Posts from — July 2008

I’ve just finished Clay Shirky’s Here Comes Everybody, an interesting book describing the impact of online social networking tools like Flickr, blogs and mailing lists on top down and bottom up organisations.

The basic idea of the book is these tools dramatically lower the transactions costs associated with running a complex organisation (which Ronald Coase, Shirky notes, highlighted a long time ago). We see bottom-up organisations and ‘happenings’ like flash mobs, meetups for single moms, helping mailing lists for software, and open source software development.

Crucially, these online networking tools provide us with the ability to fail much more easily and cheaply, while allowing some projects, businesses, and other online entities to become superstars, something Arthur DeVany and Nassim Taleb are experts on.

The book is entertaining, witty, and a quick read. If you’re interested in this area at all, pick it up.

Also, I’ve been messing around with data visualisation tools after reading Edward Tufte’s latest book, and here’s a tool which creates mindmaps out of simple indented text. This will be great for making lecture notes. It’s free. For nuthin’. Here’s my first go at a mindmap of Shirky’s work using the Text2mindmap tool.

Click on the link below to take a look at Here Comes Everybody.

Here Comes Everybody

I’ve been on a bit of a data visualisation trip since talking to Liam Delaney about it a few days ago, and just came across this.

You must click here!

This is so cool, I might just write a course based on it.

Liam Delaney has some interesting thoughts on this post I wrote earlier on this week. One of Liam’s points concerns increased flexibility of degree offerings, the idea being students will work full time and take modularised ‘for credit’ courses along the American model which will add up to a degree as and when the students decide to complete their course.

Liam puts it better than I could:

Modularisation will resolve some of this as students can stagger their degree more and, in many cases, embed evening courses and so on. If attendances are declining at traditional lecture formats, more flexibility of this nature seems a likely route. As someone who took a distance masters in philosophy (still completing some) while i was working on a very hectic job, I was extremely grateful for the flexibility of the format and it mellowed me out from thinking that such courses lowered the standards in higher education. They simply provide more choice and we should think more about these as options where students are clearly signalling that they do not want the traditional format. The market will then decide how much they value the qualifications.

I’ve no issue with the modular degree format, but I don’t think it’s the answer to why student attendance is dropping.

Increased choice will result in more students showing up to take their classes, but the problem will persist, in my opinion, because even if you offer the students lectures at times which suits them better, there will still be a cohort of student who haven’t bought into the idea of the degree.

One way to increase the level of ‘buy in’ is to make students financially liable for each module they take, again on the American model. When I taught in the US, each module cost close to 5 grand per student. If you missed a lecture, it was costing you 250 dollars, so you’d want a pretty good reason not to show up.

Professors knew the students were expecting a good lecture from them—you can’t go into an MBA class costing the students that much money and waffle for 40 minutes and leave, they’ll rip your head off—and so put in a better effort, again in my opinion, because of that.

This did also lead to rampant grade inflation, but even hyper inflated, the grades produced a ranking of students, which is all that mattered.

So perhaps one way to encourage attendance is to create financial incentives and disincentives to showing up and not showing up.

Behavioural economics has something to say about that, I think, see: “Nudge: Improving Decisions About Health, Wealth, and Happiness” (Richard H. Thaler, Cass R. Sunstein)

Update: here’s a piece of software to help stop online cheating. Sounds cool.

Congratulations to my friend and colleague Donal Palcic, who passed his viva yesterday.

Congratulations to my Charlie Larkin, who also passed his viva recently.

New doctors all over the place!

Remember, don’t use your new superpowers for evil. Just don’t.

How cool is this?

Click on the thumbnail below to see all the words used on the site in the last few days. The ranking system really does well. It’s a lovely little web 2.0 app.

It’s from a text visualisation site called Wordle. Check it out.

Link found via Economic Investigations

Complexity and obscurity have professional value—they are the academic equivalents of apprenticeship rules in the building trades. They exclude the outsiders, keep down the competition, preserve the image of a privileged or priestly class. The man who makes things clear is a scab. He is criticized less for his clarity than for his treachery.

Additionally, and especially in the social sciences, much unclear writing is based on unclear or incomplete thought. It is possible with safety to be technically obscure about something you haven’t thought out. It is impossible to be wholly clear on something you do not understand. Clarity thus exposes flaws in the thought. The person who undertakes to make difficult matters clear is infringing on the sovereign right of numerous economists, sociologists, and political scientists to make bad writing the disguise for sloppy, imprecise, or incomplete thought. One can understand the resulting anger. Adam Smith, John Stuart Mill, John Maynard Keynes were writers of crystalline clarity most of the time. Marx had great moments, as in The Communist Manifesto. Economics owes very little, if anything, to the practitioners of scholarly obscurity. If any of my California students should come to me from the learned professions, I would counsel them in all their writing to keep the confidence of their colleagues. This they should do by being always complex, always obscure, invariably a trifle vague.

Writing, Typing, and Economics

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Why don’t students go to class anymore?

This is a constant refrain in faculty lounges around the world. One answer MIT surveyors found was when students didn’t feel they were learning, they didn’t go.

This is certainly because the penalty to not going to a lecture is reduced by the presence of online learning materials like power point slides and handouts.

However, the old standards still apply–the researchers found students were less likely to go if they thought the lecturer wasn’t very good, and if they thought they wouldn’t learn much, which makes perfect sense, and, it should be noted, has nothing to do with technology.

Several other interesting articles on this issue have been summarised by Dr. Paul L. Latreille in this article for the economics network. They make for fascinating reading, but re run many of the arguments my colleagues and I have about the relationship between student access to notes online and their attendance. In particular, Dr. Latreille takes attendance in his lectures, and allows access to that lecture’s notes using a password system. He found this strategy increased student attendance throughout the term.

My stand on issuing notes, etc, to students is pretty clear from this website. I give students everything I can think of to help them learn—lecture notes, handouts, podcasts, readings, coursepacks, software, solutions to past tests, links to current and past events that I think help them learn, and more. I don’t care where they get the information from, I just want to see them learn the material. I happen to think the best place to do that is inside of a lecture hall, but if a student decides to spend their time elsewhere, then so be it.

So I don’t take attendance at any lectures, and I won’t. Ever. I don’t remove bits of power point slides or other reductive strategies like that to make students show up. I’ll try to tell a decent story as best I can on the day, and hope most of them make it out of bed on time to hear it.

I will, however, quite cheerfully fail people who don’t make the grade, precisely because of this laissez faire policy.

Here are Dr Lattreille’s references in his case study, all of which are fascinating.

Barrett, R., Rainer, A. and Marczyk, O. (2007) “Managed Learning Environments and an Attendance Crisis?“, The Electronic Journal of e-Learning, 5(1), pp. 1-10

Burd, E. and Hodgson, B. (2006) “Attendance and Attainment: A Five Year Study“, Innovation in Teaching And Learning in Information and Computer Sciences, 5(2)

Clay, T. and Breslow, L. (2006) “Why Students Don’t Attend Class“, MIT Faculty Newsletter, XVIII(4)

Clearly-Holdforth, J. (2007) “Student non-attendance in higher education. A phenomenon of student apathy or poor pedagogy?“, DIT Level 3, 5

Related articles

• Making Sense Of New Technologies And Media: An Opinionated Digest by George Siemens - July 19 08
• Should Instructors Provide Students with Complete Notes?

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Duncan Foley, who taught me advanced microeconomics and mathematics, and in my opinion the best economic educator around, gives a lecture on the history of economic thought in his development.

You can watch the lecture here, but you may need to download a silverlight plugin. It’s well worth your time.

Foley’s latest book, Adam’s Fallacy, is here.

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.

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From the press blurb:

Sir Clive Granger, recipient of the 2003 Nobel Prize for Economics, will be visiting CIDS at IT Tralee(www.cids.ie). He will present two talks on Sep 9 at ITT. Please see www.cids.ie/sep9_programme.html for details. You are cordially welcome. Please register online or by e-mail andrew.shields@staff.ittralee.ie

Thanks to recent developments in Quantum Physics, I can in simultaneously have and eat a superposition of my cake.

Only German Sausage can prevent forest fires

link

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The 19th Hebrew University summer school in economics has videos of all of its presenters, amongst whom are 6 Nobel Laureates in economics. The summer school is organised around the economics of Kenneth Arrow, right, and well worth watching. My favourite video so far is Herbert Scarf’s presentation on The Core.

Link to the videos, via Gill Kalai

Also, Scarf’s master work, and an index to a large part of the recent history of economic thought, is here.

Even cooler, here’s a game you can play to learn Sperner’s Lemma. 

• The Cowles Foundation Monographs in Economics

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.

A new paper, “On the Implications of Micro Price Data for Macro Models” (SSRN download here) finds a striking lack of correspondence between highly detailed micro level models and their mappings into macro level models.

The paper comes up with a few good suggestions to increase the accuracy of the mapping between levels of modeling, but the main findings are pretty negative. They find (pg. 3):

We provide two examples to show that there may be no simple mapping between the frequency of price changes in micro data and the speed of impulse responses of prices and quantities to shocks.

The key finding is that macro-level time series models built from micro data don’t much behave like the DSGE impulse-response models predict they should. It’s a poor approximation, especially when trying to measure a macro-property like the Phillips Curve (of New Keynesian vintage).

This is kind of depressing, because we should be able to go from microscales to macroscales if the modeling methodology is correct. Readers of this blog know I’m no fan of the DSGE approach, but, with some approximation, the model should work, if micro founded macro models are accurate descriptors of economic reality. If they are not, perhaps another methodology might provide more insight?

A new paper in this issue of the AER by LeBaron and Tesfatsion (link to final version) gives an example of an alternative modeling framework, the Agent Based Computational (ACE) framework. Say the authors (pg. 2)

ACE is a culture-dish approach to the study of economic worlds. Once initial conditions have been specified by the modeler, all subsequent world events are driven by agent interactions.

The idea here is to have macroeconomic fundamentals (the Phillips curve, Okun’s law, a country’s income distribution, what have you) emerge from the localised interaction of informationally constrained actors with their environment and with each other. Delli Gatti et al have a new book, Emergent Macroeconomics (download the full book here), devoted to this very topic, where an ACE based micro model evolves into a set of observed macroeconomic regularities.

The idea I’d like to talk about here is emergence of macrostates from microstates. A standard reductionist approach is to assume we have a (noisy) mapping from the individual micro level to the aggregate macro level. In a new book, Reinventing the Sacred, Stuart Kaufmann refutes this naive reductionist approach. Kaufmann’s metaphor is the human heart.

The heart is an evolved piece of biological hardware. It’s primary purpose is to pump blood about the body. It is true to say it is a clump of cells, and those cells are made from molecules, and those molecules from atoms, so the physicist’s description is true, but only partially true. While the heart is one collection of atoms arranged in a specific fashion, the physicist cannot describe the heart at the atomic level, nor can she identify or predict that arrangement of atoms purpose. Even assuming the physicist could describe the heart using the atom as the bottom level of description, she could not tell us which of the heart’s characteristics—its electrical impulse function, its pumping function, and the sound it emits while pumping—are the most important in securing the continuance of life.

So the physicist’s description of the aggregate ‘heart’ as the sum of the interactions of particles is correct, but only partially so (even though some macro properties can be deduced from micro relations in thermodynamics: see this paper for details). The heart is an emergent property of the evolutionary process, whose description at lower levels is incomplete. Descriptions of things, concepts built from these descriptions, and models designed to produce predictions about the movements of these concepts, all need to be couched at the right level for an appropriate analysis to take place.

All of which begs the question, what is the appropriate level of analysis for the economic system? Marx begins from commodities. Marshall from consumers. Modern mainstream theorists consider Homo Economicus and his behavioural cousins as the atoms of the economic system. Individuals group together into households. Firms are considered next, then regions, then countries. then supranational entities like the EU. All in all, economists span a wide range of analytical macrostates—from the mind of an individual and their preferences to the movements of pan governmental organisations. It’s a lot to deal with.

So, how do we classify macrostates? Two other really interesting pieces of work on the problem come from Statistical Mechanics and Philosophy.

Shalizi et al (download the paper) try to classify macrostates by the level of predictive ability a certain set of states has. Once a set of states has the ability to predict itself, Shalizi et al argue the predictor thus dervived is the optimal statistical predictor of observed values.

From philosophy, AJ Julius (download the paper) shows a basic structure argument where the most just state emerges from the collision of several unjust states.

Where do we go from here? In economics, using these views, there are as many potential macrostates as microstates. We have each individual, groups of them, coalitions of these groups, and so on, all modeled game theoretically or using the new tools of neuroeconomics.

Really, we’re looking for an ontologically appropriate analogy, as Douglas Hofstadter shows in chapter 9 of his I am a Strange Loop. What matters to just about every non-physicist adult person is not the atoms they are composed of, but the world at their level of description: what we perceive as every day reality. And generating an analogy (or mapping, or isomorphism or translation, whatever jargon applies to your field) appropriate for those individuals is not the same as the analogy you might apply to another level of abstraction. The stories at different levels don’t mesh.

Consider the micro founded macro model, which only works when we constrain each household to be godlike and identical before our description of the micro and macro economies coincide. Or try to move the other way: to go from macro phenomena like Okun’s law to micro phenomena like regional or sectoral unemployment and growth patterns. It is very hard to do this, because we don’t have the tools to dig back down, unless the analogies or stories or models, are rather strained as descriptions of the real world.

Let me ask you, dear reader, a question, email me if you’ve any thoughts on the subject.

What is the appropriate macrostate in neuroeconomics?

There’s definitely more on this topic, perhaps even a paper. We’ll see.

Here are Mathematica notes, slides, and code for a course I taught at NUI, Galway in 2006. I recall it didn’t go down that well, but the notes are pretty cool implementations of standard Solow/Ramsey type models, so I think, on reflection, I’ll put them out there.

You’ll need Mathematica or the free mathematica player to view the .nb files, but look at the .pdfs if you’re interested.

You’ll need to evaluate the mathematica file to generate the figures and solutions. Right click the link below to get the files in a .zip file.

Some thoughts on using Wolfram’s typesetting software, Publicon.

Publicon is a pimped version of Mathematica’s front end, a graphical interface for the processing wizard that is the Mathematica kernel. I’ve tried it now for a while, and my thoughts on it are pretty negative, which I didn’t want. I was looking for a way to write some of my papers without having to enter the cumbersome latex markups which generate the mathematical notation you see in some of my papers. I didn’t find it here. Publicon has a lot of cool features, like different stylesheets for different article styles, and ‘pallettes’ of different notations and structural elements, which is why I downloaded the thing in the first place.

The problem is the export features: Publicon just doesn’t play well with professional-grade , so, for me, it’s not a runner.

Sad.

Some issues with the software:

1. Too expensive

2. Bibliography integration sucks

3. Latex export sucks.

4. Basic features of typesetting missing.

So, Publicon is still no way near persuading me to go from 40 dollar textmate+free latex solution.

The search continues.

It’s official: sucking dead-dinosaur juice out of the ground and burning it is officially uncool.

link

Tags: Oil, Credit, Post+Oil+World, Cool+Phrases

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Professor Edward Nell of the New School for Social Research is coming in the spring as a Fulbright professor. I’m chuffed, and I think he will make a positive contribution to the research and teaching objectives of the department.

More details on Prof. Nell’s work and his books are here, and his New York Review columns are here. His books in print are here.

Incidentally, UL has 5 of the 29 awards in this category. Nice!