RIP Gary Becker

Gary Becker, winner of the Nobel prize for economics in 1992, died on Saturday. There have appeared several obituaries and appreciations. I want to pay tribute to him by linking to three very good ones.

The first was written by Justin Wolfers in the Upshot. Wolfers explains well Becker’s influence on all the social sciences.

The second was written by Steven Levitt in the Freakonomics blog. The remarkable story of how Becker stepped in, unasked, to teach Levitt’s courses for some weeks when Levitt’s son died suddenly, stands out for me.

Finally, Kevin Bryan wrote a good appreciation of Becker’s influence in his blog, A Fine Theorem.

My own take is that, while Becker extended the homo economicus approach forcefully and with a recognition of the limitations that prevent any decision maker from being “perfectly rational”, he relied too much on methodological individualism, which may yet turn out to be the Achilles heel of the entire enterprise of economics. The way I see it, a very big step forward for economics has not yet been taken. This step would keep some aspect of methodological individualism while seriously, and tractably, incorporating the influence that people have on others’ preferences. De gustibus est disputandum. Until I make a contribution of some importance in this direction, however, I must simply tip my virtual hat to Becker’s fertile mind and his prodigious and influential output. If I ever make a contribution, then I will want to acknowledge that it was motivated, partly, by grappling with Becker’s ideas (on this, see the first link above, too).

Author thinks he has the key to revolutionizing economics

Blood circulation and economics?

The Economist’s latest issue has a review of Money, Blood and Revolution: How Darwin and the doctor of King Charles I could turn economics into a science, by George Cooper. The review intrigued me enough to take the morning of my last weekday of Spring Break and devote it to purchasing the Kindle version of the book and reading the crucial chapters. These are the chapters where Cooper discusses his view of economics and what ails it and then proposes a new conceptual framework for economics

I am disappointed. I spent my time investigating this book because I agree that economics needs change. I also liked that the author does not simply complain that economics needs changing, but has a proposal to make. However, the proposal was disappointingly vague. I started suspecting it would be so before I reached the proposal: the review does mention that economists would be unlikely to pay attention to it because it is not presented precisely enough, but, also, the description of economics that Cooper offers before coming to his proposal shows clearly that he does not know enough of the field he is criticizing.

To give an example of the gaps in Cooper’s knowledge, he says in section 7.3 that

The problem for mainstream economic theory is that the experimental evidence suggests that the way we choose to arrange our societies has enormous influence on how our economies actually work. However, there is simply no coherent way to integrate this observation into the neoclassical paradigm…

Really? Cooper does not seem to be aware of mechanism design theory, or its offspring that is making tremendous strides lately, market design. There is a lot riding on his usage of “coherent”, without which his ignorance of these fields would be utterly condemning of his diagnosis here.

A little later in the same section, Cooper complains that

Given the empirical evidence, it is unscientific not to at least consider whether democracy and government play a role in the promotion of economic growth.

Really? I have to exclaim again. Cooper has apparently not heard of the work of Acemoglu and Robinson, not to mention a legion of other mainstream economists who have examined exactly this question. Oh yes, and let’s throw in all of modern institutional economics, to boot. Cooper has a lot to learn, it appears.

Here is one more piece of evidence on the partial nature of Cooper’s knowledge of economics, as revealed in this book. In the entirety of section 7.5 he conflates all of mainstream economics with DSGE (dynamic stochastic general equilibrium) models. These models are indeed used in mainstream macroeconomics, but they are not the entirety of mainstream macroeconomics and of course macroeconomics is not ALL of economics.

And what about Cooper’s discussion in section 8.1 of competition, in the Darwinian sense, as opposed to individual maximization as in microeconomic theory. Methinks someone ought to show Robert Frank’s works to Cooper, not to mention the entire evolutionary game theory literature.

So, do I care for the proposal for reform that Cooper advances? I would, if he had told me how to formulate a model or two of the economy. He does not do so in this book. Instead, he gives a vague story about a flow that resembles (at least in Cooper’s mind) the circulation of blood in a body (hence the title of the book and the heading of this post). This flow is created by the social mobility that democracy enables, Cooper says. Yet, it is not clear what flows here, although I suspect it is money. Cooper does talk about income inequality in this connection. I suspect more serious thinkers, concerned with income inequality and the nature of contemporary economists (as am I), may be able yet to build on the vague suggestions of Cooper. Maybe Thomas Piketty, now that he has finished the labor of his upcoming (in English) magnum opus (which I am eager to read when it arrives in my Kindle in a few days). Maybe an economist well-versed in political economy, Acemoglu-style, can bring Cooper’s project to fruition. Maybe someone else. It seems certain to me, though, that, by giving us nothing precise to build on, Cooper has not advanced his self-professed goal to make economics more scientific.

Cooper is diagnosing the sickness of economics without having examined all parts of the patient, and it’s as though he’s showing us a bottle of colored liquid that supposedly has the needed medicine, but he does not explain the medicine’s formulation or how it is going to improve what he thinks is the entirety of the economic theory patient. If the medicine is ever made and administered and gets to improve some part of economics, I will be glad. But it would take someone with the theoretical chops needed to do the job that Cooper has only started. And the medicine may prove to be sugared water.

Criticizing economics

Critics of economics abound. The criticisms have become more pointed since the financial and economic disaster that started in 2007 and promises to trouble us for years to come yet. But the critics often take unproductive approaches. I came across an excellent post by Diane Coyle today, on exactly the topic of how to criticize economics. In it, I also noticed a link to a previous good post by Chris Dillow.

All in all, I spent a rewarding period of time in reading these posts today and following their links, so I wanted to note this here. I am also looking forward to the review by Coyle of Mirowski’s new book, which she promises in her post is forthcoming soon.

RIP Ronald Coase (1910–2013)

I am late to the commemoration of Ronald Coase’s contribution to economics, on the occasion of his death yesterday at the age of 102 years. You will find a large number of online posts about this with a simple search. The New York Times publishes its obituary here. The Economist points to its article published two years ago on the occasion of Coase’s 100th birthday.

After reading a number of other posts on Coase’s legacy, I decided to offer here this fantastic piece by Kevin Bryan. I heartily recommend a careful reading of it and the links it offers. In the Toolbox for Economic Design there are several cautions against taking the “Coase Theorem” seriously. After studying Bryan’s post and the links he offers in it (especially that to McCloskey’s article), you will have a better idea why this nomenclature (Stigler’s baby, Coase proclaimed no theorems) is wrong and misleading, while Coase’s contributions to institutional economics, stemming from his 1960 article The Problem of Social Cost, are important.

Let us also not forget Coase’s 1937 (!) article The Nature of the Firm, an early and fundamental contribution to the way economists ought to view the limits of the efficacy of markets.

On the quality of academic software

On the quality of academic software:

Software is eating the world. Despite a poor year, Facebook has a market capitalization of $65 billion. This little company with barely 2000 developers is worth as much as a car marker.

Students should take notice. I would expect countless students to come to college demanding top-notch software training. I would expect graduate students to focus on building gorgeous software programs.

Yet software produced in universities and colleges is awful, and it is not getting better. I have a few explanations:

(Via Daniel Lemire’s blog) Highly recommended. Read all about it at the link on the top.

My encapsulation of mathematical modeling in economics

I wrote this in a comment on Google+ today, in a discussion of the death of Elinor Ostrom. I kind of like it, so I am putting it here, too:

Economic theory building proceeds by using abstractions to encapsulate what are thought to be important basic principles of interactive decision-making and then uses mathematics as a language for analysis to reach conclusions. There is also an empirical side that tries to find appropriate values for some of the parameters that are used in theorizing, with disputed success. The social side of human behavior is quite important in some parts of mathematical economic modeling; economic network analysis and the analysis of information diffusion and information cascades being some prominent examples that spring to mind.

The repugnant conclusion and economic theory denialism

Many macroeconomic theorists appear utterly reluctant to accept the abject disaster that macroeconomic theory has become, as made evident by the crisis that started in 2007.

It just occurred to me today that, since most economists, and even more so most macroeconomists, are unquestioningly utilitarian (why else are they always looking to maximize criteria such as a representative agent’s discounted present value of lifetime utility?), they may be just unaware of the repugnant conclusion from population ethics and yet their work pushes the world towards it. Here is a succinct presentation from the page just linked:

In Derek Parfit’s original formulation the Repugnant Conclusion is characterized as follows: “For any possible population of at least ten billion people, all with a very high quality of life, there must be some much larger imaginable population whose existence, if other things are equal, would be better even though its members have lives that are barely worth living” (Parfit 1984).

This seems to me to be consonant with the approach of the austerians, who rely on mainstream DSGE models in macroeconomics to make recommendations to make a great number of presently living people more and more miserable with austerity measures, in order to safeguard the well-being of future generations, which the austerians think they only know how to do. Never mind the obvious fact that austerity measures don’t even advance the cause of a market economy and economic growth; all they do is give political power to neonazis and leftist extremists.

Can you tell I am first and foremost an incorrigible economic theorist? Here the world may well be entering a second Great Depression, to be capped off with widespread war, and I am talking about arcane philosophical topics. Except that, there may, just may, be a glimmer of hope in getting austerians to realize just how badly their recommendations are behaving and to learn a bit more social welfare criteria than utilitarianism. (I know, I will keep dreaming.)

Playing “taboo” with jargon

Eliezer Yudkovsky has an excellent suggestion in this blog post. The idea is that clarity in scientific or philosophical discussions would be enhanced if participants were not allowed to use certain terms. For economists, for instance, this could include terms such as “welfare” or “efficiency”. Instead of “welfare”, you would have to use the operational definition of welfare you have in mind, and so on. I endorse this suggestion heartily, mindful that it would make defenders of elegance in verbal expression cringe. Most often, what intended-to-be-taken-seriously speech lacks is a manic devotion to exactitude.

ADDENDUM: hat tip to Ole Rogeberg on Google+.

John Quiggin makes an excellent point

I don’t often discuss macroeconomics here but this blog post by John Quiggin is well worth my attention and yours. He totally demolishes a review of his book by Steven Williamson, who is quoted as saying “Market efficiency is simply an assumption of rationality. As such it has no implications. If it has no implications, it can’t be wrong.” Williamson is also quoted as saying “Like the “efficient markets hypothesis,” DSGE has no implications, and therefore can’t be wrong.”

If you wonder why I have not gone over to my university’s online library site to read the review, it’s because I am utterly disgusted by the Williamson quotations and do not want to waste my time reading his review. Macroeconomics is supposed to be a science, not a part of analytic philosophy or logic; “economists” with such enormous blindfolds as Williamson have too much sway in the discipline and have corrupted its very core.

Like Tjalling Koopmans said in response to Milton Friedman’s methodological emanations, every assumption you make in building a theory is automatically, and rather obviously, also a prediction of the theory. Saying “I like the assumption of rationality, I will always make it, and it’s my mode so you can’t tell me not to play with it to my heart’s content” is odious nonsense. When the efficient market hypothesis or DSGE is part of a model that produces predictions that keep being smacked by the data, insisting that these assumptions (and since when is DSGE an assumption, rather than a whole modeling technique?) can’t be wrong is tantamount to saying that your theory can’t be useful and in fact is eminently ignorable. If you peddle it to the world and your students as science, you are at the minimum corrupting the notion of science itself.

Object-oriented thinking in economics

I have been meddling with the programming language Python for some years now in order to become self-sufficient in programming simulations. In doing so, I learned the basics of object-oriented computer programming (OOP for short).

OOP is a style of writing computer programs, with some languages, such as Java, heavily supporting it (Java enforces it, in fact) and other languages, such as Python, supporting it strongly. Coming to OOP from economics was conceptually easy. Now I am thinking that OOP has something to tell us regarding how we teach economics or how we present it to the wider world. This post is my first written rumination on this topic.

First, let me define an object in the OOP sense. I need to give you some background first. I expect you are aware, even if you never wrote any computer code, that such code is a string of data, ultimately encoded in binary notation, that tells the computer to do certain things with part of this data. To take a simple example, if you want the computer to choose randomly between the names of participants in a lottery, you have to include the names in your program (data) and write commands that result in one of these names being chosen randomly and this choice being communicated to the user. This little program has data (it “knows” some things) and it has ways of acting on these data (it “knows” what to do with the things it knows).

In any reasonably complicated programming problem, it helps us as programmers to compartmentalize the code. We make a chunk of code to perform task A, another to perform task B, and so on, and finally we write code to coordinate these chunks as they go along merrily doing their thing. Each one of these code chunks has some data it knows and some things it can do with the data it knows. (Computer scientists, I know I am simplifying. I only want to convey the basics of OOP here.) An object in OOP language is a chunk of code that has some data and some things it can do (usually called “methods”, but you can think of them as commands specific to this chunk). The programming language in which you are specifying these objects provides ways for objects to communicate with each other, passing data around, and to ask each other to perform one of its methods (execute one of the commands it has).

How is this relevant to economics? You are probably already chomping at the bit to answer, but here is my take. When we set up any model in economics, at least if our model is “microfounded” as we say, we have some agents who know certain things and do certain things. Suppose, for example, that you want to make a computer model of an exchange economy. You need a number of individuals, each with an endowment and a preference relation over the commodity space. These individuals need to be able to perceive prices, decide which net trades are feasible given a particular price vector, and, finally, they need to be able to propose and execute trades with each other. (OK, maybe you wanted a Walrasian auctioneer thrown in as well? I leave it as an exercise then to determine what data and which methods the auctioneer object in the code will need.)

I cannot think of a mainstream economics model that cannot be conceptualized in these terms. Indeed, computational economics has flourished in the last several years and you can find plenty of examples of what I am talking about by visiting, for example, the amazingly comprehensive website that Leigh Tesfatsion has set up. (This kind of “microfounded” computational economics goes by the name agent-based computational economics.)

So why don’t we teach our students using the concept that each economic agent in our models is an object (in the OOP sense) that interacts with others based on rules set up by the institutional infrastructure of the economy and physical feasibility conditions?

I can think of two answers right now, one deeper than the other. I will examine each one and argue that they do not convince me, starting with the shallower one.

The shallow answer is that our students will find it hard to understand the OOP language of objects and their interactions. I am much more likely to agree that our students in introductory courses will not understand supply and demand graphs or the simple linear equations we try to use to overcome their math anxieties. But almost all students in introductory courses will have played computer games. For them, all you have to do to introduce OOP objects is to refer to an avatar in an online role-playing game or a tile in Tetris. Stepping from these examples to explaining economic objects in code does not in fact impose the need for a computer language at all. You can use pseudo-code and the ideas stand; any time your students feel shaky, just bring in another computer-game inspired example.

The deeper answer is that while encapsulating an individual in an exchange economy in the language of OOP objects is easy, if we start thinking in the algorithmic terms this mental shift suggests, things like arriving at a Walrasian equilibrium become hard problems. There might even be a student in your class who knows enough computer science and will tell you to your face that our cavalier approach of taking shortcuts like calculating an equilibrium with Lagrange multiplier techniques and setting supply equal to demand is a rotten approach, as it hides the remarkably difficult problem of arriving at an equilibrium.

So how can I deal with this answer? My point is that we should adopt the OOP viewpoint precisely so as to force our students or readers to confront the fact that reaching an equilibrium in an economic model is much trickier a proposition than the typical paper in Econometrica or JET lets on. As economists, we have internalized the mental shortcuts that make us jump to equilibria in fairly complicated models and then analyze the properties of these equilibria. But there is much to be learned by confronting the need to specify exactly how economic agents interact in time, each with its own data an abilities to perform actions such as buying and selling, manage to get to an equilibrium (if they do). Do we really want a Walrasian auctioneer who gropes around in price space to find an equilibrium? What if the economy happens not to have a stable equilibrium (the question can arise whether we are looking at a market model or any other kind of agent-based model). These are not idle concerns; they show clearly some of the limitations of economic theorizing and to ignore them is intellectual arrogance at best, dishonesty at worst.

There is one final point in my mind about this, which I will leave to be developed in a future post. Thinking in terms of institutional infrastructure can be considered the overall code of our economic computer program. This can encompass the ways information gets passed around from agent to agent as the economy operates as well as the outcome function that determines what allocations occur and when as the agents take various actions. The more precise we are in specifying these the better, just as much as the more careful we are to specify computable ways to reach an equilibrium (see the previous paragraph) the better. As this post already exceeds 1250 words, however, this final point will have to be explored later.