College students receive any number of recommended introductory courses. But according to New York Times columnist Nicholas Kristof, one of the key classes you might need to take to succeed in life might be an introductory economics course.
As part of the Marketplace Morning Report’s “Econ Extra Credit” project, host David Brancaccio spoke with Kristof about how an Econ 101 class can provide a student with a robust toolbox that could be used later in life to both understand and address larger issues like rent control or how to fund a tax cut.
“We’ve repeatedly mangled macro economic policy in the U.S.,” Kristof said. “It’s pretty obvious that even lawmakers kind of have no clue about really basic issues, like you know, what a fiscal stimulus is.”
Click on the player above to hear their conversation on the merits of Econ 101, as well as Kristof’s thoughts on how introductory economics has adapted to better reflect real world economic issues.
This interview is part of our “Econ Extra Credit” project, where we read a new introductory economics textbook provided by the non-profit Core-Econ together with our listeners. If you’d like to join us in this project, email MorningReport@marketplace.org and let us know you’re reading along with Marketplace through the end of Spring.
I love the idea that Marketplace is planning on using an OER (open educational resources) economics textbook to do a public bookclub/MOOC/guided self-study of Economics 101.
Naturally I worry that the participation rates will start high and end low, but the fact that they’re encouraging their listeners to expand themselves and delve a bit deeper than just listening to their show is fantastic.
And honestly, who couldn’t use an ECON refresher from time to time–particularly one that takes a dramatically different approach to the subject than the one many of us took?
This collection of essays explores the authors’ work in, inquiry into, and critique of online learning, educational technology, and the trends, techniques, hopes, fears, and possibilities of digital pedagogy. For more information, visit urgencyofteachers.com.
I can subscribe to his feed of posts for the class (or an aggregated one he’s made–sometimes known as a planet) and use the feed reader of choice to consume the content (and that of my peers’) at my own pace to work my way through the course.
This is a lot closer to what I think online pedagogy or even the use of a Domain of One’s Own in an educational setting could and should be. I hope other educators might follow suit based on our examples. As an added bonus, if you’d like to try it out, Greg’s three week course is, in fact, an open course for using IndieWeb and DoOO technologies for teaching. It’s just started, so I hope more will join us.
He’s focusing primarily on using WordPress as the platform of choice in the course, but one could just as easily use other Webmention enabled CMSes like WithKnown, Grav, Perch, Drupal, et al. to participate.
Probability and statistics have long helped scientists make sense of data about the natural world — to find meaningful signals in the noise. But classical statistics prove a little threadbare in today’s landscape of large datasets, which are driving new insights in disciplines ranging from biology to ecology to economics. It's as true in biology, with the advent of genome sequencing, as it is in astronomy, with telescope surveys charting the entire sky.
The data have changed. Maybe it's time our data analysis tools did, too.
During this three-month online course, starting June 11th, instructors Hector Zenil and Narsis Kiani will introduce students to concepts from the exciting new field of Algorithm Information Dynamics to search for solutions to fundamental questions about causality — that is, why a particular set of circumstances lead to a particular outcome.
Algorithmic Information Dynamics (or Algorithmic Dynamics in short) is a new type of discrete calculus based on computer programming to study causation by generating mechanistic models to help find first principles of physical phenomena building up the next generation of machine learning.
The course covers key aspects from graph theory and network science, information theory, dynamical systems and algorithmic complexity. It will venture into ongoing research in fundamental science and its applications to behavioral, evolutionary and molecular biology.
Students should have basic knowledge of college-level math or physics, though optional sessions will help students with more technical concepts. Basic computer programming skills are also desirable, though not required. The course does not require students to adopt any particular programming language for the Wolfram Language will be mostly used and the instructors will share a lot of code written in this language that student will be able to use, study and exploit for their own purposes.
The course will begin with a conceptual overview of the field.
Then it will review foundational theories like basic concepts of statistics and probability, notions of computability and algorithmic complexity, and brief introductions to graph theory and dynamical systems.
Finally, the course explores new measures and tools related to reprogramming artificial and biological systems. It will showcase the tools and framework in applications to systems biology, genetic networks and cognition by way of behavioral sequences.
Students will be able apply the tools to their own data and problems. The instructors will explain in detail how to do this, and will provide all the tools and code to do so.
The course runs 11 June through 03 September 2018.
Tuition is $50 required to get to the course material during the course and a certificate at the end but is is free to watch and if no fee is paid materials will not be available until the course closes. Donations are highly encouraged and appreciated in support for SFI's ComplexityExplorer to continue offering new courses.
In addition to all course materials tuition includes:
Six-month access to the Wolfram|One platform (potentially renewable by other six) worth 150 to 300 USD.
Free digital copy of the course textbook to be published by Cambridge University Press.
Several gifts will be given away to the top students finishing the course, check the FAQ page for more details.
Best final projects will be invited to expand their results and submit them to the journal Complex Systems, the first journal in the field founded by Stephen Wolfram in 1987.
About the Instructor(s):
Hector Zenil has a PhD in Computer Science from the University of Lille 1 and a PhD in Philosophy and Epistemology from the Pantheon-Sorbonne University of Paris. He co-leads the Algorithmic Dynamics Lab at the Science for Life Laboratory (SciLifeLab), Unit of Computational Medicine, Center for Molecular Medicine at the Karolinska Institute in Stockholm, Sweden. He is also the head of the Algorithmic Nature Group at LABoRES, the Paris-based lab that started the Online Algorithmic Complexity Calculator and the Human Randomness Perception and Generation Project. Previously, he was a Research Associate at the Behavioural and Evolutionary Theory Lab at the Department of Computer Science at the University of Sheffield in the UK before joining the Department of Computer Science, University of Oxford as a faculty member and senior researcher.
Narsis Kiani has a PhD in Mathematics and has been a postdoctoral researcher at Dresden University of Technology and at the University of Heidelberg in Germany. She has been a VINNOVA Marie Curie Fellow and Assistant Professor in Sweden. She co-leads the Algorithmic Dynamics Lab at the Science for Life Laboratory (SciLifeLab), Unit of Computational Medicine, Center for Molecular Medicine at the Karolinska Institute in Stockholm, Sweden. Narsis is also a member of the Algorithmic Nature Group, LABoRES.
TA: Alyssa Adams has a PhD in Physics from Arizona State University and studies what makes living systems different from non-living ones. She currently works at Veda Data Solutions as a data scientist and researcher in social complex systems that are represented by large datasets. She completed an internship at Microsoft Research, Cambridge, UK studying machine learning agents in Minecraft, which is an excellent arena for simple and advanced tasks related to living and social activity. Alyssa is also a member of the Algorithmic Nature Group, LABoRES.
The development of the course and material offered has been supported by:
The Foundational Questions Institute (FQXi)
John Templeton Foundation
Santa Fe Institute
Swedish Research Council (Vetenskapsrådet)
Algorithmic Nature Group, LABoRES for the Natural and Digital Sciences
Living Systems Lab, King Abdullah University of Science and Technology.
Department of Computer Science, Oxford University
Cambridge University Press
London Mathematical Society
ItBit for the Natural and Computational Sciences and, of course,
the Algorithmic Dynamics lab, Unit of Computational Medicine, SciLifeLab, Center for Molecular Medicine, The Karolinska Institute
This tutorial will present you with the basics of how to use NetLogo to create an agent-based modeling. During the tutorial, we will briefly discuss what agent-based modeling is, and then dive in to hands-on work using the NetLogo programming language, which is developed and supported at Northwestern University by Uri Wilensky. No programming background or knowledge is required, and the methods examined will be useable in any number of different fields.
WE’RE LAUNCHING A NEW TUTORIAL!
Fundamentals of NetLogo, a primer on the most used agent-based modeling software, will be available tomorrow.
Stay tuned for our launch announcement, and check out all our tutorials at https://t.co/APIkME07y5pic.twitter.com/M8qIJp1R6x
A very beautiful classical theory on field extensions of a certain type (Galois extensions) initiated by Galois in the 19th century. Explains, in particular, why it is not possible to solve an equation of degree 5 or more in the same way as we solve quadratic or cubic equations. You will learn to compute Galois groups and (before that) study the properties of various field extensions.
We first shall survey the basic notions and properties of field extensions: algebraic, transcendental, finite field extensions, degree of an extension, algebraic closure, decomposition field of a polynomial.
Then we shall do a bit of commutative algebra (finite algebras over a field, base change via tensor product) and apply this to study the notion of separability in some detail.
After that we shall discuss Galois extensions and Galois correspondence and give many examples (cyclotomic extensions, finite fields, Kummer extensions, Artin-Schreier extensions, etc.).
We shall address the question of solvability of equations by radicals (Abel theorem). We shall also try to explain the relation to representations and to topological coverings.
Finally, we shall briefly discuss extensions of rings (integral elemets, norms, traces, etc.) and explain how to use the reduction modulo primes to compute Galois groups.
I’ve been watching MOOCs for several years and this is one of the few I’ve come across that covers some more advanced mathematical topics. I’m curious to see how it turns out and what type of interest/results it returns.
It’s being offered by National Research University – Higher School of Economics (HSE) in Russia.
Introduction to Dynamical Systems and Chaos (Summer, 2016)
About the Course:
In this course you’ll gain an introduction to the modern study of dynamical systems, the interdisciplinary field of applied mathematics that studies systems that change over time.
Topics to be covered include: phase space, bifurcations, chaos, the butterfly effect, strange attractors, and pattern formation. The course will focus on some of the realizations from the study of dynamical systems that are of particular relevance to complex systems:
Dynamical systems undergo bifurcations, where a small change in a system parameter such as the temperature or the harvest rate in a fishery leads to a large and qualitative change in the system’s
Deterministic dynamical systems can behave randomly. This property, known as sensitive dependence or the butterfly effect, places strong limits on our ability to predict some phenomena.
Disordered behavior can be stable. Non-periodic systems with the butterfly effect can have stable average properties. So the average or statistical properties of a system can be predictable, even if its details are not.
Complex behavior can arise from simple rules. Simple dynamical systems do not necessarily lead to simple results. In particular, we will see that simple rules can produce patterns and structures of surprising complexity.
About the Instructor:
David Feldman is Professor of Physics and Mathematics at College of the Atlantic. From 2004-2009 he was a faculty member in the Santa Fe Institute’s Complex Systems Summer School in Beijing, China. He served as the school’s co-director from 2006-2009. Dave is the author of Chaos and Fractals: An Elementary Introduction (Oxford University Press, 2012), a textbook on chaos and fractals for students with a background in high school algebra. Dave was a U.S. Fulbright Lecturer in Rwanda in 2011-12.
5 Jul 2016 9am PDT to
20 Sep 2016 3pm PDT
A familiarity with basic high school algebra. There will be optional lessons for those with stronger math backgrounds.
Fortunately, most are pretty reasonable, though vary in their coverage of topics. The introductory lectures don’t require as much mathematics and can probably be understood by those at the high school level with just a small amount of basic probability theory and an understanding of the logarithm.
The top three in the advanced section (they generally presume a prior undergraduate level class in probability theory and some amount of mathematical sophistication) are from professors who’ve written some of the most commonly used college textbooks on the subject. If I recall a first edition of the Yeung text was available via download through his course interface. MacKay’s text is available for free download from his site as well.
Feel free to post other video lectures or resources you may be aware of in the comments below.
Editor’s Update: With sadness, I’ll note that David MacKay died just days after this was originally posted.
Many readers often ask me for resources for delving into the basics of information theory. I hadn’t posted it before, but the Santa Fe Institute recently had an online course Introduction to Information Theory through their Complexity Explorer, which has some other excellent offerings. It included videos, fora, and other resources and was taught by the esteemed physicist and professor Seth Lloyd. There are a number of currently active students still learning and posting there.
Introduction to Information Theory
About the Tutorial:
This tutorial introduces fundamental concepts in information theory. Information theory has made considerable impact in complex systems, and has in part co-evolved with complexity science. Research areas ranging from ecology and biology to aerospace and information technology have all seen benefits from the growth of information theory.
In this tutorial, students will follow the development of information theory from bits to modern application in computing and communication. Along the way Seth Lloyd introduces valuable topics in information theory such as mutual information, boolean logic, channel capacity, and the natural relationship between information and entropy.
Lloyd coherently covers a substantial amount of material while limiting discussion of the mathematics involved. When formulas or derivations are considered, Lloyd describes the mathematics such that less advanced math students will find the tutorial accessible. Prerequisites for this tutorial are an understanding of logarithms, and at least a year of high-school algebra.
About the Instructor(s):
Professor Seth Lloyd is a principal investigator in the Research Laboratory of Electronics (RLE) at the Massachusetts Institute of Technology (MIT). He received his A.B. from Harvard College in 1982, the Certificate of Advanced Study in Mathematics (Part III) and an M. Phil. in Philosophy of Science from Cambridge University in 1983 and 1984 under a Marshall Fellowship, and a Ph.D. in Physics in 1988 from Rockefeller University under the supervision of Professor Heinz Pagels.
From 1988 to 1991, Professor Lloyd was a postdoctoral fellow in the High Energy Physics Department at the California Institute of Technology, where he worked with Professor Murray Gell-Mann on applications of information to quantum-mechanical systems. From 1991 to 1994, he was a postdoctoral fellow at Los Alamos National Laboratory, where he worked at the Center for Nonlinear Systems on quantum computation. In 1994, he joined the faculty of the Department of Mechanical Engineering at MIT. Since 1988, Professor Lloyd has also been an adjunct faculty member at the Sante Fe Institute.
Professor Lloyd has performed seminal work in the fields of quantum computation and quantum communications, including proposing the first technologically feasible design for a quantum computer, demonstrating the viability of quantum analog computation, proving quantum analogs of Shannon’s noisy channel theorem, and designing novel methods for quantum error correction and noise reduction.
Professor Lloyd is a member of the American Physical Society and the Amercian Society of Mechanical Engineers.
Yoav Kallus is an Omidyar Fellow at the Santa Fe Institute. His research at the boundary of statistical physics and geometry looks at how and when simple interactions lead to the formation of complex order in materials and when preferred local order leads to system-wide disorder. Yoav holds a B.Sc. in physics from Rice University and a Ph.D. in physics from Cornell University. Before joining the Santa Fe Institute, Yoav was a postdoctoral fellow at the Princeton Center for Theoretical Science in Princeton University.