six comparisons are necessary to apply Bonferroni method</b> (Bonferroni Adjustment = 0.05 / 6 = 0.008).<br />
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</div>
<h2>
3.3 Moderation</h2>
</b><br />
<div class="Unindented">
Major depression did not moderate
the association between drinking quantity and alcohol abuse or
dependence among middle adult drinkers, i.e. drinking quantity is
positively and significantly associated with alcohol abuse or dependence
for those with major depression (corresponding to 1 drink of any
alcohol usually consumed, i.e. USQUAN=1, we have a total of 14.9% of
daily, or nearly daily, middle adult drinkers meeting the criteria for
alcohol abuse or dependence; for USQUAN=2 have a total of 25.0%; for
USQUAN=3 have a total of 50%; for USQUAN=8 have a total of 73.3%) and
without major depression (for USQUAN=1 have a total of 3.5%; for
USQUAN=2 have a total of 15.2%; for USQUAN=3 have a total of 31.9%; for
USQUAN=8 have a total of 52.9%). Moreover, among daily, or nearly daily,
middle adult drinkers at each level of drinking the probability of
alcohol abuse or dependence was higher among those with major depression
than those without major depression.</div>
<b>
</b><br />
<div class="Unindented">
<br />
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<br />
<br /></div>
<b>
</b>
<br />
<h2>
<b>
4 Discussion</b></h2>
<b>
</b>
<br />
<h2>
<b>
4.1 What might the results mean?</b></h2>
<b>
</b>
<br />
<ul><b>
<li><span style="font-weight: normal;">Drinking quantity is positively and significantly associated
with alcohol abuse or dependence among middle adult drinkers both in
case they had a major depression at some point in their life and in case
they had not.</span></li>
<span style="font-weight: normal;">
</span>
<li><span style="font-weight: normal;">Individuals with major depression seem to be more sensitive to
alcohol abuse or dependence across a range of drinking quantity levels.</span></li>
</b></ul>
<b>
</b>
<br />
<h2>
<b>
4.2 Strengths</b></h2>
<b>
</b>
<br />
<div class="Unindented">
Results are based on a large nationally representative sample of U.S. middle adults daily, or nearly daily, drinkers.</div>
<b>
</b>
<br />
<h2>
<b>
4.3 Limitations</b></h2>
<b>
</b>
<br />
<div class="Unindented">
<ul><b>
<li><span style="font-weight: normal;">The present findings are based on data collected by observing
middle adults daily, or nearly daily, drinkers with and without major
depression and they do not show at which drinking quantity levels or
drinking frequencies alcohol abuse or dependence emerges, i.e. in case
of middle adults drinking less than every day or nearly every day (e.g. 3
to 4 times a week, 2 times a week, etc.)</span></li>
<span style="font-weight: normal;">
</span>
<li><span style="font-weight: normal;">Moreover these findings are based on data collected observing middle
adults daily, or nearly daily, drinkers in the past year and do not
show drinking quantity levels or drinking frequencies of previous years.</span></li>
</b></ul>
</div>
<b>
</b>
<br />
<h2>
<b>
4.4 Recommended Future Research</b></h2>
<b>
</b>
<br />
<div class="Unindented">
Further research is needed to determine whether
current alcohol abuse or depression in middle adult drinkers may be
correlated to high levels of drinking quantity or drinking frequency
when such subjects were younger.</div>
<b>
</b>
<br />
<h2>
<b>
5 SAS Source code</b></h2>
<div class="separator" style="clear: both; text-align: center;">
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gghttp://www.blogger.com/profile/10478964622265922809noreply@blogger.com0tag:blogger.com,1999:blog-12089192.post-57422879047083112452013-05-13T15:43:00.000-07:002013-05-13T15:58:03.149-07:00<html lang="en" xml:lang="en" xmlns="http://www.w3.org/1999/xhtml">
<head>
<meta name="description" content="Social Network Analysis">
<meta name="keywords" content="Social Network Analysis, NetLogo, Political Elections, Cascading Behavior">
<meta name="author" content="Gino Tesei">
<link href="http://elyxer.nongnu.org/lyx.css" media="all" rel="stylesheet" type="text/css"></link>
<title>An application of Cascading Behavior to Political Elections</title>
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<body>
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<h1 class="title">
An application of Cascading Behavior to Political Elections
</h1>
<h2 class="author">
Gino Tesei - April 24, 2013
</h2>
<div class="Verse">
This work was born as my peer assignment inside the course of Social Network Analysis by Prof. Lada Adamic, University of Michigan on <a href="http://www.coursera.org">Coursera</a>. I would like to express my appreciation to Prof. Lada Adamic for the NetLogo model I extended and to the students who review this work for their precious point of view.
</div>
<h2 class="author"><b>Abstract</b></h2>
Political elections are getting hotter and hotter on social networks when such an event occurs. In this work, I consider how new behaviors, practices and conventions can spread new political opinions from person to person through a social network in that case. I focused on two key characteristics of a given social network in case of political elections, i.e. the subjectivity of political issues and the level of resilience inside the network. Hence, two findings are showed. First, higher levels of resilience increases the predictability of political outcome no matter the initial allocation of political opinions. Second, higher levels of variability in payoffs to change opinion decreases the predictability of political outcome.
<h1 class="Part">
Part I. Constructing the model
</h1>
<h2 class="Section">
1 Introduction
</h2>
<div class="Unindented">
In [1] Easley and Kleinber considered how new behaviors, practices, opinions, conventions, and technologies spread from person to person through a social network, as people influence their friends to adopt new ideas. The first basic model considered is the so called <i>Networked Coordination Game</i>, where the situation where each node has a choice between two possible behaviors, labeled A and B, is studied. If nodes v and w are linked by an edge, then there is an incentive for them to have their behaviors match:
</div>
<ul>
<li>
if v and w both adopt behavior A, they each get a payoff of a > 0;
</li>
<li>
if they both adopt B, they each get a payoff of b > 0; and
</li>
<li>
if they adopt opposite behaviors, they each get a payoff of 0.
</li>
</ul>
<div class="Unindented">
Suppose that a p fraction of v’s neighbors have behavior A, and a (1 − p) fraction have behavior B, hence it is showed that A is the better choice if <span class="formula"><i>p</i></span><span class="formula"> ≥ <i>b</i> ⁄ (<i>a</i> + <i>b</i>)</span>. Moreover, it is used the following terminology:
</div>
<blockquote class="Quote">
<b><i>Definition 1 </i></b>Consider a set of initial adopters who start with a new behavior A, while every other node starts with behavior B. Nodes then repeatedly evaluate the decision to switch from B to A using a threshold of q. If the resulting cascade of adoptionsof A eventually causes every node to switch from B to A, then we say that the set of initial adopters causes a complete cascade at threshold q.
</blockquote>
<blockquote class="Quote">
<b><i>Definition 2 </i></b>We say that a cluster of density p is a set of nodes such that each node in the set has at least a p fraction of its network neighbors in the set.
</blockquote>
<blockquote class="Quote">
Hence the following Claim is proved.
</blockquote>
<blockquote class="Quote">
<b><i>Claim 1 </i></b>Consider a set of initial adopters of behavior A, with a threshold of q for nodes in the remaining network to adopt behavior A.
</blockquote>
<blockquote class="Quote">
<i>(i) If the remaining network contains a cluster of density greater than 1 − q, then the set of initial adopters will not cause a complete cascade. </i>
</blockquote>
<blockquote class="Quote">
<i>(ii) Moreover, whenever a set of initial adopters does not cause a complete cascade with threshold q, the remaining network must contain a cluster of density greater than 1 − q. </i>
</blockquote>
<div class="Unindented">
Hence, the <i>heterogeneous thresholds model</i> is introduced where each node v, we define a payoff <span class="formula"><i>a</i><sub><i>v</i></sub></span> — labeled so that it is specific to v — that it receives when it coordinates with someone on behavior A, and we define a payoff <span class="formula"><i>b</i><sub><i>v</i></sub></span> that it receives when it coordinates with someone on behavior B.
</div>
<div class="Indented">
Finally, the concept of <i>cascade capacity</i> of the network is introduced as the largest value of the threshold q for which some finite set of early adopters can cause a complete cascade. So it is proved that the cascade capacity of the infinite path is at least 1/2 while and there is no network in which the cascade capacity exceeds 1/2 .
</div>
<div class="Indented">
Simulating side, the NetLogo model in [2] shows 2 examples from [1] and Lada Adamic Facebook network with the following parameters:
</div>
<ul>
<li>
payoffs a, b, c > 0 fixed for any node;
</li>
<li>
bilingual option;
</li>
<li>
initial probability blue value.
</li>
</ul>
<h2 class="Subsection">
1.1 Assumptions, parameters, how the model works
</h2>
<div class="Unindented">
Here I will model to the case of <i>political elections, </i>an hot topic on social networks when such an event occurs. Specifically, I’ll build in [3] an extension of model [2] with
</div>
<ol>
<li>
<i>heterogeneous thresholds</i> in order to take into account that political issues are subjective and payoffs to change opinion are subjective as well; hence, for each node v, payoffs <span class="formula"><i>a</i><sub><i>v</i></sub></span> = <span class="formula"><span class="overline"><i>a</i></span></span><span class="formula">±</span><span class="formula"><i>r</i><sub><i>a</i></sub></span> , with <span class="formula"><i>r</i><sub><i>a</i></sub></span><span class="formula"><i>ϵ</i></span>[-<span class="formula"><i>k</i><sub><i>a</i></sub></span>, <span class="formula"><i>k</i><sub><i>a</i></sub></span>] is random and where 0<span class="formula"> ≤ </span><span class="formula"><i>k</i><sub><i>a</i></sub></span><span class="formula"> ≤ </span><span class="formula"><span class="bar"><i>a</i></span></span> is a futher parameter of the model; on the same way, <span class="formula"><i>b</i><sub><i>v</i></sub></span> = <span class="formula"><span class="bar"><i>b</i></span></span><span class="formula">±</span><span class="formula"><i>r</i><sub><i>b</i></sub></span> with <span class="formula"><i>r</i><sub><i>b</i></sub></span><span class="formula"><i>ϵ</i></span>[-<span class="formula"><i>k</i><sub><i>b</i></sub></span>, <span class="formula"><i>k</i><sub><i>b</i></sub></span>] is random and where 0<span class="formula"> ≤ </span><span class="formula"><i>k</i><sub><i>b</i></sub></span><span class="formula"> ≤ </span><span class="formula"><span class="bar"><i>b</i></span></span> is a futher parameter of the model;
</li>
<li>
no bilingual option (= i.e. adopting both A and B), that is not applicable in case of political elections;
</li>
<li>
% of “resilient” nodes (<span class="formula"><i>R</i></span>) impossible to influence and that never change their initial opinion. This assumption is made to take into account people that, for instance, will vote ever a given party, no matter what their neighbors do.
</li>
</ol>
<div class="Unindented">
In order to control the above assumptions, in addition to UI items of NetLogo model in [2], the user has (likewise, in order to get the second point, the bilingual slider has been removed and the related source code commented)
<ul>
<li>
the reslience slider (0 to 100) to set up the % of nodes (drawn with the shape of a “cow”) resilient, i.e. that never change their initial opinion no matter what their neighbors think;
</li>
<li>
<span class="formula"><i>k</i><sub><i>a</i></sub></span> and <span class="formula"><i>k</i><sub><i>b</i></sub></span> sliders (0 to 100) to set up the maximum payoffs <span class="formula"><i>a</i><sub><i>v</i></sub></span> and <span class="formula"><i>b</i><sub><i>v</i></sub></span>, so that <span class="formula"><i>a</i><sub><i>v</i></sub></span> = <span class="formula"><span class="overline"><i>a</i></span></span><span class="formula">±</span><span class="formula"><i>r</i><sub><i>a</i></sub></span> , with <span class="formula"><i>r</i><sub><i>a</i></sub></span><span class="formula"><i>ϵ</i></span>[-<span class="formula"><i>k</i><sub><i>a</i></sub></span>, <span class="formula"><i>k</i><sub><i>a</i></sub></span>] is random and where 0<span class="formula"> ≤ </span><span class="formula"><i>k</i><sub><i>a</i></sub></span><span class="formula"> ≤ </span><span class="formula"><span class="bar"><i>a</i></span></span>; and <span class="formula"><i>b</i><sub><i>v</i></sub></span> = <span class="formula"><span class="bar"><i>b</i></span></span><span class="formula">±</span><span class="formula"><i>r</i><sub><i>b</i></sub></span> with <span class="formula"><i>r</i><sub><i>b</i></sub></span><span class="formula"><i>ϵ</i></span>[-<span class="formula"><i>k</i><sub><i>b</i></sub></span>, <span class="formula"><i>k</i><sub><i>b</i></sub></span>] is random and where 0<span class="formula"> ≤ </span><span class="formula"><i>k</i><sub><i>b</i></sub></span><span class="formula"> ≤ </span><span class="formula"><span class="bar"><i>b</i></span></span>.
</li>
</ul>
<div class="Unindented">
After such initial setting, the user can
</div>
<ul>
<li>
choose a network topology among “setup-fb” (Lada Adamic Facebook network), “setup19_4” (a network topology from [1]), “setup19_3” (another network topology from [1]), “setup-line” (line network topology), and
</li>
<li>
allocate opinions,
</li>
<li>
update opinions untill getting an equilibrium.
</li>
</ul>
<div class="Unindented">
In order to accomplish the latter step, the user can either use the “alloc-opinion” button after tuning up the initial probability of blue nodes or set up one by one each node as red or blue. It is pobbile to play with a partner with the first person picking one blue node and the second one picking onother red one, and so on. Moreover, the first person can choose a “cow” node (i.e. a resilient node) and, as soon as they are available, the second person can do the same.
</div>
<div class="Indented">
Finally, it is possbile to update the color of nodes with the “update” button in order to get start the game. After some hundreds of ticks an equilibrium is typically reached as it can be shown by the following chart.
</div>
<div class="Indented"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjAxqylj81R4FA25oO8QDiaTia5_Bp5RVs-RA8GSNCxGxss_v2rMaBAg5RukOrjRnWGOI606QVzIGplQHnBcXQOu3wIu3rYWp9PUpbQzy61G1SRsENf-NMuIF_V6zgt3RMcZB7N/s1600/equil.png" imageanchor="1" ><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjAxqylj81R4FA25oO8QDiaTia5_Bp5RVs-RA8GSNCxGxss_v2rMaBAg5RukOrjRnWGOI606QVzIGplQHnBcXQOu3wIu3rYWp9PUpbQzy61G1SRsENf-NMuIF_V6zgt3RMcZB7N/s320/equil.png" /></a>
</div>
<div class="Indented">
Hence, I inserted the stop command after 500 ticks. In order to monitor network evolution, the following monitors have been inserted:
</div>
<ul>
<li>
<i>init-blue</i>: initial number of blue nodes;
</li>
<li>
<i>init-red</i>: initial number of red nodes;
</li>
<li>
<i>resilient-red</i>: number of red cows;
</li>
<li>
<i>resilient-blue</i>: number of blue cows;
</li>
<li>
<i>num-red</i>: number of red nodes;
</li>
<li>
<i>num-blue</i>: number of blue nodes;
</li>
<li>
<i>delta-in / delta-out</i> (<i>ratio</i>): <span class="formula"><span class="fraction"><span class="ignored">(</span><span class="numerator">(<i>num</i> − <i>blue</i>) − (<i>num</i> − <i>red</i>)</span><span class="ignored">)/(</span><span class="denominator">(<i>init</i> − <i>blue</i>) − (<i>init</i> − <i>red</i>)</span><span class="ignored">)</span></span></span>.
</li>
</ul>
<div class="Unindented">
In the following, I will refer to the ratio <i>delta-in/delta-ou</i>t just with <i>ratio</i>.
</div>
<div class="Indented">
For instance, let suppose we choose se the “setup-fb” with
</div>
<ul>
<li>
identical payoffs to change opinion, i.e. a = b = 3,
</li>
<li>
probability to have initally A opinion equal to have inially B opinion, i.e. init-blue-prob = 0.5,
</li>
<li>
34% of persons impossible to influence, i.e. resilience level = 34,
</li>
<li>
no variability in payoffs to change opinion, i.e. <span class="formula"><i>k</i><sub><i>a</i></sub></span>= <span class="formula"><i>k</i><sub><i>b</i></sub> = 0</span>.
</li>
</ul>
<div class="Unindented">
These settings get the following results after simulating elections:
</div>
<ul>
<li>
<i>init-blue</i>: 76;
</li>
<li>
<i>init-red</i>: 74;
</li>
<li>
<i>resilient-red</i>: 27;
</li>
<li>
<i>resilient-blue</i>: 25;
</li>
<li>
<i>num-red</i>: 106;
</li>
<li>
<i>num-blue</i>: 44;
</li>
<li>
<i>delta-in / delta-out</i> (<i>ratio</i>): -31.
</li>
</ul>
<div class="Unindented">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj7QwdiD1ny2otxI5TghnvCscKCc8NGE2K1UOr5lrNGJkYwkBFzF6jAcSUjvDtMkAXwA2-cLNyPsoWq9HW7w1uTPdZkRfRHXdEtuoigvJTcI_ElZpxzaZY0YfWMg12AoDwR85xT/s1600/simul-1.png" imageanchor="1" ><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEj7QwdiD1ny2otxI5TghnvCscKCc8NGE2K1UOr5lrNGJkYwkBFzF6jAcSUjvDtMkAXwA2-cLNyPsoWq9HW7w1uTPdZkRfRHXdEtuoigvJTcI_ElZpxzaZY0YfWMg12AoDwR85xT/s320/simul-1.png" /></a>
</div>
<div class="Indented">
<i>Ceteris paribus</i> (i.e. with the same input parameters), the output <i>ratio</i> can be different. The first question is how much it is different. In order to study statistically this problem, the “100 series” button has been created. By using it, for 100 times NetLogo will
</div>
<ul>
<li>
setup the Facebook network (setup-fb) with the same input input parameters,
</li>
<li>
allocate and update opinions untill getting an equilibrium.
</li>
</ul>
<div class="Unindented">
At the end of such 100 simulations,
</div>
<ul>
<li>
the monitor “<i>mean</i>” will report the statistical mean of ratio,
</li>
<li>
the monitor “<i>variance</i>” will report the statistical mean of ratio.
</li>
<li>
the monitor “<i>standard deviation</i>” will report the standard deviation of ratio.
</li>
</ul>
<div class="Unindented">
The second question is why the ratio can be different starting with the same input parameters. I will answer to this question in the following sections.
</div>
<h2 class="Subsection">
1.2 Source Code
</h2>
<div class="Unindented">
Source code can be fully download from [3].
</div>
<h2 class="Section">
2 An interpretation of the model
</h2>
<div class="Unindented">
The model in [3] is an extension of the one in [2]. Specifically, the model in [3] collapses in the one in [2] by setting
</div>
<ul>
<li>
0% of persons impossible to influence, i.e. resilience level = 0,
</li>
<li>
homogeneous payoffs to change political opinion, i.e. <span class="formula"><i>k</i><sub><i>a</i></sub></span>= <span class="formula"><i>k</i><sub><i>b</i></sub> = 0</span>.
</li>
</ul>
<div class="Unindented">
So, we can ask
</div>
<ol>
<li>
what kind of impacts we have in the model assuming that a part of nodes (“cows”) are politically resilient to external influence as it happens effectively during political elections?
</li>
<li>
what kind of impacts we have in the model assuming that nodes have <i>heterogeneous thresholds</i> in order to take into account the subjectivity of political issues (= the payoffs to change opinion are subjective)?
</li>
</ol>
<div class="Unindented">
I show an interesting finding. The more the percentage of persons politically resilient to external influence inside the network (“cows”) the less the importance of network topology in order to predict the final political outcome. This can be esily seen thinking at the extreme case, i.e. <span class="formula"><i>resilience</i></span> = 100 corresponding to the case where any node is a “cow”. In that case we would easily predict ratio = 1, i.e. no one will change opinion. In section 3.1 I will show that such a correlation holds in the general case.
</div>
<h1 class="Part">
Part II. Model testing
</h1>
<h2 class="Section">
3 Misuring two key characteristics of the model
</h2>
<div class="Unindented">
In this section two key characteristics of the model will be measured
</div>
<ul>
<li>
higher levels of resilience increases the predictability of political outcome no matter the network topology and the initial allocation of red and blue nodes;
</li>
<li>
higher levels of variability in payoffs to change opinion decreases the predictability of political outcome.
</li>
</ul>
<h2 class="Subsection">
3.1 Higher levels of resilience increases the predictability of political outcome no matter the initial allocation of red and blue nodes
</h2>
<div class="Unindented">
In order to study such a problem, I performed the following simulations.
</div>
<div class="Indented">
<table>
<tr>
<td align="center" valign="top">
resilience
</td>
<td align="center" valign="top">
<span class="formula"><i>k</i><sub><i>a</i></sub></span>
</td>
<td align="center" valign="top">
<span class="formula"><i>k</i><sub><i>b</i></sub></span>
</td>
<td align="center" valign="top">
init-blue-prob.
</td>
<td align="center" valign="top">
<span class="formula"><i><span class="withsymbol"><span class="symbolover">^</span><span class="undersymbol">mean(ratio)</span></span></i></span>
</td>
<td align="center" valign="top">
<span class="formula"><i><span class="withsymbol"><span class="symbolover">^</span><span class="undersymbol">variance(ratio)</span></span></i></span>
</td>
<td align="center" valign="top">
<span class="formula"><i><span class="withsymbol"><span class="symbolover">^</span><span class="undersymbol">std − dev(ratio)</span></span></i></span>
</td>
</tr>
<tr>
<td align="center" valign="top">
0
</td>
<td align="center" valign="top">
0
</td>
<td align="center" valign="top">
0
</td>
<td align="center" valign="top">
0.5
</td>
<td align="center" valign="top">
3.83
</td>
<td align="center" valign="top">
899.18
</td>
<td align="center" valign="top">
29.98
</td>
</tr>
<tr>
<td align="center" valign="top">
10
</td>
<td align="center" valign="top">
0
</td>
<td align="center" valign="top">
0
</td>
<td align="center" valign="top">
0.5
</td>
<td align="center" valign="top">
3.35
</td>
<td align="center" valign="top">
520.49
</td>
<td align="center" valign="top">
22.81
</td>
</tr>
<tr>
<td align="center" valign="top">
20
</td>
<td align="center" valign="top">
0
</td>
<td align="center" valign="top">
0
</td>
<td align="center" valign="top">
0.5
</td>
<td align="center" valign="top">
3.60
</td>
<td align="center" valign="top">
218.32
</td>
<td align="center" valign="top">
14.77
</td>
</tr>
<tr>
<td align="center" valign="top">
50
</td>
<td align="center" valign="top">
0
</td>
<td align="center" valign="top">
0
</td>
<td align="center" valign="top">
0.5
</td>
<td align="center" valign="top">
3.21
</td>
<td align="center" valign="top">
111.95
</td>
<td align="center" valign="top">
10.58
</td>
</tr>
<tr>
<td align="center" valign="top">
60
</td>
<td align="center" valign="top">
0
</td>
<td align="center" valign="top">
0
</td>
<td align="center" valign="top">
0.5
</td>
<td align="center" valign="top">
2.01
</td>
<td align="center" valign="top">
82.29
</td>
<td align="center" valign="top">
9.34
</td>
</tr>
<tr>
<td align="center" valign="top">
70
</td>
<td align="center" valign="top">
0
</td>
<td align="center" valign="top">
0
</td>
<td align="center" valign="top">
0.5
</td>
<td align="center" valign="top">
1.42
</td>
<td align="center" valign="top">
81.96
</td>
<td align="center" valign="top">
9.05
</td>
</tr>
<tr>
<td align="center" valign="top">
90
</td>
<td align="center" valign="top">
0
</td>
<td align="center" valign="top">
0
</td>
<td align="center" valign="top">
0.5
</td>
<td align="center" valign="top">
1.32
</td>
<td align="center" valign="top">
13.78
</td>
<td align="center" valign="top">
1.94
</td>
</tr>
<tr>
<td align="center" valign="top">
100
</td>
<td align="center" valign="top">
0
</td>
<td align="center" valign="top">
0
</td>
<td align="center" valign="top">
0.5
</td>
<td align="center" valign="top">
0.92
</td>
<td align="center" valign="top">
0.07
</td>
<td align="center" valign="top">
0.27
</td>
</tr>
</table>
</div>
<div class="Indented">
A regression of observed mean of ratio has been performed. The value of <span class="formula"><i>R</i><sup>2</sup></span>tells us the proportion of the variance in the forecast variable (i.e. the mean of ratio) that can be accounted by the predictor variable (i.e. resilience). Hence, if we know the resilience we can predict 89% of the variability we will see in the mean of ratio. It is a pretty good result (a typical threshold value for <span class="formula"><i>R</i><sup>2</sup></span> is 0.37).
</div>
<div class="Indented">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgp1ePH075XEacbRey8FuuBrpWCq5OarNXCXFiDrzFuWUjbx8wHRKVmt-qrac_vL_nTek5tHBW-GrPojK3fWueDU3m5AN17Sv-N9-uF5CZMuLMOUnId_-zMN2FJ94xSB_SBzyg0/s1600/corr_1.png" imageanchor="1" ><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgp1ePH075XEacbRey8FuuBrpWCq5OarNXCXFiDrzFuWUjbx8wHRKVmt-qrac_vL_nTek5tHBW-GrPojK3fWueDU3m5AN17Sv-N9-uF5CZMuLMOUnId_-zMN2FJ94xSB_SBzyg0/s320/corr_1.png" /></a>
</div>
<div class="Indented">
In the same way, if we know the resilience we can predict 72% of the variability we will see in the variance of ratio. It is a pretty good result (a typical threshold value for <span class="formula"><i>R</i><sup>2</sup></span> is 0.37).
</div>
<div class="Indented">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiRGo_uIHhxGRKNiCTUXara5d6VOmD3E4_BPy_A0nDZpiu9-lO2riHP_jZ6YZgNSXEp3LFMAerK2isvxMnmJFpy7RafLkcz_MbRiUG-enorr31gXq8ptWmtrmkbnaf3RhcwyqPR/s1600/var_1.png" imageanchor="1" ><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiRGo_uIHhxGRKNiCTUXara5d6VOmD3E4_BPy_A0nDZpiu9-lO2riHP_jZ6YZgNSXEp3LFMAerK2isvxMnmJFpy7RafLkcz_MbRiUG-enorr31gXq8ptWmtrmkbnaf3RhcwyqPR/s320/var_1.png" /></a>
</div>
<h2 class="Subsection">
3.2 Higher levels of variability in payoffs to change opinion decreases the predictability of political outcome
</h2>
<div class="Unindented">
In order to study such a problem, I performed the following simulations.
</div>
<div class="Indented">
<table align="left">
<tr>
<td align="center" valign="top">
resilience
</td>
<td align="center" valign="top">
<span class="formula"><i>k</i><sub><i>a</i></sub></span>
</td>
<td align="center" valign="top">
<span class="formula"><i>k</i><sub><i>b</i></sub></span>
</td>
<td align="center" valign="top">
init-blue-prob.
</td>
<td align="center" valign="top">
<span class="formula"><i><span class="withsymbol"><span class="symbolover">^</span><span class="undersymbol">mean(ratio)</span></span></i></span>
</td>
<td align="center" valign="top">
<span class="formula"><i><span class="withsymbol"><span class="symbolover">^</span><span class="undersymbol">variance(ratio)</span></span></i></span>
</td>
<td align="center" valign="top">
<span class="formula"><i><span class="withsymbol"><span class="symbolover">^</span><span class="undersymbol">std − dev(ratio)</span></span></i></span>
</td>
</tr>
<tr>
<td align="center" valign="top">
0
</td>
<td align="center" valign="top">
0
</td>
<td align="center" valign="top">
0
</td>
<td align="center" valign="top">
0.5
</td>
<td align="center" valign="top">
4.46
</td>
<td align="center" valign="top">
705.60
</td>
<td align="center" valign="top">
26.56
</td>
</tr>
<tr>
<td align="center" valign="top">
0
</td>
<td align="center" valign="top">
10
</td>
<td align="center" valign="top">
10
</td>
<td align="center" valign="top">
0.5
</td>
<td align="center" valign="top">
4.54
</td>
<td align="center" valign="top">
731.32
</td>
<td align="center" valign="top">
27.04
</td>
</tr>
<tr>
<td align="center" valign="top">
0
</td>
<td align="center" valign="top">
20
</td>
<td align="center" valign="top">
20
</td>
<td align="center" valign="top">
0.5
</td>
<td align="center" valign="top">
2.99
</td>
<td align="center" valign="top">
583.03
</td>
<td align="center" valign="top">
24.14
</td>
</tr>
<tr>
<td align="center" valign="top">
0
</td>
<td align="center" valign="top">
30
</td>
<td align="center" valign="top">
30
</td>
<td align="center" valign="top">
0.5
</td>
<td align="center" valign="top">
2.27
</td>
<td align="center" valign="top">
407.61
</td>
<td align="center" valign="top">
20.18
</td>
</tr>
<tr>
<td align="center" valign="top">
0
</td>
<td align="center" valign="top">
40
</td>
<td align="center" valign="top">
40
</td>
<td align="center" valign="top">
0.5
</td>
<td align="center" valign="top">
6.34
</td>
<td align="center" valign="top">
429.23
</td>
<td align="center" valign="top">
20.71
</td>
</tr>
<tr>
<td align="center" valign="top">
0
</td>
<td align="center" valign="top">
50
</td>
<td align="center" valign="top">
50
</td>
<td align="center" valign="top">
0.5
</td>
<td align="center" valign="top">
6.19
</td>
<td align="center" valign="top">
617.39
</td>
<td align="center" valign="top">
24.84
</td>
</tr>
<tr>
<td align="center" valign="top">
0
</td>
<td align="center" valign="top">
60
</td>
<td align="center" valign="top">
60
</td>
<td align="center" valign="top">
0.5
</td>
<td align="center" valign="top">
6.64
</td>
<td align="center" valign="top">
447.33
</td>
<td align="center" valign="top">
21.15
</td>
</tr>
<tr>
<td align="center" valign="top">
0
</td>
<td align="center" valign="top">
80
</td>
<td align="center" valign="top">
80
</td>
<td align="center" valign="top">
0.5
</td>
<td align="center" valign="top">
8.69
</td>
<td align="center" valign="top">
424.75
</td>
<td align="center" valign="top">
20.61
</td>
</tr>
<tr>
<td align="center" valign="top">
0
</td>
<td align="center" valign="top">
100
</td>
<td align="center" valign="top">
100
</td>
<td align="center" valign="top">
0.5
</td>
<td align="center" valign="top">
5.00
</td>
<td align="center" valign="top">
827.24
</td>
<td align="center" valign="top">
28.76
</td>
</tr>
</table>
</div>
<div class="Indented">
Here a regression of observed mean of ratio has been performed. The value of <span class="formula"><i>R</i><sup>2</sup></span>tells us the proportion of the variance in the forecast variable (i.e. the mean of ratio) that can be accounted by the predictor variable (i.e. threshold variability <span class="formula"><i>k</i><sub><i>a</i></sub> = <i>k</i><sub><i>b</i></sub></span>). Hence, if we know threshold variability we can predict 31% of the variability we will see in the mean of ratio. It is not a good result (a typical threshold value for <span class="formula"><i>R</i><sup>2</sup></span> is 0.37).
</div>
<div class="Indented">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjJvGJxAPsd8lJiwj0bbD4vPdliRHe_wvtSCLe5dyOxrpuC-YicDIy2Bb87TPGuxiaWuY1XkDbqwE-4HEoNuwyKIQ0h2Zkm8-AmaEMjc59vgcm7TfxCUVYurGQpTyWNpV5My9Gz/s1600/mean_2.png" imageanchor="1" ><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjJvGJxAPsd8lJiwj0bbD4vPdliRHe_wvtSCLe5dyOxrpuC-YicDIy2Bb87TPGuxiaWuY1XkDbqwE-4HEoNuwyKIQ0h2Zkm8-AmaEMjc59vgcm7TfxCUVYurGQpTyWNpV5My9Gz/s320/mean_2.png" /></a>
</div>
<div class="Indented">
In the same way, if we know threshold variability we can predict 0.12% of the variability we will see in the variance of ratio. It is not a good result (a typical threshold value for <span class="formula"><i>R</i><sup>2</sup></span> is 0.37).
</div>
<div class="Indented">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiyJJQQVMChI69tHomYtC7oUbBv5MTqPDa_vYjbrZMNbuHCKXHaumsG0l_aQF7KvZRMH9nxTNeQ4OiAGdlGhNAgvNK0Q8PAvcew16vH8jtP206Vxw31SqXoBbDArFPXm0UTLITf/s1600/var_2.png" imageanchor="1" ><img border="0" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiyJJQQVMChI69tHomYtC7oUbBv5MTqPDa_vYjbrZMNbuHCKXHaumsG0l_aQF7KvZRMH9nxTNeQ4OiAGdlGhNAgvNK0Q8PAvcew16vH8jtP206Vxw31SqXoBbDArFPXm0UTLITf/s320/var_2.png" /></a>
</div>
<h2 class="Section">
4 A comparable existing model chosen and simulated
</h2>
<div class="Unindented">
The comparable existing model chosen is the one in [2], as the model in [3] is an extension of the one in [2]. Specifically, the model in [3] simulates in the one in [2] by setting
</div>
<ul>
<li>
0% of persons impossible to influence, i.e. resilience level = 0,
</li>
<li>
homogeneous payoffs to change political opinion, i.e. <span class="formula"><i>k</i><sub><i>a</i></sub></span>= <span class="formula"><i>k</i><sub><i>b</i></sub> = 0</span>.
</li>
</ul>
<div class="Unindented">
I simulated the model in [2] with the one in [3] both in sub-section 3.1 and in sub-section 3.2. For instance, in the former case the following results are produced.
</div>
<div class="Indented">
<table>
<tr>
<td align="center" valign="top">
resilience
</td>
<td align="center" valign="top">
<span class="formula"><i>k</i><sub><i>a</i></sub></span>
</td>
<td align="center" valign="top">
<span class="formula"><i>k</i><sub><i>b</i></sub></span>
</td>
<td align="center" valign="top">
init-blue-prob.
</td>
<td align="center" valign="top">
<span class="formula"><i><span class="withsymbol"><span class="symbolover">^</span><span class="undersymbol">mean(ratio)</span></span></i></span>
</td>
<td align="center" valign="top">
<span class="formula"><i><span class="withsymbol"><span class="symbolover">^</span><span class="undersymbol">variance(ratio)</span></span></i></span>
</td>
<td align="center" valign="top">
<span class="formula"><i><span class="withsymbol"><span class="symbolover">^</span><span class="undersymbol">std − dev(ratio)</span></span></i></span>
</td>
</tr>
<tr>
<td align="center" valign="top">
0
</td>
<td align="center" valign="top">
0
</td>
<td align="center" valign="top">
0
</td>
<td align="center" valign="top">
0.5
</td>
<td align="center" valign="top">
3.83
</td>
<td align="center" valign="top">
899.18
</td>
<td align="center" valign="top">
29.98
</td>
</tr>
</table>
</div>
<h2 class="Section">
5 A comparison between two models
</h2>
<div class="Unindented">
The questions that the model in [3] can answer and that the model in [2] can not are
</div>
<ol>
<li>
what kind of impacts we have in the model assuming that a part of nodes (“cows”) are politically resilient to external influence as it happens effectively during political elections?
</li>
<li>
what kind of impacts we have in the model assuming that nodes have <i>heterogeneous thresholds</i> in order to take into account the subjectivity of political issues (= the payoffs to change opinion are subjective)?
</li>
</ol>
<div class="Unindented">
At this regard, sub-section 3.1 and 3.2 claim that
</div>
<ul>
<li>
higher levels of resilience increases the predictability of political outcome no matter the initial allocation of red and blue nodes;
</li>
<li>
higher levels of variability in payoffs to change opinion decreases the predictability of political outcome.
</li>
</ul>
<h2 class="Section">
6 Step by step instructions and key points in source code
</h2>
<div class="Unindented">
In this section I will provide
</div>
<ul>
<li>
step by step instructions for replicating the evaluation; and
</li>
<li>
the related key points in source code.
</li>
</ul>
<h2 class="Subsection">
<a class="toc" href="http://www.blogger.com/null" name="toc-Subsection-6.1">6.1</a> Step by step instructions
</h2>
<div class="Unindented">
Download NetLogo model from [3]. Open and set up
</div>
<ul>
<li>
Facebook network (“setup-fb” button),
</li>
<li>
identical payoffs to change opinion, i.e. a = b = 3,
</li>
<li>
probability to have initally A opinion equal to have inially B opinion, i.e. init-blue-prob = 0.5,
</li>
<li>
0% of persons impossible to influence, i.e. resilience level = 0,
</li>
<li>
homogeneous payoffs to change political opinion, i.e. <span class="formula"><i>k</i><sub><i>a</i></sub></span>= <span class="formula"><i>k</i><sub><i>b</i></sub> = 0</span>.
</li>
</ul>
<div class="Unindented">
Hence,
</div>
<ul>
<li>
allocate opinions (“alloc-opinion” button),
</li>
<li>
update opinions (“alloc-opinion” button) untill getting an equilibrium.
</li>
</ul>
<div class="Unindented">
On the other side, download NetLogo model from [2]. Open and set up
</div>
<ul>
<li>
Facebook network (“setup-fb” button),
</li>
<li>
identical payoffs to change opinion, i.e. a = b = 3,
</li>
<li>
probability to have initally A opinion equal to have inially B opinion, i.e. init-blue-prob = 0.5.
</li>
</ul>
<div class="Unindented">
Hence,
</div>
<ul>
<li>
allocate opinions (“alloc-opinion” button),
</li>
<li>
update opinions (“alloc-opinion” button) untill getting an equilibrium.
</li>
</ul>
<h2 class="Subsection">
<a class="toc" href="http://www.blogger.com/null" name="toc-Subsection-6.2">6.2</a> Key points in source code
</h2>
<div class="Unindented">
In order to implement resilient nodes (“cows”) and heterogeneous thresholds, the following turtles-own variables must be declared.
</div>
<div class="Indented">
<div class="listing">
<pre class="listing">turtles-own [
<i>resilient
a-node ;; heterogeneous payoff
b-node ;; heterogeneous payoff </i>
]
</pre>
</div>
</div>
<div class="Indented">
Hence, the update procedure must be properly modified.
</div>
<div align="left" class="Indented">
<div align="left" class="listing">
<pre align="left" class="listing">...
<i>ask turtles [
if resilient = 0 [
if any? link-neighbors with [color = blue or color = red] [
let payoff-a a-node * count link-neighbors with [color = blue]
let payoff-b b-node * count link-neighbors with [color = red]
set color blue
if (payoff-a < payoff-b) [
set color red
]
if ((payoff-a = payoff-b) and (random 100 < 50)) [
set color red
]
]
] ]
...
tick
...</i>
</pre>
</div>
</div>
<div class="Indented">
For other minor modifications, please refer to source code from [3].
</div>
<h1 class="Part">
Part III. Interpretation
</h1>
<h2 class="Section">
7 Limitations of data
</h2>
<div class="Unindented">
The main limitation is that conclusions of sub-section 3.1 and 3.2 are derived from a single network topology (Lada Adamic Facebook network). In order to extend such conclusions to the general case, simulations and related regressions of sub-section 3.1 and 3.2 should be performed in a wider range of network topologies.
</div>
<div class="Indented">
Another limitations is that conclusions of sub-section 3.1 and 3.2 are derived starting from a statistically initial equal situation (i.e. initial number of red nodes statistically equal to the number of initial blue nodes). In order to extend such conclusions to the general case, simulations and related regressions of sub-section 3.1 and 3.2 should be performed in a wider range of inital political allocation patterns.
</div>
<h2 class="Section">
8 Insights from the analysis
</h2>
<div class="Unindented">
Conclusions of sub-section 3.1 and 3.2 claim that
</div>
<ul>
<li>
higher levels of resilience increases the predictability of political outcome no matter the network topology and the initial allocation of red and blue nodes;
</li>
<li>
higher levels of variability in payoffs to change opinion decreases the predictability of political outcome.
</li>
</ul>
<div class="Unindented">
Hence,
</div>
<ul>
<li>
samples of social network comminities could be studied in order to asses the level of political resilience (e.g. members can answer to some questionaries). If such a level is high (e.g. forums where people have same political opinions), then the model could be used in order to predict and influence political outcome inside that community (e.g. putting strategically some “cows” inside the network).
</li>
<li>
samples of social network comminities could be studied in order to asses the level of variability in payoffs to change political opinion (e.g. members can answer to some questionaries). If such a level is high, we know is very hard to influence and predict political outcome inside the whole community. A strategy could be trying to cut the community (e.g. putting strategically some “cows” around weak ties of community) and repeat the process on the two seprate communities.
</li>
</ul>
<h2 class="biblio">
References
</h2>
<div class="biblio">
[1] Easley and Kleinberg, Networks, Crowds and Markets, Ch19.Cascading Behavior in Networks
</div>
<div class="biblio">
[2] Lada Adamic, NetLogo Model (<a href="http://spark-public.s3.amazonaws.com/sna/netlearn/NetLogo502/CascadeModel.html">http://spark-public.s3.amazonaws.com/sna/netlearn/NetLogo502/CascadeModel.html</a>)
</div>
<div class="biblio">
[3] Gino Tesei, NetLogo Model for political elections (<a href="https://docs.google.com/file/d/0B1zSgDfZgjLTY1REdjZlR3JCcU0/edit?usp=sharing">https://docs.google.com/file/d/0B1zSgDfZgjLTY1REdjZlR3JCcU0/edit?usp=sharing</a>)
</div>
<hr class="footer" />
</div>
</body>
</html>
gghttp://www.blogger.com/profile/10478964622265922809noreply@blogger.com0tag:blogger.com,1999:blog-12089192.post-51261758422422755122013-04-07T22:16:00.002-07:002013-04-07T22:16:07.541-07:00My Facebook network 2 <div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhGJdvc2UNi69f9_VMX8sIQp93VNglqV8vjVh8EzgQTHgjQYxwck1H478B5-xmXwt5NgPZm1gltKUaI0tFtVxJfR_n6_4FPQ_nGlDc93HMUZQfaz_dP4m_lEOlC0a1AxVAZav03/s1600/my_facebook_netwoork_2.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhGJdvc2UNi69f9_VMX8sIQp93VNglqV8vjVh8EzgQTHgjQYxwck1H478B5-xmXwt5NgPZm1gltKUaI0tFtVxJfR_n6_4FPQ_nGlDc93HMUZQfaz_dP4m_lEOlC0a1AxVAZav03/s320/my_facebook_netwoork_2.png" width="320" /></a></div>
<br />gghttp://www.blogger.com/profile/10478964622265922809noreply@blogger.com0tag:blogger.com,1999:blog-12089192.post-10169035907252316662013-03-16T11:49:00.001-07:002013-03-16T11:52:21.902-07:00My facebook network<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjeBKLsx8tkFcKk1Kc2N-kWuvpfhc4BpjPirsG2PWmqU3ToV-7KoU6BX7I3pEYLWSsv-fVgG2_D9dsr6KUsxbawBy2iCXlJXMG9zqrEEL3FByUtIkqXsDHMdMOPA2Z4H4zY8o-o/s1600/735072_301599469967243_20359529_n.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="240" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjeBKLsx8tkFcKk1Kc2N-kWuvpfhc4BpjPirsG2PWmqU3ToV-7KoU6BX7I3pEYLWSsv-fVgG2_D9dsr6KUsxbawBy2iCXlJXMG9zqrEEL3FByUtIkqXsDHMdMOPA2Z4H4zY8o-o/s320/735072_301599469967243_20359529_n.jpg" width="320" /></a></div>
<br />gghttp://www.blogger.com/profile/10478964622265922809noreply@blogger.com0tag:blogger.com,1999:blog-12089192.post-23649343333031087642013-02-09T05:24:00.001-08:002013-02-09T13:30:19.948-08:00Will users' awareness of Facebook inability to protect privacy be the Facebook killer?<br />
<u><b>Facebook </b></u>monthly active users (MAUs) were <b><a href="http://investor.fb.com/releasedetail.cfm?ReleaseID=736911" target="_blank">1.06 billion as of <span class="xn-chron">December 31 2012</span></a></b>. The company noted that now stores over <b><a href="http://techcrunch.com/2012/02/02/visualizing-facebooks-media-store-how-big-is-100-petabytes/" target="_blank">100 petabytes of media</a></b> (photos and videos). Hence, <u><b>each Facebook user needs on average almost 100 Mbyte</b></u> for photos and videos. In addiction, there's data necessary for personal user profile information, but as it's text based it's likely to be some size level lower. <br />
<br />
<span style="background-color: yellow;"><u><b>A great deal of data</b>. <b>But why does Facebook business model need so much data</b></u>?</span> "<i>Our goal is to help every person stay connected and every product they use be a great social experience</i>," Mark Zuckerberg says. In fact, Facebook business model is focused <b><u>On Line Adverstising, payments and other fees</u></b> (aka <u><b>virtual goods</b></u>; e.g. fees from games). <a href="http://news.cnet.com/8301-1023_3-57480865-93/facebook-earnings-match-estimates-955-million-users/" target="_blank">Sandberg also views local businesses as a growth opportunity for the company</a>. "<i>Local is the holy grail of the Internet, but local businesses
are not very tech savvy. More than 40 percent have no Web presence at
all. Facebook has a huge competitive advantage because they are using
Facebook personally, and seeing messages from other businesses," she
said. "It's a smaller leap (to use Facebook), and the numbers bear that
out -- 7 million small businesses are using <a href="http://www.facebook.com/FacebookPages">Facebook Pages</a> on a monthly basis, and hundreds of thousands are upsold to become advertisers.</i>" <u><b> </b></u><br />
<br />
<u><b>Average Revenue per User (ARPU)</b></u> <a href="http://news.cnet.com/8301-1023_3-57480865-93/facebook-earnings-match-estimates-955-million-users/" target="_blank">Q2'12 was <b>$1.28 </b></a>with some differences:<br />
<ul>
<li>US & Canada: $3.20 </li>
<li>Europe: $1.43</li>
<li>Asia: $0.55</li>
<li>Rest of the world: $0.44 </li>
</ul>
Q2'12 revenue totaled <span class="xn-money">$1.18 billion</span>, an increase of 32%, compared with <span class="xn-money">$895 million</span> of Q2'11: <br />
<ol>
<li><b>Revenue from advertising was <span class="xn-money">$992 million</span>, representing 84% of total revenue</b> and a 28% increase from the same quarter last year. </li>
<li><b>Payments and other fees revenue for the second quarter was <span class="xn-money">$192 million</span></b>. </li>
</ol>
This<b> <span style="background-color: yellow;"><u>revenue structure</u></span></b> is rather consilidated as <a href="http://investor.fb.com/releasedetail.cfm?ReleaseID=736911" target="_blank">full year 2012 results</a> shows<u><b>:</b></u><br />
<ul>
<li><span style="background-color: yellow;"><u><b>84% revenue from adv </b></u></span></li>
<li><span style="background-color: yellow;"><u><b>16% revenue from payments and other fees.</b></u></span></li>
</ul>
Interesting that <b>mobile revenue represented approximately 23% of advertising revenue for Q4'12 up from 14% Q3'12</b>. Globally, <u><b>2012 revenue increased 27%</b></u> up to $5.089 billions from $3.711 billions of 2011. <br />
<br />
<b><u><span style="background-color: yellow;">Different music on operating margin & net income side.<span style="background-color: yellow;"> <span style="background-color: yellow;"><span style="background-color: white;"></span></span><span style="background-color: white;"></span></span></span></u></b><span style="background-color: yellow;"><span style="background-color: yellow;"><span style="background-color: white;"> 2012 (non-GAAP) income from operations decreased to $538 millions from </span></span></span><span style="background-color: yellow;"><span style="background-color: yellow;"><span style="background-color: white;"></span></span></span> $1.756 billions of 2011.<b> </b>Looking at the main costs and expenses<b>:</b><br />
<ul>
<li><span style="background-color: yellow;"><b>Research and development (+261% vs 2011): $1.399 mln </b></span></li>
<li>Marketing and sales (+128% vs 2011): $896 mln </li>
<li>General and administrative (+184% vs 2011): $892 mln<b><br /></b></li>
</ul>
<span style="background-color: yellow;"><span style="background-color: yellow;"><span style="background-color: white;"><span style="background-color: yellow;"><u><b>Facebook is obviously investing heavily on R&D</b></u></span> , MKTG & Sales and it's completing its expansion. So, the 2012 net income shouldn't be interesting (!!!). Actually, 2012 net income is $53 mln vs $1 billion of 2011 (before IPO). That's a clear sign that Facebook is a long term investment and not a speculative bet for</span></span></span><span style="background-color: yellow;"><span style="background-color: yellow;"><span style="background-color: white;"> extempore investors, but that's not the point here. </span></span></span><span style="background-color: white;"><b>The point is that Facebook went public to get the necessary resources do do these technology/product/corporate investments and developing a </b></span><span style="background-color: white;"><b><span style="background-color: white;"><b>unique </b></span>competitive avdantage (Facebook </b></span><span style="background-color: yellow;"><span style="background-color: yellow;"><span style="background-color: white;"><span style="background-color: white;"><b>MAUs are actually unique!) and how the return could be affected by the untechnological factor of users' awareness of Facebook inability to protect privacy. </b></span></span></span></span><br />
<span style="background-color: yellow;"><span style="background-color: yellow;"><span style="background-color: white;"><br /></span></span></span>
<span style="background-color: yellow;"><span style="background-color: yellow;"><span style="background-color: white;"><span style="background-color: yellow;"><u><b>Where is Facebook investing and where it can get a unique advantage? <span style="background-color: white;"></span></b></u><span style="background-color: white;"></span></span></span></span></span><br />
<ul>
<li><span style="background-color: yellow;"><b>Big Data</b> (<b>for final users</b>: search, social graph search, recommendation engine, etc.; <b>for clients</b>: Sentiment Analysis, Marketing Campaign Analysis, Customer Churn Analysis, Customer Experience Analytics, etc.)<b></b></span></li>
<li><span style="background-color: yellow;"><span style="background-color: yellow;"><span style="background-color: white;"><span style="background-color: yellow;"><span style="background-color: white;"></span></span></span></span></span><span style="background-color: yellow;"><span style="background-color: yellow;"><span style="background-color: white;"><span style="background-color: yellow;"><span style="background-color: white;"><span style="background-color: yellow;"><span style="background-color: yellow;"><span style="background-color: white;"><span style="background-color: yellow;"><span style="background-color: white;"><b>Mobile support & integration</b> (iOS, Andrioid, etc.)</span></span></span></span></span></span></span></span></span></span></li>
<li><span style="background-color: yellow;"><span style="background-color: yellow;"><span style="background-color: white;"><span style="background-color: yellow;"><span style="background-color: white;"><span style="background-color: yellow;"><span style="background-color: yellow;"><span style="background-color: white;"><span style="background-color: yellow;"><span style="background-color: white;"><b>Product development</b> (Messenger for Android and iOS, Facebook Camera available in 18 languages, new advertising products such as Custom Audiences, Facebook Exchange, Offers, and mobile app install ads, Created Facebook Stories, global App Center, etc. ) </span></span></span></span></span></span></span></span></span></span></li>
<li><span style="background-color: yellow;"><span style="background-color: yellow;"><span style="background-color: white;"><span style="background-color: yellow;"><span style="background-color: white;"><span style="background-color: yellow;"><span style="background-color: yellow;"><span style="background-color: white;"><span style="background-color: yellow;"><span style="background-color: white;"><b>Corporate development </b>(first international engineering office in London, etc.)</span></span></span></span></span></span></span></span></span></span></li>
</ul>
<span style="background-color: yellow;"><span style="background-color: yellow;"><span style="background-color: white;"><span style="background-color: yellow;"><span style="background-color: white;"><span style="background-color: yellow;"><span style="background-color: yellow;"><span style="background-color: white;"><span style="background-color: yellow;"><span style="background-color: white;">Talking about product, Google+ is considered actually a better product by someone, but unluckily Google+ hasn't the Facebook MAUs. <span style="background-color: yellow;"><b>I think MAUs and its </b></span></span></span></span></span></span></span></span></span></span></span><span style="background-color: yellow;"><b>exploitation is the core asset Facebook can use to get a unique competitive advantage over competitors and get satisfactory return to investors. </b> </span><br />
<br />
Talking about MAUs explotation, <u><b>technologies behind Big Data</b></u> are <br />
<ul>
<li><b>Hadoop </b>& related fameworks (Hadoop Distributed File System, MapReduce, Hive, Pig, HBase, Flume, Oozie, Flume, Ambari, Avro, Mahout, Sqoop, HCatalog, BigTop, etc.), <b> </b></li>
<li><b>NoSQL databases</b> (HBase, Cassandra, Aerospike, MongoDB, Accumulo, Riak, CouchDB, DynamoDB, etc.). </li>
</ul>
Effectively, some of these technologies were initially developed in Google and Facebook. Now, they most live as open source projects and applied by Facebook in order to make <b><u>applications / services interesting for Facebook users/clients, such as</u>: </b><br />
<ul>
<li><b>Recommendation Engine:</b> Web properties and online retailers use
Hadoop to match and recommend users to one another or to products and
services based on analysis of user profile and behavioral data. <b>LinkedIn
</b>uses this approach to power its “<i>People You May Know</i>” feature, while
Amazon uses it to suggest related products for purchase to online
consumers. Initial question turns here into the following one:<b> <span style="background-color: yellow;">how </span></b><span style="background-color: yellow;"><span style="background-color: yellow;"><span style="background-color: white;"><span style="background-color: white;"><b><span style="background-color: yellow;">users' awareness of Facebook inability to protect privacy can prevent users to recommend some kinds products or users? Is there particular products or relationships between users more sensitive than others? </span> </b></span></span></span></span></li>
<li><b>Sentiment Analysis:</b> Used in conjunction with Hadoop,
advanced text analytics tools analyze the unstructured text of social
media and social networking posts, including Tweets and Facebook posts,
to determine the user sentiment related to particular companies, brands, products or politic parties. Analysis can focus on macro-level sentiment down to
individual user sentiment.
Initial question turns here into the following one:<b> <span style="background-color: yellow;">how users' awareness of Facebook inability to protect privacy can prevent users to express their opinion about companies, brands
or products? Is there particular companies, brands, products or politic parties </span></b><span style="background-color: yellow;"><span style="background-color: yellow;"><span style="background-color: white;"><span style="background-color: white;"><b><span style="background-color: yellow;">more sensitive than others? </span></b></span></span></span></span><b></b><span style="background-color: yellow;"><span style="background-color: yellow;"><span style="background-color: white;"><span style="background-color: white;"><b><span style="background-color: yellow;"></span></b></span></span></span></span>
</li>
<li><b>Social Graph Analysis:</b> In conjunction with Hadoop and NoSQL databases, social networking data is mined
to determine which customers pose the most influence over others inside
social networks. This helps enterprises determine which are their “most
important” customers, who are not always those that buy the most
products or spend the most but those that tend to influence the buying
behavior of others the most.
<b> <span style="background-color: yellow;">how users' awareness of Facebook inability to protect privacy can prevent users to influence others users? Is </span></b><b><span style="background-color: yellow;">here particular companies, brands, products, political issues or arguments </span></b><span style="background-color: yellow;"><span style="background-color: yellow;"><span style="background-color: white;"><span style="background-color: white;"><b><span style="background-color: yellow;">more sensitive than others?</span></b></span></span></span></span><span style="background-color: yellow;"><span style="background-color: yellow;"><span style="background-color: white;"><span style="background-color: white;"><b><span style="background-color: yellow;"></span></b></span></span></span></span></li>
</ul>
<span style="background-color: yellow;"><u><b>A Final metaphor </b></u></span><br />
In information theory, the <b>Shannon–Hartley theorem</b> tells the maximum rate at which information can be transmitted over a communications channel of a specified bandwidth in the presence of noise.<br />
<br />
<br />
<br />
<img alt=" C = B \log_2 \left( 1+\frac{S}{N} \right) " class="tex" src="http://upload.wikimedia.org/math/5/d/a/5da4ee296242ff06ad38fd97d1911cb3.png" /><br />
<span style="background-color: yellow;"><span style="background-color: yellow;"><span style="background-color: white;"><span style="background-color: white;"></span></span></span></span><br />
where C is the channel capacity (bits per second), B is the bandwidth of the channel (hertz), S is the average received signal power over the bandwidth, N is the average noise or interference power over the bandwidth (watts), S/N is the signal-to-noise ratio (SNR). Hence, <span style="background-color: yellow;"><b>the more the noise power, the more the signal power necessary to get a given capaci<span style="background-color: yellow;">ty.</span></b> <b>Or, fixed the signal power, the more the noise power, the less </b><b>the channel capacity. <span style="background-color: white;"> </span></b></span><br />
<br />
<span style="background-color: yellow;"><b>Perhaps, we can think the channel capacity (C) as the value delivered to Facebook clients by services such as Recommendation Engine, Sentiment Analysis or Social Graph Analysis; the signal power (S) as the effectiveness of Big Data technology in order to discover such information; the noise power (N) as the int<span style="background-color: yellow;">erference because of </span><span style="background-color: yellow;">users' awareness of Facebook inability to protect privacy in letting users<span style="background-color: white;"><span style="background-color: yellow;"> having a full social experience; the bandwidth (B) as the Facebook MAUs. </span></span></span></b></span><br />
<br />
<span style="background-color: yellow;"><span style="background-color: yellow;"><span style="background-color: white;">Hence, if that metaphor works<b>, <span style="background-color: yellow;">given Facebook MAUs, the more the </span></b></span></span><span style="background-color: yellow;"><b>users' awareness of Facebook inability to protect privacy, the more the </b></span></span><span style="background-color: yellow;"><b>effectiveness of Big Data technology to get the same value to clients, the more R&D investments, the higher the price for Facebook clients ... the less the likelihood that Facebook business model works... </b></span><b><span style="background-color: yellow;"> </span></b>gghttp://www.blogger.com/profile/10478964622265922809noreply@blogger.com0tag:blogger.com,1999:blog-12089192.post-525124970232417922013-02-03T15:11:00.002-08:002013-02-03T15:14:03.181-08:00WSJ: Big Data Pay Premium ‘Highest It Will Ever Be’<a href="http://blogs.wsj.com/cio/2013/01/31/big-data-pay-premium-highest-it-will-ever-be/?mod=wsj_ciohome_cioreport"> <i>According to a </i></a><i><a href="http://www.footepartners.com/SSCP.htm">January report</a>
from the IT research firm Foote Partners LLC, which gathered
compensation data from 2,435 employers. For example, expertise in <b>Hadoop
</b>and <b>Cassandra</b>, platforms capable of processing massive amounts of
unstructured data like feeds from social media, commanded pay premiums
of up to 16% and 14% respectively. </i><br />
<br />
<i> </i>
gghttp://www.blogger.com/profile/10478964622265922809noreply@blogger.com0tag:blogger.com,1999:blog-12089192.post-42734045978719471952013-01-28T04:53:00.000-08:002013-02-03T14:37:37.079-08:00Global MBA Ranking 2013 - Weighted salary (US$). Is SDA underperforming?<iframe src="http://rankings.ft.com/businessschoolrankings/embeds/chart?set=global-mba-ranking-2013&variable=27242&type=single&entityentries=287152,286782,286772,286822,286802,286952&title=1" width="400" height="250" frameborder="0"></iframe>gghttp://www.blogger.com/profile/10478964622265922809noreply@blogger.com0tag:blogger.com,1999:blog-12089192.post-22197040250649424412013-01-25T13:26:00.005-08:002013-01-25T13:41:05.348-08:00A simple way for data cleaning in VBA Excel In data analysis <i>data cleaning</i> is the act of detecting and either removing or correcting inaccurate records from a record set. In case data is fetched from a Data Base Relational Systems, we're talking about incorrect or inaccurate records from a table. For instance, in Excel 2007+ you can fetch data from a DBMS such as SQL Server in the Get External Data group. The following step is removing or correcting inaccurate records. A typical way to do it is scanning the Excel data sheet following from the top and from left to rigth, processing only columns storing data validity information. Hence, you need a VBA function such as the following:<br />
<span style="color: blue;"><span style="font-family: "Courier New",Courier,monospace;"><br /></span></span>
<span style="color: blue;"><span style="font-family: "Courier New",Courier,monospace;"><span style="background-color: yellow;">clean</span>("</span></span><span style="color: blue;"><span style="font-family: "Courier New",Courier,monospace;"><span style="font-family: "Courier New",Courier,monospace;"><span style="color: blue;">FromSheet</span></span>", "</span></span><span style="color: blue;"><span style="font-family: "Courier New",Courier,monospace;"><span style="font-family: "Courier New",Courier,monospace;"><span style="color: blue;">ToSheet</span></span>", </span></span><span style="color: blue;"><span style="font-family: "Courier New",Courier,monospace;"><span style="font-family: "Courier New",Courier,monospace;"><span style="color: blue;">CellCondition</span></span>, </span></span><span style="color: blue;"><span style="font-family: "Courier New",Courier,monospace;"><span style="font-family: "Courier New",Courier,monospace;"><span style="color: blue;">Condition</span></span>, True)</span></span><br />
<br />
i.e. a VBA function with a signature like <br />
<br />
<span style="font-family: "Courier New",Courier,monospace;"><span style="color: blue;">Function <span style="background-color: yellow;"><b>clean</b></span>(FromSheet As String, ToSheet As String, CellCondition As Variant, Condition As Variant, Caption As Boolean) As Long </span> </span><br />
<br />
<br />
<span style="font-size: small;"><span style="font-family: Arial,Helvetica,sans-serif;">A typical safe way to implement such a <span style="font-size: small;">a</span>ction is copying in a new sheet the clean data. That works especially in case you refresh data sheet periodically from an external data source. Here's the VBA code. </span></span> <br />
<br />
<br />
<span style="font-family: "Courier New",Courier,monospace;"><span style="font-size: x-small;"><span style="color: blue;"> Set wsI = Sheets(FromSheet)<br /> Set wsO = Sheets(ToSheet)<br /><br /> LastRow = wsI.Range("A" & Rows.Count).End(xlUp).Row<br /> <br /> j = 1<br /> With wsI<br /> For i = 1 To LastRow<br /> <br /> ok = False<br /> For N = LBound(CellCondition) To UBound(CellCondition)<br /> If Trim(.Range(CellCondition(N) & i).Value) = Condition(N) Then<br /> ok = True<br /> End If<br /> Next N<br /> <br /> If Caption And i = 1 Then<br /> ok = True<br /> End If<br /> <br /> If ok Then<br /> wsI.Rows(i).Copy wsO.Rows(j)<br /> j = j + 1<br /> End If<br /> <br /> Next i<br /> End With</span></span> </span><br />
<br />
<br />gghttp://www.blogger.com/profile/10478964622265922809noreply@blogger.com0tag:blogger.com,1999:blog-12089192.post-91694853930495673152013-01-22T08:22:00.003-08:002013-01-22T17:02:24.392-08:00Yet another piece i missed at the time <b>June 14th, 2012</b> - <b>The Climate Corporation Raises $50M For Big Data Driven Weather Insurance</b><br />
<br />
<a href="http://techcrunch.com/2012/06/14/founders-fund-leads-the-climate-corporations-colossal-50m-funding-round/">Formerly known as Weatherbill, The Climate Corporation is announcing its $50 million Series C round today, led by new investor Founders Fund and followed on by existing investors Khosla, Google Ventures, NEA, Index Ventures and Atomico. The round comes after another formidable $42 million raise from the aforementioned group sans Founders Fund.</a>gghttp://www.blogger.com/profile/10478964622265922809noreply@blogger.com0tag:blogger.com,1999:blog-12089192.post-15580806905897332252013-01-21T18:15:00.001-08:002013-01-21T18:24:11.901-08:00Trends: Italy / USA / UK / World <br />
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<a class="active" href="http://data.worldbank.org/indicator/NY.GDP.PCAP.CD/countries/1W-IT-US-GB?display=graph" style="color: white; text-decoration: none;">GDP per capita (current US$)</a></div>
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<script type="text/javascript">widgetContext = { "url": "http://data.worldbank.org/widgets/indicator/0/web_widgets_3/NY.GDP.PCAP.CD/countries/1W-IT-US-GB", "width": 300, "height": 225, "widgetid": "web_widget_iframe_bca75b0de18cd02603d21eaf1acd1d4e" };</script><br />
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<script src="http://data.worldbank.org/profiles/datafinder/modules/contrib/web_widgets/iframe/web_widgets_iframe.js"></script><br />
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Data from <a href="http://data.worldbank.org/indicator/NY.GDP.PCAP.CD/countries/1W-IT-US-GB?display=graph" style="color: #cccccc;">World Bank</a></div>
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<a class="active" href="http://data.worldbank.org/indicator/NY.GNP.PCAP.PP.CD/countries/1W-IT-US-GB?display=graph" style="color: white; text-decoration: none;">GNI per capita, PPP (current international $)</a></div>
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<script type="text/javascript">widgetContext = { "url": "http://data.worldbank.org/widgets/indicator/0/web_widgets_3/NY.GNP.PCAP.PP.CD/countries/1W-IT-US-GB", "width": 300, "height": 225, "widgetid": "web_widget_iframe_84f9f4b56753b3145bd74940d54eee4f" };</script><br />
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<script src="http://data.worldbank.org/profiles/datafinder/modules/contrib/web_widgets/iframe/web_widgets_iframe.js"></script><br />
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Data from <a href="http://data.worldbank.org/indicator/NY.GNP.PCAP.PP.CD/countries/1W-IT-US-GB?display=graph" style="color: #cccccc;">World Bank</a></div>
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<a class="active" href="http://data.worldbank.org/indicator/SE.XPD.TOTL.GB.ZS/countries/1W-IT-US-GB?display=graph" style="color: white; text-decoration: none;">Public spending on education, total (% of government expenditure)</a></div>
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<script type="text/javascript">widgetContext = { "url": "http://data.worldbank.org/widgets/indicator/0/web_widgets_3/SE.XPD.TOTL.GB.ZS/countries/1W-IT-US-GB", "width": 300, "height": 225, "widgetid": "web_widget_iframe_1ab0c8f04cdc8a61d75aef75309068a8" };</script><br />
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<script src="http://data.worldbank.org/profiles/datafinder/modules/contrib/web_widgets/iframe/web_widgets_iframe.js"></script><br />
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Data from <a href="http://data.worldbank.org/indicator/SE.XPD.TOTL.GB.ZS/countries/1W-IT-US-GB?display=graph" style="color: #cccccc;">World Bank</a></div>
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<a class="active" href="http://data.worldbank.org/indicator/SL.UEM.1524.FE.ZS/countries/1W-IT-US-GB?display=graph" style="color: white; text-decoration: none;">Unemployment, youth female (% of female labor force ages 15-24)</a></div>
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<script type="text/javascript">widgetContext = { "url": "http://data.worldbank.org/widgets/indicator/0/web_widgets_3/SL.UEM.1524.FE.ZS/countries/1W-IT-US-GB", "width": 300, "height": 225, "widgetid": "web_widget_iframe_942f9d90cfab473647d3a68805792f04" };</script><br />
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<script src="http://data.worldbank.org/profiles/datafinder/modules/contrib/web_widgets/iframe/web_widgets_iframe.js"></script><br />
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Data from <a href="http://data.worldbank.org/indicator/SL.UEM.1524.FE.ZS/countries/1W-IT-US-GB?display=graph" style="color: #cccccc;">World Bank</a></div>
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<a class="active" href="http://data.worldbank.org/indicator/SL.UEM.1524.MA.ZS/countries/1W-IT-US-GB?display=graph" style="color: white; text-decoration: none;">Unemployment, youth male (% of male labor force ages 15-24)</a></div>
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<script type="text/javascript">widgetContext = { "url": "http://data.worldbank.org/widgets/indicator/0/web_widgets_3/SL.UEM.1524.MA.ZS/countries/1W-IT-US-GB", "width": 300, "height": 225, "widgetid": "web_widget_iframe_f1912eb3459c19b3f3e25e16741bb1ac" };</script><br />
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<script src="http://data.worldbank.org/profiles/datafinder/modules/contrib/web_widgets/iframe/web_widgets_iframe.js"></script><br />
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Data from <a href="http://data.worldbank.org/indicator/SL.UEM.1524.MA.ZS/countries/1W-IT-US-GB?display=graph" style="color: #cccccc;">World Bank</a></div>
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<a class="active" href="http://data.worldbank.org/indicator/SL.UEM.LTRM.MA.ZS/countries/1W-IT-US-GB?display=graph" style="color: white; text-decoration: none;">Long-term unemployment, male (% of male unemployment)</a></div>
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<script type="text/javascript">widgetContext = { "url": "http://data.worldbank.org/widgets/indicator/0/web_widgets_3/SL.UEM.LTRM.MA.ZS/countries/1W-IT-US-GB", "width": 300, "height": 225, "widgetid": "web_widget_iframe_16b1bbd4fb41d3564cb70e5e1ee0b9e1" };</script><br />
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<script src="http://data.worldbank.org/profiles/datafinder/modules/contrib/web_widgets/iframe/web_widgets_iframe.js"></script><br />
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Data from <a href="http://data.worldbank.org/indicator/SL.UEM.LTRM.MA.ZS/countries/1W-IT-US-GB?display=graph" style="color: #cccccc;">World Bank</a></div>
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gghttp://www.blogger.com/profile/10478964622265922809noreply@blogger.com0tag:blogger.com,1999:blog-12089192.post-35279277357375381902013-01-18T13:32:00.003-08:002013-01-18T13:35:08.260-08:00Why yet another blog about conputing and after so many years?<div style="font-family: Noteworthy; font-size: 18px; line-height: 24px;">
It
was April 2005 when I started this blog. I was a Java-Oracle
developer and I was at the begin of my career as team leader at <a href="http://virgilio.it/" rel="nofollow" target="_blank">virgilio.it</a>,
at that time # 1 Italian web portal. I was amazed by what at the time
was an incoming revolution, the Web 2.0. I read and read again the
<a href="http://oreilly.com/web2/archive/what-is-web-20.html?page=1" rel="nofollow" target="_blank">article by Tim O'Really</a> about Web 2.0.
<a href="http://technologyhyperboles.blogspot.it/2005/10/welcome-to-web-20.html" rel="nofollow" target="_blank">Starting a blog</a> or just trying to start it seemed a mandatory step. The
following mandatory step was becoming the fonder of an open source
project. So, <a href="http://www.javaworld.com/javaworld/jw-02-2005/jw-0228-pippo.html" rel="nofollow" target="_blank">Pippoproxy </a>was born, a 100 percent pure Java HTTP proxy designed/implemented for Tomcat that can be used instead of standard Apache-Tomcat
solutions. </div>
<div style="font-family: Noteworthy; font-size: 18px; line-height: 24px;">
<br /></div>
<div style="font-family: Noteworthy; font-size: 18px; line-height: 24px;">
It
was before my MBA and my incursion in the private equity arena where I
must confess I lost a bit the touch for technology and the attraction
for <b>SEXY TECHNOLOGY</b>. I started to find sexy discounted cash flows Excel
models or amazing PowerPoint presentations aimed to convince investors
to put money on some fund or listed company. Again, the more the time
passed the more I was convinced that nothing new was under the sun.
Java, PHP, Apache projects <i>... the same stuff again and again... </i></div>
<div style="font-family: Noteworthy; font-size: 18px; line-height: 24px;">
<br /></div>
<div style="font-family: Noteworthy; font-size: 18px; line-height: 24px;">
Now
I know I was wrong. Exactly at that time Hadoop was born as well as other other innovative open source projects. A new revolution, nowadays
known with the <i>buzzword</i> Big Data, was born. Now I feel as excited as at
that time. The same excitement of when I discovered a hack ... the same
excitement of when I was child and I realized a program to predict
football matches with my mythical Commodore Vic 20. Just for fun! </div>
<div style="font-family: Noteworthy; font-size: 18px; line-height: 24px;">
<br /></div>
<div style="font-family: Noteworthy; font-size: 18px; line-height: 24px;">
In
the next posts I'm going to analyze tools, open source projects,
algorithms, statistical methods, products and I'll give them a 1-5
score. No strict methodology, no committees, just personal judgment.<i>
Just for fun!</i></div>
gghttp://www.blogger.com/profile/10478964622265922809noreply@blogger.com0tag:blogger.com,1999:blog-12089192.post-70831540713109207062013-01-17T06:07:00.000-08:002013-01-17T07:04:09.470-08:00If Facebook new Graph Search is your Personal "Big Data" why Facebook's shares were flat at $30.10 in early trading on Wednesday?<br />
Last Tuesday Facebook announced a new way to "<i>navigate connections and make them more useful</i>": <b>Graph Search</b> (beta version).<br />
<br />
Graph Search will allow users to ask real time questions to find friends and information within the Facebook universe. Searches like “<i>find friends of friends who live in New York and went to Stanford</i>” would come back with anyone who fit the bill, provided that information had been cleared to share by the users.<br />
<br />
<a href="http://www.dailymail.co.uk/sciencetech/article-2262906/Facebook-Graph-Search-Social-network-takes-Google.html">Graph Search will appear as a bigger search bar at the top of each page</a>. When you search for something, that search not only determines the set of results you get, but also serves as a title for the page. You can edit the title - and in doing so create your own custom view of the content you and your friends have shared on Facebook.<br />
<br />
The first version of Graph Search focuses on four main areas -- <b>people</b>, <b>photos</b>, <b>places</b>, and <b>interests</b>.<br />
<ul>
<li><b>People</b>: "friends who live in my city," "people from my hometown who like hiking," "friends of friends who have been to Yosemite National Park," "software engineers who live in San Francisco and like skiing," "people who like things I like," "people who like tennis and live nearby"</li>
<li><b>Photos</b>: "photos I like," "photos of my family," "photos of my friends before 1999," "photos of my friends taken in New York," "photos of the Eiffel Tower"</li>
<li><b>Places</b>: "restaurants in San Francisco," "cities visited by my family," "Indian restaurants liked by my friends from India," "tourist attractions in Italy visited by my friends," "restaurants in New York liked by chefs," "countries my friends have visited"</li>
<li><b>Interests</b>: "music my friends like," "movies liked by people who like movies I like," "languages my friends speak," "strategy games played by friends of my friends," "movies liked by people who are film directors," "books read by CEOs"</li>
</ul>
<div>
Forbes talks about <i><a href="http://www.forbes.com/sites/larrymagid/2013/01/16/first-impressions-of-facebooks-new-graph-search-its-your-personal-big-data/">your Personal "Big Data"</a></i>.<br />
<br /></div>
<h3>
<b>Differences with web search</b></h3>
<a href="http://www.techradar.com/news/internet/web/facebook-graph-search-what-is-it-and-how-do-you-use-it-1125281">Graph Search and web search are very different.</a> Web search is designed to take a set of <b>keywords </b>(for example: "hip hop") and provide the best possible results that match those keywords. With Graph Search you combine phrases (for example: "my friends in New York who like Jay-Z") to get that set of people, places, photos or other content that's been shared on Facebook. We believe they have very different uses.<br />
<br />
Another big difference from web search is that every piece of content on Facebook has its own <b>audience</b>, and most content isn't public. We've built Graph Search from the start with privacy in mind, and it respects the privacy and audience of each piece of content on Facebook. It makes finding new things much easier, but you can only see what you could already view elsewhere on Facebook.<br />
<br />
<h3>
<a href="http://www.4-traders.com/YELP-INC-10083578/news/Facebook-search-to-generate-revenue-no-rival-to-Google-analysts-15828860/">Lack of a timeline for the possible launch of graph search on mobile devices + lacks the depth of review content = NO GOOGLE KILLER?</a></h3>
<br />
BofA Merrill Lynch analysts estimated Facebook could add $500 million in annual revenue if it can generate just one paid click per user per year, and raised its price target on the stock by $4 to $35.<br />
<br />
Facebook's shares were flat at $30.10 in early trading on Wednesday. They have jumped about 50 percent since November to Tuesday's close after months of weakness following its bungled Nasdaq listing in May.<br />
<br />
However, analysts at J.P. Morgan Securities said the lack of a timeline for the possible launch of graph search on mobile devices may weigh on the tool's prospects.<br />
<br />
The success of the graph search, which will rely heavily on local information, depends on Facebook launching a mobile product, the analysts said. Half of all searches on mobile devices seek local information, according to Google.<br />
<br />
Graph search also lacks the depth of review content of Yelp Inc, the analysts added.<br />
<br />
Pivotal Research Group analyst Brian Wieser said monetization potential would be largely determined by Facebook's ability to generate a significant portion of search query share volumes and he expects that quantity to be relatively low.<br />
<br />
"Consumers are likely to continue prioritizing other sources, i.e. Google. Advertisers would consequently only use search if they can, or are perceived to, satisfy their goals efficiently with Facebook," Wieser said.<br />
<br />
<b>NO GOOGLE KILLER</b><br />
<br />
Analysts mostly agreed that Facebook's search tool was unlikely to challenge Google's dominance in web search at least in the near term.gghttp://www.blogger.com/profile/10478964622265922809noreply@blogger.com0tag:blogger.com,1999:blog-12089192.post-1129736663535144162005-10-19T08:35:00.000-07:002005-10-25T07:41:06.780-07:00Welcome to Web 2.0<a href="http://www.oreillynet.com/pub/a/oreilly/tim/news/2005/09/30/what-is-web-20.html?page=1">In What Is Web 2.0</a> O’Reilly Media President and CEO Tim O'Reilly explains what it means Web 2.0. Basically, it's based on a set of core principles:<br /><ol> <li>The web as platform</li> <li>Harnessing Collective Intelligence</li> <li>Data is the Next Intel Inside</li> <li>End of the Software Release Cycle</li> <li>Lightweight Programming Models</li> <li>Software Above the Level of a Single Device</li> <li>Rich User Experiences</li> </ol> <span style="font-weight: bold;">P1. The web as platform. </span><font>Explained by example:<span style="font-weight: bold;"><br /><ul> <li><span style="font-weight: bold;">Netscape vs. Google</span>. Netscape framed the <span style="font-style: italic; font-weight: normal;">web as platform</span><span style="font-weight: normal;"> in terms of the old software paradigm: their flagship product was the web browser, a desktop application, and their strategy was to use their dominance in the browser market to establish a market for high-priced server products. Google, by contrast, began its life as a native web application, never sold or packaged, but delivered as a service, with customers paying, directly or indirectly, for the use of that service. None of the trappings of the old software industry are present. No scheduled software releases, just continuous improvement. At bottom, Google requires a competency that Netscape never needed: database management. In fact, to Tim, </span><span style="font-style: italic; font-weight: normal;">the value of the software is proportional to the scale and dynamism of the data it helps to manage</span><span style="font-weight: normal;">. While both Netscape and Google could be described as software companies, it's clear that Netscape belonged to the same software world as Lotus, Microsoft, Oracle, SAP, and other companies that got their start in the 1980's software revolution, while Google's fellows are other internet applications like eBay, Amazon, Napster, and yes, DoubleClick and Akamai.</span></li><li style="font-weight: normal;"><font>DoubleClick vs. Overture and AdSense. DoubleClick was ultimately limited by its business model. <span style="font-style: italic;">It bought into the '90s notion that the web was about publishing, not participation;</span> Overture and Google's success came from an understanding of what Chris Anderson refers to as <span style="font-style: italic;">the long tail</span>, the collective power of the small sites that make up the bulk of the web's content. DoubleClick's offerings require a formal sales contract, limiting their market to the few thousand largest websites. The Web 2.0 lesson: <span style="font-style: italic;">leverage customer-self service and algorithmic data management to reach out to the entire web, to the edges and not just the center, to the long tail and not just the head.</span></span></li><font><font> <li style="font-weight: normal;"><font>Akamai vs. BitTorrent.<span style="font-style: italic;"> </span>BitTorrent, like other pioneers in the P2P movement, takes a radical approach to internet decentralization. <span style="font-style: italic;">Every client is also a server</span>; BitTorrent thus demonstrates a key Web 2.0 principle: <span style="font-style: italic;">the service automatically gets better the more people use it</span>.</span></li><font><font> </span></span></span></span></ul><span style="font-weight: normal;"><font><font><font><font><font><font>P2. Harnessing Collective Intelligence.</span></span></span></span></span></span></span></span><span style="font-weight: normal;"><font><font><font><font><font><font><font><font> Explained by success story.</span></span></span></span></span></span></span></span></span><font><font><font><font><font><font><font><font><font><font><br /></span></span></span></span></span></span></span></span></span></span></span><ul style="font-weight: normal;"><font><font><font><font><font><font><font><font><font><font><font><font><font> <li><font>eBay's product is the collective activity of all its users; like the web itself, eBay grows organically in response to user activity, and the company's role is as an enabler of a context in which that user activity can happen.</span></li><font><font> <li><font>Amazon sells the same products as competitors such as Barnesandnoble.com, and they receive the same product descriptions, cover images, and editorial content from their vendors. <span style="font-style: italic;">But Amazon has made a science of user engagement.</span></span></li><font><font> <li><font>Wikipedia, an online encyclopedia based on the unlikely notion that an entry can be added by any web user, and edited by any other, is a radical experiment in trust, applying Eric Raymond's dictum (originally coined in the context of open source software) that with <span style="font-style: italic;">enough eyeballs, all bugs are shallow</span>, to content creation.</span></li><li><font><font>Sites like <font>del.icio.us and <font>Flickr, two companies that have received a great deal of attention of late, have pioneered a concept that some people call <span style="font-style: italic;">folksonomy </span>(in contrast to taxonomy), a style of collaborative categorization of sites using freely chosen keywords, often referred to as tags. Tagging allows for the kind of multiple, overlapping associations that the brain itself uses, rather than rigid categories.</span></span></span></span></li><font><font><font><font><font><font><font> <li>Even much of the infrastructure of the web--including the <font>Linux, <font>Apache, <font>MySQL, and <font>Perl, <font>PHP, or <font>Python code involved in most web servers--relies on the peer-production methods of open source, in themselves an instance of collective, net-enabled intelligence. There are more than 100,000 open source software projects listed on <font>SourceForge.net.</span></span></span></span></span></span></span></li><font><font><font><font><font><font><font><font><font><font><font><font><font><font> <li><font>Blogging and the Wisdom of Crowds. At this regard, tom remembers that RSS allows someone to link not just to a page, but to subscribe to it, with notification every time that page changes. Skrenta calls this <span style="font-style: italic;">the incremental web</span>. Others call it the <span style="font-style: italic;">live web</span>.</span></li><font><font> </span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></ul><span style="font-weight: normal;"><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font>P3. Data is the Next Intel Inside. </span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span><span style="font-weight: normal;"><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font>The race is on to own certain classes of core data: location, identity, calendaring of public events, product identifiers and namespaces. In many cases, where there is significant cost to create the data, there may be an opportunity for an Intel Inside style play, with a single source for the data. In others, </span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span><span style="font-style: italic; font-weight: normal;"><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font>the winner will be the company that first reaches critical mass via user aggregation, and turns that aggregated data into a system service.</span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><br /><br /><span style="font-weight: normal;">P4. End of the Software Release Cycle.</span><span style="font-weight: normal;"> Operations must become a core competency. Google's or Yahoo!'s expertise in product development must be matched by an expertise in daily operations. So fundamental is the shift fr</span><span style="font-style: italic; font-weight: normal;">om software as artifact to software as service</span><span style="font-weight: normal;"> that the software will cease to perform unless it is maintained on a daily basis.</span><br /><br /><span style="font-weight: normal;">It's also no accident that scripting languages such as Perl, Python, PHP, and now Ruby, play such a large role at web 2.0 companies. Perl was famously described by Hassan Schroeder, Sun's first webmaster, as "the duct tape of the internet."</span><br /> <br /><span style="font-weight: normal;">Users </span><span style="font-weight: normal;">must be treated as </span><span style="font-weight: normal;">co-developers</span><span style="font-weight: normal;">, in a reflection of open source development practices (even if the software in question is unlikely to be released under an open source license).</span><br /><br /><span style="font-weight: normal;">P5. Lightweight Programming Models. </span><span style="font-weight: normal;"> RSS, SOAP, XML data over HTTP, REST, AJAX support lightweight programming models that allow for </span><span style="font-weight: normal;">loosely coupled systems</span><span style="font-weight: normal;">. Think syndication, not coordination. Design for </span><span style="font-style: italic; font-weight: normal;">hackability </span><span style="font-weight: normal;">and </span><span style="font-style: italic; font-weight: normal;">remixability</span><span style="font-weight: normal;">.</span><br /><span style="font-weight: normal;">Moreover, when commodity components are abundant, you can create value simply by assembling them in novel or effective ways (</span><span style="font-weight: normal;">Innovation in Assembly</span><span style="font-weight: normal;">:).</span><br /><br /><span style="font-weight: normal;">P6. Software Above the Level of a Single Device. </span><span style="font-weight: normal;">To date, </span><span style="font-weight: normal;">iTunes </span><span style="font-weight: normal;">is the best exemplar of this principle. This application seamlessly reaches from the handheld device to a massive web back-end, with the PC acting as a local cache and control station. There have been many previous attempts to bring web content to portable devices, but the iPod/iTunes combination is one of the first such applications designed from the ground up to span multiple devices. </span><span style="font-weight: normal;">TiVo </span><span style="font-weight: normal;">is another good example.</span><br /><br /><span style="font-weight: normal;">P7. Rich User Experiences. </span><span style="font-weight: normal;">Ajax isn't a technology. It's really several technologies, each flourishing in its own right, coming together in powerful new ways. Ajax incorporates:</span><br /></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span><ul style="font-weight: normal;"><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font> <li>standards-based presentation using XHTML and CSS;</li> <li>dynamic display and interaction using the Document Object Model;</li> <li>data interchange and manipulation using XML and XSLT;</li> <li>asynchronous data retrieval using XMLHttpRequest;</li> <li>and JavaScript binding everything together.</li> </span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></ul><span style="font-weight: normal;"><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font> A Web 2.0 word processor would support wiki-style collaborative editing, not just standalone documents. But it would also support the rich formatting we've come to expect in PC-based word processors. Writely is a good example of such an application, although it hasn't yet gained wide traction</span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span><span style="font-weight: normal;" id="intelliTxt"><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font><font>. <a href="http://www.writely.com/">Writely</a> is a good example of such an application, although it hasn't yet gained wide traction.</span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span>gghttp://www.blogger.com/profile/10478964622265922809noreply@blogger.com2