Archive for category Classes
Grad Classes in Course 14 (Part 2)
I decided to delay the posting of this Part 2 (of a two-part series) in order to get more experience in these classes. I’m currently enjoying 14.121/14.122, 14.771, and Harvard’s Market Design class (Ec 2056a). All three of my grad classes are also being taken by undergrad friends whom I work on psets with and talk to about the course material. This shared experience makes the subjects a whole lot more fun. I’ll wrap up this series by presenting my characterizations of first-year and second-year classes.
First-year classes (14.121-14.124, 14.381-14.382, 14.451-14.454) are usually described as very functional. These are “toolbox” classes meant to equip the budding economist with an arsenal for tackling problems. The core material you’re learning can be very dry, but awesome theoretical or empirical applications are not in short supply. For instance, 14.121 covered consumer theory, producer theory, aggregation, and other core micro concepts, but also featured numerous General Equilibrium applications. My favorite was the testing of an elaborate theoretical risk-sharing model with data from three poor ICRISAT villages in India.
Second-year classes offer a much more narrow, yet concentrated view of a particular field in economics. These classes inundate you with papers upon papers of studies of that particular field. If you’re truly passionate about a field of economics and feel that you can handle tons of reading, take a field class! Not only will you hone your research skills; you might also discover that idea that leads to an exciting new research project in economics.
Economists Saving Lives (and doing other awesome stuff)
Posted by Jing Li in Classes, News and Current Events on October 21, 2010
This term I am cross-registering at Harvard to take Market Design, taught by Professors Alvin Roth and Peter Coles. The class is offered jointly by the Harvard Economics Department and the Harvard Business School. I have interests in computer science and economics, and I actually first heard about market/mechanism design from the computer science literature. However, the research done in these two fields are quite different. Generally, economists are concerned about efficiency, equilibrium stability, and incentives, while computer scientists try to optimize the algorithms behind the markets.
Before I list off too many vague terms, here’s an example of market design at work: kidney matching. Every year, more and more Americans are placed on the waitlist for transplant kidneys to replace their own failing or diseased kidneys. Thousands of people either die or become too sick to receive a transplant while waiting for a matching kidney. It is important to note is that people have two kidneys, but can lead normal lives with just one. A lot of people on the waitlist have loving relatives or friends who would donate one of their kidneys, but they unfortunately do not have the same blood types and other characteristics necessary for transplant.
Kidney exchange first came about when two relatives met at a dialysis center waiting room. They realized that the relative of one patient is a match for the other patient, and vice versa — they could swap kidney donations, and both families would be better off! Professor Roth has worked to establish databases of recipient and donor pairs, and they run a stable matching algorithm on the database every two weeks. The frequency at which we run the matching can change the number of total matches we get over time, since new patients are being added to the database and current patients can be removed for various reasons. Two weeks has just worked out well in practice.
[I actually had a problem on my 6.046 take-home exam last fall motivated by kidney matching. We were supposed to find the fastest algorithm possible to match people, given a set of donor parameters and recipient requirements -- computer science and economics working together 
]
Theoretically, it is possible to have longer exchange cycles than just two. (Let A1, B1 be a recipient-donor pair, A2, B2 be a second donor pair, etc, for a total of n pairs. Then we can imagine a situation where A1 can donate to B2, A2 can donate to B3, A3 can donate to B4, etc, until we loop back and An donates to Bn.) In practice — and this is also where economic incentives come in — we need to have all the operations simultaneously to make sure that all the donations actually happen. For a 2-way swap, that’s 4 simultaneous operations, requiring 4 surgical teams, 4 operating rooms, the works. Some longer chains and larger swaps have happened, but they are rare.
One of our MIT professors, Parag Pathak, was a graduate student at Harvard and has worked with Al Roth and co-authors to design allocation mechanisms for public school lotteries in New York, Boston, and San Francisco. The goal is to design the system for which it is a dominant strategy for people to submit their true preferences for schools. This removes the confusion from an important decision like choosing schools and makes the school-matching process stable and efficient.
The applications that we have discussed include job market matching, job markets for couples, and dating websites. We will have a guest speaker this week to present his findings in generalized matching and another guest speaker to talk about the shortage of IP addresses. Did you know that IP addresses were in shortage? Luckily, MIT got a lot of IP addresses way back when the internet first came about. I would just be listing off a bunch of terms if I wanted to talk about the theoretical topics in the course, so visit the course site if you’re interested (link below).
For my final project, I’m exploring the market for vacation house swaps. I just came across a news article about the Iraqi government auctions for their gas fields, though, so I might switch topics. Any suggestions for topics I should consider for my final project in market design would be appreciated.
If you’re interested in learning more about market design, here are some useful links:
Market Design blog by Al Roth and Peter Coles: http://marketdesigner.blogspot.com/
2056a course site: http://isites.harvard.edu/icb/icb.do?keyword=k72549
Al Roth’s game theory, experimental economics, and market design page: http://kuznets.fas.harvard.edu/~aroth/alroth.html
The Market Design class that I am taking (2056a in Harvard code) is a graduate course, with game theory as a prerequisite. If you are interested in taking this class, you should take game theory (e.g. 14.12) as soon as possible because 2056a and 14.12 are only offered in the fall (and also because game theory is awesome!).
I will likely blog next about Development Economics and 14.771 next, stay tuned!
Teaching and Economics
Posted by Anthony Rindone in Classes, Uncategorized on April 9, 2010
Note that I did not title this “Education Economics.” Rather, the focus is on the element of teaching the subject, and not the field of economics that examines problems relating to education as a whole.
Over the past three years, I have tutored around 14 or so different students in microeconomics (14.01) Probability (14.30) and Econometrics (14.32). I have also been able to lead the 14.01 Exam Review sessions in large lecture halls for the the past two semesters. During this time, I have gathered a bit of “data,” if you will, on how students learn economics.
At MIT, probability and econometrics are not tough subjects to tutor, due to the fact that most have such strong mathematics backgrounds, and thus most questions are centered on how to apply equation X to problem Y. “How does Bayes’ theorem apply here? What could the omitted variable be in this problem?” And so on…
But introductory microeconomics (14.01) gives many people a bit of trouble. Much of it stems from a textbook that focuses on math over concepts; yet much of it lies in developing a new mindset, a new set of intuitions. Thinking as an economist is matched by few others. And development takes time, little of which MIT students have.
One student asks me: “What’s the difference between ‘long term’ and ‘short term’ in production?” Another asks: “If perfect competition ensured 0 profits in the long run, why would any firms go through the trouble of starting a business?” Finally: “Isn’t this model an oversimplification?”
All are good questions; yet they are also quite revealing. The students could recall from memory the exact equations involving solving optimization problems, but the students also desire a deeper qualitative understanding, which may not be emphasized enough.
(Though the observant economics student could point out that this may be a problem of a small sample size, of which I will not hesitate to agree on the possibility.)
Thus if you are reading this and are struggling through the introductory classes, relax…The moment when it all clicks is a beautiful one that will make the effort all the more satisfying.
What Math classes should I take for an Econ major?
As a second-term senior, I feel the need to leave my experiences at MIT somewhere permanently. Here’s edition 1 of that.
The Economics classes at MIT are very math-heavy in comparison to what I’ve heard about economics elsewhere, and it’s a good idea to take a few math classes concurrent with your economics curriculum, especially if you’re aiming to go to grad school. I’ll list a few here that I’ve taken with econ classes they’re useful in.
You should take these math classes:
18.01/.02: required for 14.01/.02, I think, and it’s a good idea to have basic calculus down pat before you take any economics classes at MIT. Especially get comfortable with Lagrange multipliers; they will be with you for the rest of your economics career.
18.03: You’ll need this in the macro sequence, to solve the Solow model and some other differential equation problems. Other than that, this stuff doesn’t appear until grad-level classes. I took a PhD labor class, and there were a good number of people in it that never took a diffeq class in undergrad.
6.041/18.440/14.30: A statistics/probability class is immensely useful in the higher level economics classes; this comes up over and over and over, in econometrics, in finance, in labor, etc. etc. The ostensible rank in difficulty, hardest to easiest, is 18.440>6.041>14.30, though I think the curve is worse in 6.041 because of the course 6 masters students. Take this and know it well; it’s a requirement anyway. If you like this stuff, I hear 14.381, the PhD econ stats class is pretty good, too.
18.06: A linear algebra class is a needed to understand econometrics past the very basics. I don’t think anyone actually enjoys 18.06, but it’ll teach you what you need to know and more. Take this with Strang if you can.
18.100A/B/C: Real Analysis comes into play if you want to think about theory and proofs. This class is necessary if you want to go do a PhD in economics. Which level of the class you take will depend on your comfort with proofs and reading math. 100B/C is taught out of an incredibly dense book (Rudin) that is written in all math, and at a speed that was too much for me. 100A has more guidance for intuition, and is generally easier. I haven’t had to do any proofs in my undergrad classes; I think this comes in useful if you want to take the PhD intro classes.
Some other math classes that I took that aren’t so related:
18.443: More statistics, somewhat useful as a reference for econometrics, but I haven’t used it
18.310C: CI-M for math majors, discrete math with random topics about compsci, signals, sorting, etc. Makes you do dumb things like fourier transforms in Excel.
18.311: No tests. Ever. Continuous version of .310
If you have taken any interesting math classes that are relevant, leave it in comments.
Animal Behavior and Game Theory
For Acemoglu and Ozdalgar’s Networks class (6.207/14.15) I am writing my term paper on the producer-scrounger game. The producer-scrounger game is just one application of game theory to animal behavior. Last year I took Animal Behavior (9.20) and I really enjoyed it. After learning about evolutionary game theory in 14.15, I became even more intrigued. The producer-scrounger game is a game within social foraging. Individual animals decide (or evolution decides for them) whether to look for food themselves, or exploit others’ discoveries. There is plenty of literature on the subject, I would recommend Dugatkin’s book Game Theory and Animal Behavior. This field is great for anyone interested in 7 and 14
fallacies of experimental economics
Posted by Maggie L. in Classes, News and Current Events on September 30, 2009
In Professor Pathak’s 14.04 class today, we touched upon the fallacies of experimental economics. When economists take surveys on a randomly selected audience, they often ignore the caveat that people behave differently when scruitinized/ when they know that they are experimented on. Even when they know that their submissions are anonymous, they usually feel more obligated towards making a decision that seems ‘right’.
Professor Pathak called up two students. One was deemed a dictator and given $10. The other was deemed the commandee. The dictator was allowed to give the commandee a portion of the money and could keep the rest. The chosen student decided to keep all $10, hence, demonstrating the ‘rational’ mindset of undergrad economic students and defying the usual behavior of the tested subjects.
Studies show that the average amount of money that the dictator normally gives is $2, partly because of unconscious social pressures and the desire to do things the ‘right’ way. There are three main peaks and they are at $5, $2, and $0. Those ones who do not give away any money are the rationalists (hence, in most of these experiments, undergraduate economics students were excluded). The ones who gave the other $5 did what they thought ‘fair’ and the ones who gave $2 did so out of obligation and social niceties.
In our class example however, the student represented the minority unaffected by caveats that calls the tactics of experimental economics into question. Think about it this way, if you knew you were in an experiment and you were asked to handle the $10- with the eyes of forty people upon you- what would you do?
Come to the lecture of guest speaker Ernst Fehr if you are interested in the reasoning behind these strategies. Professor Fehr will be giving a talk on “Social Preferences- A Foundation of Cooperation, Competition, and Incentives” on Tuesday, October 13, 2009 at 4 pm in Room E51-325!
Do I take 14.01 or 14.02?
I’ve been here three years now, and the first question from freshmen is always, “Which should I take, 14.01 (Microeconomics) or 14.02 (Macroeconomics)?” Short answer, it doesn’t really matter. Long answer, it doesn’t really matter. If you, young freshman, are in for the long haul and are going to stay as a course 14 major, you’ll need to take both classes fairly early on, usually within a semester. The level of work is not really any different and there’s no order, contrary to what the numbers would have you think. If you’re interested in questions of individual choices, firm-level decision making, and game theory, take 14.01. If you’re interested in big-picture questions of labor, capital, trade, and GDP, take 14.02. I would say find an intermediate class you would like to take afterward and then take the intro class that is a prereq. There’s no trick here, just take what you think you’ll enjoy. In economics, 2 does not necessarily come after 1.