Constrained Bliss Point

Memes are little replicators, kind of like genes for culture (or ideas or general mind-things). Memes have a few properties, like how transmittable they are, or how “attractive” they are to the mind they occupy. Like that song you just can’t get out of your head, some memes are maddeningly sticky. For reasons unclear to me, one meme occupying my brain is bent on expressing itself through the medium of the blagoweb. You’ve been warned.

I’ve had Lady Gaga stuck in my head all week. Frakking Poker Face. It started innocently enough, I was watching South Park and Cartman was Rock-Banding it up. He was hilarious enough that I had to watch the clip a few more times on the YouTube. By that time, it was probably already too late.

This song is addicting on a dangerous level. I think there are at least three parts that are independently catchy — basically all the repeating parts: the opening mah-mah-mah-mah, the repetition of po-po-po-poker face, and the can’t-read-my can’t-read-my part, for those of you keeping track at home. Now, I wasn’t planning on writing about Lady Gaga. I don’t know firsthand how good her albums are. I won’t comment on whether I think all her hype is justified. I just know that forces acting beyond my control are propagating this absurdity over the intertubes.

The meme made me do it.

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 was lucky enough to have my essay submission accepted to Harvard’s Free Culture Research Workshop 2009, so it looks like I’ll be in Boston on October 23rd. I revised the draft based on the comments of some very fabulous reviewers, and here is what I came up with.

Normative statements – statements of what ought to be – comprise much of the basis of the free culture movement. As an economist, I am wary of jumping to the normative before the establishment of positive science – knowledge about what is. Thus my research on free culture is in developing a foundational, quantitative, descriptive science for the field. I believe that free culture research, and free culture activism more broadly, can benefit greatly from the application of quantitative methods to understanding social systems, for example, in learning to design systems that promote a freer culture thereon. I propose that the new field of web ecology can provide a descriptive science for free culture. In the same way civil engineers design bridges with knowledge derived from physical sciences, so too could free culture engineers design social systems that promote the ends of the free culture movement with knowledge provided by web ecology.

First I will describe some current limitations I see the free culture movement facing. Then I will provide an introduction to the new field of web ecology, whose goal is to develop a foundational science of social activity on the internet. Last I discuss how a web ecology perspective can apply to free culture research.

As it stands the free culture movement knows much more about where it wants to go than about the best way to get there. Open licensing, open access, and open education are all strong, prescriptive goals. But right now the hard work is in implementing those goals. What is the best way to foster broader adoption of open licensing for artistic works? What is the most effective approach to encouraging the creation and reuse of open education resources around the world? How can we design social networks so that they encourage a read-write culture, instead of a read-only culture? To be sure, these are difficult questions. I think what is lacking is a body of quantitative research to develop more precise answers. In free culture research there is more argument about best approaches and less empirical basis for these claims.

For example, at the end of Lessig’s Free Culture, he laid out an argument for why a standardized licensing system is a good idea and would further the principles of free culture. It was not based on science, nor did it claim to be. And while the argument was undeniably strong, I think it could have been even stronger if it had a quantitative empirical basis. I believe this descriptive foundation can be provided by a new field called web ecology, which I will now describe.

A group of colleagues and I have begun work on an academic discipline whose focus is a study of activity on the internet. The name “web ecology” emphasizes 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, seeing 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 particular interest in web ecology is in its axiomatic approach to the complex phenomena that comprise the internet. There is opportunity for innovative theorists to develop novel, simple, quantitative models that will comprise a scientific basis for future free culture research and activism. I look to the example the field of economics has set, as I think there is significant value in its approach: by rigorously formalizing, even “oversimplifying,” the complexity of the market system, 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 broader social and cultural activity, especially when applied to the rich social systems and associated quantitative data available for study on the internet.

Since this conception is somewhat abstract, let me build an example. Perhaps web ecology seeks to understand the creation of content 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. A number of studies, both experimental and observational, are undertaken; researchers systematically attribute the creation of remixes to certain treatments, and are able to make quantitative statements about these relationships. For example, perhaps a group of web ecologists finds that the proportion of anonymous users on an image board is directly proportional to the amount of remixes that are created. Furthermore, they precisely measure a coefficient that relates the proportion of anonymous users to the creation of remixes, holding other things equal. Then people who want to promote free culture by encouraging remixes can leverage this descriptive knowledge to achieve their ends. Perhaps a start-up company wants to build a platform for the creation of remixes, and it uses the findings of web ecology in its design, adding an anonymous user option and encouraging the use of anonymous accounts. Or perhaps a government policy maker decides that the act of remixing 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 field of web ecology.

Web ecology must define itself as a solid foundation of knowledge for use in free culture research and activism. This hard work will be taken up by academics and business people with an interest in actually understanding the internet’s social dynamics, rather than so-called “experts” seeking to sell social media services based on shoddy data and methods.

2. Building a standardized set of tools and models for web ecology.

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 that will uniquely benefit the free culture movement.

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 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.

I think my generation will see a robot war in our lifetimes. I have made a 50-year bet with Tim Hwang that the singularity will come to be (the winner receives one barrel of oil come 2058).

What if the intelligence is born in the network, but it just can’t communicate? What if the consciousness already exists, but will spend decades learning how to flip bits appropriately to communicate with humanity? What if she’s out there right now, understanding everything we’re saying, but she can’t respond?

Terrifying. I hope this blog post doesn’t piss her off.

Nowadays, virtually all reasonable people will admit that global warming is real. It’s true that the world is not always warmer — for that reason a number of cool kids are calling it “climate change” instead of “global warming” — but one thing is certain: the times they are a-changin’.

I don’t much care whose fault it is, whether natural or anthropogenic. I do think we should do something about it, not least of all because the long-term survival of our species and civilization is at stake. “But what can I do to stop such a monstrous force as manbearpig global warming?” you might ask.

There is actually one very simple thing you can do that would go further than any other single step to fight climate change. Stop eating cows. Seriously. The UN put out a nice report to this end. Cattle ranching produces more greenhouse gas than cars, or even all other forms of transportation put together. It also drives deforestation and consumes massive amounts of water.

Do your part. Try not to eat meat. Tell your friends and family of the folly of cows. And when you have to eat meat, please, for the love of God, eat chickens.

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.

Biases should be avoided if one is trying to do objective science. Some biases are relatively easy to see, like race or sex discrimination. Others are harder to combat, or even to identify. On a related note, Wikipedia has a pretty comprehensive list of cognitive biases if anyone is interested.

I’ve found one that’s very, very sneaky. I call it “measurability bias”, which I’m pretty sure I read somewhere but now I forget the exact reference. It comprises a few similar phenomena. For one, it is a tendency for researchers — notably those who pride themselves as scientists — to spend more time and resources focusing on problems where data are readily available, rather than what they identify as the most interesting or important problems.

A more general conception of measurability bias is when a decision maker weights more heavily the set of things that are quantitatively or accurately measurable when making decisions. For example, when Oreos increase in price by 20%, that’s very easy to see, but I’m not as good at noticing if the increase in quality leads to a greater-than-20% increase in the satisfaction I derive from said Oreos.

Cost-benefit analyses suffer from this bias a great deal. For example, in the case of climate change, the costs of carbon reduction programs are known relatively accurately, whereas things like the mitigated risk from rising sea levels, loss in biodiversity, chronic water scarcities in developing countries, etc. are much harder to measure. In this case, measurability bias is used as an excuse to do nothing — since the costs are large and the benefits are uncertain, we should defer climate stabilization policies.

The point to take away is that just because something is difficult to quantify does not mean it is not incredibly important.

As hard as it is to do science on one’s self, I’m confident in the hypothesis that my weight and meat consumption are inversely proportional. I was pescetarian for around fifteen months back in college — or as I described it, I just didn’t eat meat. Though I constantly changed my reasons behind my pescetarianism, I was intent on the conclusion that I should not eat meat, and I enjoyed the lifestyle. It was certainly difficult, both at first and throughout. I gave it up for no apparent reason, and then went pescetarian again for the summer I spent in San Francisco.

I do miss not eating meat. There is some amount of deep contentment through self-control and self-denial. The community is nice, too; you become part of a special group where the only shibboleth is your diet. And there are a lot of really strong reasons not to eat meat. My favorites were the inefficiency of converting calories up the food chain, the various sorts of environmental damage that factory meat production entails, and an argument that a society which treat its animals better is also one which treats its people better.

But right now I’m focusing on the dieting aspect. I decided I need to lose some weight. I’m also pretty lazy, and have been struggling with enacting a consistent exercise regimen for months. And since the easiest way to lose weight is to restrict your caloric intake, it’s pretty much win-win. Now I just need to get to the point where I can make that commitment not to eat meat anymore.

Or I guess I could just not eat meat as much, but that’s actually just as hard. I’m pretty bad at self control when I don’t have clearly-delineated monolithic rules for my own decision making.

I want to see myself as a scientist. Doing science is maybe one of the most awesome activities possible. It’s like, hey, we could keep talking in circles like idiots, or we can actually go figure things out. The scientific method is likely mankind’s greatest innovation.

But what if I want to figure things out in the very important realm of human relationships — specifically the romantic kind? Tough luck. I mean, first of all, how can you do science on relationships? There are too few data points! Even if you are Mr. Playboy and date, say, one new girl per week, it would be nearly seven months before you could use the OLS large sample assumption. And that’s no good, because then you’d just be testing something about short relationships — probably not the kind you’re interested in studying.

There is no control group: you can’t clone yourself to see how you would act under two different relationships. Besides, you’re not a static person either. Each relationship you’re in changes who you are to a great extent. Even if you determined some profound result, it would probably only be valid on a past self.

You would need to use a between-subjects design. It would be virtually impossible to organize two groups of people in relationships and keep the treatments the same. How would you control for all the possible differences in peoples’ personalities and relationship dynamics?

No, doing science on relationships is all but impossble. We’ll probably have to settle for psychology or something. (See what I did there? Thinly-guised psychology insults are the first thing a budding economist learns.)

As a child I wondered where units came from — physical units, like amps or pounds or furlongs. I didn’t know what a volt was, and I had this desire to come up with another unit. For that, I needed a quantity that needs to be quantified, and soon discovered that all the good ones are taken.

All the easy ones, that is. But what about qualitative units, the ones that are hard or impossible to quantify? Like love, or happiness? I don’t know how to do it. I got this idea from a book I read: “Great Feuds in Science,” where the author suggests naming the unit of optimism a Leibniz. Because, I guess, Gottfried Leibniz was a really optimistic guy (and with a name like Gottfried, who wouldn’t be?).

So how do we attack the problem of qualitative units? Observability is one problem: one meter is the same length in any reasonable inertial reference frame, but one man’s happiness cannot be easily observed. But subjectivity looks like the biggest stumbling block. A unit of pleasure or pain has to mean the same thing to all people.

Here is my first proposed solution: massively better measuring techniques. And cyborg-quality computer chips in everyone’s head. Scientists first need to become experts at figuring out which brain chemicals make us happy, sad, angry, etc. Then they need to set up a scale based on relative proportions of brain chemicals or something. At last we’ll be able to make statements like “oh man, I am sixteen Leibniz’s optimistic about the future right now!”

I can’t wait.