BEGIN:VCALENDAR
VERSION:2.0
X-WR-CALNAME;VALUE=TEXT:Sendhil Mullainathan: Prediction Policy Problems: Using Machine Learning to Address Social Problems
PRODID:-//Harvard events data//EN
BEGIN:VEVENT
UID:event_349501_0
SUMMARY:Sendhil Mullainathan: Prediction Policy Problems: Using Machine Learning to Address Social Problems
DESCRIPTION:<p><strong>Sendhil Mullainathan</strong>, P<em>rofessor of Economics, Harvard University.</em></p><p><em><!--break--></em></p><p><em><drupal-media data-entity-type="media" data-entity-uuid="4acd3ac3-1a5b-471a-9ece-2eb430f7996e" data-align="right" data-view-mode="hwp_small"></drupal-media></em>There is a class of policy problems that require predictive tools, rather than causal tools to solve. As such as they are ideal for machine learning applications.</p><p>I will illustrate with the case of bail decisions and highlight that (1) the potential social gain for this approach is high; (2) there is an intimate link to behavioral economics; and (3) new issues for machine learning arise in solving these problems.</p><p>Finally I will try to argue that prediction policy problems are quite common and as conceptually interesting as causal problems.</p><p><strong><a href="/file_url/695" data-fid="2879686">View paper </a></strong><br><br></p><p><strong>About the speaker</strong></p><p>Sendhil Mullainathan is a Professor of Economics at Harvard University. His real passion is behavioral economics. His work runs a wide gamut: the <a href="http://www2.warwick.ac.uk/fac/soc/economics/staff/academic/mani/mani_science_976.full.pdf">impact</a> of poverty on mental bandwidth; whether CEO pay is <a href="http://www.chicagobooth.edu/~/media/B7263C57CBF74B15844A49432FB1D365.pdf">excessive</a>; using fictitious resumes to measure <a href="http://www.chicagobooth.edu/pdf/bertrand.pdf">discrimination</a>; showing that higher cigarette taxes makes smokers <a href="http://www.degruyter.com/view/j/bejeap.2005.5.issue-1/bejeap.2005.5.1.1412/bejeap.2005.5.1.1412.xml">happier</a>; modeling how competition affects <a href="http://scholar.harvard.edu/files/shleifer/files/market_aea.pdf">media bias</a>; and a model of coarse <a href="http://scholar.harvard.edu/files/shleifer/files/coarse_thinking_qje.pdf">thinking</a>. His latest research focuses on using machine learning and data mining techniques to better understand human behavior.  </p><p>He enjoys writing, having recently co-authored<em> </em><strong><a href="http://www.amazon.com/Scarcity-Having-Little-Means-Much/dp/0805092641">Scarcity: Why Having too Little Means so Much</a> </strong>and writes <a href="http://query.nytimes.com/search/sitesearch/?action=click&amp;contentCollection=Business%20Day&amp;region=TopBar&amp;module=SearchSubmit&amp;pgtype=article#/*/since1851/allresults/1/bySendhil%20Mullainathan/">regularly</a> for the New York Times.  The current issue of <strong><a href="http://harvardmagazine.com/2015/05/the-science-of-scarcity" data-url="http://harvardmagazine.com/2015/05/the-science-of-scarcity">Harvard Magazine</a></strong> (May 2015) features an in-depth profile and discussion of this work.</p><p>He helped co-found a non-profit to apply behavioral science (ideas42), co-founded a center to promote the use of randomized control trials in development (the <a href="http://www.povertyactionlab.org/" data-url="http://www.povertyactionlab.org/">Abdul Latif Jameel Poverty Action Lab</a>), serves on the board of the MacArthur Foundation, and has worked in government in various roles, including most recently as Assistant Director of Research at the Consumer Financial Protection Bureau.</p><p>He is a recipient of the MacArthur “genius” Award, has been designated a “Young Global Leader” by the World Economic Forum, labeled a “Top 100 Thinker” by Foreign Policy Magazine, and named to the “Smart List: 50 people who will change the world” by Wired Magazine (UK). His hobbies include basketball, board games, googling and fixing-up classic espresso machines.</p>
LOCATION:Harvard Kennedy School: Allison Dining Room
STATUS:CONFIRMED
DTSTART:20150427T160000Z
DTEND:20150427T174500Z
END:VEVENT
END:VCALENDAR