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The Precision Medicine Issue Medicine Is Becoming Hyper
The precision medicine issue Vol. 121 Nov/Dec $9.99 USD No. 6 2018 $10.99 CAD $2 million would save her life. The precision medicine issue Could you pay? Should you? Medicine is becoming hyper-personalized, hyper-accurate ... and hyper-unequal. p.38 The AIs taking over Curing cancer How to plan your from doctors with customized digital afterlife vaccines p.22 p.46 p.76 ND18_cover.indd 1 10/3/18 4:16 PM Artificial intelligence is here. Own what happens next. ND18 ETD19 Spread 16x10.875 D3.indd 2 9/28/18 10:59 AM March 25–26, 2019 St. egis Hotel San Francisco, CA Meet the people leading the next wave of intelligent technologies. Andrew Kathy Daphne Sergey Harry Anagnost Gong Koller Levine Shum President and Cofounder and Founder and Assistant Executive Vice CEO, Autodesk CEO, WafaGames CEO, Insitro Professor, EECS, President, UC Berkeley Microsoft Artifi cial Intelligence and Research Group EmTechDigital.com/2019 ND18 ETD19 Spread 16x10.875 D3.indd 3 9/28/18 10:59 AM 02 From the editor It’s been nearly two decades since the help meet the ballooning healthcare first human genome was sequenced. needs of an aging population. That achievement opened up the prom The problem? As medicine gets ise of drugs and treatments perfectly more personalized, it risks getting more tailored to each person’s DNA. But per unequal. Our cover story is Regalado’s sonalized or “precision” medicine has gripping and troubling account page always felt a bit like flying cars, sexbots, 38 of the parents who raise millions of or labgrown meatone of those things dollars to finance genetherapy cures for we’re perpetually being promised but their children’s ultrarare diseases. -
GPI's Research Agenda
A RESEARCH AGENDA FOR THE GLOBAL PRIORITIES INSTITUTE Hilary Greaves, William MacAskill, Rossa O’Keeffe-O’Donovan and Philip Trammell February 2019 (minor changes July 2019) We acknowledge Pablo Stafforini, Aron Vallinder, James Aung, the Global Priorities Institute Advisory Board, and numerous colleagues at the Future of Humanity Institute, the Centre for Effective Altruism, and elsewhere for their invaluable assistance in composing this agenda. 1 Table of Contents Introduction 3 GPI’s vision and mission 3 GPI’s research agenda 4 1. The longtermism paradigm 6 1.1 Articulation and evaluation of longtermism 6 1.2 Sign of the value of the continued existence of humanity 8 1.3 Mitigating catastrophic risk 10 1.4 Other ways of leveraging the size of the future 12 1.5 Intergenerational governance 14 1.6 Economic indices for longtermists 16 1.7 Moral uncertainty for longtermists 18 1.8 Longtermist status of interventions that score highly on short-term metrics 19 2. General issues in global prioritisation 21 2.1 Decision-theoretic issues 21 2.2 Epistemological issues 23 2.3 Discounting 24 2.4 Diversification and hedging 28 2.5 Distributions of cost-effectiveness 30 2.6 Modelling altruism 32 2.7 Altruistic coordination 33 2.8 Individual vs institutional actors 35 Bibliography 38 Appendix A. Research areas for future engagement 46 A.1 Animal welfare 46 A.2 The scope of welfare maximisation 48 Appendix B. Closely related areas of existing academic research 51 B.1 Methodology of cost-benefit analysis and cost-effectiveness analysis 51 B.2 Multidimensional economic indices 51 B.3 Infinite ethics and intergenerational equity 53 B.4 Epistemology of disagreement 53 B.5 Demandingness 54 B.6 Forecasting 54 B.7 Population ethics 55 B.8 Risk aversion and ambiguity aversion 55 B.9 Moral uncertainty 57 1 B.10 Value of information 58 B.11 Harnessing and combining evidence 59 B.12 The psychology of altruistic decision-making 60 Appendix C. -
Should the Randomistas (Continue To) Rule?
Should the Randomistas (Continue to) Rule? Martin Ravallion Abstract The rising popularity of randomized controlled trials (RCTs) in development applications has come with continuing debates on the pros and cons of this approach. The paper revisits the issues. While RCTs have a place in the toolkit for impact evaluation, an unconditional preference for RCTs as the “gold standard” is questionable. The statistical case is unclear on a priori grounds; a stronger ethical defense is often called for; and there is a risk of distorting the evidence-base for informing policymaking. Going forward, pressing knowledge gaps should drive the questions asked and how they are answered, not the methodological preferences of some researchers. The gold standard is the best method for the question at hand. Keywords: Randomized controlled trials, bias, ethics, external validity, ethics, development policy JEL: B23, H43, O22 Working Paper 492 August 2018 www.cgdev.org Should the Randomistas (Continue to) Rule? Martin Ravallion Department of Economics, Georgetown University François Roubaud encouraged the author to write this paper. For comments the author is grateful to Sarah Baird, Mary Ann Bronson, Caitlin Brown, Kevin Donovan, Markus Goldstein, Miguel Hernan, Emmanuel Jimenez, Madhulika Khanna, Nishtha Kochhar, Andrew Leigh, David McKenzie, Berk Özler, Dina Pomeranz, Lant Pritchett, Milan Thomas, Vinod Thomas, Eva Vivalt, Dominique van de Walle and Andrew Zeitlin. Staff of the International Initiative for Impact Evaluation kindly provided an update to their database on published impact evaluations and helped with the author’s questions. Martin Ravallion, 2018. “Should the Randomistas (Continue to) Rule?.” CGD Working Paper 492. Washington, DC: Center for Global Development. -
Heterogeneous Treatment Effects in Impact Evaluation†
American Economic Review: Papers & Proceedings 2015, 105(5): 467–470 http://dx.doi.org/10.1257/aer.p20151015 Heterogeneous Treatment Effects in Impact Evaluation† By Eva Vivalt* The past few years have seen an exponential which programs work best where. To date, it growth in impact evaluations. These evalua- has conducted 20 meta-analyses and systematic tions are supposed to be useful to policymakers, reviews of different development programs.1 development practitioners, and researchers Data gathered through meta-analyses are the designing future studies. However, it is not yet ideal data with which to answer the question of clear to what extent we can extrapolate from how much we can extrapolate from past results, past impact evaluation results or under which as what one would want is a large database of conditions Deaton 2010; Pritchett and Sandefur impact evaluation results. Since data on these 20 2013 . Further,( it has been shown that even a topics were collected in the same way, we can similar) program, in a similar environment, can also look across different types of programs to yield different results Duflo et al. 2012; Bold see if there are any more general trends. et al. 2013 . The different( findings that have The rest of this paper proceeds as follows. been obtained) in such similar conditions point First, I briefly discuss the framework of hetero- to substantial context-dependence of impact geneous treatment effects. I then describe the evaluation results. It is critical to understand this data in more detail and provide some illustrative context-dependence in order to know what we results. -
Daniel J. Benjamin (As of 10/4/2019)
Daniel J. Benjamin (as of 10/4/2019) Contact Information University of Southern California Center for Economic and Social Research Dauterive Hall 635 Downey Way, Suite 301 Los Angeles, CA 90089-3332 Phone: 617-548-8948 Professional Experience: Professor (Research) of Economics, CESR, USC, 2019-present Associate Professor (Research) of Economics, CESR, USC, 2015-2019 Visiting Associate Professor (Research) of Economics, CESR, USC, 2014-2015 Associate Professor (with tenure), Economics Department, Cornell University, 2013-2015 Assistant Professor, Economics Department, Cornell University, 2007-2013 Visiting Research Fellow, National Bureau of Economic Research (NBER), 2011-2012 Research Associate, NBER, 2013-present Faculty Research Fellow, NBER, 2009-2013 Research Fellow, Research Center for Group Dynamics, Institute for Social Research, 2006-2017 Research Fellow, Population Studies Center, Institute for Social Research, 2006-2007 Graduate Studies: Ph.D., Economics, Harvard University, 2006 M.Sc., Mathematical Economics, London School of Economics, 2000 A.M., Statistics, Harvard University, 1999 Undergraduate Studies: A.B., Economics, Harvard University, summa cum laude, prize for best economics student, 1999 Honors, Scholarships, and Fellowships: 2013 Norwegian School of Economics Sandmo Junior Fellowship (prize for a “promising young economist”) 2012-2013 Cornell’s Institute for Social Science (ISS) Faculty Fellowship 2009-2012 ISS Theme Project Member: Judgment, Decision Making, and Social Behavior 2011-2012 NBER Visiting Fellowship -
New App for Effective Altruism Released Aidgrade’S New Tool Helps the Public Understand Latest Research Results in International Development
Contact: Eva Vivalt For Immediate Release 202-630-6763 [email protected] New App for Effective Altruism Released AidGrade’s new tool helps the public understand latest research results in international development November 7, 2013 — AidGrade, a research institute dedicated to discovering what works in international development, has just released a much-expanded version of its meta-analysis app which allows people to download and analyze results of individual studies. Meta-analysis combines findings from academic studies in a statistically sound way. It is to be contrasted with “vote counting”, which simply tallies how many studies found positive or negative effects and can be misleading. AidGrade previously released an app that allowed people to conduct their own meta-analyses online. AidGrade simultaneously built a database of impact evaluation results from academic studies, such as those produced by the Abdul Latif Jameel Poverty Action Lab, at MIT; Innovations for Poverty Action, based around Yale; and the Center for Effective Global Action, at the University of California, Berkeley, among others. More than 5,000 results from 300 papers have now been coded and are coming online. “It’s awesome to see these results,” enthused Eva Vivalt, founder of the project and post- doctoral fellow at New York University. “You hear so much in the media about ‘this study says this’ or ‘this study says that’, but each academic paper only gives a small part of the story. We need to look at the aggregate results, but in a way that makes sense. Economics isn’t like other disciplines where results can be easily replicated across contexts. -
JOSHUA TASOFF August 28, 2019
JOSHUA TASOFF August 28, 2019 https://scholar.cgu.edu/joshua-tasoff/ Department of Economics, Phone: 909-621-8782 School of Politics & Economics, Fax: 909-621-8460 Claremont Graduate University, [email protected] Harper E. 204 160 E. Tenth Street Claremont, CA 91711 EMPLOYMENT Associate Professor (with tenure), Claremont Graduate University, July 2016 – Present Assistant Professor, Claremont Graduate University, July 2010 – July 2016 Visiting Professor, Carnegie Mellon University, Spring 2018 Visiting Professor, University of Southern California, Fall 2013 EDUCATION Ph.D. University of California, Berkeley, Economics, June 2010 Dissertation: “Essays in psychological and political economics” Advisors: Botond Kőszegi and Matthew Rabin B.S. Massachusetts Institute of Technology, Economics, June 2003 RESEARCH INTERESTS Behavioral Economics, Experimental Economics, Economics of Microbial Communities, Food Choice REFEREED PUBLICATIONS 10. “The Role of Time Preferences and Exponential-Growth Bias in Retirement Savings”with Gopi Shah Goda, Matthew Levy, Colleen Manchester, and Aaron Sojourner. Economic Inquiry, Vol. 57 (3), July 2019, pp1636-1658 9. “Exponential-Growth Bias in Experimental Consumption Decisions” with Matthew Levy. Economica, 20 March 2019, https://doi.org/10.1111/ecca.12306 8. “Fantasy and Dread: The Demand for Information and the Consumption Utility of the Future”, Management Science, Vol. 63 (12), December 2017, pp. 4037–4060, http://dx.doi.org/10.1287/mnsc.2016.2550 with Ananda Ganguly 7. “When Higher Productivity Hurts: The Interaction Between Overconfidence and Capital” Journal of Behavioral and Experimental Economics, Vol. 67, April 2017, pp 131-142 with Andrew Royal 6. “Exponential-Growth Bias and Overconfidence”, Journal of Economic Psychology, (Lead Article) Vol. 58, Feb (2017), pp 1-14 with Matthew Levy 5. -
Why Is It Hard to Find Genes That Are Associated with Social Science Traits? Theoretical and Empirical Considerations
“Why Is It Hard to Find Genes …” – p. 1 Why Is It Hard to Find Genes that are Associated with Social Science Traits? Theoretical and Empirical Considerations Christopher F. Chabris1†* James J. Lee2† Daniel J. Benjamin3 Jonathan P. Beauchamp4 Edward L. Glaeser4 Gregoire Borst2 Steven Pinker2 David I. Laibson4 1. Department of Psychology, Union College, Schenectady, NY, USA 2. Department of Psychology, Harvard University, Cambridge, MA, USA 3. Department of Economics, Cornell University, Ithaca, NY, USA 4. Department of Economics, Harvard University, Cambridge, MA, USA † These authors contributed equally to the work. * Address correspondence to: Christopher F. Chabris Department of Psychology Union College 807 Union Street Schenectady, NY 12308 USA [email protected] “Why Is It Hard to Find Genes …” – p. 2 Abstract Though most behavioral traits are moderately to highly heritable, the genes that influence them are elusive: many published genetic associations fail to replicate. With physical traits like eye color and skin pigmentation, in contrast, several genes with large effects have been discovered and replicated. We draw on R.A. Fisher’s geometric model of adaptation to explain why traits of interest to behavioral scientists may have a genetic architecture featuring hundreds or thousands of alleles with tiny individual effects, rather than a few with large effects, and why such an architecture makes it difficult to find robust associations between traits and genes. In the absence of strong directional selection on a trait, alleles with large effect sizes will probably remain rare, and such a lack of strong directional selection is likely to characterize most traits currently of interest in social science. -
Economics Without Borders
ECONOMICS WITHOUT BORDERS Economic Research for European Policy Challenges Edited by Richard Blundell Estelle Cantillon Barbara Chizzolini Marc Ivaldi Wolfgang Leininger Ramon Marimon Laszlo Matyas (Lead Editor) and Frode Steen March 8, 2016 Cambridge University Press Contents List of illustrations page xii List of tables xiv Foreword xv 0 Introduction Blundell, Cantillon, Chizzolini, Ivaldi, Leininger, Ma- rimon, Matyas, and Steen 1 1 Innovation and Growth: The Schumpeterian Perspective Philippe Aghion and Ufuk Akcigit 30 1.1 Introduction 30 1.2 Looking for Growth Paradigms to Think about Growth Policy 32 1.2.1 The Neoclassical Growth Model 32 1.2.2 The AK Model 33 1.2.3 The Product-variety Model 33 1.2.4 The Schumpeterian Model 34 1.3 Some Main Applications and Extensions of Schumpeterian Growth Theory 36 1.3.1 Growth Meets IO 36 1.3.2 Schumpeterian Growth and Firm Dynamics 38 1.3.3 Growth Meets Development: Appropriate Institutions 40 1.3.4 Growth Meets Development: Firm Dynamics in Developing Countries 42 1.3.5 Growth and Unemployment 43 1.4 Enhancing Productivity Growth in Advanced Countries 45 1.4.1 Pillars of Innovation-led Growth 45 1.4.2 Competition Policy against Industrial Policy 45 1.4.3 Reforming the State 48 1.4.4 Macroeconomic Policy 49 iv Contents 1.4.5 Innovation, Inequality, and Social Mobility: Making Growth Inclusive 50 1.5 Technological Waves and the Debate on Secular Stagnation 53 1.5.1 The Debate 53 1.5.2 Historical Wave Patterns 54 1.6 Schumpeterian Insights into R&D Policy 56 1.6.1 R&D Policies and Firm Selection -
Essays on Genetics and the Social Sciences Genoeconomics, Social-Science Genetics, Or Biological Economics
413 AYSU OKBAY Evidence from behavior-genetic studies of twins, adoptees and other pairs of relatives shows that virtually all human traits, including economic preferences and behaviors, are at least moderately heritable. With increasing availability of genetic data, it is now becoming feasible to identify specific genetic variants associated with complex outcomes, thus blurring the disciplinary boundaries between biological and Essays on Genetics and social sciences. Building on these developments, this thesis explores questions at the intersection of economics and biology and thus contributes to an emerging field of research commonly referred to as: - Essays on Genetics and the Social Sciences OKBAY AYSU genoeconomics, social-science genetics, or biological economics. the Social Sciences The research described here identifies specific genetic variants robustly associated with a suite of complex outcomes – ranging from educational attainment, to subjective well-being, neuroticism and depression – and shows how these findings can be used, in conjunction with quasi-experimental research designs from economics, to conduct rigorous and well-powered investigations of the interactions between genes and environment. It illustrates some hard-won lessons about the relative merits of various research strategies that have been proposed for efforts to discover genetic associations with complex traits. Furthermore, it provides a framework for quantifying tradeoffs between outcome heterogeneity and sample-size in gene-discovery efforts. The results reported in this thesis strongly suggest that there will be many settings in which genetic data will prove valuable to social sciences. In addition to broadening our knowledge about the biological mechanisms underlying behavioral outcomes, these advances will provide researchers from many disciplines with increasingly powerful tools that can be used to integrate genetic factors into a wide range of empirical models. -
Weighing the Evidence: Which Studies Count?
Weighing the Evidence: Which Studies Count? Eva Vivalt∗ Aidan Coville† Sampada KC‡ April 30, 2021 Abstract We present results from two experiments run at World Bank and Inter- American Development Bank workshops on how policy-makers, practi- tioners and researchers weigh evidence and seek information from impact evaluations. We find that policy-makers care more about attributes of studies associated with external validity than internal validity, while for researchers the reverse is true. These preferences can yield large differ- ences in the estimated effects of pursued policies: policy-makers indicated a willingness to accept a program that had a 6.3 percentage point smaller effect on enrollment rates if it were recommended by a local expert, larger than the effects of most programs. ∗Department of Economics, University of Toronto, [email protected] †Development Impact Evaluation group, World Bank, [email protected] ‡Institutions and Political Inequality, Berlin Social Science Center (WZB), [email protected]. The findings, interpretations, and conclusions expressed in this paper are entirely those of the au- thors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. 1 1 Introduction The basic motivation behind evidence-based policy-making is simple: rigorous ev- idence on what works best could help policy-makers make more informed decisions that improve policy effectiveness. This could take the form of, for example, scale up or adaptation of programs based on evidence on the project of interest or simi- lar projects elsewhere. -
Deaton Randomization Revisited V7 2020
Randomization in the tropics revisited: a theme and eleven variations Angus Deaton School of Public and International Affairs, Princeton University Schaeffer Center for Health Policy and Economics, University of Southern California National Bureau of Economic Research December 2019, Revised July 2020 Forthcoming in Florent Bédécarrats, Isabelle Guérin and François Roubaud, Randomized controlled trials in the field of development: a critical perspective, Oxford University Press. For (generous, helpful, and amazingly rapid) comments on an earlier version, I am most grateful to Nancy Cartwright, Anne Case, Shoumitro Chatterjee, Nicolas Côté, Jean Drèze, William Easterly, Reetika Khera, Lant Pritchett, C. Rammanohar Reddy, François Roubaud, Dean Spears and Bastian Steuwer. I acknowledge generous support from the National Institute on Aging through the NBER, Grant number P01AG05842. Development economists have been using randomized controlled trials (RCTs) for the best part of two decades1, and economists working on welfare policies in the US have been doing so for much longer. The years of experience have made the discussions richer and more nuanced, and both proponents and critics have learned from one another, at least to an extent. As is often the case, researchers seem reluctant to learn from earlier mistakes by others, and the lessons from the first wave of experiments, many of which were laid out by Jim Heckman and his collaborators2 a quarter of a century ago, have frequently been ignored in the second wave. In this essay, I do not attempt to reconstruct the full range of questions that I have written about elsewhere3, nor to summarize the long running debate in economics.