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<oembed><version>1.0</version><provider_name>Microsoft Research</provider_name><provider_url>https://www.microsoft.com/en-us/research</provider_url><author_name>Alex Slivkins</author_name><author_url>https://www.microsoft.com/en-us/research/people/slivkins/</author_url><title>Multiworld Testing - Microsoft Research</title><type>rich</type><width>600</width><height>338</height><html>&lt;blockquote class="wp-embedded-content" data-secret="GWW1oBGCph"&gt;&lt;a href="https://www.microsoft.com/en-us/research/project/multi-world-testing-mwt/"&gt;Multiworld Testing&lt;/a&gt;&lt;/blockquote&gt;&lt;iframe sandbox="allow-scripts" security="restricted" src="https://www.microsoft.com/en-us/research/project/multi-world-testing-mwt/embed/#?secret=GWW1oBGCph" width="600" height="338" title="&#x201C;Multiworld Testing&#x201D; &#x2014; Microsoft Research" data-secret="GWW1oBGCph" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"&gt;&lt;/iframe&gt;&lt;script type="text/javascript"&gt;
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</html><description>Exponentially better than A/B testing. Multiworld Testing&#xA0;(MWT) is the capability to test and optimize over K policies (context-based decision rules) using an amount of data and computation that scales logarithmically in K, without necessarily knowing these policies before or during data collection. MWT can answer exponentially more detailed questions compared to traditional A/B testing. The [&hellip;]</description><thumbnail_url>https://www.microsoft.com/en-us/research/wp-content/uploads/2015/06/loop-300x227.png</thumbnail_url></oembed>
