BEGIN:VCALENDAR VERSION:2.0 PRODID:-//IDSS STAGE - ECPv6.15.11//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:IDSS STAGE X-ORIGINAL-URL:https://idss-stage.mit.edu X-WR-CALDESC:Events for IDSS STAGE REFRESH-INTERVAL;VALUE=DURATION:PT1H X-Robots-Tag:noindex X-PUBLISHED-TTL:PT1H BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20150308T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20151101T060000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20160313T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20161106T060000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20170312T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20171105T060000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20180311T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20181104T060000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20190310T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20191103T060000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20200308T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20201101T060000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 TZNAME:EDT DTSTART:20210314T070000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:-0400 TZOFFSETTO:-0500 TZNAME:EST DTSTART:20211107T060000 END:STANDARD END:VTIMEZONE BEGIN:VTIMEZONE TZID:UTC BEGIN:STANDARD TZOFFSETFROM:+0000 TZOFFSETTO:+0000 TZNAME:UTC DTSTART:20160101T000000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;TZID=America/New_York:20200505T160000 DTEND;TZID=America/New_York:20200505T170000 DTSTAMP:20260406T103910 CREATED:20200124T155644Z LAST-MODIFIED:20200205T182920Z UID:11484-1588694400-1588698000@idss-stage.mit.edu SUMMARY:Michael Kearns - Professor and National Center Chair\, Department of Computer and Information Science\, University of Pennsylvania DESCRIPTION: URL:https://idss-stage.mit.edu/calendar/idss-distinguished-speaker-seminar-michael-kearns-upenn/ LOCATION:E18-304\, United States CATEGORIES:IDSS Distinguished Seminar Series END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20200413T160000 DTEND;TZID=America/New_York:20200413T170000 DTSTAMP:20260406T103910 CREATED:20200129T182413Z LAST-MODIFIED:20200205T183630Z UID:11662-1586793600-1586797200@idss-stage.mit.edu SUMMARY:David Blei - Professor of Computer Science and Statistics\, Columbia University DESCRIPTION: URL:https://idss-stage.mit.edu/calendar/david-blei-columbia-university/ LOCATION:E18-304\, United States CATEGORIES:IDSS Distinguished Seminar Series END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20200407T160000 DTEND;TZID=America/New_York:20200407T170000 DTSTAMP:20260406T103910 CREATED:20191220T134359Z LAST-MODIFIED:20200205T184008Z UID:11488-1586275200-1586278800@idss-stage.mit.edu SUMMARY:Guido Imbens - The Applied Econometrics Professor and Professor of Economics\, Graduate School of Business\, Stanford University DESCRIPTION:About the author: Prof. Guido Imbens’ primary field of interest is Econometrics. Research topics in which he is interested include: causality\, program evaluation\, identification\, Bayesian methods\, semi-parametric methods\, instrumental variables. Guido Imbens does research in econometrics and statistics. His research focuses on developing methods for drawing causal inferences in observational studies\, using matching\, instrumental variables\, and regression discontinuity designs. Guido Imbens is Professor of Economics at the Stanford Graduate School of Business and the department of Economics. After graduating from Brown University Guido taught at Harvard University\, UCLA\, and UC Berkeley. He holds an honorary degree from the University of St Gallen. Professor Imbens joined the GSB in 2012 where he specializes in econometrics\, and in particular methods for drawing causal inferences. Guido Imbens is a fellow of the Econometric Society and the American Academy of Arts and Sciences. URL:https://idss-stage.mit.edu/calendar/idss-distinguished-speaker-seminar-series-guido-imbens-stanford-university/ LOCATION:E18-304\, United States CATEGORIES:IDSS Distinguished Seminar Series END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20200303T160000 DTEND;TZID=America/New_York:20200303T170000 DTSTAMP:20260406T103910 CREATED:20200124T155554Z LAST-MODIFIED:20200205T184442Z UID:11607-1583251200-1583254800@idss-stage.mit.edu SUMMARY:Rohini Pande - Henry J. Heinz II Professor of Economics and Director\, Economic Growth Center (Yale University) DESCRIPTION:About the speaker: Rohini Pande is the Henry J. Heinz II Professor of Economics and Director of the Economic Growth Center\, Yale University. She is a co-editor of American Economic Review: Insights. \nPande’s research is largely focused on how formal and informal institutions shape power relationships and patterns of economic and political advantage in society\, particularly in developing countries. She is interested the role of public policy in providing the poor and disadvantaged political and economic power\, and how notions of economic justice and human rights can help justify and enable such change. Her most recent work focuses on testing innovative ways to make the state more accountable to its citizens\, such as strengthening women’s economic and political opportunities\, ensuring that environmental regulations reduce harmful emissions\, and providing citizens effective means to voice their demand for state services. In 2018\, Pande received the Carolyn Bell Shaw Award from the American Economic Association for promoting the success of women in the economics profession. She is the co-chair of the Political Economy and Government Group at Jameel Poverty Action Lab (J-PAL)\, a Board member of Bureau of Research on Economic Development (BREAD) and a former co-editor of The Review of Economics and Statistics. Before coming to Yale\, Pande was the Rafik Harriri Professor of International Political Economy at Harvard Kennedy School\, where she co-founded Evidence for Policy Design. \nPande received a PhD in economics from London School of Economics\, a BA/MA in Philosophy\, Politics and Economics from Oxford University and a BA in Economics from Delhi University. \n  URL:https://idss-stage.mit.edu/calendar/rohini-pande-yale-university/ LOCATION:E18-304\, United States CATEGORIES:IDSS Distinguished Seminar Series END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20191202T160000 DTEND;TZID=America/New_York:20191202T170000 DTSTAMP:20260406T103910 CREATED:20190619T191750Z LAST-MODIFIED:20191203T191735Z UID:9784-1575302400-1575306000@idss-stage.mit.edu SUMMARY:Automating the Digitization of Historical Data on a Large Scale DESCRIPTION:Over the past two centuries\, we have transitioned from an overwhelmingly agricultural world to one with vastly different patterns of economic organization. This transition has been remarkably uneven across space and time\, and has important implications for some of the most central challenges facing societies today. Deepening our understanding of the determinants of economic transformation requires data on the long-run trajectories of individuals and firms. However\, these data overwhelmingly remain trapped in hard copy\, with cost estimates for manual digitization totaling millions of dollars for even relatively modestly sized datasets. Automation has the potential to massively scale up the extraction of historical quantitative data from hard copy documents\, significantly expanding and democratizing access. However\, the synthesis of methodology required to digitize and catalog most historical data is not available off-the-shelf through commercial OCR software\, which performs poorly at recognizing irregular document layouts. Off-the-shelf tools for assembling raw unstructured output into structured databases likewise do not exist. \nWe develop methods for automating the digitization and classification of historical data on a large scale\, illustrating their application to a rich corpus of historical Japanese documents about firms and individuals. An array of methods from computer vision\, natural language processing\, and machine learning are used to detect complex document layouts and assemble a rich structured dataset that tracks the evolution of network relationships between Japanese managers\, government officials\, and firms across the 20th century. \nAbout the Speaker: Melissa Dell is a professor in the Economics Department and a faculty research fellow at the National Bureau of Economic Research. Her research focuses on long-run economic development\, primarily in Latin America and Asia. She has examined the impacts of weather on economic growth and is currently conducting research about the long-run effects of agrarian reform and agricultural technology investments in Mexico and East Asia. She received a PhD in Economics from MIT\, a masters degree in Economics from Oxford\, and a BA from Harvard College. \nReception to follow. URL:https://idss-stage.mit.edu/calendar/idss-distinguished-speaker-seminar-with-melissa-dell-harvard-university/ LOCATION:E18-304\, United States CATEGORIES:IDSS Distinguished Seminar Series END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20191104T160000 DTEND;TZID=America/New_York:20191104T170000 DTSTAMP:20260406T103910 CREATED:20190722T171135Z LAST-MODIFIED:20191105T195746Z UID:10365-1572883200-1572886800@idss-stage.mit.edu SUMMARY:Causal Inference in the Age of Big Data DESCRIPTION:The rise of massive data sets that provide fine-grained information about human beings and their behavior offers unprecedented opportunities for evaluating the effectiveness of social\, behavioral\, and medical treatments. With the availability of fine-grained data\, researchers and policymakers are increasingly unsatisfied with estimates of average treatment effects based on experimental samples that are unrepresentative of populations of interest. Instead\, they seek to target treatments to particular populations and subgroups. Because of these inferential challenges\, Machine Learning (ML) is now being used for evaluating and predicting the effectiveness of interventions in a wide range of domains from technology firms to clinical medicine and election campaigns. However\, there are a number of issues that arise with the use of ML for causal inference. For example\, although ML and related statistical models are good for prediction\, they are not designed to estimate causal effects. Instead\, they focus on predicting observed outcomes. In this talk\, a number of meta-algorithms are presented that can take advantage of any supervised learning method to estimate the Conditional Average Treatment Effect function. Also\, discussed are new theoretical results on confidence intervals and overlap in high-dimensional covariates and a new algorithm for optimal linear aggregation functions for tree-based estimators. \nAbout the speaker: Jasjeet Sekhon is the Robson Professor of Political Science and Statistics at the University of California\, Berkeley. His current research focuses on creating new machine learning methods for estimating causal effects in observational and experimental studies and evaluating social science\, digital\, public health\, and medical interventions. He is also the Head of Causal Inference at Bridgewater Associates. \nReception to follow. URL:https://idss-stage.mit.edu/calendar/idss-distinguished-speaker-seminar-with-jasjeet-sekhon-uc-berkeley/ LOCATION:E18-304\, United States CATEGORIES:IDSS Distinguished Seminar Series END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20191007T160000 DTEND;TZID=America/New_York:20191007T170000 DTSTAMP:20260406T103910 CREATED:20190722T170917Z LAST-MODIFIED:20191009T193356Z UID:10361-1570464000-1570467600@idss-stage.mit.edu SUMMARY:Theoretical Foundations of Active Machine Learning DESCRIPTION:Title:\nTheoretical Foundations of Active Machine Learning\nAbstract:\nThe field of Machine Learning (ML) has advanced considerably in recent years\, but mostly in well-defined domains using huge amounts of human-labeled training data. Machines can recognize objects in images and translate text\, but they must be trained with more images and text than a person can see in nearly a lifetime.  The computational complexity of training has been offset by recent technological advances\, but the cost of training data is measured in terms of the human effort in labeling data. People are not getting faster nor cheaper\, so generating labeled training datasets has become a major bottleneck in ML pipelines. Active ML aims to address this issue by designing learning algorithms that automatically and adaptively select the most informative examples for labeling so that human time is not wasted labeling irrelevant\, redundant\, or trivial examples. This talk explores the development of active ML theory and methods over the past decade\, including recently proposed approaches to active ML with nonparametric or overparameterized models such as neural networks. \nSpeaker: Rob Nowak\, University of Wisconsin\, Madison\nReception to follow. URL:https://idss-stage.mit.edu/calendar/idss-distinguished-speaker-seminar-with-rob-nowak-university-of-wisconsin-madison/ LOCATION:E18-304\, United States CATEGORIES:IDSS Distinguished Seminar Series END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20190930T160000 DTEND;TZID=America/New_York:20190930T170000 DTSTAMP:20260406T103910 CREATED:20190619T144446Z LAST-MODIFIED:20191001T175118Z UID:9778-1569859200-1569862800@idss-stage.mit.edu SUMMARY:Selection and Endogenous Bias in Studies of Health Behaviors DESCRIPTION:Abstract:\nStudies of health behaviors using observational data are prone to bias from selection in behavior choices. How important are these biases? Are they dynamic – that is\, are they influenced by the recommendations we make? Are there formal assumptions under which we can use information we have about selection on observed variables to learn about the possible bias from unobserved selection? \nAbout the Speaker:\nEmily Oster is a professor of economics. Prior to coming to Brown she was an associate professor at the University of Chicago Booth School of Business. She is affiliated with the National Bureau of Economic Research. She earned her BA and her PhD from Harvard\, in 2002 and 2006\, respectively. \n  \nReception to follow. URL:https://idss-stage.mit.edu/calendar/selection-and-endogenous-bias-in-studies-of-health-behaviors/ LOCATION:E18-304\, United States CATEGORIES:IDSS Distinguished Seminar Series END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20190507T160000 DTEND;TZID=America/New_York:20190507T170000 DTSTAMP:20260406T103910 CREATED:20190129T150047Z LAST-MODIFIED:20190514T131508Z UID:8800-1557244800-1557248400@idss-stage.mit.edu SUMMARY:Design and Analysis of Two-Stage Randomized Experiments DESCRIPTION:Abstract:\nIn many social science experiments\, subjects often interact with each other and as a result\, one unit’s treatment can influence the outcome of another unit. Over the last decade\, a significant progress has been made towards causal inference in the presence of such interference between units. In this talk\, we will discuss two-stage randomized experiments\, which enable the identification of the average spillover effects as well as that of the average direct effect of one’s own treatment. In particular\, we consider the setting with noncompliance\, in which some units in the treatment group do not receive the treatment while others in the control group may take up one. This implies that there may exist the spillover effect of the treatment assignment on the treatment receipt as well as the spillover effect of the treatment receipt on the outcome. To address this complication\, we generalize the instrumental variables method by allowing for interference between units and show how to identify the average complier direct effect. We also establish the connections between our nonparametric randomization-inference approach and the two-stage least squares regression. The proposed methodology is motivated by and applied to an ongoing randomized evaluation of the India’s National Health Insurance Program (RSBY). Joint work with Zhichao Jiang and Anup Malani. \nAbout the Speaker:\nKosuke Imai is Professor in the Department of Government and the Department of Statistics at Harvard University. He is also an affiliate of the Institute for Quantitative Social Science where his primary office is located. Before moving to Harvard in 2018\, Imai taught at Princeton University for 15 years where he was the founding director of the Program in Statistics and Machine Learning. He specializes in the development of statistical methods and their applications to social science research and is the author of Quantitative Social Science: An Introduction (Princeton University Press\, 2017). Outside of Harvard\, Imai is currently serving as the President of the Society for Political Methodology. He is also Professor of Visiting Status in the Graduate Schools of Law and Politics at The University of Tokyo. URL:https://idss-stage.mit.edu/calendar/idss-distinguished-speaker-seminar-may/ LOCATION:E18-304\, United States CATEGORIES:IDSS Distinguished Seminar Series END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20190402T160000 DTEND;TZID=America/New_York:20190402T170000 DTSTAMP:20260406T103910 CREATED:20190129T145834Z LAST-MODIFIED:20190404T145710Z UID:8793-1554220800-1554224400@idss-stage.mit.edu SUMMARY:A Particulate Solution: Data Science in the Fight to Stop Air Pollution and Climate Change | IDSS Distinguished Speaker Seminar DESCRIPTION:Abstract:\nWhat if I told you I had evidence of a serious threat to American national security – a terrorist attack in which a jumbo jet will be hijacked and crashed every 12 days. Thousands will continue to die unless we act now. This is the question before us today – but the threat doesn’t come from terrorists. The threat comes from climate change and air pollution. \nWe have developed an artificial neural network model that uses on-the-ground air-monitoring data and satellite-based measurements to estimate daily pollution levels across the continental U.S.\, breaking the country up into 1-square-kilometer zones. We have paired that information with health data contained in Medicare claims records from the last 12 years\, and for 97% of the population ages 65 or older. We have developed statistical methods and computational efficient algorithms for the analysis over 460 million health records. \nOur research shows that short and long term exposure to air pollution is killing thousands of senior citizens each year. This data science platform is telling us that federal limits on the nation’s most widespread air pollutants are not stringent enough. \nThis type of data is the sign of a new era for the role of data science in public health\, and also for the associated methodological challenges. For example\, with enormous amounts of data\, the threat of unmeasured confounding bias is amplified\, and causality is even harder to assess with observational studies. These and other challenges will be discussed. \nReferences:\nDi Q\, Wang Y\, Zanobetti A\, Wang Y\, Koutrakis P\, Dominici F\, Schwartz J. (2017). Air Pollution and Mortality in the Medicare Population. New England Journal of Medicine\, 376:2513-2522\, June 29\, 2017\, DOI: 10.1056/NEJMoa1702747\nDi Q\, Dai L\, Wang Y\, Zanobetti A\, Dominici F\, Schwartz J. (2017) A Nationwide Case-crossover Study on Air Pollution and Mortality in the United States\, 2000-2012\, Journal of American Medical Association\, AMA. 2017;318(24):2446-2456. doi:10.1001/jama.2017.17923 \nAbout the Speaker:\nFrancesca Dominici is Professor of Biostatistics at the Harvard T.H.Chan School of Public Health and co-Director of the Harvard Data Science Initiative.  \nHer research focuses on the development of statistical methods for the analysis of large and complex data; she leads several interdisciplinary groups of  scientists with the ultimate goal of addressing important questions in environmental health science\, climate change\, comparative effectiveness research  in cancer\, and health policy. Currently\, Dominici’s team uses satellite data and multiple data sources to estimate exposure to air pollution in rural areas in the US\, in India\, and in other developing countries. Her studies have directly and routinely impacted air quality policy and led to more stringent ambient air quality standards in the United States. \n \nDominici was recognized on the Thomson Reuters 2015 Highly Cited Researchers list\, ranking in the top 1 percent of scientists cited in her field. In 2017\, she was named one of the top 10 Italian women scientists with the largest impact in biomedical sciences across the world. In addition to her research interests and administrative leadership roles\, Dominici has demonstrated a career-long commitment to promoting diversity in academia. For her contributions\, she has earned the Jane L. Norwood Award for Outstanding Achievement by a Woman in the Statistical Sciences and the Florence Nightingale David Award. Dominici currently chairs the University Committee for the Advancement of Women Faculty at the Harvard T.H. Chan School of Public Health. Prior to Harvard\, she was on the faculty of the Johns Hopkins Bloomberg School of Public Health\, where she also co-chaired the University Committee on the Status of Women. Dominici has degrees from University La Sapienza and University of Padua. \n  \nPress coverage links\nNPR: http://www.npr.org/sections/health-shots/2017/06/28/534594373/u-s-air-pollution-still-kills-thousands-every-year-study-concludes\nLos Angeles Times: http://www.latimes.com/science/sciencenow/la-sci-sn-air-pollution-death-20170628-story.html\nNew York Times: https://www.nytimes.com/2017/06/28/well/even-safe-pollution-levels-can-be-deadly.html?_r=0\nPodcast: https://www.hsph.harvard.edu/news/multimedia-article/harvard-chan-this-week-in-health-archive/ URL:https://idss-stage.mit.edu/calendar/idss-distinguished-speaker-seminar-francesca-dominici/ LOCATION:E18-304\, United States CATEGORIES:IDSS Distinguished Seminar Series END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20190305T160000 DTEND;TZID=America/New_York:20190305T170000 DTSTAMP:20260406T103910 CREATED:20190129T144952Z LAST-MODIFIED:20190312T132639Z UID:8791-1551801600-1551805200@idss-stage.mit.edu SUMMARY:A Theory for Representation Learning via Contrastive Objectives DESCRIPTION:Abstract:\nRepresentation learning seeks to represent complicated data (images\, text etc.) using a vector embedding\, which can then be used to solve complicated new classification tasks using simple methods like a linear classifier. Learning such embeddings is an important type of unsupervised learning (learning from unlabeled data) today. Several recent methods leverage pairs of “semantically similar” data points (eg sentences occuring next to each other in a text corpus). We call such methods contrastive learning (another term would be “like word2vec”) and propose a theoretical framework for analysing them. The challenge for theory here is that the training objective seems to have little to do with the downstream task. Our framework bridges this challenge and can show provable guarantees on the performance of the learnt representation on downstream classification tasks. I’ll show experiments supporting the theory.\nThe talk will be self-contained.\n(Joint work with Hrishikesh Khandeparkar\, Mikhail Khodak\, Orestis Plevrakis\, and Nikunj Saunshi.) \nAbout the Speaker:\nSanjeev Arora is Charles C. Fitzmorris Professor of Computer Science at Princeton University and Visiting Professor in Mathematics at the Institute for Advanced Study. He works on theoretical computer science and theoretical machine learning. He has received the Packard Fellowship (1997)\, Simons Investigator Award (2012)\, Gödel Prize (2001 and 2010)\, ACM Prize in Computing (formerly the ACM-Infosys Foundation Award in the Computing Sciences) (2012)\, and the Fulkerson Prize in Discrete Math (2012). He is a fellow of the American Academy of Arts and Sciences and member of the National Academy of Science. URL:https://idss-stage.mit.edu/calendar/idss-distinguished-speaker-seminar-march/ LOCATION:32-155\, United States CATEGORIES:IDSS Distinguished Seminar Series END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20190205T160000 DTEND;TZID=America/New_York:20190205T170000 DTSTAMP:20260406T103910 CREATED:20190116T163023Z LAST-MODIFIED:20190206T203341Z UID:8749-1549382400-1549386000@idss-stage.mit.edu SUMMARY:Collective Decision Making: Theory and Experiments DESCRIPTION:Abstract:\nRanging from jury decisions to political elections\, situations in which groups of individuals determine a collective outcome are ubiquitous. There are two important observations that pertain to almost all collective processes observed in reality. First\, decisions are commonly preceded by some form of communication among individual decision makers\, such as jury deliberations\, or election polls. Second\, even when looking at a particular context\, say U.S. civil jurisdiction\, there is great variance in the type of institutions that are employed to aggregate private information or preferences into group decisions. In this talk\, I will present some theoretical models and experimental results that provide insight into how groups aggregate information and opinions\, and the sorts of instruments that might be beneficial for improving collective outcomes in various settings. \nAbout the speaker:\nLeeat Yariv is the Uwe E. Reinhardt Professor of Economics at Princeton University. She is also the director of the Princeton Experimental Laboratory for the Social Sciences (PExL)\, which provides a platform for experimental research in the social sciences. Yariv’s research combines experimental and empirical evidence together with economic theory to study how individuals connect with one another and how they make decisions together. Her research has touched upon a wide range of topics within the areas of social networks\, political economy\, and market design. Yariv received a B.Sc. in Mathematics\, a B.Sc. in Physics\, and an M.Sc. in Mathematics from Tel-Aviv University. She received an M.A. and a Ph.D. in Economics from Harvard University. Prior to joining Princeton University\, she was a professor at UCLA and Caltech. Yariv is a fellow of the Econometric Society and of the Society for the Advancement of Economic Theory. She has served on multiple journal editorial boards\, including those of Econometrica\, American Economic Review\, and Journal of Economic Literature. URL:https://idss-stage.mit.edu/calendar/idss-distinguished-speaker-seminar-leeat-yariv/ LOCATION:32-155\, United States CATEGORIES:IDSS Distinguished Seminar Series END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20181203T160000 DTEND;TZID=America/New_York:20181203T170000 DTSTAMP:20260406T103910 CREATED:20180712T160757Z LAST-MODIFIED:20181206T141227Z UID:7987-1543852800-1543856400@idss-stage.mit.edu SUMMARY:The Opportunity Atlas: Mapping the Childhood Roots of Social Mobility DESCRIPTION:Abstract:\nWe construct a publicly available atlas of children’s outcomes in adulthood by Census tract using anonymized longitudinal data covering nearly the entire U.S. population. For each tract\, we estimate children’s earnings distributions\, incarceration rates\, and other outcomes in adulthood by parental income\, race\, and gender. These estimates allow us to trace the roots of outcomes such as poverty and incarceration back to the neighborhoods in which children grew up. We find that children’s outcomes vary sharply across nearby areas: for children of parents at the 25th percentile of the income distribution\, the standard deviation of mean household income at age 35 is $5\,000 across tracts within counties. We illustrate how these tract-level data can provide insight into how neighborhoods shape the development of human capital and support local economic policy using two applications. First\, the estimates permit precise targeting of policies to improve economic opportunity by uncovering specific neighborhoods where certain subgroups of children grow up to have poor outcomes. Neighborhoods matter at a very granular level: conditional on characteristics such as poverty rates in a child’s own Census tract\, characteristics of tracts that are one mile away have little predictive power for a child’s outcomes. Our historical estimates are informative predictors of outcomes even for children growing up today because neighborhood conditions are relatively stable over time. Second\, we show that the observational estimates are highly predictive of neighborhoods’ causal effects\, based on a comparison to data from the Moving to Opportunity experiment and a quasi-experimental research design analyzing movers’ outcomes. We then identify high-opportunity neighborhoods that are affordable to low income families\, providing an input into the design of affordable housing policies. Our measures of children’s long-term outcomes are only weakly correlated with traditional proxies for local economic success such as rates of job growth\, showing that the conditions that create greater upward mobility are not necessarily the same as those that lead to productive labor markets. Read the whole paper here.\n \nAbout the speaker:\nRaj Chetty is the William A. Ackman Professor of Economics at Harvard University. He is also the Director of the Equality of Opportunity Project\, which uses “big data” to understand how we can give children from disadvantaged backgrounds better chances of succeeding. Chetty’s research combines empirical evidence and economic theory to help design more effective government policies. His work on topics ranging from tax policy and unemployment insurance to education and affordable housing has been widely cited in academia\, media outlets\, and Congressional testimony. \nChetty received his Ph.D. from Harvard University in 2003 and is one of the youngest tenured professors in Harvard’s history. Before joining the faculty at Harvard\, he was a professor at UC-Berkeley and Stanford University. Chetty has received numerous awards for his research\, including a MacArthur “Genius” Fellowship and the John Bates Clark medal\, given to the economist under 40 whose work is judged to have made the most significant contribution to the field. URL:https://idss-stage.mit.edu/calendar/idss-distinguished-speaker-series/ LOCATION:32-155 CATEGORIES:IDSS Distinguished Seminar Series END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20181105T160000 DTEND;TZID=America/New_York:20181105T170000 DTSTAMP:20260406T103910 CREATED:20180712T160448Z LAST-MODIFIED:20181121T141808Z UID:7982-1541433600-1541437200@idss-stage.mit.edu SUMMARY:The Regression Discontinuity Design: Methods and Applications DESCRIPTION:Abstract:\nThe Regression Discontinuity (RD) design is one of the most widely used non-experimental strategies for the study of treatment effects in the social\, behavioral\, biomedical\, and statistical sciences. In this design\, units are assigned a score and a treatment is offered if the value of that score exceeds a known threshold—and withheld otherwise. In this talk\, I will discuss the assumptions under which the RD design can be used to learn about treatment effects\, and how to make valid inferences about them based on modern theoretical results in nonparametrics that emphasize the importance of extrapolation of regression functions and misspecification biases near the RD cutoff. I will also discuss the common approach of augmenting nonparametric regression models using predetermined covariates in RD setups\, and how this affects nonparametric identification of as well as statistical inference about the RD parameter. If time permits\, I will also discuss a more general version of the RD design based on multiple cutoffs\, which expands the generalizability of the standard RD design by allowing researchers to test richer hypotheses regarding the heterogeneity of the treatment effect and\, under additional assumptions\, to extrapolate the treatment effect to score values far from the cutoff. \n \nRocío Titiunik is the James Orin Murfin Professor of Political Science at the University of Michigan. She specializes in quantitative methodology for the social sciences\, with emphasis on quasi-experimental methods for causal inference and political methodology. Her research interests lie at the intersection of political science\, political economy\, and applied statistics\, particularly on the development and application of quantitative methods to the study of political institutions. Her recent methodological research includes the development of statistical methods for the analysis and interpretation of treatment effects and program evaluation\, with emphasis on regression discontinuity (RD) designs. Her recent substantive research centers on democratic accountability and the role of party systems in developing democracies. Rocio’s work appears in various journals in the social sciences and statistics\, including the American Political Science Review\, the American Journal of Political Science\, the Journal of Politics\, Econometrica\, the Journal of the American Statistical Association\, and the Journal of the Royal Statistical Society. In 2016\, she received the Emerging Scholar Award from the Society for Political Methodology\, which honors a young researcher who is making notable contributions to the field of political methodology. She is a member of the leadership team of the Empirical Implications of Theoretical Models (EITM) Summer Institute\, member-at-large of the Society for Poltical Methodology\, and member of Evidence in Governance and Politics (EGAP). She is also an Associate Editor for Political Science Research and Methods and the American Journal of Political Science\, and has served in the advisory panel for the Methodology\, Measurement\, and Statistics program of the National Science Foundation. URL:https://idss-stage.mit.edu/calendar/idss-distinguished-speaker-seminar-rocio-titiunik/ LOCATION:E18-304\, United States CATEGORIES:IDSS Distinguished Seminar Series END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20181016T160000 DTEND;TZID=America/New_York:20181016T170000 DTSTAMP:20260406T103910 CREATED:20180712T160153Z LAST-MODIFIED:20181024T145321Z UID:7973-1539705600-1539709200@idss-stage.mit.edu SUMMARY:Can machine learning survive the artificial intelligence revolution? DESCRIPTION:  \nAbstract:\nData and algorithms are ubiquitous in all scientific\, industrial and personal domains. Data now come in multiple forms (text\, image\, video\, web\, sensors\, etc.)\, are massive\, and require more and more complex processing beyond their mere indexation or the computation of simple statistics\, such as recognizing objects in images or translating texts. For all of these tasks\, commonly referred to as artificial intelligence (AI)\, significant recent progress has allowed algorithms to reach performances that were deemed unreachable a few years ago and that make these algorithms useful to everyone.\nMany scientific fields contribute to AI\, but most of the visible progress come from machine learning and tightly connected fields such as computer vision and natural language processing. Indeed\, many of the recent advances are due to the availability of massive data to learn from\, large computing infrastructures and new machine learning models (in particular deep neural networks).\nBeyond the well publicized visibility of some advances\, machine learning has always been a field characterized by the constant exchanges between theory and practice\, with a stream of algorithms that exhibit both good empirical performance on real-world problems and some form of theoretical guarantees. Is this still possible? \nIn this talk\, Francis Bach will present recent illustrating machine learning successes and propose some answers to the question above. \nFrancis Bach is a researcher at Inria\, leading since 2011 the machine learning team which is part of the Computer Science Department at Ecole Normale Supérieure. He graduated from Ecole Polytechnique in 1997 and completed his Ph.D. in Computer Science at U.C. Berkeley in 2005\, working with Professor Michael Jordan. He spent two years in the Mathematical Morphology group at Ecole des Mines de Paris\, then he joined the computer vision project-team at Inria/Ecole Normale Supérieure from 2007 to 2010. Francis Bach is primarily interested in machine learning\, and especially in graphical models\, sparse methods\, kernel-based learning\, large-scale convex optimization\, computer vision and signal processing. He obtained in 2009 a Starting Grant and in 2016 a Consolidator Grant from the European Research Council\, and received the Inria young researcher prize in 2012\, the ICML test-of-time award in 2014\, as well as the Lagrange prize in continuous optimization in 2018. In 2015\, he was program co-chair of the International Conference in Machine learning (ICML)\, and general chair in 2018; he is now co-editor-in-chief of the Journal of Machine Learning Research. URL:https://idss-stage.mit.edu/calendar/idss-distinguished-speaker-seminar-francis-bach/ LOCATION:32-141\, United States CATEGORIES:IDSS Distinguished Seminar Series END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20180911T160000 DTEND;TZID=America/New_York:20180911T170000 DTSTAMP:20260406T103910 CREATED:20180731T142245Z LAST-MODIFIED:20180731T142245Z UID:8102-1536681600-1536685200@idss-stage.mit.edu SUMMARY:Science for Policy 2.0 DESCRIPTION:We live in an increasingly polarized present\, looking to a complex and uncertain future while basing our legislative decisions on systems of the past. We need the processes and structures that underpin our political decision-making to be aligned with the complexities of the 21st century. Such changes cannot be undertaken by a technocratic elite\, potentially disenfranchising citizens further from their governing institutions. Rather\, political institutions must seek to improve transparency\, openness\, and accountability. The great divide between science and policy must be bridged\, not through advisers and external counsel but through involvement in a co-creation process that would from the outset\, allow interested parties\, experts and policymakers to work together to gain a shared understanding of a specific issue\, clarity of the objectives of regulatory action as well as alternative regulatory measures. Yet we know that knowledge is not the only driver of political decision-making\, emotion\, self-interest\, power relations and values all play their role in decision-making and political discourse. Through co-creation\, interested parties\, experts\, and policymakers could potentially compare and weigh the risks\, costs\, and benefits and their distribution against self-declared biases.\nAs the European Commission’s in-house science service providing independent scientific advice and support to EU policy\, the Joint Research Centre is at the forefront of such research and seeking innovative opportunities to implement such measures. \nAbout the speaker: \nVladimír Šucha is Director-General of the Joint Research Centre\, the European Commission’s science and knowledge service. He was Deputy Director-General of the JRC between 2012 and 2013. Prior to that\, he spent 6 years in the position of director for culture and media in the Directorate-General for Education and Culture of the European Commission. Before joining the European Commission\, he held various positions in the area of European and international affairs. Between 2005 and 2006\, he was director of the Slovak Research and Development Agency\, national body responsible for funding research. He was principal advisor for European affairs to the minister of education of the Slovak Republic (2004-2005). He worked at the Slovak Representation to the EU in Brussels as research\, education and culture counselor (2000-2004). In parallel\, he has followed a long-term academic and research career\, being a full professor in Slovakia and visiting professor/scientist at different academic institutions in many countries. He published more than 100 scientific papers in peer reviewed journals. URL:https://idss-stage.mit.edu/calendar/science-for-policy-2-0/ LOCATION:32-141\, United States CATEGORIES:IDSS Distinguished Seminar Series END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20180501T160000 DTEND;TZID=America/New_York:20180501T170000 DTSTAMP:20260406T103910 CREATED:20171228T155922Z LAST-MODIFIED:20180427T191821Z UID:7193-1525190400-1525194000@idss-stage.mit.edu SUMMARY:IDSS Distinguished Seminar - Conflict in Networks: The Rise and Fall of Empires DESCRIPTION:Abstract \nIn the study of war\, a recurring observation is that conflict between two opponents is shaped by third parties. The actions of these parties are in turn influenced by other proximate players. These considerations lead us to propose a model of conflict in a network. We study the influence of resources\, technology\, and the network of connections on the dynamics of war and the prospects of peace. \nBio \nSanjeev Goyal is Professor of Economics at the University of Cambridge and Fellow of Christ’s College\, Cambridge. His early research in the 1990’s laid the foundations of an economic approach to the study of networks by providing a framework for the study of the effects of social networks on human behaviour and by developing a model of how the costs and benefits of linking shape the formation of networks. In subsequent work\, he has explored applications of network ideas in a variety of fields including industrial organisation\, economic development\, international trade\, and conflict. In 2007\, Princeton University Press published his book\, Connections: An Intrduction to the Economics of Networks. Sanjeev Goyal is a Fellow of the British Academy and was the founding Director of the Cambridge-INET Institute URL:https://idss-stage.mit.edu/calendar/idss-distinguished-seminar-sanjeev-goyal-university-of-cambridge/ LOCATION:MIT Building 32\, Room 141\, The Stata Center (32-141)\, 32 Vassar Street\, Cambridge\, MA\, 02139\, United States CATEGORIES:IDSS Distinguished Seminar Series ATTACH;FMTTYPE=image/jpeg:https://idss-stage.mit.edu/wp-content/uploads/2017/10/IMG_1788.jpg END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20180403T160000 DTEND;TZID=America/New_York:20180403T170000 DTSTAMP:20260406T103910 CREATED:20171228T155630Z LAST-MODIFIED:20180405T154714Z UID:7191-1522771200-1522774800@idss-stage.mit.edu SUMMARY:Computational Social Science: Exciting Progress and Future Challenges DESCRIPTION: \nAbstract\nThe past 15 years have witnessed a remarkable increase in both the scale and scope of social and behavioral data available to researchers\, leading some to herald the emergence of a new field: “computational social science.” In this talk I highlight two areas of research that would not have been possible just a handful of years ago: first\, using “big data” to study social contagion on networks; and second\, using virtual labs to extend the scale\, duration\, and complexity of traditional lab experiments. Although these examples were all motivated by substantive problems of longstanding interest to social science\, they also illustrate how new classes of data can cast these problems in new light. At the same\, they illustrate some important limitations faced by our existing data generating platforms. I then conclude with some thoughts on how CSS might overcome some of these obstacles to progress. \nBio\nDuncan Watts is a principal researcher at Microsoft Research and a founding member of the MSR-NYC lab. He is also an AD White Professor at Large at Cornell University. Prior to joining MSR in 2012\, he was from 2000-2007 a professor of Sociology at Columbia University\, and then a principal research scientist at Yahoo! Research\, where he directed the Human Social Dynamics group. His research on social networks and collective dynamics has appeared in a wide range of journals\, from Nature\, Science\, and Physical Review Letters to the American Journal of Sociology and Harvard Business Review\, and has been recognized by the 2009 German Physical Society Young Scientist Award for Socio and Econophysics\, the 2013 Lagrange-CRT Foundation Prize for Complexity Science\, and the 2014 Everett Rogers M. Rogers Award. He is also the author of three books: Six Degrees: The Science of a Connected Age (W.W. Norton\, 2003) and Small Worlds: The Dynamics of Networks between Order and Randomness (Princeton University Press\, 1999)\, and most recently Everything is Obvious: Once You Know The Answer (Crown Business\, 2011). Watts holds a B.Sc. in Physics from the Australian Defence Force Academy\, from which he also received his officer’s commission in the Royal Australian Navy\, and a Ph.D. in Theoretical and Applied Mechanics from Cornell University. URL:https://idss-stage.mit.edu/calendar/idss-distinguished-seminar-duncan-watts-microsoft-research-nyc/ LOCATION:MIT Building 32\, Room 141\, The Stata Center (32-141)\, 32 Vassar Street\, Cambridge\, MA\, 02139\, United States CATEGORIES:IDSS Distinguished Seminar Series ATTACH;FMTTYPE=image/jpeg:https://idss-stage.mit.edu/wp-content/uploads/2017/10/IMG_1788.jpg END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20180206T160000 DTEND;TZID=America/New_York:20180206T170000 DTSTAMP:20260406T103911 CREATED:20171228T155151Z LAST-MODIFIED:20180226T211720Z UID:7189-1517932800-1517936400@idss-stage.mit.edu SUMMARY:Machine Learning and Causal Inference DESCRIPTION:Abstract: \nThis talk will review a series of recent papers that develop new methods based on machine learning methods to approach problems of causal inference\, including estimation of conditional average treatment effects and personalized treatment assignment policies. Approaches for randomized experiments\, environments with unconfoundedness\, instrumental variables\, and panel data will be considered. \nBio: \nSusan Athey is The Economics of Technology Professor at Stanford Graduate School of Business. She received her bachelor’s degree from Duke University and her Ph.D. from Stanford\, and she holds an honorary doctorate from Duke University. She previously taught at the economics departments at MIT\, Stanford and Harvard. In 2007\, Professor Athey received the John Bates Clark Medal\, awarded by the American Economic Association to “that American economist under the age of forty who is adjudged to have made the most significant contribution to economic thought and knowledge.” She was elected to the National Academy of Science in 2012 and to the American Academy of Arts and Sciences in 2008. Professor Athey’s research focuses on marketplace design and the intersection of computer science\, machine learning and economics. URL:https://idss-stage.mit.edu/calendar/idss-distinguished-seminar-susan-athey-stanford-university/ LOCATION:MIT Building 32\, Room 141\, The Stata Center (32-141)\, 32 Vassar Street\, Cambridge\, MA\, 02139\, United States CATEGORIES:IDSS Distinguished Seminar Series END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20171212T163000 DTEND;TZID=America/New_York:20171212T173000 DTSTAMP:20260406T103911 CREATED:20171010T165615Z LAST-MODIFIED:20171227T201302Z UID:6594-1513096200-1513099800@idss-stage.mit.edu SUMMARY:IDSS Distinguished Seminar - Essential Concepts of Causal Inference: A Remarkable History DESCRIPTION:  \nAbstract \nI believe that a deep understanding of cause and effect\, and how to estimate causal effects from data\, complete with the associated mathematical notation and expressions\, only evolved in the twentieth century. The crucial idea of randomized experiments was apparently first proposed in 1925 in the context of agricultural field trails but quickly moved to be applied also in studies of animal breeding and then in industrial manufacturing. The conceptual understanding\, to me at least\, was tied to ideas that were developing in quantum mechanics. The key ideas of randomized experiments evidently were not applied to studies of human beings until the 1950s\, when such experiments began to be used in controlled medical trials\, and then in social science\, in education and economics. Humans are more complex than plants and animals\, however\, and with such trials came the attendant complexities of non-compliance with assigned treatment and the occurrence of Hawthorne and placebo effects. The formal application of the insights from earlier simpler experimental settings to more complex ones dealing with people\, started in the 1970s and continue to this day\, and include the bridging of classical mathematical ideas of experimentation\, including fractional replication and geometrical formulations from the early twentieth century\, with modern ideas that rely on powerful computing to implement many of the tedious aspects of design and analysis. \nBio \nDonald B. Rubin is John L. Loeb Professor of Statistics\, Harvard University\, where he has been professor since 1983\, and Department Chair for 13 of those years. He has been elected to be a Fellow/Member/Honorary Member of: the Woodrow Wilson Society\, Guggenheim Memorial Foundation\, Alexander von Humboldt Foundation\, American Statistical Association\, Institute of Mathematical Statistics\, International Statistical Institute\, American Association for the Advancement of Science\, American Academy of Arts and Sciences\, European Association of Methodology\, the British Academy\, and the U.S. National Academy of Sciences. As of 2017\, he has authored/coauthored over 400 publications (including ten books)\, has four joint patents\, and for many years has been one of the most highly cited authors in the world\, with currently over 200\,000 citations and nearly 20\,000 in 2016 alone (Google Scholar). He has received honorary doctorate degrees from Otto Friedrich University\, Bamberg\, Germany; the University of Ljubljana\, Slovenia; Universidad Santo Tomás\, Bogotá\, Colombia; Uppsala University\, Sweden; and Northwestern University\, Evanston\, Illinois. He has also received honorary professorships from the University of Utrecht\, The Netherlands; Shanghai Finance University\, China; Nanjing University of Science & Technology\, China; Xi’an University of Technology\, China; and University of the Free State\, Republic of South Africa. \n[16Mar2017] URL:https://idss-stage.mit.edu/calendar/idss-distinguished-seminar-series-donald-rubin-harvard-university/ LOCATION:MIT Building 32\, Room 141\, The Stata Center (32-141)\, 32 Vassar Street\, Cambridge\, MA\, 02139\, United States CATEGORIES:IDSS Distinguished Seminar Series END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20171107T163000 DTEND;TZID=America/New_York:20171107T173000 DTSTAMP:20260406T103911 CREATED:20171010T145759Z LAST-MODIFIED:20171207T153512Z UID:6591-1510072200-1510075800@idss-stage.mit.edu SUMMARY:Social Network Experiments - Nicholas Christakis (Yale University) DESCRIPTION:  \n\n  \nAbstract \nHuman beings choose their friends\, and often their neighbors\, and co-workers\, and we inherit our relatives; and each of the people to whom we are connected also does the same\, such that\, in the end\, we humans assemble ourselves into face-to-face social networks with particular structures. Why do we do this? And how might an understanding of human social network structure and function be used to intervene in the world to make it better? Here\, I review recent research from our lab describing several classes of interventions involving both offline and online networks that can help make the world better\, including: (1) interventions that rewire the connections between people\, and (2) interventions that manipulate social contagion\, facilitating the flow of desirable properties within groups. I will illustrate what can be done using a variety of experiments in settings as diverse as fostering cooperation in networked groups online\, to fostering health behavior change in developing world villages\, to facilitating the diffusion of innovation or coordination in groups. I will also focus on our recent experiments with “heterogenous systems” involving both humans and “dumb AI” bots\, interacting in small groups. By taking account of people’s structural embeddedness in social networks\, and by understanding social influence\, it is possible to intervene in social systems to enhance desirable population-level properties as diverse as health\, wealth\, cooperation\, coordination\, and learning. \n  \nBiography \nNicholas A. Christakis\, MD\, PhD\, MPH\, is a social scientist and physician who conducts research in the area of biosocial science\, investigating the biological predicates and consequences of social phenomena. He directs the Human Nature Lab at Yale University\, where he is appointed as the Sol Goldman Family Professor of Social and Natural Science\, with appointments in the Departments of Sociology\, Medicine\, Ecology and Evolutionary Biology\, and Biomedical Engineering. He is the Co-Director of the Yale Institute for Network Science. \nPrior to moving his lab to Yale in 2013\, Dr. Christakis was Professor of Sociology and Professor of Medicine at Harvard University\, since 2001. Prior to that\, he served in the same capacities at the University of Chicago. URL:https://idss-stage.mit.edu/calendar/idss-distinguished-series-seminar-nicholas-christalkis-yale-university/ LOCATION:MIT Building 32\, Room 141\, The Stata Center (32-141)\, 32 Vassar Street\, Cambridge\, MA\, 02139\, United States CATEGORIES:IDSS Distinguished Seminar Series END:VEVENT BEGIN:VEVENT DTSTART;TZID=UTC:20171003T163000 DTEND;TZID=UTC:20171003T173000 DTSTAMP:20260406T103911 CREATED:20170831T225119Z LAST-MODIFIED:20170926T131728Z UID:6072-1507048200-1507051800@idss-stage.mit.edu SUMMARY:IDSS Distinguished Seminar Series: Latanya Sweeney (Harvard University) DESCRIPTION:Title: How Technology Design will Dictate Our Civic Future \nAbstract:\nTechnology designers are the new policymakers. No one elected them\, and most people do not know their names\, but the decisions they make when producing the latest gadgets and online innovations dictate the code by which we conduct our daily lives and govern our country. Challenges to the privacy and security of our personal data are part of the first wave of this change; as technology progresses\, says Latanya Sweeney\, every demographic value and every law comes up for grabs and will likely be redefined by what technology does or does not enable. How will it all fit together or fall apart? Join Sweeney\, who after serving as chief technology officer at the U.S. Federal Trade Commission\, has been helping others unearth unforeseen consequences and brainstorm on how to engineer the way forward. \nBio:\nLatanya Sweeney is a Professor at Harvard University; Faculty Dean at Harvard’s Currier House; Editor-in-Chief of Technology Science; Director and Founder of Harvard’s Data Privacy Lab; the former Chief Technology Officer at the U.S. Federal Trade Commission; and Commissioner in the U.S. Commission on Evidence-based Policy Making. Dr. Sweeney holds four patents and is credited with more than 100 academic publications. She is a recipient of the prestigious American Psychiatric Association’s Privacy Advocacy Award\, an elected fellow of the American College of Medical Informatics\, and has testified before government bodies worldwide. Dr. Sweeney became the first African American woman to earn a PhD in computer science from MIT in 2001. More information about her is available at latanyasweeney.org. URL:https://idss-stage.mit.edu/calendar/idss-distinguished-seminar-series-latanya-sweeney-harvard-university/ LOCATION:MIT Building 32\, Room 141\, The Stata Center (32-141)\, 32 Vassar Street\, Cambridge\, MA\, 02139\, United States CATEGORIES:IDSS Distinguished Seminar Series END:VEVENT BEGIN:VEVENT DTSTART;TZID=UTC:20170912T163000 DTEND;TZID=UTC:20170912T173000 DTSTAMP:20260406T103911 CREATED:20170831T223845Z LAST-MODIFIED:20171006T183252Z UID:6066-1505233800-1505237400@idss-stage.mit.edu SUMMARY:Fast and Slow Learning from Reviews DESCRIPTION:Speaker: Daron Acemoglu (MIT)\nMany online platforms present summaries of reviews by previous users. Even though such reviews could be useful\, previous users leaving reviews are typically a selected sample of those who have purchased the good in question\, and may consequently have a biased assessment. In this paper\, we construct a simple model of dynamic Bayesian learning and profit-maximizing behavior of online platforms to investigate whether such review systems can successfully aggregate past information and the incentives of the online platform to choose the relevant features of the review system. \nOn the consumer side\, we assume that each individual cares about the underlying quality of the good in question\, but in addition has heterogeneous ex ante and ex post preferences (meaning that she has a different strength of preference for the good in question than other users\, and her enjoyment conditional on purchase is also a random variable). After purchasing a good\, depending on how much they have enjoyed it\, users can decide to leave a positive or a negative review (or leave no review if they do not have strong preferences). New users observe a summary statistic of past reviews (such as fraction of all reviews that are positive or fraction of all users that have left positive review etc.). Our first major result shows that\, even though reviews come from a selected sample of users\, Bayesian learning ensures that as the number of potential users grows\, the assessment of the underlying state converges almost surely to the true quality of the good. More importantly\, we provide a tight characterization of the speed of learning (which is a contribution relative to most of the works in this area that focus on whether there is learning or not). \nUnder the assumption that the online platform receives a constant revenue from every user that purchases (because of commissions from sellers or from advertising revenues)\, we then show that\, in any Bayesian equilibrium\, the profits of the online platform are a function of the speed of learning of users. Using this result\, we study the design of the review system by the online platform\, and show the possibility of both fast and slow learning from reviews.\nAuthors: Daron Acemoglu\, Ali Makhdoumi\, Azarakhsh Malekian and Asu Ozdadaglar. \nBiography\nDaron Acemoglu is the Elizabeth and James Killian Professor of Economics at MIT. In 2005 he received the John Bates Clark Medal awarded to economists under forty judged to have made the most significant contribution to economic thought and knowledge. Among many other awards\, in 2017 he was given an Honorary Doctorate (Bath University)\, Great Immigrant List of the Carnegie foundations\, BBVA Frontiers of Knowledge Award in Economics and a Carnegie Fellow. URL:https://idss-stage.mit.edu/calendar/fast-and-slow-learning-from-reviews/ LOCATION:MIT Building 32\, Room 141\, The Stata Center (32-141)\, 32 Vassar Street\, Cambridge\, MA\, 02139\, United States CATEGORIES:IDSS Distinguished Seminar Series END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20170425T160000 DTEND;TZID=America/New_York:20170425T160000 DTSTAMP:20260406T103911 CREATED:20190627T212126Z LAST-MODIFIED:20190627T212126Z UID:10090-1493136000-1493136000@idss-stage.mit.edu SUMMARY:Recent Methodological Advances in Automated Causal Discovery DESCRIPTION:IDSS Distinguished Seminars is a monthly lecture series featuring prominent global leaders and academics sharing research in areas that include social networks\, causal inference\, data privacy\, computational social science and other areas that are impacted by the emergence of big data.   URL:https://idss-stage.mit.edu/calendar/recent-methodological-advances-in-automated-causal-discovery-2/ LOCATION:32-141\, United States CATEGORIES:IDSS Distinguished Seminar Series END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20170307T160000 DTEND;TZID=America/New_York:20170307T160000 DTSTAMP:20260406T103911 CREATED:20190627T212134Z LAST-MODIFIED:20190627T212134Z UID:10104-1488902400-1488902400@idss-stage.mit.edu SUMMARY:How the Chinese Government Fabricates Social Media Posts for Strategic Distraction\, not Engaged Arguments DESCRIPTION:IDSS Distinguished Seminars is a monthly lecture series featuring prominent global leaders and academics sharing research in areas that include social networks\, causal inference\, data privacy\, computational social science and other areas that are impacted by the emergence of big data.   URL:https://idss-stage.mit.edu/calendar/how-the-chinese-government-fabricates-social-media-posts-for-strategic-distraction-not-engaged-arguments-2/ LOCATION:32-141\, United States CATEGORIES:IDSS Distinguished Seminar Series END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20161213T160000 DTEND;TZID=America/New_York:20161213T160000 DTSTAMP:20260406T103911 CREATED:20190627T212143Z LAST-MODIFIED:20190627T212143Z UID:10118-1481644800-1481644800@idss-stage.mit.edu SUMMARY:The Impact of Expanding Medicaid: Evidence from the Oregon Health Insurance Experiment  DESCRIPTION:IDSS Distinguished Seminars is a monthly lecture series featuring prominent global leaders and academics sharing research in areas that include social networks\, causal inference\, data privacy\, computational social science and other areas that are impacted by the emergence of big data.   URL:https://idss-stage.mit.edu/calendar/the-impact-of-expanding-medicaid-evidence-from-the-oregon-health-insurance-experiment-2/ LOCATION:32-141\, United States CATEGORIES:IDSS Distinguished Seminar Series END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20161108T160000 DTEND;TZID=America/New_York:20161108T160000 DTSTAMP:20260406T103911 CREATED:20190627T212144Z LAST-MODIFIED:20190627T212144Z UID:10123-1478620800-1478620800@idss-stage.mit.edu SUMMARY:IDSS Distinguished Seminar  DESCRIPTION:IDSS Distinguished Seminars is a monthly lecture series featuring prominent global leaders and academics sharing research in areas that include social networks\, causal inference\, data privacy\, computational social science and other areas that are impacted by the emergence of big data.   URL:https://idss-stage.mit.edu/calendar/idss-distinguished-seminar-3/ LOCATION:32-141\, United States CATEGORIES:IDSS Distinguished Seminar Series END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20161018T160000 DTEND;TZID=America/New_York:20161018T160000 DTSTAMP:20260406T103911 CREATED:20190627T212146Z LAST-MODIFIED:20190627T212146Z UID:10129-1476806400-1476806400@idss-stage.mit.edu SUMMARY:The Moral Character of Cryptographic Work DESCRIPTION:IDSS Distinguished Seminars is a monthly lecture series featuring prominent global leaders and academics sharing research in areas that include social networks\, causal inference\, data privacy\, computational social science and other areas that are impacted by the emergence of big data.   URL:https://idss-stage.mit.edu/calendar/the-moral-character-of-cryptographic-work-2/ LOCATION:32-141\, United States CATEGORIES:IDSS Distinguished Seminar Series END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20161011T160000 DTEND;TZID=America/New_York:20161011T160000 DTSTAMP:20260406T103911 CREATED:20190627T212146Z LAST-MODIFIED:20190627T212146Z UID:10131-1476201600-1476201600@idss-stage.mit.edu SUMMARY:Innovations for the 21st Century Electricity Grid DESCRIPTION:IDSS Distinguished Seminars is a monthly lecture series featuring prominent global leaders and academics sharing research in areas that include social networks\, causal inference\, data privacy\, computational social science and other areas that are impacted by the emergence of big data.   URL:https://idss-stage.mit.edu/calendar/innovations-for-the-21st-century-electricity-grid-2/ LOCATION:32-141\, United States CATEGORIES:IDSS Distinguished Seminar Series END:VEVENT BEGIN:VEVENT DTSTART;TZID=America/New_York:20160405T160000 DTEND;TZID=America/New_York:20160405T160000 DTSTAMP:20260406T103911 CREATED:20190627T212157Z LAST-MODIFIED:20190627T212157Z UID:10155-1459872000-1459872000@idss-stage.mit.edu SUMMARY:Distributed Learning Dynamics Convergence in Routing Games DESCRIPTION:IDSS Distinguished Seminars is a monthly lecture series featuring prominent global leaders and academics sharing research in areas that include social networks\, causal inference\, data privacy\, computational social science and other areas that are impacted by the emergence of big data.   URL:https://idss-stage.mit.edu/calendar/distributed-learning-dynamics-convergence-in-routing-games-2/ LOCATION:32-155\, United States CATEGORIES:IDSS Distinguished Seminar Series END:VEVENT END:VCALENDAR