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<title>Risky, Sifty! …pt. 2</title>
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<body>
<div class="container-fluid main-container">
<div class="fluid-row" id="header">
<h1 class="title toc-ignore">Risky, Sifty! …pt. 2</h1>
<h4 class="author"><table style='table-layout:fixed;width:100%;border:0;padding:0;margin:0'>
<col width='10%'>
<col width='10%'>
<tr style="border:none">
<td style="display:block;width:100%;text-align:left;vertical-align:bottom;padding:0;margin:0;border:none" nowrap>
CDS Hackathon
</td>
<td style="width:100%;vertical-align:bottom;text-align:right;padding:0;margin:0;border:none">
<img src='https://raw.githubusercontent.com/therbootcamp/therbootcamp.github.io/master/_sessions/_image/by-sa.png' style='height:15px;width:80px'/>
</td>
</tr>
</table></h4>
</div>
<p align="center">
<img width="100%" src="image/minority.gif" margin=0><br> <font style="font-size:10px">from <a href="https://gifer.com/en/6Hql">gifer.com</a></font>
</p>
<div id="section" class="section level1 tabset">
<h1></h1>
<div id="overview" class="section level2">
<h2>Overview</h2>
<p><em>Washington, D.C., Anno 2054.</em></p>
<p>In an utopian, future world mankind has achieved a state of perfect orderliness. In this world, humans have become ideal decision makers, who always choose rationally to satisfy both their preferences and the law. That is, almost all humans. A small resistance continues to disobey, by letting themselves be guided by situational greed and excitement, leading them to engage in ruthlessly risky behavior. To identify these troublemakers, the Secretary of Homeland Security funded a new task force headed by your friend, Tom Cruise.</p>
<p>Knowing himself little about regression analysis, Tom Cruise approached you to help him predict analyze risk taking based on the data of an recent meta-analysis (<a href="lit/Wulff2018GapMeta.pdf"><strong>download</strong></a>). Your task is to use the meta analysis’ data to develop a model that predicts risky choices, based on the features available.</p>
<p>Specifically, your goal is to reveal hidden patterns by identifying which features predict risk taking and by creating visualizations that make these patterns abundantly clear. Follow the instructions under the <em>Instructions</em> tab.</p>
<p>The competition ends in…</p>
<font style="font-size:32px">
<p id="demo" align="center">
</p>
<p></font></p>
<script>
// Set the date we're counting down to
var countDownDate = new Date("November 8, 2019 16:45:00").getTime();
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var hours = Math.floor((distance % (1000 * 60 * 60 * 24)) / (1000 * 60 * 60));
var minutes = Math.floor((distance % (1000 * 60 * 60)) / (1000 * 60));
var seconds = Math.floor((distance % (1000 * 60)) / 1000);
// Display the result in the element with id="demo"
document.getElementById("demo").innerHTML = days + "d " + hours + "h "
+ minutes + "m " + seconds + "s ";
// If the count down is finished, write some text
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clearInterval(x);
document.getElementById("demo").innerHTML = "EXPIRED";
}
}, 1000);
</script>
</div>
<div id="instructions" class="section level2">
<h2>Instructions</h2>
<div id="preliminaries" class="section level3">
<h3>Preliminaries</h3>
<ol style="list-style-type: decimal">
<li>Load the set of packages listed in the functions section above.</li>
</ol>
<pre class="r"><code>## NAME
## DATE
## Modeling competition
library(tidyverse)
library(lme4)</code></pre>
<ol start="2" style="list-style-type: decimal">
<li>Load the <code>meta</code> data set and change any character variables to factors.</li>
</ol>
<pre class="r"><code># Load bbrs data
meta <- read_csv(file = "https://cdsbasel.github.io/hackathon_nov/meta.csv")</code></pre>
</div>
<div id="competition-rules" class="section level3">
<h3>Competition rules</h3>
<ol style="list-style-type: decimal">
<li><p>The goal of the competition is to <strong>predict <code>risky_choice</code></strong> (whether a person chose the riskier of two options) using linear-mixed models.</p></li>
<li><p>Entering the competition grants you a chance to win 🍾🍾🍾.</p></li>
<li><p>To enter the competition, you must prepare a short presentation of up to max 5 slides (including title and conclusion slide) containing the results .</p></li>
<li><p>Use any weapon in your arsenal. Feel free to transform existing variables or to create new ones. Whatever leads to the most compelling analysis.</p></li>
</ol>
</div>
</div>
<div id="dataset" class="section level2">
<h2>Dataset</h2>
<div id="data-set" class="section level4">
<h4>Data set</h4>
<table>
<thead>
<tr class="header">
<th align="left">File</th>
<th align="left">Rows</th>
<th align="left">Columns</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left"><a href="%22https://cdsbasel.github.io/hackathon_nov/meta.csv%22">meta</a></td>
<td align="left">7685</td>
<td align="left">21</td>
</tr>
</tbody>
</table>
</div>
<div id="variable-descriptions" class="section level4">
<h4>Variable descriptions</h4>
<table>
<thead>
<tr class="header">
<th align="left">Variable</th>
<th align="left">Description</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td align="left">study</td>
<td align="left">study identifier</td>
</tr>
<tr class="even">
<td align="left">subject</td>
<td align="left">subject identifier</td>
</tr>
<tr class="odd">
<td align="left">problem</td>
<td align="left">problem identifier</td>
</tr>
<tr class="even">
<td align="left">risky_choice</td>
<td align="left">risky choice (criterion)</td>
</tr>
<tr class="odd">
<td align="left">dom</td>
<td align="left">domain of choice: loss, gain, or mixed</td>
</tr>
<tr class="even">
<td align="left">cert</td>
<td align="left">is the safe option certain</td>
</tr>
<tr class="odd">
<td align="left">ev_risky</td>
<td align="left">expected value of risky option</td>
</tr>
<tr class="even">
<td align="left">ev_safe</td>
<td align="left">expected value of safe option</td>
</tr>
<tr class="odd">
<td align="left">ev_diff</td>
<td align="left">difference in expected values</td>
</tr>
<tr class="even">
<td align="left">cv_risky</td>
<td align="left">coefficient of variation of risky option</td>
</tr>
<tr class="odd">
<td align="left">cv_safe</td>
<td align="left">coefficient of variation of safe option</td>
</tr>
<tr class="even">
<td align="left">cv_diff</td>
<td align="left">difference in coefficient of variations</td>
</tr>
<tr class="odd">
<td align="left">first_author</td>
<td align="left">first three letters of the study’s first author</td>
</tr>
<tr class="even">
<td align="left">out_risky_1</td>
<td align="left">first outcome of risky option</td>
</tr>
<tr class="odd">
<td align="left">out_risky_2</td>
<td align="left">second outcome of risky option</td>
</tr>
<tr class="even">
<td align="left">prob_risky_1</td>
<td align="left">probability of first outcome of risky option</td>
</tr>
<tr class="odd">
<td align="left">prob_risky_2</td>
<td align="left">probability of second outcome of risky option</td>
</tr>
<tr class="even">
<td align="left">out_safe_1</td>
<td align="left">first outcome of safe option</td>
</tr>
<tr class="odd">
<td align="left">out_safe_2</td>
<td align="left">second outcome of safe option</td>
</tr>
<tr class="even">
<td align="left">prob_safe_1</td>
<td align="left">probability of first outcome of safe option</td>
</tr>
<tr class="odd">
<td align="left">prob_safe_2</td>
<td align="left">probability of second outcome of safe option</td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
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