Constrained Bliss Point

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 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.

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.

A few weeks ago I got involved in a fledgling research group in Boston. We’re doing research on the internets. But this isn’t just any lame internet research! No no. See, too many times you get people drawing conclusions about a social network like they know what’s what, but really they’re just making things up. “Well I use Facebook sometimes and I read some of my friends’ profiles so clearly I can pontificate about broad generalizations with no data to back up my claims.”

Friends, that isn’t science. Science is about testing hypotheses with empirical data. And who has the data? We have the data. Let me introduce the Web Ecology Project.  This project is, among other things, about basing qualitative analysis on firm quantitative footing. Check out our first report on the election in Iran to see some real Twitter research.

Already we’re getting some sweet press. It’s only going to get more awesome from here, folks. Stay tuned.