Ascent in competitive arenas: From Fenway Park to Mass Ave The Science of Success: Measurements and Predictions June 17th Harvard University http://www.barabasilab.com/success! Follow us on Facebook for updates: www.facebook.com/SuccessScience The availability of massive data on individual performance has prompted scientists to start exploring patterns that govern the path to individual success. The topic is diverse — from exploring citations to influential scientific papers to the emergence of runaway videos on YouTube, from the popularity of hash tags on Twitter to the path to success for countries. As such the tools and perspectives vary, engaging social scientists, computer scientists, economists, physicists, mathematicians, and many other disciplines. The goal of this symposium is to bring together these diverse communities. We invite contributions and participants in two tracks: Ignite Talk Submit a one-page abstract. Selected participants in this track are invited to present an Ignite talk about their research — a five minutes talk accompanied by 20 slides. Slides are automatically advanced with 15 seconds each — A prize committee willAlexander judge and select M. Petersenthe best talk for an award sponsored by IBM Research. Submission should be emailed to [email protected], with subject indicating 'Ignite Talk'. The IMTemail shouldInstitute include for the Advanced information about Studies, the presenter, Lucca including Italy name, current position and affiliation. Guest If you wish to attend the event without giving a presentation, apply for a guest seat. Tell us a bit about yourself by emailing us a short note that indicates your motivation for participating as well as your name, position, and affiliation at [email protected], with subject indicating 'Guest'. Submissions will be evaluated in a rolling basis. Evaluation will continue until all seats are filled. Space is limited, so those interested should submit their abstracts as soon as possible. Submission Deadline: May 17, 2013. Acceptance Notification: May 24, 2013. There is no registration fee, but proof for acceptance to either of the two tracks is needed for participation. Venue The Institute for Quantitative Social Science, Harvard University, Cambridge, MA, USA Organized by: Albert-László Barabási, David Lazer, Dashun Wang Center for Complex Network Research, Northeastern University, Boston, MA, USA Confirmed Speakers as of April, 2013 Pierre Azoulay, Sloan School of Management, MIT James A. Evans, Professor of Sociology, University of Chicago Santo Fortunato, Associate Professor in Complex Systems, Aalto University, Finland Gautam Mukunda, Harvard Business School Alexander M. Petersen, IMT Institute for Advanced Studies Lucca, Italy Nicola Perra, Northeastern University Chaoming Song, CCNR, Northeastern University Camille Sweeney, author of "The Art of Doing" Christoph Riedl, IQSS, Harvard University Arnout van de Rijt, Department of Sociology, SUNY Stony Brook Brian Uzzi, the Kellogg School of Management, Northwestern University Duncan Watts, Principal Researcher at Microsoft Research 103 Mozart x(t) = compositions stellar career growth is ) t 102 αi =1.42 ( i a non-linear process X 101 age 8 100 101 cumulative output ~ reputation Babe Ruth 101 x(t) = OPS t αi =1.24 ) αi t X (t) x (τ) t ( i i i ≡ ∼ X 100 τ=1 α 1 constant output, no growth 100 101 i 105 ≈ ⇒ H. Eugene Stanley super-linear growth 104 αi > 1 x(t) = annual citations ⇒ ) 103 t αi =2.41 ( i 102 X 101 100 100 101 career age, t Coevolution of reputation and impact over the academic career Alexander M. Petersen,1 Santo Fortunato,2 Raj K. Pan,2 Orion Penner,3 Massimo Riccaboni,3, 4 H. Eugene Stanley,5 and Fabio Pammolli1, 5, 6 1Laboratory for the Analysis of Complex Economic Systems, IMT (Institutions Markets Technologies) Institute for Advanced Studies Lucca, 55100 Lucca, Italy 2Department of Biomedical Engineering and Computational Science, Aalto University, 12200 Aalto, Finland 3Laboratory of Innovation Management and Economics, IMT Institute for Advanced Studies Lucca, 55100 Lucca, Italy 4Department of Managerial Economics, Strategy and Innovation, Katholieke Universiteit Leuven, 3000 Leuven, Belgium 5Center for Polymer Studies and Department of Physics, Boston University, Boston, Massachusetts 02215, USA 6Crisis Lab, IMT Lucca Institute for Advanced Studies, Lucca 55100, Italy (Dated: July 26, 2012) The cumulative citations to a paper is a universal measure of impact, but the role that author reputation plays in the life-cycle of the citation rate remains poorly understood. As a result, models of citation dynamics and career trajectories overlook the collaboration and reputation spillovers constitute a cumulative advantage underlying the competitive aspects of science. To better understand the reputation effect in science, we analyze the longitudinal citation dynamics of 350 leading scientists from biology, physics, and mathematics. We uncover statistical regularities in the evolution of productivity and impact which we use as benchmarks for a theoretical model of career growth that we test and validate on real careers. Our model incorporates the life-cycle effect for individual papers, the cumulative advantage arising from scientific reputation, and the preferential attachment effect for citation dynamics. We find that the author reputation effect dominates in the initial phase of the citation life-cycle, but that the preferential attachment mechanism emerges as the main component behind the sustained citation rate of highly cited papers. Comparing betweenCoevolution the three disciplines, of reputation we show and that impact the impact over life-cycle the academic career differs between fields: the axiomatic discoveries in mathematics have a very long shelf-life, whereas the rapid pace in biology and physics results in a shorter half-lifeAlexander despite the M. intense Petersen, citation1 Santo rate Fortunato, in the field.2 Raj K. Pan,2 Orion Penner,3 Massimo Riccaboni,3, 4 H. Eugene Stanley,5 and Fabio Pammolli1, 5, 6 1 Todo: by constructionLaboratory for (L thecorresponds Analysis of Complex to the Economic career length Systems, of in- IMT (Institutions Markets Technologies)i Institute for Advanced Studies Lucca, 55100 Lucca, Italy 2 dividual i at the time of data extraction). Fig. 1 shows 2 Department of Biomedical Engineering and Computational Science, Aalto University, 12200 Aalto, Finland Perform statistical χ significance tests on the DGBD 3 • the characteristicLaboratory of production Innovation Management trajectory and obtained Economics, by averag- profiles for datasets [D] and [E] and put in SI. ingIMT together Institute the forA Advancedindividual Studies trajectories Lucca, 55100N˜i(t Lucca,) belonging Italy to 4Department of Managerial Economics,A Strategy and Innovation, each dataset, N˜(t) Ni(t)/A ni , where we define Calculate πi, ρi, and τi for each of 350 scientists, put in Katholieke Universiteit Leuven,i=1 3000 Leuven, Belgium 5 ≡ • N (t)Center= N˜ for(t Polymer) / N˜(1) Studies. and Department of Physics, tables, and look for relations to other factors ThisBoston regularity University, reflects Boston, the Massachusetts abundance of 02215, of careers USA with 6Crisis Lab, IMT Lucca Institute for Advanced Studies, Lucca 55100, Italy αi > 1 corresponding(Dated: to accelerated July 26, 2012) career growth. This ac- celeration is consistent with increasing returns arising from I. INTRODUCTION The cumulativeknowledge citations to a and paper production is a universal spillovers. measure of impact, but the role that author reputation plays in the life-cycle of the citation rate remains poorly understood. As a result, models of citation dynamics and career trajectories overlook the collaboration and reputation spillovers constitute a cumulative advantage We analyze a large longitudinal career dataset coveringunderlying 350 the competitive aspects of science. To better understand the reputation effect in science, we analyze leading scientists comprising 83,693 papers and 7,577,084the longitudinal ci- citation dynamics of 350 leading scientists from biology, physics, and mathematics. We uncover tations tracked over 384,407 paper years. statistical regularities in the evolutionB. of Longitudinalproductivity and citation impact which dynamics we use as benchmarks for a theoretical model of career growth that we test and validate on real careers. Our model incorporates the life-cycle effect for individual papers, the cumulative advantage arising from scientific reputation, and the preferential attachment effect for citation dynamics.Paper We quality find that is the universally author reputation measured effect dominates according in the to initial the cu- phase of the citation τ II. RESULTS life-cycle, but that themulative preferential number attachment of citations mechanismc(τ emerges)= ast=1 the∆ mainc(t component), where we behind the sustained citation rate of highlydefine cited papers.∆c(t) Comparingas the number between of the citations three disciplines, received we show by the that pa- the impact life-cycle differs between fields:per the in axiomatic year t where discoveriesτ = int mathematicst +1defines have a very the long relation shelf-life, be- whereas the rapid A. Longitudinal productivity dynamicspace in biology and physics results in a shorter half-life despite0 the intense citation rate in the field. tween the paper age τ, the career− age t, and the first year the We model the career trajectory as a sequence of scientific paper was cited,