DOCSLIB.ORG

How Networks Learn an Interview with Cesar Hidalgo

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
How Networks Learn an Interview with Cesar Hidalgo Episode 085 – How Networks Learn An Interview with Cesar Hidalgo Aired on May 10, 2018 [00:00] Commercial about The Ninth International Conference on Complex Systems hosted by ​ the New England Complex Systems Institute. [Intro Music] [02:30] Haley: Today on the Human Current we have with us Cesar Hidalgo, he’s a physicist, ​ complexity scientist and the director of the Collective Learning Group at MIT. Hi Cesar, thank you for being on the Human Current. [02:42] Cesar: Hi, it’s my pleasure to be here ​ [02:44] Cesar: Would you start us off by introducing yourself to our listeners along with telling ​ us about the work you’re currently doing and what makes you passionate? [02:50] Cesar: Of course, I run the Collective Learning Group at MIT and what we do here is to ​ study how teams, cities and nations learn. We study how they learn to develop new activities for instance; how countries started exporting new products but also we create data democratization and facilitation tools can that can help facilitate learning within organizations. [03:11] Haley: Some very exciting and much needed work that you’re doing there and in our ​ introduction, we said that you are a complexity scientist so we’d like to talk a little bit about how you became interested into complex systems. [03:23] Cesar: Well, I remember being an undergrad in Chile and at that time we had to do this ​ huge weekly problem test, I was studying physics and there were problem test on Quantum Mechanics, Electro-dynamics and other tough courses. So, when my friends would spend a huge amount of time in the library, when I would take a break, I would walk around and try to think about how to solve the problems that were being assigned. I would look at books on chaos and fractures I started to become interested in this other physics which I thought it was quite interested, it was quite new, and it was not what I was being taught in the classroom and this was the physics of complex systems; it was the physics of things that were deterministic but yet not predictable or things that would have geometry that would be extremely interesting and complex. Eventually I got from there to the physics of networks which at that time was growing very fast and at that time I was sold, I knew that was the path I needed to follow. [04:17] Haley: When I looked you up it said that Laszlo Barabasi was your PhD advisor? ​ [04:21] Cesar: Yes, that’s correct I did my PhD with Laszlo. ​ [04:23] Haley: That’s got to be an amazing experience to work with him as well? ​ © The HumanCurrent 2018 [04:27] Cesar: Oh yes, it was amazing because he’s actually someone that has been extremely ​ successful in this area; so you not only learn about ideas you learn very well how to interact with editors, with other people, how to identify certain problems, learn how to shape them or communicate them and I think all of those are skills that are vital to survive as a scholar. [04:49] Haley: Yeah definitely and I think that it’s one thing to talk about network theory another ​ to put it into practice in your own life as well, right? [04:56] Cesar: Yep. ​ [04:47] Haley: If you would give us your definition of complexity. ​ [05:00] Cesar: So, I usually use a couple of definition of complexity; one of them is functional ​ and we can talk about complex systems as those systems that have the ability to adapt and evolve. So, you can think of life forms, you can think of society, you can think of a economies of complex systems. So, think of a car, a car is very complicated but it’s not complex because it doesn’t have the ability to adapt or evolve but a car company does have that ability; so they can change the type of cars that they do over time or the way that they organize, so that’s adapting and evolving. Another definition of complexity which we owe to Warren Weaver is that complex systems are those in which the identity of the parts involve, and the interactions cannot be ignored. Think about gas, a gas is not a complex system because if you swap one of them with oxygen with another atom nobody cares, nothing changes. But think now of a company, a company is a complex system because if you were to change one person for another it does matter the identity of the elements involved and their patterns of interaction it’s important. So, complex systems are systems that are intricate in the sense that they do have a large diversity of parts whose identities matter and cannot be ignored and also, they have this ability to adapt and evolve and when you find these systems that have these properties you can think that you are in the presence of a complex system. [06:20] Haley: Great thank you for sharing your definition. We definitely see complex systems ​ all around us every day and Angie and I definitely see the importance of them and we like talking about them especially in our human systems and we’re just wondering why do you think complexity thinking is so important? [06:37] Cesar: Thinking about complexity is important because people have the tendency to ​ jump into micro explanations for macro-phenomenon, so when we start some changes in the world we tend to attribute those to certain individual actors that have perform certain actions and we have a large tendency to go into those explanations but sometimes in reality the world is a little bit different; sometimes the behavior off people is constrained by large system variables so it could be the other way around, it could be that the constraints of the systems could be the ones that are imposing and restricting the behaviors that kind of emerge within the individuals. Sometimes the outcomes that individuals generate are the macro scales from the micro-actions are outcomes that are unintended or even not understood by the individuals that perform them. So, complexity thinking is very open at trying to understand how these different scales affect each other, how micro behaviors can have emerging property than large scales and also how constraints of the macro-scales can shape and even rule what happens on the micro-scale because their force can be larger than that of the individual agency of the agent. So, then it’s © The HumanCurrent 2018 important to think about complexity, to conceal all of these options and not to jump to conclusions that the outcomes that will serve are always a result of the actions of the individuals. [07:54] Haley: That’s well said and we definitely agree with that. Our mission here at the ​ Human Current is to get the word out about complexity science and explain and share stories of how it is valuable to our lives. We recently spoken to Jean Bolton on a show and she talked about complexity really being our reality and that we are constantly trying to control a machine that doesn’t exist and if we just embrace complexity that we would have a better way of navigating the world and being innovative and creative and working within the systems that we are a part of. [08:29] Cesar: I completely agree. In reality is like we are always so myopic at understanding ​ what’s going to happen or how our actions affect certain outcomes especially when we’re working in large organizations or with many people that you do need to have some sort of taste and acceptance of that uncertainty. I think complex systems give you not only some practical tools to think about world but also some sort of humbleness because you have to understand that your knowledge and your understanding of how the systems work is always very limited and that humbleness I think gives a different attitude and perspective and gives you some peace. [09:06] Haley: Yeah absolutely that’s definitely a word that we would use to describe a lot of the ​ guests that we’ve had on the show and it plays out in their work and how they approach problem solving and it’s definitely something that we think you need self-awareness and a humble nature to approach these complex systems and to work within them, so yeah well said. I would love to dive a little bit deeper into this topic of complexity and get your take on entropy because this is something that you talk about in your book which we’ll get to here in a minute. I was hoping you could give your definition of entropy and explain how it shows up in your work? [09:42] Cesar: Yes, so entropy is a tough one because it’s a word it is used a lot and it has ​ been introduced by multiple communities so there is a lot of confusion around it. The concept of entropy emerge originally in physics in the 1850s and it was in the context of understanding thermodynamics which was the big technological breakthrough of that time, it was in the middle of the industrial revolution when the idea of entropy came about but then that concept was also developed a century layer by Cloud Shannon in the context of information theory. So, I think to get a definition of entropy it’s important to look at both of these communities.
Load more

Recommended publications
  • Evolution of the Product Space and a New Proposal
    EVOLUTION OF THE PRODUCT SPACE AND A NEW PROPOSAL FOR TURKEY’S EXPORT INCENTIVE SYSTEM OF THE PRODUCT SPACE EVOLUTION EVOLUTION OF THE PRODUCT SPACE AND A NEW PROPOSAL FOR TURKEY’S EXPORT INCENTIVE SYSTEM 768 759 736 737 .tr 755 431 620 728 v 622 407 623 606 607 149 748 764 462 621 625 713 o 632 619 154 767 750 649 618 409 715 758 309 459 517 689 717 735 646 648 653 143 664 752 386 747 497 645 613 647 403 457 615 633 630 396 585 628 612 515 608 204 458 642 357 397 144 662 616 644 554 663 712 .sbb.g 513 617 400 762 766 46 421 629 402 643 w 417 390 751 652 631 761 398 710 520 126 412 540 654 401 410 395 716 720 391 239 708 627 498 121 685 711 703 641 650 256 359 120 383 763 415 371 ww 774 241 704 315 320 80 702 592 624 151 452 249 705 701 240 424 706 734 638 155 456 388 509 399 427 351 756 611 634 700 519 657 454 697 709 635 559 148 698 753 66 426 444 745 147 731 521 692 651 558 423 694 312 416 602 81 695 699 524 157 36 392 358 422 723 729 60 54 746 508 406 85 209 690 730 605 345 86 610 523 437 688 696 205 356 573 434 514 707 203 64 442 510 255 455 348 420 350 77 30 555 570 291 361 404 393 394 201 535 460 73 389 343 118 556 156 347 639 116 71 346 26 757 577 448 575 760 614 687 296 693 362129 349 145 65 586 669 342 772 25 574 3 355 344 101 539 224 353 405 714 79 461 564 438 259 182 742 600 329 289 170 482 372 317 674 518 50 31 557 512 670 70 88 593 661 10 56 471 146 130 691 680 321 52 516 732 326 214 479 413 686 445 579 352 55 199 228 626 484 102 29 576 83 316 583 447 104 215 159 235 322 451 136 572 328 360 604 494 446 449 430 103 542 63
    [Show full text]
  • Institute-Wide Task Force on the Future of Libraries
    Institute-wide Task Force on the Future of Libraries Preliminary Report October 24, 2016 https://future-of-libraries.mit.edu © 2016 Massachusetts Institute of Technology Institute-wide Task Force on the Future of Libraries—Preliminary Report iii CONTENTS Introduction ................................................................................................. 4 A Vision for MIT Libraries ............................................................................ 4 Community and Relationships ..................................................................... 7 Discovery and Use ......................................................................................10 Stewardship and Sustainability ..................................................................14 Research and Development ........................................................................16 Tensions and Concerns ...............................................................................18 Summary and Conclusions .........................................................................18 Appendices Appendix 1: Task Force Charge .......................................................20 Appendix 2: Task Force Members ....................................................23 Appendix 3: Task Force Activities ....................................................25 Appendix 4: Idea Bank .....................................................................26 Appendix 5: Glossary .......................................................................27 Institute-wide
    [Show full text]
  • A Trans-Disciplinary Review of Network Science
    Disconnected, fragmented, or united? A trans-disciplinary review of network science César A. Hidalgo | Associate Professor | The MIT Media Lab [email protected] Abstract: During decades the study of networks has been divided between the efforts of social scientists and natural scientists, two groups of scholars who often do not see eye to eye. In this review I present an effort to mutually translate the work conducted by scholars from both of these academic fronts hoping to continue to unify what has become a diverging body of literature. I argue that social and natural scientists fail to see eye to eye because they have diverging academic goals. Social scientists focus on explaining how context specific social and economic mechanisms drive the structure of networks and on how networks shape social and economic outcomes. By contrast, natural scientists focus primarily on modeling network characteristics that are independent of context, since their focus is to identify universal characteristics of systems instead of context specific mechanisms. In the following pages I discuss the differences between both of these literatures by summarizing the parallel theories advanced to explain link formation and the applications used by scholars in each field to justify their approach to network science. I conclude by providing an outlook on how these literatures can be further unified. Introduction: Born fragmented “Science must, over the next 50 years, learn to deal with these problems of organized complexity.”—Warren Weaver, 1948 How science evolves? And how is scientific progress tied to improvements in mathematics? In 1948 Warren Weaver, the director of the Rockefeller Foundation’s division of natural sciences, published an essay hoping to answer these questions.
    [Show full text]
  • Curriculum Vitae
    Last Update: 8/04/2020 CURRICULUM VITAE Albert-László Barabási Northeastern University Department of Physics Center for Complex Network Research (617) 373-2355; [email protected] 1100-177 Huntington Avenue Place and Date of Birth: Karcfalva, 30 March 1967 Boston, MA 02115 Citizenship: Hungarian, Romanian, US Education: • 1986-1989, University of Bucharest, major in physics and engineering • M.Sc., 1991, Eötvös Loránd University, Budapest, in physics; advisor T. Vicsek • Ph.D., 1994, Boston University, in physics; advisor H.E. Stanley Employment and Teaching Experience: ¾ 1989-91, Research Institute for Technical Physics, Hungarian Academy of Sciences, Budapest, Research Assistant ¾ 1991-92, Boston University, Teaching Assistant ¾ 1992-94, Boston University, Research Assistant ¾ 1994-95, IBM, T.J. Watson Research Center, Physical Sciences Department, Postdoctoral Associate ¾ 1995-99, University of Notre Dame, Assistant Professor ¾ 2000, Institute of Advanced Studies, Collegium Budapest, Senior Fellow ¾ 1999-2000, University of Notre Dame, Associate Professor ¾ 2005-2006, Harvard University, Dana Farber Cancer Institute, Visiting Professor ¾ 2004-2007, Center for Complex Network Research, University of Notre Dame, Director ¾ 2000-2007, University of Notre Dame, Emil T. Hofman Professor ¾ 2007-present, University of Notre Dame, Adjunct Professor of Computer Science and Engineering ¾ 2007-present, Northeastern University, Departments of Physics and the College of Computer and Information Sciences, Distinguished University Professor ¾ 2007-present,
    [Show full text]
  • Complexity and Policy Making I Ty an Ty D P Ol Ic Y Mak I Ng
    OECD INSIGHTS EDITED BY PATRICK LOVE AND JULIA STOCKDAle-OTÁROLA AND JU AND EDIT E D BY PATRICK LOV PATRICK D BY L IA STOCKDA IA le - O E , TÁRO L A DEBATE THE ISSUES: DEBATE THE ISSUES: COMPLEX ISSUES: THE DEBATE COMPLEXITY AND POLICY MAKING I TY AN D P OL ic Y MAK I NG OECD Insights Debate the Issues: Complexity and policy making This work is published under the responsibility of the Secretary-General of the OECD. The opinions expressed and arguments employed herein do not necessarily reflect the official views of OECD member countries. This document and any map included herein are without prejudice to the status of or sovereignty over any territory, to the delimitation of international frontiers and boundaries and to the name of any territory, city or area. Please cite this publication as: OECD (2017), Debate the Issues: Complexity and policy making, OECD Insights, OECD Publishing, Paris, http://dx.doi.org/10.1787/9789264271531-en. ISBN 978-92-64-27152-4 (print) ISBN 978-92-64-27153-1 (PDF) ISBN 978-92-64-27158-6 (epub) Series: OECD Insights ISSN 1993-6745 (print) ISSN 1993-6753 (online) Revised version, June 2017 Details of revisions available at: http://www.oecd.org/about/publishing/Corrigendum_Debate_the_Issues- Complexity_and_policy_making.pdf Corrigenda to OECD publications may be found on line at: www.oecd.org/about/publishing/corrigenda.htm. © OECD 2017 You can copy, download or print OECD content for your own use, and you can include excerpts from OECD publications, databases and multimedia products in your own documents, presentations, blogs, websites and teaching materials, provided that suitable acknowledgement of OECD as source and copyright owner is given.
    [Show full text]
  • Latent Dirichlet Allocation Models for World Trade Analysis
    Latent Dirichlet Allocation Models for World Trade Analysis Diego Kozlowski∗1, Viktoriya Semeshenko2, and Andrea Molinari2 1DRIVEN, FSTM, University of Luxembourg, Luxembourg 2Universidad de Buenos Aires. Facultad de Ciencias Económicas. Buenos Aires, Argentina. CONICET-Universidad de Buenos Aires. Instituto Interdisciplinario de Economía Política de Buenos Aires. Buenos Aires, Argentina Abstract The international trade is one of the classic areas of study in economics. Nowadays, given the availability of data, the tools used for the analysis can be complemented and enriched with new methodologies and techniques that go beyond the traditional approach. The present paper shows the application of the Latent Dirichlet Allocation Models, a well known technique from the area of Natural Language Processing, to search for latent dimensions in the product space of international trade, and their distribution across countries over time. We apply this technique to a dataset of countries’ exports of goods from 1962 to 2016. The findings show the possibility to generate higher level classifications of goods based on the empirical evidence, and also allow to study the distribution of those classifications within countries. The latter show interesting insights about countries’ trade specialisation. Keywords: COMTRADE Data, Data Analysis, Topic Modelling, Latent Dirichlet Alloca- tion, Unsupervised Learning 1 Introduction The role that countries play in the global market is profoundly determined by their insertion into global value chains, and by the types of goods they produce for the global market (Coe et al. 2004; Gereffi, Humphrey, and Sturgeon 2005; Gereffi 1994). Production systems, which were traditionally analyzed as almost independent national systems, are now continuously more connected on a global scale.
    [Show full text]
  • Curriculum Vitae As of August 8, 2008
    Curriculum Vitae as of August 8, 2008 Personal Information Full Name: C´esarA. Hidalgo. Current Positions: Adjunct Lecturer in Public Policy, Harvard Kennedy School Research Fellow, Center for International Development, Harvard University. Mailing Address: Center for International Development, Harvard University, 79 JFK St, Cambridge, MA, 02138 Personal Phone: 1-574-315-5494 E-Mail: cesar [email protected] Web Page: http://www.chidalgo.com Education 2004 Licenciado en F´ısica,Pontificia Universidad Cat´olicade Chile, Santiago, Chile. 2008 Ph.D. Physics, University of Notre Dame, Notre Dame, IN, U.S.A. Work Experience Teaching Experience At the Pont´ıficiaUniversidad Cat´olicade Chile (as Teaching Assistant) 2000 Spring Heat and Waves. Civil Construction. Theoretical. 2001 Fall Heat and Waves. Civil Construction. Theoretical. Physics 1. Agronomy, Biology and Agronomy. Experimental. 2001 Spring Heat and Waves. Civil Construction. Theoretical. Heat and Waves. Civil Construction. Experimental. 1 2002 Fall Classical Mechanics. Civil Construction. Experimental. 2002 Spring Heat and Waves. Physics and Astronomy. Experimental. Classical Mechanics. Civil Enginering. Theoretical. 2003 Fall Physics for Science. Agronomy and Biology. Theoretical. Classical Mechanics. Physics and Astronomy. Experimental. 2003 Spring Physics for Science. Agronomy and Biology. Theoretical. Classical Mechanics. Physics and Astronomy. Experimental 2004 Fall Statistical Mechanics. Physics and Astronomy. Theoretical. Electricity and Magnetism. Physics and Astronomy. Experimental. at the University of Notre Dame (as Teaching Assistant) 2004 Fall Physics I, Phys 221. Theoretical. Physics I Phys 115. Theoretical. 2005 Spring Physics II, Phys 222. Theoretical. Physics for Poets, Phys 192. Theoretical. Introductory Astronomy, Phys 110. Theoretical. 2006 Fall Special Topics, Complex Networks. Theoretical. 2007 Summer Complex Network Workshop.
    [Show full text]
  • The Amenity Mix of Urban Neighborhoods
    Habitat International xxx (xxxx) xxx Contents lists available at ScienceDirect Habitat International journal homepage: http://www.elsevier.com/locate/habitatint The amenity mix of urban neighborhoods C´esar A. Hidalgo a,b,c,*, Elisa Castaner~ d, Andres Sevtsuk e a ANITI Chair, University of Toulouse, France b Alliance Manchester Business School, University of Manchester, UK c School of Engineering and Applied Sciences, Harvard University, USA d Collective Learning Group, The MIT Media Lab, USA e Department of Urban Studies and Planning, Massachusetts Institute of Technology, USA ABSTRACT Advances in computational urbanism have stimulated the rise of generative and parametric approaches to urban design. Yet, most generative and parametric ap­ proaches focus on physical characteristics, such as a neighborhoods walkability, energy efficiency, and urban form. Here, we study the colocation patterns of more than one million amenities in 47 U.S. cities to model the amenity mix of neighborhoods, and to identify the amenities that are over- or under-supplied in a neighborhood. We build this model by combining a clustering algorithm, designed to identify amenity-dense neighborhoods, and a network, connecting amenities that are likely to collocate. Our findingsextend generative and parametric urban design approaches to the amenity mix of neighborhoods, by leveraging the idea of relatedness from the economic geography literature, to evaluate and optimize a neighborhood’s amenity mix. 1. Introduction principle explains the emergence and coherence of economic clusters and changes in international specialization patterns (Hidalgo et al., In recent years urbanism has witnessed the growth of parametric and 2007). Yet, to bring these ideas to the neighborhood scale, and use them generative design approaches (Mehaffy, 2008; Nagy et al., 2018; Schu­ in generative urban design approaches, requires solving a few technical macher, 2009).
    [Show full text]
  • DFG Forschungsstipendium
    Final report / Abschlussbericht: DFG Forschungsstipendium 1. General information DFG sign / Geschäftszeichen: SCHI 1065/2-1 Applicant: Dr. Maximilian Schich Institute: Center for Complex Network Research, Northeastern University, Boston Chair: Prof. Albert-László Barabási Subject: Complex Networks in Art Research – Exemplary Proofs of Concept deutsch: Komplexe Netzwerke in den Kunstwissenschaften – Beispielanwendungen zur Etablierung eines Forschungszweiges. Report and funding time-frame: April 1, 2009 – March 31, 2012 List of most important project publications: 1. Schich, Maximilian: Revealing Matrices. in: Beautiful Visualization. Looking at Data through the Eyes of Experts. ed. Julie Steele and Noah Iilinsky. Sebastopol, CA: O‘Reilly 2010 pp. 227-254. URL: http://revealingmatrices.schich.info 2. Schich, Maximilian: Netzwerke komplexer Netzwerke in der (Kunst)Wissenschaft. in: Die Dynamik sozialer und sprachlicher Netzwerke. ed. Job, Barbara; Mehler, Alexander & Sutter, Tilmann. Wiesbaden: Springer VS 2012. [in print] Preprint-URL: http://www.schich.info/pub/2011/Schich_2011_Bielefeld_Preprint.pdf 3. Schich, Maximilian; Coscia, Michele: Exploring Co-Occurrence on a Meso and Global Level Using Network Analysis and Rule Mining. MLG‘11 Proceedings of the Ninth Workshop on Mining and Learning with Graphs. ACM KDD San Diego, CA, USA. URL: http://www.cs.purdue.edu/mlg2011/papers/paper_22.pdf 4. Blumm, Nicholas; Ghoshal, Gourab; Forró, Zalan; Schich, Maximilian; Bianconi, Ginestra; Bouchaud, Jean-Philippe & Barabási Albert-László: Dynamics of Ranking Processes in Complex Systems. Physical Review Letters [to appear Sept. 4] (2012) URL: http://prl.aps.org/vtoc/PRL/v109 Schich, Maximilian; Meirelles, Isabel; Edmonds, Ernest (eds.): Special Section: Arts, Humanities, and Complex Networks. [ongoing] Leonardo Journal 43:3, June 2010, pp. 212, URL: http://www.mitpressjournals.org/toc/leon/43/3 Leonardo Journal 44:3, June 2011, pp.
    [Show full text]
  • Mas-581: Networks, Information, and the Evolution of Complex Systems
    1 THE MIT MEDIA LABORATORY, MASSACHUSETTS INSTITUTE OF TECHNOLOGY MAS-581: NETWORKS, INFORMATION, AND THE EVOLUTION OF COMPLEX SYSTEMS SPRING 2018 First Day: February 8 Meet Day: Thursdays 3pm-5pm Room: E14-633* (6th Floor of the MIT Media Lab) Office Hours: None DESCRIPTION This course covers the basics of networks science. The course will combine lectures, readings in group discussions, and projects, to familiarize students with ideas of network science in a research context. Instructor: César A. Hidalgo PhD TA: Cristian Jara E15-392B E: [email protected] The Media Laboratory 75 Amherst St, Cambridge, MA, 02142 EVALUATION This is a pass/fail class. Attendance and participation are mandatory. Required readings are mandatory and students will be quizzed on them in front of the class. Working in groups is encouraged. The class requires students to present one individual essay at the end of the semester. 100% Class Participation & Completing an Essay READINGS UNLESS OTHERWISE NOTED, STUDENTS NEED TO FIND THE READINGS BY THEMSELVES USING EITHER THE WEB OR THE LIBRARY RESOURCES FROM THEIR UNIVERSITIES. Part I Networks and their implications 2 Class 1: Feb 8: Class Introduction. Intro to Networks Class 2: Feb 15: Network Structure and its consequences COMPLEXITY --Warren Weaver. “Science and Complexity” American Scientist, 36: 536 (1948). NETWORK STRUCTURE --Watts, DJ, & SH. Strogatz. "Collective dynamics of ‘small-world’ networks." nature 393.6684 (1998): 440-442. --Barabási, A-L, and Réka Albert. "Emergence of scaling in random networks." science 286.5439 (1999): 509-512. Class 3: Feb 22: Implications of Network Structure --Pastor-Satorras, R, & A Vespignani.
    [Show full text]
  • Recovering Better: Economic and Social Challenges and Opportunities a Compilation of the High-Level Advisory Board
    ECONOMIC AND SOCIAL CHALLENGES AND OPPORTUNITIES A Compilation of the United Nations High-level Advisory Board on Economic and Social Affairs COPYRIGHT: Recovering better: economic and social challenges and opportunities A compilation of the High-level Advisory Board on Economic and Social Affairs Published by the United Nations, Department of Economic and Social Affairs New York, New York 10017, United States of America Copyright © 2020 United Nations All rights reserved This publication in its entirety may not be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording or any information storage and retrieval system now known or to be invented, without written permission from the publisher. All queries on rights and licences, Including subsidiary rights, should be addressed to: [email protected] Designed and produced by: Office of the Under-Secretary-General, Department of Economic and Social Affairs, United Nations, New York List of Acronyms AfCFTA African Continental Free Trade Area ICRICT Independent Commission for the Reform of International AfDB African Development Bank Taxation AI artificial intelligence ICT information and communications technology AREAER Annual Report on Exchange Arrangements and Exchange ICUs ICU - intensive care unit Restrictions ID identity/identification ASEAN Association of Southeast Asian Nations IFFs Illicit Financial Flows AU African Union ILO International Labour Organization BADEHOG Banco de Datos de Encuestas de Hogares / Household Survey IMF International
    [Show full text]
  • The Political Economy of Economic Complexity: Theory, Data, Methods Section 2 Introducing the Economic Complexity Index (ECI)
    Introductory remarks The fundamental building blocks Computing the ECI Underlying theory Summary References The Political Economy of Economic Complexity: Theory, Data, Methods Section 2 Introducing the Economic Complexity Index (ECI) Claudius Gräbner 1,2,3 1University of Duisburg-Essen Institute for Socioeconomics 2Johannes Kepler University Linz Institute for Comprehensive Analysis of the Economy 3ZOE. Institute for Future-Fit Economies EAEPE Pre-Conference September 11-12 2019 Gräbner Economic Complexity 2: Intro to the ECI 11-12.09.2019 1 / 47 Introductory remarks The fundamental building blocks Computing the ECI Underlying theory Summary References Outline 1. General introduction & motivation: drivers of economic development 2. Introducing the Economic Complexity Index (ECI) 2.1 Historical genesis 2.2 How to compute economic complexity 2.3 Theories underlying economic complexity 2.4 Advantages and critiques of the measure 3. Practice: using data from the Atlas of Economic Complexity 4. Selected applications 5. Outlook: using economic complexity in your own research Gräbner Economic Complexity 2: Intro to the ECI 11-12.09.2019 2 / 47 Introductory remarks The fundamental building blocks Computing the ECI Underlying theory Summary References What is economic complexity? • Distinction between complexity economics and economic complexity as used here • Complexity economics considers the economy as a complex system and uses corresponding methods from various sciences • More a school of thought or a research program a la Lakatos • Economic
    [Show full text]