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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>Jake Hofman</author_name><author_url>https://www.microsoft.com/en-us/research/people/jmh/</author_url><title>Prediction and explanation in social systems - Microsoft Research</title><type>rich</type><width>600</width><height>338</height><html>&lt;blockquote class="wp-embedded-content" data-secret="d2BnkNEb5w"&gt;&lt;a href="https://www.microsoft.com/en-us/research/publication/prediction-explanation-social-systems/"&gt;Prediction and explanation in social systems&lt;/a&gt;&lt;/blockquote&gt;&lt;iframe sandbox="allow-scripts" security="restricted" src="https://www.microsoft.com/en-us/research/publication/prediction-explanation-social-systems/embed/#?secret=d2BnkNEb5w" width="600" height="338" title="&#x201C;Prediction and explanation in social systems&#x201D; &#x2014; Microsoft Research" data-secret="d2BnkNEb5w" 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>Historically, social scientists have sought out explanations of human and social phenomena that provide interpretable causal mechanisms, while often ignoring their predictive accuracy. We argue that the increasingly computational nature of social science is beginning to reverse this traditional bias against prediction; however, it has also highlighted three important issues that require resolution. First, current [&hellip;]</description></oembed>
