Constrained Bliss Point

Yesterday was unequivocally the high point in my graduate school career to date. The big event was our first Micro 2 class, in game theory. Micro was the only class we hadn’t had yet, and my expectations were high: Econometrics is typically dry and exceedingly difficult, and our Macro class is shaping up to be intense, courtesy of our new professor. I was hoping that Micro could be the class to keep me sane this semester.

Luca Anderlini is our professor for Micro. He’s the new Director of Graduate Studies too, so my performance in Micro serves the dual role of not failing out of the program and not embarrassing myself in front of the guy running things. I had seen him present a paper last semester, and this gave me high hopes. He had a sense of humor, an entertaining manner of lecturing, and a way of making the topics at hand seem relevant.

Let me cut to the chase: my hopes were realized. The lecture was interesting, but most important, something crucial happened, something I have been waiting my entire time at Georgetown to hear someone admit. Before Professor Anderlini got into the meat of the lecture, he made a caveat. He expressed to us, in no uncertain terms, that math is not the point of what we’re doing. While, he explained, he enjoys math a great deal, and even considered a career in math, he stressed that math is a just a tool to clarify our thinking. Anyone can reason, he argued, and make a convincing case. The key is that math is a rigorous formal language to express our ideas,  so that we can make sure we are not just deluding ourselves with words. Again, math is not the end, it is only the means.

That was the breath of fresh air I needed.

In a previous post I challenged Georgetown’s economics department to “bring it” and kick my ass as best it could. I’m happy to report that they succeeded in winning the first round. Their tactics were a bit sneaky and underhanded, but nothing I couldn’t have anticipated. The fact is that I haven’t been putting in enough time and effort to be learning the most and getting the best grades I can. But that will change: I’m gearing up for round two.

See, what they don’t tell you about serious graduate school programs is the extent to which you are expected to know everything without having been taught it. In undergrad, the professor would only test you on things you covered in class. In grad school, you are lucky if what the professor covers in class is vaguely useful. More than half of what I’ve learned so far has been outside the classroom. This trend will surely continue into the future.

There’s also the little caveat about expectations. Some classes grade homework assignments strictly and care more about answers than methods and effort; some classes don’t even bother to grade homeworks (though if you don’t do them you’re almost guaranteed to fail the exams). Some exams test whether you can regurgitate proofs seen in class, some test mechanical problem solving skills and intuition, and some want you to know damn near everything. Sometimes you may spend more than half of your allotted time on two questions that, you find out after the fact, were only actually worth 18% of the exam grade, and you didn’t get any partial credit on them besides, but that your last five minutes of scribbling on a seemingly unimportant question netted you the majority of your points (a question which, by the way, was worth almost half of the points on the exam). No, you shouldn’t expect all exam questions to be weighted equally, either.

I know how I’m studying for the next round of exams: memorizing proofs, practicing my mechanics, and trying to learn damn near everything. And I am not making any more assumptions about how many points each question is worth.

I’m an intuitive person: I’m more concerned with broad-reaching theories than with any particular instantiation of fact. I scored a solid N on the Myers-Briggs Type Indicator. That’s not to say I don’t like facts. On the contrary, theories need to be generalized from somewhere, and starting with the known state of the world is common sense. But I don’t operate in Sensing-land — I need to be able to clearly see the general pattern in order to understand something.

Framed this way, I like economics for the same reason I like engineering. The extensive maths used in both fields are a means of grounding one’s intuition in something substantial, something provable. Maths allow one to derive, from a set of axioms and real-world data, a bunch of consistent theorems, equations, etc. that describe the system the axioms (and data) generate. If your intuition clashes with your results, either you’re wrong or you made a math mistake. Here you have a very solid check to balance out your intuition.

This allows me to satisfy two competing objectives: first, I don’t want to spend all my time in math land; second, I don’t want my intuitions to be wildly off base. So I ponder my intuition, I learn the requisite maths, and then I can go write a bunch of equations to ensure that my intuition is correct.

The internet is an unprecedented treasure-trove of quantitative data on social systems. Before now researchers could only dream of such data. I believe this provides the potentially fruitful opportunity to engage in formal, mathematical analysis of social systems (while remaining in a social-scientific vein, of course).

In the past century the field of economics underwent a mathematical revolution, focusing theoretical research on formal (mathematical) models to aid analysis and intuition, and empirical research to strengthen or rebut those models. This would have been impossible if the phenomena economics studies hadn’t been quantitative in nature; for example, if GDP numbers and employment data weren’t regularly compiled by developed nations, then modern macroeconomics could not have developed. This is one example of readily accessible quantitative data fueling a course of research, and surely there are others. (And perhaps the causation runs the other way too: when a group wants to study something, they do all they can to find good data, but this seems like the weaker direction.) Notable is the opposite case, though difficult to observe: when a particular field or direction of research is impossible because of a lack of useful data. Particular objects of study I’m interested in include “attention” or “influence,” which do not have clearly observable and measurable forms (what is the smallest unit of influence, anyway?); maybe something like “reputation”; or even abstract measures of happiness (utility, anyone?), which can explain why economics often treats consumption as a drop-in replacement. And so I turn to a study of the internet.

Out of the epic amounts of information generated by the plethora of internet-born social structures, data are lurking that could revolutionize the way we study social behavior. These data haven’t been compiled in appropriate forms yet — there is a lot of theoretical work to be done on what paradigm to use, how to aggregate the data, etc. I task Web Ecology with making sense of the morass of information, and turning it into useful data for researchers to study, build models around, and predict emergent phenomena. Once relevant data are identified and available, we can get to work on the fun stuff: the science of understanding people.

Economics has the most developed mathematical tool set for studying social behavior of any social science. With access to the data described above, researchers have an opportunity to apply those tools to something completely different, and maybe supremely worthwhile. For example, perhaps everyday social interaction (which we can approximate by interactions on the internet, maybe) can be modeled using the tried-and-true machinery of neoclassical microeconomics. Consumers of “reputational” or “social” goods would optimize utility given their (expanded notion of) budget constraints. What would be the arguments to their utility functions? What would prices and income represent in this system? All the quantities would represent something different, but all the relationships would stay the same.

Possible? Sure. Likely? Not really. But when we are harvesting the right data from the internet, we will be able to put these ideas to the test. We will be able to make changes to the theoretical apparatuses we have, develop some we haven’t even imagined, and set those to work helping us better understand human behavior.

I just finished reading The Structure of Scientific Revolutions by T.S. Kuhn, so that now I know what all the fuss was about. The book was rather brilliant, a good read, and a thorough examination (or re-examination) of the whole enterprise of science. Science, Kuhn argues, is not a linear process of knowledge accumulation, but instead exhibits a sort of punctuated equilibrium. Scientific communities adhere to a particular paradigm at a particular point in time, and occasionally shift to a new one. These scientific revolutions occur when cracks appear in the dominant paradigm and another paradigm emerges to try to unseat the first. So, most of the time a scientific community engages in normal science, which is the sort of problem solving we typically think of scientists engaging in, with a commonly agreed-upon paradigm underlying scientific research in a field. Revolutionary science is like when Darwin was all “Yo guys I got this idea about evolution” and then Huxley and Wilberforce started duking it out in the Scopes Trial. (This is more or less how it happened; I am clearly taking some liberties with history here).

The reason I was reading Scientific Revolutions had to do with my conception of economics as a science. A big question I am set on resolving is the nature of the very clear distinction between the natural sciences, on one hand, and the social sciences, on the other. What makes physics or chemistry so much more science-y than economics or — wince — sociology? Now I feel a lot closer to having an answer.

The last chapter in particular was elucidating. Perhaps (along with some methods, mindsets, and other similarities) the common point among the sciences is the homogeneity of their adherence to one particular paradigm. Science seems to progress so linearly because of an almost Orwellian process of rewriting history from the standpoint of the dominant paradigm.  When all a field’s practitioners are trained using one set of textbooks, and after each revolution the textbooks are rewritten, is it any wonder that this adherence of all a field’s scientists to one paradigm is achieved? From the other direction, is it a black mark on macroeconomics to have enough factions, and enough different text books that are not agreed upon, that there is not one dominant paradigm in use that constitutes the field?

More on this to come.

I’m in my first year of the economics PhD program at Georgetown University, and classes started last week. Everyone I talk to says how hard the first year is. Everyone. They are so certain of the time and effort we’ll have to put in, the sixty hours a week, the studying every day in the library. I’m going to be honest and say that I don’t entirely believe them.

Of course it will be tough. Tough is what I signed up for. But economics is not rocket science. And let’s be honest, I’m kind of good at school. At this point in my life I’ve been through a lot of it, and I’ve gotten my ass kicked a fair number of times. I am certainly no n00b.

So you know what? I hope the econ department gives me its worst. I’m ready. I can take it.

What have I been doing with myself this past week? you might ask. Well, I’ve been drafting a proposal for Harvard’s Free Culture Research Workshop 2009. Now that I finished my draft and submitted it, I figured I could give everyone else a peek.

My work on free culture to date has been broad. I wrote my undergraduate thesis on the economics of public copyright licensing, specifically studying Creative Commons licenses and adoption. I was a technology intern at Creative Commons in 2008, and I continue working as a contractor furthering internal metrics work on license adoption and API usage. While at Rensselaer Polytechnic Institute I started a chapter of Students for Free Culture. Currently I am a researcher with the Web Ecology Project out of Cambridge, MA, studying activity on the internet. Additionally, along with my colleague Tim Hwang and others, we are drafting a standard of best practices for ensuring fair dealings in Terms of Services, which we call FriendlyTOS.

I see free culture and the internet as fundamentally dichotomous: the internet is the most effective means of connecting people humanity has yet developed, and the culture that develops when people interact is naturally free. My perspective is that to study free culture, one must necessarily study the internet. Similarly, to understand the internet one must understand what makes for a free culture. Thus my research agenda for studying free culture begins with studying the internet.

My work on the internet has another motivation. Through my work and studies I have felt a common thread: issues of free culture must be expressed more fundamentally and approached from a more essential angle. When I first studied free culture I used the lens of economics, trying to fit issues of copyright licensing, peer production, and personal freedom into models optimizing utility and minimizing cost. We all have our own “home” fields, be they sociology, law, cultural anthropology, philosophy, or computer science. But to study free culture, or to study the internet, one needs to transcend particular fields. A multidisciplinary approach to these topics is a good first approximation. However, I have come to believe that both the internet and free culture more broadly are important enough topics of study that they deserve their own specialized field.

I, along with a group of colleagues, have begun work to chart out a new academic discipline whose focus is a study of the internet. We call this field “web ecology,” emphasizing the interconnected nature of the social and technological systems that comprise the web. I see web ecology as an attempt to do science on the internet in much the same way as environmental science studies our natural environment. Web ecology takes a holistic view of the internet, viewing users and code as associated and dependent elements. It is empirical and experimentally-driven, creating falsifiable theories and models that will be refined or rejected based on observable data.

My interest in web ecology is in building an axiomatic approach to the complex phenomena that comprise the internet and free culture thereon. The time is right for innovative theorists to develop novel, simple, quantitative models that describe activity on the internet. I look to the example the field of economics has set, as I think there is great value in its approach: by rigorously formalizing, even “oversimplifying,” the complex dynamics of markets, economics deduces profound insight from first principles. The basic models of perfect competition and consumer choice theory conveniently summarize the salient features of the objects under study, and provide a stepping stone toward more complicated analyses. I believe the same approach will prove useful in studying the internet.

Next comes the small step from the internet to free culture. Arguments for free culture are prescriptive at their core. As an economist I am wary of moving on to normative statements (“what ought to be”) before positive science (“what is”) has been well-established. Here is how I see this process evolving. First, web ecology will provide foundational science of the internet. From the knowledge and findings of web ecology, policy makers and other interested parties will design policies and incentives to ensure a freer culture. Prescriptive work, like working for a free culture, will inform the direction descriptive research should take, like studying particular classes of online platforms.

Since this conception is somewhat abstract, let me build an example. Perhaps web ecology seeks to understand content production on the internet. It develops a model for the creation of a certain type of content, say a “remix,” and begins exploring different social and technological treatments that increase or decrease the number of remixes produced by an internet platform. Through studies of existing online platforms, and experiments on the same, web ecology can make stronger and more quantitative statements. For example, perhaps web ecologists find that the proportion of anonymous users on an image board is proportional to the amount of remix that happens, and more precise metrics can be related through a measurable coefficient. Then a start-up company that wants to build a platform for the creation of remixes can use the findings of web ecology to design its platform, adding an anonymous user option and encouraging the use of anonymous accounts. Or a government policy maker may decide that remixes should be encouraged, and authors legislation to protect the right to anonymity on the internet.

The three key challenges I see arising from the work laid out above are as follows:

1. Defining the principles and approach for a rigorous study of the internet.

Web ecology must define itself as a solid foundation of knowledge about the internet. This hard work will be taken up by academics and business people with an interest in actually understanding the web, rather than “experts” seeking to sell social media services based on shoddy data and methods.

2. Building a standardized set of tools and models for studying the internet.

Web ecology will adopt the tools and models of other fields when appropriate, and will build its own when no suitable work exists. Many fields will no doubt have a large body of work to contribute. At the same time, web ecology will express these models in a common language and a common framework uniquely suited to study activity on the internet.

My interest is in the interface of economics and the internet, notably building better economic models of hybrid economies and open licensing. The next step for economics is moving away from studying the scarcity of goods and services to more fundamental scarcities: those of time, attention, and reputation. I see this same process occurring in other fields, which will support the work of web ecology.

3. Expressing the tenets of free culture from the axioms of web ecology.

The bits and pieces which make up free culture – things like open licenses, remixes, sharing, and peer production – will be endemic to the models and methods of web ecology from the start. Having a focus on free culture inform the development of descriptive web ecology will be formative and fruitful, for both web ecology and free culture.

If we move in these directions, we will be on our way to building a first-principles approach to the study of free culture.

The economics of slavery is an incredibly interesting topic to me. More accurately, what I imagine “the economics of slavery” entails is very interesting, as I have only begun studying it, and only at an amateur level.

And what got me started thinking about the economics of slavery? Robots. Specifically the movie “I, Robot”. I’m a fan of imagining in vivid detail post-apocalyptic futures, and a robot revolution is up there with a Class 4 zombie outbreak. At the end of that movie, the overtones of Sonny freeing a generation of robot “slaves” got me thinking about the economic distinction between capital and labor (I was taking a class in intermediate macroeconomics at the time).

If we had an entire workforce of humanoid robots doing things that people usually do, what would this make the economy look like? Traditional economics would probably treat the robots as capital. But then your capital is kind of producing labor, and that is weird. The only other time that happens (that I can think of) is slavery.

I still think it’s insane that people used to own people. But, from a strictly analytical perspective, if people are owning people (and not just purchasing their labor from them), doesn’t it make sense to treat owned people as capital? There are similarities, like people tend to depreciate rather quickly if you don’t feed them and give them shelter. I read somewhere that slavery is the only time when capital and labor are equal.

I guess I’m still not convinced that there should be a tight delineation between capital and labor. Need to look into that in the future.

I think a lot about peoples’ preferences and how individuals make decisions to achieve their desired ends. Microeconomic theory touches on these sorts of questions, which I think is why I’m drawn to it. But while micro theory does some good explaining, it doesn’t go the whole way. I don’t just want a descriptive framework that maybe works good enough in most cases. I want some heuristic models that I can apply to my everyday life.

So I’m going to bastardize microeconomics and use it as I see fit. Here goes:

A rational decision maker will be better off the more malleable his preferences are. Imagine a continuum of control over one’s preferences. At one end, individuals are endowed with perfectly stable, static preferences at birth. At the other end, individuals can change their preferences however they see fit, so that only if they starve to death will their utility be less than infinite.

Now, I’m pretty sure standard utility theory presupposes stable preferences. With malleable preferences, you’re essentially optimizing two things at the same time: the mix of goods and services you purchase, and how you feel about the goods and services you purchase. That would probably lead to some pretty ridiculous maths assuming a tractable solution exists.

Behavioral economics has shown us that, no, preferences are not always pre-formed and stable. For a very cool paper on this point, try reading “Tom Sawyer and the construction of value” by Ariely, Lowenstein, and Prelec. I mean, sometimes our preferences are well-defined, like if I know I dislike strawberries. But to me the interesting case is when we are in a new situation and don’t have our preferences defined. For example: Do I like guava? I’m not sure. Allow me to assume that I do. Some other behavioral economics studies have shown that you can actually frame experiences so that you are more inclined to like (or dislike) them.

Obviously if your preferences are malleable enough, you will be happy with pretty much anything you consume. On first face this may seem like a trivial point. But I think the question “What shall I choose to consume to maximize my happiness?” is one step too far. The question instead should be “How shall I best maximize my happiness?”. Utility theory should help answer that question, I think, in as rigorous a manner as possible.

I wonder if I can formalize this idea using a neoclassical utility framework.

I am glad that nowadays I don’t need to download TV shows illegally in order to catch up when I miss an episode. To my knowledge most shows are available streaming online from whoever is lucky enough to own their respective distribution rights. Not only is firing up a web browser easier than torrenting, but I feel good that I’m doing The Right Thing. Studios can make some money from advertising, and the ads are shorter than commercials. And I can watch whenever I want. Nice and easy.

Except studios don’t want to make my life easy. Fox and ABC are not interested in making me happy. I used to be a die-hard fanatic of House and Grey’s Anatomy. I did not miss an episode until my senior year class schedule got really crazy, when the opportunity cost of my time became too much. “I will catch up on these shows later,” I thought. “After all, they are available for free online from the good folks over at Fox and ABC.”

No. Never bank on the goodness of TV studios. They are self-interested profit-maximizing vampires. After I realized that the episodes available for both House and Grey’s were restricted to the first in season five, and the last few of the same season, my dreams of catching up were defenestrated. I guess I’ll have to go back to illegal downloads, or wait until I have Netflix to catch up.

Why would pissing me off be in any studio’s best interest? It can’t be because of technical issues, since the previous episodes must have been taped and posted earlier, but then taken down. The issue must be economic. Studios are trying to get me to substitute TV-over-internets for a more lucrative medium, like TV-over-TV or TV-over-DVD. I hope they did their cost-benefit analysis carefully. Weighing foregone internet ad revenue against 1) the number of people who would then download the shows and 2) the number of people who would buy DVDs or watch it on reruns on TV, I guess they decided that they’d make more money making peoples’ lives difficult. My inclination is that they’re wrong: those watching TV online are disproportionately young, who are probably more likely to download illegally rather than have the money to spend on DVDs.

Well, good for them. As it stands I probably won’t be catching up on House or Grey’s any time soon, which means next season they’ll both have one fewer viewer (hehe, inadvertant rhyming). Pissing off your customers probably isn’t the best business model.