Category — Notes To Self

I’ve been going to lots of conferences recently, and I’ve been asked to chair a few sessions here and there. Some have gone well, others have been disasters, but I think after doing a few and seeing a lot in a short space of time, I’ve come up with a nice list of things to do to try and keep things on track.

The idea is to moderate (or chair) a session to balance the speaker’s need to do their thing and say what they want to, with the audience and conference organiser(s) need to get on with the bloody conference and get out on time for food and coffee.

Once I went looking for a list like this, I couldn’t find anywhere. (This chap’s list is useless for academic presentations, though might be useful for other types of gigs I just don’t go to, hence the uselessness of his post.)

Not one thing here is my idea, I just saw some behaviours I thought worth writing down and noting to myself. If anyone has any more insights, leave them in the comments and I’ll update the post if there is sufficient interest. If anyone wants a Nobel Laureate’s ideas about conference ettiqutte, go here, it’s hillarious.

Here’s the list:

Communicate with the speakers. At the usual coffee break between sessions, speak to the people in the session, say hi, and get their presentations loaded. Make sure their presentations are uploaded on the computer in a folder they can recognise with their name on the presentation (because everyone is going to name their presentation for ABC conference ‘ABC presentation.ppt’). Click the file once, change it to ‘blogs.ppt’ in a folder you can recognise on the desktop, and you’re golden. Load each file and play all the way through with media bits and everything while the audience is chatting. There is nothing worse than watching someone’s funny bit fall flat on them. Don’t let speakers change computers halfway through the session unless they can do it really, really quickly and it is necessary beyond belief. Most of the time it’s not. This practice eats time and upsets the audience.

Get the timing right. Explain directly to each speaker how much time they have. If there are, say, two papers in a 1.5 hour session with discussants, you want each speaker to have 20 minutes for their presentation, 10 minutes for the discussant, and 15 minutes for general questions. When each speaker starts, note the time exactly on a piece of paper, then add 20 minutes. This will be important in seeming fair.

Give the speakers time warnings. Explain to the speakers you are going to give them TWO warnings on time, one at five minutes towards the end of the session, and one at one minute. This allows the speaker enough time to land their main points home before finishing up on or under the time. It also chills everybody in the audience out if the speaker happens to be awful, or if the speaker is brilliant and the audience really want to quiz them. Either way it keeps people on track. With the warnings, don’t be too obvious. Making eye contact and putting your hand up should do it. You don’t need a sign or anything.

Give the discussant their time. Whatever happens, you must allow the discussant their time to make whatever points they want. Usually discussants have read the papers and have thoughtful, useful, and constructive comments to make on the presenter’s paper. Budget 10 minutes, but if it takes 15, so be it, it is only eating into the audience’s question time, which you are going to monitor for time anyway. These comments are important for the speaker, and usually lead the audience’s discussion. If the discussant obviously hasn’t read the paper and is making stuff up, hew to this rule anyway. It’s just good form.

Take questions from the audience in ‘batch mode’. The audience members ask questions, the speaker writes them down, and responds to all of the questions one by one at the end. All (and I mean all) academics give general comments followed by questions, making each individual question last 1-2 minutes at least. If you allow one windy chap (or chapette) to monopolise the speaker’s time with a roundabout half lecture and the speaker’s reply followed up by a comment from Mr/Mrs Windy, this will upset those in the audience who wished to speak, and generally disrupt the flow of people’s happiness if audience members didn’t want to speak. Don’t let it happen. Budget for 3-5 questions in 15 minutes. After thanking the discussant, call for brief questions and note that the session has 15/14/12/etc minutes for questions. Participants put their hands up, amd you note down who wants to speak, and let them speak in the order you see them. This method is more time efficient and avoids overlapping of topics, as well as keeping everyone on a nice, respectful keel, and lets the speaker come up with more complete answers to boot.

Finish off early if you can. Thank everyone with applause, and hand the floor back to the main organiser of the conference for logistical details and more information on the day’s programme.

Book cover via Amazon

It’s a wet day before term starts, so I’m reading little bits and piece to keep me sane (also: ranting about dead socialists). I’ve just finished Seth Godin’s The Dip, an 80 page little rant about quitting, and when it is smart to do so. Godin’s idea can be summarised in one phrase:

quit the wrong stuff, stick with the right stuff, and have the guts to do one or the other.

The idea is that we are all stuck doing things we’re not really ever going to be the best at. And that is a waste of our time. By saying yes to too many things, we create excessive commitments on our time and energy, which are scarce resources, and ensure we won’t do well at any of these things.

Being the best at something implies scarcity and therefore value, by dint of a simple ranking: there is only one Michael Phelps, and he only got that good by being really selective about what he did with his time. In his own words, “Eat, sleep, swim”.

The obvious questions are ‘which things should I quit?’, and, ‘what do I do if I can’t quit something?’

To answer the first question, Godin calls up the image of The Dip. The dip is defined as the slog between learning and mastery of a skill. So the first week of college is brilliant fun, but after four years, it really is a slog. But, on the other side of a degree, most people can see that they are changed for the better.

The slope of the Dip also matters. Microsoft, for example, created such a huge dip in the word processor market that no one can ride it through for long enough to challenge them. Competitors have to seek other paths to world domination.

There are other curves with respect to projects Godin identifies: the cul de sac, which is obvious, and the cliff. The cliff is a situation where you don’t have any incentive to quit until you fall off it, for example, smoking.

In a world where the dip dominates, diversification is a lie. Quit lots, until you find a dip you can beat for the right reasons, argues Godin. The practice this translates to is to envision what circumstances you’ll quit under, before you get into those circumstances.

Several things Godin doesn’t talk about is minor things you’d love to quit, but which have dependencies on other things you love doing. I love my job as a lecturer, but I hate marking exams. I can’t quit marking them though, because marking exams is a necessary and sufficient condition of existence as a lecturer in the KBS.

Another thing Godin glosses over is the usefulness of being well rounded. Take education, for example. Godin writes that the chap who comes out of their Leaving Certificate with an A, two B’s and three C’s is wasting his time, because he hasn’t specialised, and so is in danger of becoming mediocre. He should have concentrated on that A subject. Perhaps he will later on, say in college, but I rather like the idea of a well rounded individual who can talk to me about books, the arts, and current affairs, rather than simply asking me ‘what’s that got to do with swimming?’

So there are trade offs to Godin’s point of view. In certain areas (like my job, where I am hopelessly over committed to things right now) it makes sense to think about the dip before jumping into another new project.

Related articles

• Giving Up
• Shortcuts: Winners Never Quit? Well, Yes, They Do
• Spreading the Word: 300 Blogs I Encourage You to Read
• On reading Clay Shirky’s ‘Here comes everybody’

Terrence Tao is one of the smartest and most accomplished human beings alive.

Every economist who is not Joseph Stiglitz must click this link and read what Terrence has to say about his time management approach.

Once read, I recommend printing his post out, laminating it, and stapling it to your person.

It stings a little, but it’s worth it.

Algorithmic Game Theory is the name given to a subfield where computer science and game theory overlap. The idea is to use the tools of algorithm design and analysis to try and understand economic problems, chiefly as they appear on the internet. The field is about ten years’ old, and there is already a codification of the first big results in these two books:

“Algorithmic Game Theory” (Cambridge University Press)

“Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations” (Yoav Shoham, Kevin Leyton-Brown)

There are ample web resources on the topic as well. These computer scientists love the bloggery.

Here are a few courses on the subject:

Tim Roughgarden’s Algorithmic Game Theory course at Stanford.

Another course at Stanford, this time on adwords and search.

Éva Tardos’s course from Cornell

Kousha Etessami’s course from Edinburgh, much more elementary than the first two.

I’ll add more as I find them.

One question is how the tools and concepts of Algorithmic Game Theory overlap with Velupillai’s Algorithmic Economics, which I’m working on at the moment, and with the Algorithmic Information Theory results of people like Gregory Chaitin, and the Probability and Finance work of Shafer and Vvok. Very cool, new, and fertile crossover point from computer science to game theory to economics, all in an algorithmic (and in that sense, computable) fashion.

Key Papers (I’ll add to this list as I read more)

Nissan, Algorithms for Selfish Agents

Koutsoupias and Papadimitriou Worst-case Equilibria

Akcoglu, Aspnes, et al, Graph-theoretic auctions

Papadimitriou and Yannakakis, Bounded Rationality and Computational Complexity

Singh Kearns and Mansour Nash Convergence of Gradient Dynamics in General-Sum Games

Blog

My slice of pizza has a lot of links and comments on the personalities behind many of the AGT advances.

Information and Learning in Markets represents the culmination of fifteen years of Xavier Vives‘ work in applying Bayesian ideas to game theoretic models of finance and the real economy. Vives’ homepage with his working papers is here.

{Image: Morning Meeting at the Fish Market}

The book starts off by building simple Cournot models of large markets, showing where and when the informational equilbria correspond to efficient allocative equilibria. The first few chapters are tough going (especially chapter 2), but once you’ve gotten your head around the general approach, which is, assume a continuum or assets and workers, and draw statistical inferences from the assumed interactions of these players, which correspond to equilibria in defintie settings according to standard Bayesian criteria.

The goal is to show the equivalence of anomalous results in behaviural economics to rational expectations equilibria, in different assumption-settings.

I found the first part of the book really interesting, and will use the approach Vives develops in my financial economics course, EC4024, next year. The second half of the book really loses me, however, especially chapter 9, sections 9.0–9.2, where we see a rational expectations equilibrium emerging, even though some of the traders are ’slow learners’. I can’t get my head around why a slow learner in a financial market could persist for long enough to attain an equilibrium. We see that market microstructure really matters for the information revelation properties of prices, but we don’t see the connection with the real world.

All in all though, this book is a nice, well paced, and at the end a very technical introduction to Bayesian thinking about price dynamics in markets with asymmetric information. The going is tough, but in parts the work one puts in will really worth it. Expect to see more of Vives’ work on my financial economics course next semester, in elementary from of course.

Courses this book will be good for: PhD financial economics and mathematics, industrial organisation, or a Bayesian decision making seminar.

Notes for Kendrick, Mercado and Amman’s 2006 Princeton University Press book:

(Code for the programs in this book is here)

This is a pedagogical book with a lovely idea at the core: teach standard micro and macroeconomic models and principles using computer programs like Excel, Mathematica, MATLAB, and others.

The book takes us through a series of simple and well known examples, using student’s familiarity with the textbook models as a foil to show the usefulness and extensibility of the computational approach.

So, we see partial equilibrium analysis and game theory in Mathematica, Derivatives analysis and genetic algorithms in MATLAB, and CGE models in GAMS as well as other programming languages. Throughout students are encouraged to go it alone by modifying the authors’ code (which is itself a point of interesting pedagogy, see this Journal of Economic Education article for more)

The bits of the book which are most interesting for me are the Mathematica and Excel chapters on Growth, Game Theory, and Partial Equilibrium analysis.

Each chapter follows the same structure. For example, in the first real chapter, the authors give an Excel solver routine to solve the Neoclassical growth model in it’s simplest form, as presented by, say, Romer in Advanced Macroeconomics. The exposition is clear and concise, and any student with some previous exposure to the mathematics will have no trouble fathoming the model’s basics. Then we see the Excel implementation of the model, with links to the code for the model itself so students can use and modify the parameters and basic assumptions as they like. That code, by the way, is here.

The Mathematica notebooks all run well in Version 6.02, and the use of game theoretic concepts in a LISP-like environment such as Mathematica really makes it clear what a high level and extensible computer architecture Mathematica is. The work, really, is a precursor of the currently white-hot topic of Algorithmic Game Theory, even though the authors don’t mean their work as such, just seeing the canonical models implemented in a computable set theoretic way (by definition, because it’s on a computer) is a real step forward.

One thing I would have loved to see was a comparison of one model built in two different languages: how one would go about translating, say, an Eviews program to MATLAB, or something like that. Maybe I’ll add that to the ideas list. Another idea to really sell the book would be to make each ‘program’ section of the book downloadable separately. Most institutions don’t have blanket subscriptions to Mathematica, MATLAB, Eviews, GAUSS, TSP, Stata, etc, so by allowing readers to pick and choose by program, the authors might make themselves a few more shekels. Who knows?

So all in all, a very nice pedagogical book, and well worth purchasing for a library or graduate student interested in teaching computational economics. Glad I read it.

…emails I’ve sent since Jan 1, 2008.

Number of emails recieved: 8,934.

Damn!

I’m thinking of declaring email bankruptcy.

1. The US Economy: Bureau of Labor Statistics
2. The EU: Eurostat
3. The EU with Micro data: OECD.
4. World Poverty/Income Distribution data: IMF
5. World GDP/GNP Data: The Penn World Tables
6. Historical Financial Data: Measuring Worth
7. Irish Macro Data: CSO (Measuring Ireland’s Progress is a great place to start here), the ESRI has excellent Micro data on health.
8. Current Financial Data: I use Yahoo finance or Financial Data Finder. Both are excellent.
9. Different Interest Rate Data: FRED.
10. Internet Price Competition Index: Nash-Equilibrium.

I’ll add more as I come across them.

Here’s a great book relevant to anyone who needs to make a presentation now and then. Notes to self are below the fold.

“Made to Stick: Why Some Ideas Survive and Others Die” (Chip Heath, Dan Heath)

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