Herding on the Job Market

 

 To little light and narrow room have I been  consigned, to stamp out ignorance. After 7 weeks of blood, bile and ink, it appears another cohort has been saved from barbarism, prepped and ready to be delivered into the capitalist maw.

 

 In the meantime the junior economics job market has lurched into action. It begins with the `great sorting’. Departments with large numbers of students on the market meet to sort their `product’ by quality. There is no sorting hat to do the work. Instead, advisers, who must balance the parental impulse to promote their progeny against professional propriety, execute the task. This demand to constantly judge, weigh and evaluate others is perhaps the most unpleasant duty that those who `seek for truth in the groves of academe’ must perform. It wearies the mind and corrodes the soul. 

 

 The second big step of the junior economics job market is the `great herding’. Major institutions must coordinate upon a handful of bright young things bound for greatness. In six years, these new minted pennies will be so much loose change, sic transit gloria mundi.

 

 Who are this years, superstars? Haven’t the faintest. However, one does not need to don tweed and move to the hub of the universe to find out. There is a web site that passes along rumors about the job market in which names are named, cutting remarks made and blood drawn.

 

 A puzzle, to me at least, is why herding does take place (OK, I probably need some way  to distinguish between herding and not herding, a measure of the extent of herding etc. But, this is not the pages of Econometrica). The decision to hire at the untenured assistant professor level is not irrevocable. One can let the person go if things do not work out (see what I mean about the corrosion of the soul). Thus a department should be willing to take on more risk in hiring at the junior level (I’m assuming of course that the department is willing to bite the bullet when the time comes). In particular  a department at rank x looking to make it into rank x-10, should be willing to take on more risk then the department currently at rank x-10. Thus, why so much agreement on who they interview?

 

 

Economists and falsifiability

For someone who doesn’t care much for experimental studies and data analysis, I often come up with experimental tasks or statistical surveys for other people to do. Here is one: Take a bunch of graduate students of nuclear physics and ask them about the role of falsifiability in science. Then take a bunch of graduate students of macro-economics and ask them the same question. My guess is that the physicists will not know what you are talking about, and at any rate  wouldn’t have come accross this issue during their studies, whereas the economists will know all about Popper, logical positivism, demarcation and other latin words I never heard of.

Drive-by-Theorizing

I confess. Blogging is harder than publishing. Lance Fortnow tells me that I would find Blogging easier if the orifice in my antipodes was not constricted. Perhaps this is true, but it does make one wonder about the source of a bloggers wisdom.

Very well then. I will follow his advice. Where is the milk of magnesia? Jacta alea est.

Drive-by theorizing. Practiced by applied theorists. This is the species that theorizes about phenomena, rather than theory.

Works like this. Read a book from another discipline about some unusual phenomena over the summer. In the fall, write a paper with an economic model about the phenomena. Usually with a title like `An Economic Theory of BLANK.’ A popular choice for BLANK is one of the 7 deadly sins (but these have  been exhausted) or something magesterial like empire, justice, language, family or sex.

The contents of said paper consist of a verbal intuition formalized with a patina of mathematics. Indeed, the best examples, admit no other intuition but the one advanced by the author. Of course, this makes the mathematical exercise utterly pointless. We build mathematical models to sort out competing intuitions not to codify them.

 When completed, move on. It is always best to have the first word, no matter how anemic, than the last.

Of course, one should name names. I won’t. I’ve sipped a laxative not drowned in it.

 

Dawkins on predictions

Richard Dawkins has a new book, in which he sets out to prove, once and for all, that evolution is a fact `in the same sense as it is a fact that Paris is in the northern hemisphere’, or, as he calls it, a Theorum.

More CSECON09

During a panel session at  CSECON09, Joan Feigenbaum and Yoav Shoham commented on the underepresentation of Economists. One reason offered was that the tribe of the ECON is large with varied interests: fertility, wage discrimination, returns to education etc. However, I don’t think Joan and Yoav meant all Economists but Economic Theorists.

Like the tribe at large, other topics compete for the attention of theorists. Some have gone over to the dark side, behavioral economics, to rearrange the foundations of Economics in service of the Obama administration (if Businessweek is to be believed). Others are engaged in work of national importance extending the folk theorem to repeated games with imperfect monitoring. More have gone beyond the event horizon of the decision theory black hole to be lost forever. Put differently, it is not sufficient for the topics of CSECON09 to be interesting, it must be more interesting than what currently occupies their attention. Unfortunately, some of the topics associated with the overlap between CS and ECON induce yawns amongst the economic theorists.

Consider, first, the stream of papers on the Price of Anarchy (PoA). A catchy name prompted by a natural question: how should one quantify the degree of inefficiency associated with the Nash equilibrium (NE) of a game? The Roughgarden and Tardos papers offer a proof of concept with an appealing game (traffic flow) and an elegant analysis. What next? Repeat the same for any game that comes to mind between breakfast and a bowel movement? Non (denying something always sounds better in French).

First, if the PoA is R, what does it mean for R to be economically significant? This depends on the magnitude of the total utilities involved. So, knowing R alone is not particularly informative about the magnitude of the inefficiency. Further, what exactly is to be gained from quantifying the inefficiency in a stylized model?

Second, one’s interest in quantifying the degree of inefficiency is to offer suggestions on how to improve efficiency. Thus, from an understanding of R, can we tell whether it is better, for example, to alter the network in the traffic game or modify the latencies? Which intervention will have a larger impact?

I’ve not heard a good counter to the first. I don’t recall an example of the second. As always, such statements reflect the authors deafness and absent mindedness. I confess to being both. Others may suggest that muteness would correct for this.

Next, on the docket is mechanism design (MD). Catnip to computer scientists since it is constrained optimization. Whilst many economic theorists make use of mechanism design, the deeper recesses of it’s theory are irrelevant to them. For them, MD is a convenient way to model an institution without being encumbered by it’s details. The corresponding MD problems are simple to deal with. An analogy to this situation is the classic monopoly pricing problem. It is an instance of concave programming, but a deep knowledge of concave programming is unnecessary for it’s analysis (I confess to unease with such a view because of the possibility of selection bias in models). Thus, for them, the body of work by Computer Scientists on algorithmic mechanism design (AMD) is irrelevant.

At the other extreme, are theorists who actually want to implement mechanisms. This group acknowledges the restraints that computation imposes but is largely untouched by  work in AMD. Why?
1) Some of the underlying problems seem contrived.
2) Some of the mechanisms with good approximation bounds appear unnatural (which suggests unmodeled constraints that should be articulated). The criticism is applied even handedly. Consider the debate about Cremer-McLean full surplus extraction.
3) Worst-case approximation bounds are worst-case and in some cases ad-hoc. When partial information is available, why isn’t that incorporated? Little justification is given for the  benchmarks against which the selected mechanism is compared to.
4) Perhaps, the solution to a hard MD problem is to side step the problem altogether.  For example, the allocation of spectrum may be a hard problem only because of the way the Federal government has chosen to design spectrum property rights. There are other ways in which spectrum could be managed that would lead to very different resource allocation problems. 

I close by noting some other blogs on the issues raised at CSECON09. See Lance Fortnow, Noam Nisan  and Muthu.

CSECON09

I write from Ithaca. Not Homer’s, but New York’s. The Economics and Computer Science department at Cornell hosted a workshop on the Research Issues at the Interface of Computer Science and Economics (CSECON09). 

There have been a number of these `intimate’ dances between computer scientists and economists. The first, about 10 years ago at Northwestern. Thence, Maastricht, IMA, Dagsthul, Bertinoro and DIMACS. They tend to attract more Computer Scientists than Economists. In another time and place I’d expatiate on the causes of this. At this time and place I’d rather make the argument for why Economists should pay attention to (Theoretical) Computer Science. I leave, for another post, why Economists, rightly, in some cases, turn a deaf ear.

I’ll begin with complexity. Knowing the complexity of computing an equilibrium is important. When it is polynomial (in a natural specification of the input) then, there will be a good, non-trivial characterization of the equilibrium. Further, if a problem is polynomially solvable, there is usually a simple, decentralized algorithm (best response, no-regret, fictitious play) that will compute the equilibrium. In many cases, this decentralized algorithm can be interpreted as a plausible dynamic process that terminates in the equilibrium of interest.

If the problem is hard (in a formal sense, say NP-complete, PPAD), then, there will be no good characterization and no simple decentralized algorithm that will offer a learning justification for the underlying equilibrium.

One might argue that the test is whether agents behave as if they are in equilibrium not whether they actually play an equilibrium. In this case, pretty characterizations and learning justifications for equilibrium are merely ornamental. Very well. How is it that the observed behavior might be indistinguishable from equilibrium play? Two explanations come to mind. 

First, coincidence. This  provides no good reason to use an equilibrium concept to make predictions; why should the coincidence be repeated? Second, the notions of hardness employed are worst-case. Perhaps, the world organizes itself so that equilibria are easy to compute. If so, what are the structures that lend themselves to efficient computation? How does one identify them? 

I picked the problem of computing equilibria as an example to argue the importance of thinking about complexity. I could instead have picked mechanism design the vehicle for making the same point. It will be useful to recall what the purpose of mechanism design is. It is identify what a given institution can achieve when the information necessary to make decisions is dispersed and privately held. If one takes this seriously, then one must accept that in many situations, computation is as much a constraint on economic activity as private information.

I write from Ithaca. Not Homer’s, but New York’s. The Economics and Computer Science department at Cornell hosted a workshop on the Research Issues at the Interface of Computer Science and Economics (CSECON09). 

There have been a number of these `intimate’ dances between computer scientists and economists. The first, about 10 years ago at Northwestern. Thence, Maastricht, IMA, Dagsthul, Bertinoro and DIMACS. They tend to attract more Computer Scientists than Economists. In another time and place I’d expatiate on the causes of this. At this time and place I’d rather make the argument for why Economists should pay attention to (Theoretical) Computer Science. I leave, for another post, why Economists, rightly, in some cases, turn a deaf ear.

I’ll begin with complexity. Knowing the complexity of computing an equilibrium is important. When it is polynomial (in a natural specification of the input) then, there will be a good, non-trivial characterization of the equilibrium. Further, if a problem is polynomially solvable, there is usually a simple, decentralized algorithm (best response, no-regret, fictitious play) that will compute the equilibrium. In many cases, this decentralized algorithm can be interpreted as a plausible dynamic process that terminates in the equilibrium of interest.

If the problem is hard (in a formal sense, say NP-complete, PPAD), then, there will be no good characterization and no simple decentralized algorithm that will offer a learning justification for the underlying equilibrium.

One might argue that the test is whether agents behave as if they are in equilibrium not whether they actually play an equilibrium. In this case, pretty characterizations and learning justifications for equilibrium are merely ornamental. Very well. How is it that the observed behavior might be indistinguishable from equilibrium play? Two explanations come to mind. 

First, coincidence. This  provides no good reason to use an equilibrium concept to make predictions; why should the coincidence be repeated? Second, the notions of hardness employed are worst-case. Perhaps, the world organizes itself so that equilibria are easy to compute. If so, what are the structures that lend themselves to efficient computation? How does one identify them? 

I picked the problem of computing equilibria as an example to argue the importance of thinking about complexity. I could instead have picked mechanism design the vehicle for making the same point. It will be useful to recall what the purpose of mechanism design is. It is identify what a given institution can achieve when the information necessary to make decisions is dispersed and privately held. If one takes this seriously, then one must accept that in many situations, computation is as much a constraint on economic activity as private information.

Rankings

Summer is coming to an end, students are returning to campus like swallows to Capistrano and College rankings are released. Useless News and World Misreport just published theirs. For some, this is but the deep breath before the plunge: the long awaited NRC rankings. This will be followed by Businessweek’s ranking of B-schools. Hard on the heels of each will be a flood of blood, bile and ink to drown them all (or not).

Rather than criticize the various rankings (others have already done that) I’d like to propose my own. For each pair of colleges C(i) and C(j), record the number of students who received an offer of admission from both in given time period. Denote by N(i,j) the fraction of this group who chose C(i) over C(j) and by N(j,i) the fraction who chose C(j) over C(i). Next, construct a directed graph with a vertex for each college and insert an edge directed from C(i) to C(j) if N(i,j) > N(j,i). For expositional convenience only, ignore the case N(i,j) = N(j,i).

The resulting directed graph, called a tournament in some circles, can be used to generate clusters of `like’ colleges and a ranking among clusters. One could pick as `winners’ the usual suspects: top cycle, Banks set or uncovered set. The delightful aspect of such an approach is that it relies on the revealed preference of students themselves. One could construct a similar directed graph (by college, by department), but use faculty employment choices instead. A side benefit is that it would provide another onanism opportunity for social networks scholars.

In praise of chatting

Like Michael Mitzenmacher, I also use skype to collaborate on research projects. But not  for voice calls. For me, chatting is much more convenient.  I am not sure why, but I can think of some possible reasons.

Signaling and Indian Higher Education

From Delhi, the former Mughal capital to Mathura, Krishna’s birthplace, is about 150 km, roughly 75 miles. The route traverses a portion of the Grand Trunk road. In Kipling’s time it was a “river of life as nowhere else exists in the world.” That river is now one of tar and concrete. The jostling of barber and bunnia has been replaced by that of car, lorry, scooter and the occasional tractor going the wrong way down a two-lane highway.

Two weeks ago I traveled this stretch of history and counted no less than ten private institutions of tertiary education.

The Great AD/AC Compromise

The economics profession has been for a long time divided into two camps — ADs and ACs.
On the AD camp, there is me, claiming that a bit of exciting bizarreness brought to game theory by the axiom of choice is not worth the headache of checking measurability of strategies and the embarrassment caused by undetermined gale-stewart games; The Axiom of Determinacy, on the other hand, offers a paradise in which