Category Archives: epistemolgy
If we want to understand the plethora of very specific roles computers play in today’s world, the question “What is software?” is inevitable. Many different answers have been articulated from different viewpoints and different positions – creator, user, enterprise, etc. – in the networks of practices that surround digital objects. From a scholarly perspective, the question is often tied to another one, “Where does software come from?”, and is connected to a history of mathematical thought and the will/pressure/need to mechanize calculation. There we learn for example that the term “algorithm” is derived from the name of the Persian mathematician al-Khwārizmī and that in mathematical textbooks from the middle ages, the term algorism is used to denote the basic arithmetic techniques – that we now learn in grammar school – which break down e.g. the calculation of a multiplication with large numbers into a series of smaller operations. We learn first about Pascal, Babbage, and Lady Lovelace and then about Hilbert, Gödel, and Turing, about the calculation of projectile trajectories, about cryptography, the halt-problem, and the lambda calculus. The heroic history of bold pioneers driven by an uncompromising vision continues into the PC (Engelbart, Kay, the Steves, etc.) and Network (Engelbart again, Cerf, Berners-Lee, etc.) eras. These trajectories of successive invention (mixed with a sometimes exaggerated emphasis on elements from the arsenal of “identity politics”, counter-culture, hacker ethos, etc.) are an integral part for answering our twin question, but they are not enough.
A second strand of inquiry has developed in the slipstream of the monumental work by economic historian Alfred Chandler Jr. (The Visible Hand) who placed the birth of computers and software in the flux of larger developments like industrialization (and particularly the emergence of the large scale enterprise in the late 19th century), bureaucratization, (systems) management, and the general history of modern capitalism. The books by James Beniger (The Control Revolution), JoAnne Yates (Control through Communication and more recently Structuring the Information Age), James W. Cortada (most notably The Digital Hand in three Volumes), and others deepened the economic perspective while Paul N. Edwards’ Closed World or Jon Agar’s The Government Machine look more closely at the entanglements between computers and government (bureaucracy). While these works supply a much needed corrective to the heroic accounts mentioned above, they rarely go beyond the 1960s and do not aim at understanding the specifics of computer technology and software beyond their capacity to increase efficiency and control in information-rich settings (I have not yet read Martin Campell-Kelly’s From Airline Reservations to Sonic the Hedgehog, the title is a downer but I’m really curious about the book).
Lev Manovich’s Language of New Media is perhaps the most visible work of a third “school”, where computers (equipped with GUIs) are seen as media born from cinema and other analogue technologies of representation (remember Computers as Theatre?). Clustering around an illustrious theoretical neighborhood populated by McLuhan, Metz, Barthes, and many others, these works used to dominate the “XY studies” landscape of the 90s and early 00s before all the excitement went to Web 2.0, participation, amateur culture, and so on. This last group could be seen as a fourth strand but people like Clay Shirky and Yochai Benkler focus so strongly on discontinuity that the question of historical filiation is simply not relevant to their intellectual project. History is there to be baffled by both present and future.
This list could go on, but I do not want to simply inventory work on computers and software but to make the following point: there is a pronounced difference between the questions “What is software?” and “What is today’s software?”. While the first one is relevant to computational theory, software engineering, analytical philosophy, and (curiously) cognitive science, there is no direct line from universal Turing machines to our particular landscape with the millions of specific programs written every year. Digital technology is so ubiquitous that the history of computing is caught up with nearly every aspect of the development of western societies over the last 150 years. Bureaucratization, mass-communication, globalization, artistic avant-garde movements, transformations in the organization of labor, expert movements in public administrations, big science, library classifications, the emergence of statistics, minority struggles, two world wars and too many smaller conflicts to count, accounting procedures, stock markets and the financial crisis, politics from fascism to participatory democracy,… – all of these elements can be examined in connection with computing, shaping the tools and being shaped by them in return. I am starting to believe that for the humanities scholar or the social scientist the question “What is software?” is only slightly less daunting than “What is culture?” or “What is society?”. One thing seems sure: we can no longer pretend to answer the latter two questions without bumping into the first one. The problem for the author, then, becomes to choose the relevant strands, to untangle the mess.
In my view, there is a case to be made for a closer look at the role the library and information sciences played in the development of contemporary software techniques, most obviously on the Internet, by not exclusively. While Bush’s Memex has perhaps been commented on somewhat beyond its actual relevance, the work done by people such as Eugene Garfield (citation analysis), Calvin M. Mooers (information retrieval), Hans-Peter Luhn (KWIC), Edgar Codd (relational database) or Gerard Salton (the vector space model) from the 1950s on has not been worked on much outside of specialist circles – despite the fact that our current ways of working with information (yes, this includes your Facebook profile, everything Google is doing, cloud computing, mobile applications and all the other cool stuff Wired writes about) have left behind the logic of the library catalog quite some time ago. This is also where today’s software comes from.
Gabriel Tarde is a springwell of interesting – and sometimes positively weird – ideas. In his 1899 article L’opinion et la conversation (reprinted in his 1901 book L’opinion et la foule), the French judge/sociologist makes the following comment:
Il n’y [dans un Etat féodal, BR] avait pas “l’opinion”, mais des milliers d’opinions séparées, sans nul lien continuel entre elles. Ce lien, le livre d’abord, le journal ensuite et avec bien plus d’efficacité, l’ont seuls fourni. La presse périodique a permis de former un agrégat secondaire et très supérieur dont les unités s’associent étroitement sans s’être jamais vues ni connues. De là, des différences importantes, et, entre autre, celles-ci : dans les groupes primaires [des groupes locales basés sur la conversation, BR], les voix ponderantur plutôt que numerantur, tandis que, dans le groupe secondaire et beaucoup plus vaste, où l’on se tient sans se voir, à l’aveugle, les voix ne peuvent être que comptées et non pesées. La presse, à son insu, a donc travaillé à créer la puissance du nombre et à amoindrir celle du caractère, sinon de l’intelligence.
After a quick survey, I haven’t found an English translation anywhere – there might be one in here – so here’s my own (taking some liberties to make it easier to read):
[In a feudal state, BR] there was no “opinion” but thousands of separate opinions, without any steady connection between them. This connection was only delivered by first the book, then, and with greater efficiency, the newspaper. The periodical press allowed for the formation of a secondary and higher-order aggregate whose units associate closely without ever having seen or known each other. Several important differences follow from this, amongst others, this one: in primary groups [local groups based on conversation, BR], voices ponderantur rather than numerantur, while in the secondary and much larger group, where people connect without seeing each other – blind – voices can only be counted and cannot be weighed. The press has thus unknowingly labored towards giving rise to the power of the number and reducing the power of character, if not of intelligence.
Two things are interesting here: first, Lazarsfeld, Berelson, and Gaudet’s classic study from 1945, The People’s Choice, and even more so Lazarsfeld’s canonical Personal Influence (with Elihu Katz, 1955) are seen as a rehabilitation of the significance (for the formation of opinion) of interpersonal communication at a time when media were considered all-powerful brainwashing machines by theorists such as Adorno and Horkheimer (Adorno actually worked with/for Lazarsfeld in the 30ies, where Lazarsfeld tried to force poor Adorno into “measuring culture”, which may have soured the latter to any empirical inquiry, but that’s a story for another time). Tarde’s work on conversation (the first order medium) is theoretically quite sophisticated – floating against the backdrop of Tarde’s theory of imitation as basic mechanism of cultural production – and actually succeeds in thinking together everyday conversation and mass-media without creating any kind of onerous dichotomy. L’opinion et la conversation would merit an inclusion into any history of communication science and it should come as no surprise that Elihu Katz actually published a paper on Tarde in 1999.
Second, the difference between ponderantur (weighing) and numerantur (counting) is at the same time rather self-evident – an object’s weight and it’s number are logically quite different things – and somewhat puzzling: it reminds us that while measurement does indeed create a universe of number where every variable can be compared to any other, the aspects of reality we choose to measure remain connected to a conceptual backdrop that is by itself neither numerical nor mathematical. What Tarde calls “character” is a person’s capacity to influence, to entice imitation, not the size of her social network.
I’m currently working on a software tool that helps studying Twitter and while sifting through the literature I came across this citation from a 2010 paper by Cha et al.:
We describe how we collected the Twitter data and present the characteristics of the top users based on three influence measures: indegree, retweets, and mentions.
Besides the immense problem of defining influence in non trivial terms, I wonder whether many of the studies on (social) networks that pop up all over the place are hoping to weigh but end up counting again. What would it mean, then, to weigh a person’s influence? What kind of concepts would we have to develop and what could be indicators? In our project we use the bit.ly API to look at clickstream referers – if several people post the same link, who succeeds in getting the most people to click it – but this may be yet another count that says little or nothing about how a link will be uses/read/received by a person. But perhaps this is as far as the “hard” data can take us. But is that really a problem? The one thing I love about Tarde is how he can jump from a quantitative worldview to beautiful theoretical speculation and back with a smile on his face…
Over the last year, I have been reading loads of books in and on Information Science, paying special attention to key texts in the (pre)history of the discipline. Fritz Machlup and Una Mansfield’s monumental anthology The Study of Information (Wiley & Sons, 1983) has been a pleasure to read and there are several passages in the foreword that merit a little commentary. I have always wondered why Shannon’s Mathematical Theory of Communication from 1948 has been such a reference point in the discipline I started out in, communication science. Talking about purely technological problems and pumped with formulas than very, very few social science scholars could make sense of, the whole things seems like a misunderstanding. The simplicity and clearness of the schema on page two – which has been built into the canonical sender-receiver model – cannot be the only reason for the exceptional (mostly second or third hand) reception the text has enjoyed. In Machlup & Mansfield’s foreword one can find some strong words on the question of why a work on engineering problems that excludes even the slightest reference to matters of human understanding came to be cited in probably every single introduction to communication science:
“When scholars were chiefly interested in cognitive information, why did they accept a supposedly scientific definition of ‘information apart from meaning’? One possible explanation is the fact that they were impressed by a definition that provided for measurement. To be sure, measurement was needed for the engineering purposes at hand; but how could anybody believe that Shannon’s formula would also measure information in the sense of what one person tells another by word of mouth, in writing, or in print?
We suspect that the failure to find, and perhaps impossibility of finding, any ways of measuring information in this ordinary sense has induced many to accept measurable signal transmission, channel capacity, or selection rate, misnamed amount of information, as a substitute or proxy for information. The impressive slogan, coined by Lord Kelvin, that ‘science is measurement’ has persuaded many researchers who were anxious to qualify as scientists to start measuring things that cannot be measured. As if under a compulsion, they looked for an operational definition of some aspect of communication or information that stipulated quantifiable operations. Shannon’s formula did exactly that; here was something related to information that was objectively measurable. Many users of the definition were smart enough to realize that the proposed measure – perfectly suited for electrical engineering and telecommunication – did not really fit their purposes; but the compulsion to measure was stronger than their courage to admit that they were not operating sensibly.” (p. 52)
For Machlup & Manfield – who, as trained (neoclassical) economists, should not be deemed closet postmodernists – this compulsion to measure is connected to implicit hierarchies in academia where mathematical rationality reigns supreme. A couple of pages further, the authors’ judgment becomes particularly harsh:
“This extension of information theory, as developed for communication engineering, to other quite different fields has been a methodological disaster – though the overenthusiastic extenders did not see it, and some of them, who now know that it was an aberration, still believe that they have learned a great deal from it. In actual fact, the theory of signal transmission or activating impulses has little or nothing to teach that could be extended of applied to human communication, social behavior, or psychology, theoretical or experimental.” (p. 56)
Shannon himself avoided the term “information theory” and his conception of communication obviously had nothing to do with what the term has come to mean in the social sciences and general discourse. But the need to show that the social sciences could be “real” sciences in search of laws formulated in mathematical terms proved stronger than the somewhat obvious epistemological mismatch.
Like many classic texts, Machlup & Manfield’s work offers a critique that is not based on dismissal or handbag relativism but on deep engagement with the complexities of the subject matter and long experience with interdisciplinary work, which, necessarily, makes one bump into unfamiliar concepts, methods, ontological preconceptions, modes of reasoning, vectors of explanation and epistemological urges (what is your knowledge itch? how do you want to scratch it?). The Study of Information is a pleasure to read because it brings together very different fields without proposing some kind of unifying meta-concept or imperialist definition of what science – the quest for knowledge – should look like.
My colleague Theo Röhle and I went to the Computational Turn conference this week. While I would have preferred to hear a bit more on truly digital research methodology (in the fully scientific sense of the word “method”), the day was really quite interesting and the weather unexpectedly gorgeous. Most of the papers are available on the conference site, make sure to have a look. The text I wrote with Theo tried to structure some of the epistemological challenges and problems to take into account when working with digital methods. Here’s a tidbit:
…digital technology is set to change the way scholars work with their material, how they “see” it and interact with it. The question is, now, how well the humanities are prepared for these transformations. If there truly is a paradigm shift on the horizon, we will have to dig deeper into the methodological assumptions that are folded into the new tools. We will need to uncover the concepts and models that have carried over from different disciplines into the programs we employ today…
The question of how mathematics could lay the foundation for a machine that sustains such a wide variety of practices is really quite well understood from the point of view of the mathematical theory of computation. From a humanities standpoint however, despite the number of texts commenting on the genius of key figures such as Gödel, Turing, Shannon, and Church, there is still a certain awkwardness when it comes to situating the key steps in mathematical reasoning that lead up to the birth of the computer in the larger context of mathematics itself. One of the questions I find really quite interesting is the role of the formalist stance in mathematics.
In the philosophy of mathematics, there are many different positions. The realist stance for example holds that mathematical objects exist. For the platonist, they exist in some kind of extra spatio-temporal realm of ideas. For the physicalist, they are intrinsically connected to material existence, even if that relationship is not necessarily simple. Then there is formalism and this is where things get interesting. In a tale we can read in many social sciences and humanities books on the computer, there is the young Kurt Gödel that smashes the coherent world of the “establishment” mathematician David Hilbert, inventing the metamathematical tools that will later prove essential for the practical realization of computing machinery in the process. What is most often overlooked in that story is that Hilbert’s formalist position is already an extremely important step in the preparation for what is to come. For Hilbert, the question of the ontological status of mathematical objects is already a no-go – truth is no longer defined via any kind of correspondence to an external system but as a function of the internal coherence of the symbolic system. As Bettina Heintz says, Hilbert’s work rendered mathematical concepts “self-sufficient” (autark) by liberating them from any kind of external benchmark and opening a purely mechanical world where symbolic machinery can be built at will, like in a game.
If we want to think about computing today, I think we should remember this break from an ontological concept of truth to a purely formalistic one (even if that mean Gödel put a pretty big crack in it lateron). Because in a way, programming is like a “game” with formulas and if the algorithm works, that means it is “true”. In this sense, Google’s PageRank algorithm is true. But without the reference to an external system, this “truth” is purely mechanical, internal. In a similar way, an algorithm’s claim to objectivity, impartiality, or neutrality should be seen as internal only. The moment we apply mathematics to the description of some external mechanism (gravity, for example), there is a second truth criterion that intervenes, which refers to the establishment of correspondence between the formal system and the external reality. In the same way, if an algorithm is applied to, let’s say the filtering of information, the formal world of the game is mapped onto another world. There is an important difference however. When mathematics are applied to physical phenomena, the gesture is descriptive and epistemological (verb: is). When an algorithms is applied to tasks such as information filtering, the gesture is prescriptive and political (verb: ought).
The fact than an automatic procedure works makes it true in a formal sense. The moment we apply it to a certain task, other criteria intervene. Hilbert’s formalism pulled mathematics from the empirical world and if we bring the two together again by writing software, the criteria by which we judge the quality of that action should be seen as political because there are no mathematical criteria to judge the mapping of on world onto the other. No Hilbert to hold our hand…
Over the last couple of years, the social sciences have been increasingly interested in using computer-based tools to analyze the complexity of the social ant farm that is the Web. Issuecrawler was one of the first of such tools and today researchers are indeed using very sophisticated pieces of software to “see” the Web. Sciences-Po, one of these rather strange french institutions that were founded to educate the elite but which now have to increasingly justify their existence by producing research, has recently hired Bruno Latour to head their new médialab, which will most probably head into that very direction. Given Latour’s background (and the fact that Paul Girard, a very competent former colleague at my lab, heads the R&D departement), this should be really very interesting. I do hope that there will be occasion to tackle the most compelling methodological question when in comes to the application of computers (or mathematics in general) to analyzing human life, which is beautifully framed in a rather reluctant statement from 1889 by Karl Pearson, a major figure in the history of statistics:
“Personally I ought to say that there is, in my own opinion, considerable danger in applying the methods of exact science to problems in descriptive science, whether they be problems of heredity or of political economy; the grace and logical accuracy of the mathematical processes are apt to so fascinate the descriptive scientist that he seeks for sociological hypotheses which fit his mathematical reasoning and this without first ascertaining whether the basis of his hypotheses is as broad as that human life to which the theory is to be applied.” cit. in. Stigler, Stephen M.: The History of Statistics. Harvard University Press, 1990 p. 304
This spring worked on an R&D project that was really quite interesting but – as it happens with projects – took up nearly all of my spare time. La montre verte is based on the idea that pollution measurement can be brought down to street level if sensors can be made small enough to be carried around by citizens. Together with a series of partners from the private sector, the CiTu group of my laboratory came up with the idea to put an ozone sensor and a microphone (to measure noise levels) into a watch. That way, the device is not very intrusive and still in direct contact with the surrounding air. We built about 15 prototypes, based on the fact that currently, Paris’ air quality is measured by only a handful of (really high quality) sensors and even the low resolution devices we have in our watches should therefore be able to complement that data with a geographically more fine grained analysis of noise and pollution levels. The watch produces a georeferenced measurement (a GPS is built into the watch) every second and transmits the data via Bluetooth to a Java application on a portable phone, which then sends every data packet via GPRS to a database server.
My job in the project was to build a Web application that allows people to interact with and make sense of the data produced by the watches. Despite the help from several brilliant students from our professional Masters program, this proved to be a daunting task and I spent *at lot* of time programming. The result is quite OK I believe; the application allows users to explore the data (which is organized in localized “experiments”) in different ways, either in real-time or afterward. With a little more time (we had only about three month for the whole project and we got the hardware only days before the first public showcase) we could have done more but I’m still quite content with the result. Especially the heatmap (see image) algorithm was fun to program, I’ve never done a lot of visual stuff so this was new territory and a steep learning curve.
Unfortunately, the strong emphasis on the technological side and the various problems we had (the agile methods one needs for experimental projects are still not understood by many companies) cut down the time for reflection to a minimum and did not allow us to come up with a deeper analysis of the social and political dimensions of what could be called “distributed urban intelligence”. The whole project is embedded in a somewhat naive rhetoric of citizen participation and the idea that technological innovation can solve social problems, in this case matters of urban planning and local governance. A lesson I have learned from this is that the current emphasis in funding on short-term projects that bring together universities and the industry makes it very difficult to carve out an actual space for scientific practice between all the deadlines and the heavy technical demands. And by scientific practice, I mean a *critical* practice that does not only try to base specifications and prototyping on “scientifically valid” approaches to building tools and objects but which includes a reflection on social utility that takes a wider view than just immediate usefulness. In the context of this project, this would have implied a close look at how urban development is currently configured in respect to environmental concerns in order to identify structures of governance and chains of decision-making. This way, the whole project could have targeted issues more clearly and consciously, fine-tuning both the tools and the accompanying discourse to the social dimension it aimed at.
I think my point is that we (at least I) have to learn how to better include a humanities-based research agenda into very high-tech projects. We have known for a long time now that every technical project is in fact a socio-technical enterprise but research funding and the project proposals that it generates are still pretending that the “socio-” part is some fluffy coating that decorates the manly material core where cogs and wire produce tangible effects. As I programmer I know how difficult and time-consuming technical work can be but if there is to be a conscious socio-technical perspective in R&D we have to accept that the fluffy stuff takes even more time – if it is done right. And to do it right means not only reading every book and paper relevant to a subject matter but to take the time to reflect on methodology, to evaluate every step critically, to go back to the drawing board, and to include and to produce theory every step of the way. There is a cost to the scientific method and if that cost is not figured in, the result may still be useful, interesting, thought-provoking, etc. but it will not be truly scientific. I believe that we should defend these costs and show why they are necessary; if we cannot do so, we risk confining the humanities to liberal armchair commentary and the social sciences to ex-post usage analysis.
When talking about the politics of the social Web and particularly online networking, the first issue coming up is invariably the question of privacy and its counterpart, surveillance – big brother, corporations bent on world dominance, and so on. My gut reaction has always been “yeah, but there’s a lot more to it than that” and on this blog (and hopefully a book in a not so far future) I’ve been trying to sort out some of the political issues that do not pertain to surveillance. For me, social networking platforms are more relevant to politics as marketing rather than surveillance. Not that these tools cannot function quite formidably to spy on people, but it is my impression that contemporary governance relies on other principles more than the gathering of intelligence about individual citizens (although it does, too). But I’ve never been very pleased with most of the conceptualizations of “post-disciplinarian” mechanisms of power, even Deleuze’s Post-scriptum sur les sociétés de contrôle, although full of remarkable leads, does not provide a fleshed-out theoretical tool – and it does not fit well with recent developments in the Internet domain.
But then, a couple of days ago I finally started to read the lectures Foucault gave at the Collège de France between 1971 and 1984. In the 1977-1978 term the topic of that class was “Sécurité, Territoire, Population” (STP, Gallimard, 2004) and it holds, in my view, the key to a quite different perspective on how social networking platforms can be thought of as tools of governance involved in specific mechanisms of power.
STP can be seen as both an extension and reevaluation of Foucault’s earlier work on the transition from punishment to discipline as central form in the exercise of power, around the end of the 18th century. The establishing of “good practice” is central to the notion of discipline and disciplinary settings such as schools, prisons or hospitals serve most of all as means for instilling these “good practices” into their subjects. Jeremy Bentham’s Panopticon – a prison architecture that allows a single guard to observe a large population of inmates from a central control point – has in a sense become the metaphor for a technology of power that, in Foucault’s view, is part of a much more complex arrangement of how sovereignty can be performed. Many a blogpost has been dedicated to applying the concept on social networking online.
Curiously though, in STP, Foucault calls the Panopticon both modern and archaic, and he goes as far as dismissing it as the defining element of the modern mechanics of power; in fact, the whole course is organized around the introduction of a third logic of governance besides (and historically following) “punishment” and “discipline”, which he calls “security”. This third regime is no longer focusing on the individual as subject that has to be punished or disciplined but on a new entity, a statistical representation of all individuals, namely the population. The logic of security, in a sense, gives up on the idea of producing a perfect status quo by reforming individuals and begins to focus on the management on averages, acceptable margins, and homeostasis. With the development of the social sciences, society is perceived as a “natural” phenomenon in the sense that it has its own rules and mechanisms that cannot be so easily bent into shape by disciplinary reform of the individual. Contemporary mechanisms of power are, then, not so much based on the formatting of individuals according to good practices but rather on the management of the many subsystems (economy, technology, public health, etc.) that affect a population so that this population will refrain from starting a revolution. Foucault actually comes pretty close to what Ulrich Beck’s will call, eight years later, the Risk Society. The sovereign (Foucault speaks increasingly of “government”) assures its political survival no longer primarily through punishment and discipline but by managing risk by means of scientific arrangements of security. This not only means external risk, but also risk produced by imbalance in the corps social itself.
I would argue that this opens another way of thinking about social networking platforms in political terms. First, we would look at something like Facebook in terms of population not in terms of the individual. I would argue that governmental structures and commercial companies are only in rare cases interested in the doings of individuals – their business is with statistical representations of populations because this is the level contemporary mechanisms of power (governance as opinion management, market intelligence, cultural industries, etc.) preferably operate on. And second – and this really is a very nasty challenge indeed – we would probably have to give up on locating power in specific subsystems (say, information and communication systems) and trace the interplay between the many different layers that compose contemporary society.
The concept of self-organization has recently made quite a comeback and I find myself making a habit of criticizing it. Quite generally I use this blog to sort things out in my head by writing about them and this is an itch that needs scratching. Fortunately, political scientist Steven Weber, in his really remarkable book The Success of Open Source, has already done all the work. On page 132 he writes:
Self-organization is used too often as a placeholder for an unspecified mechanism. The term becomes a euphemism for “I don’t really understand the mechanism that holds the system together.” That is the political equivalent of cosmological dark matter.
This seems really right on target: self-organization is really quite often just a means to negate organizing principles in the absence of an easily identifiable organizing institution. By speaking of self-organization we can skip closer examination and avoid the slow and difficult process of understanding complex phenomena. Webers second point is perhaps even more important in the current debate about Web 2.0:
Self-organization often evokes an optimistically tinged “state of nature” narrative, a story about the good way things would evolve if the “meddling” hands of corporations and lawyers and governments and bureaucracies would just stay away.
I would go even further and argue that especially the digerati philosophy pushed by Wired Magazine equates self-organization with freedom and democracy. Much of the current thinking about Web 2.0 seems to be quite strongly infused by this mindset. But I believe that there is a double fallacy:
- Much of what is happening on the Social Web is not self-organization in the sense that governance is the result of pure micro-negotiations between agents; technological platforms lay the ground for and shape social and cultural processes that are most certainly less evident than the organizational structures of the classic firm but nonetheless mechanisms that can be described and explained.
- Democracy as a form of governance is really quite dependent on strong organizational principles and the more participative a system becomes, the more complicated it gets. Organizational principles do not need to be institutional in the sense of the different bodies of government; they can be embedded in procedures, protocols or even tacit norms. A code repository like SourceForge.net is quite a complicated system and much of the organizational labor in Open Source is delegated to this and other platforms – coordinating the work effort between that many people would be impossible without it.
My guess is that the concept of self-organization as “state of nature” narrative (nature = good) is much too often used to justify modes of organization that would imply a shift power from traditional institutions of governance to the technological elite (the readers and editors of Wired Magazine). Researchers should therefore be weary of the term and whenever it comes up take an even closer look at the actual mechanisms at work. Self-organization is an explanandum (something that needs to be explainend) and not an explanans (an explanation). This is why I find network science really very interesting. Growth mecanism like preferential attachment allow us to give an analytical content to the placeholder that is “self-organization” and examine, albeit on a very abstract level, the ways in which dynamic systems organize (and distribute power) without central control.
This morning Jonah Bossewitch pointed me to an article over at Wired, authored by Chris Anderson which announces “The End of Theory”. The article’s main argument in itself is not very interesting for anybody with a knack for epistemology – Anderson has apparently never heard of the induction / deduction discussion and a limited idea about what statistics does – but there is a very interesting question lurking somewhere behind all the Californian Ideology and the following citation points right to it:
We can stop looking for models. We can analyze the data without hypotheses about what it might show. We can throw the numbers into the biggest computing clusters the world has ever seen and let statistical algorithms find patterns where science cannot.
One could point to the fact that the natural sciences had their experimental side for quite a while (Roger Bacon advocated his scientia experimentalis in the 13th century) and that a laboratory is in a sense a pattern-finding machine where induction continuously plays an important role. What interests me more though is Anderson’s insinuation that statistical algorithms are not models. Let’s just look at one of the examples he uses:
Google’s founding philosophy is that we don’t know why this page is better than that one: If the statistics of incoming links say it is, that’s good enough. No semantic or causal analysis is required.
This is a very limited understanding of what constitutes a model. I would argue that PageRank does in fact rely very explicitly on a model which combines several layers of justification. In their seminal paper on Google, Brin and Page write the following:
PageRank can be thought of as a model of user behavior. We assume there is a “random surfer” who is given a web page at random and keeps clicking on links, never hitting “back” but eventually gets bored and starts on another random page. The probability that the random surfer visits a page is its PageRank.
The assumption behind this graph oriented justification is that people do not randomly place links but they do so with purpose. Linking implies attribution of importance: we don’t link to documents that we’re indifferent about. The statistical exploration of the huge graph that is the Web is indeed oriented by this basic assumption and adds the quite contestable ruling according to which shall be most visible what is thought important by the greatest number of linkers. I would, then, argue that there is no experimental method that is purely inductive, not even neural networks. Sure, on the mathematical side we can explore data without limitations concerning their dimensionality, i.e. the number of characteristics that can be taken into account; the method of gathering data is however always a process of selection that is influenced by some idea or intuition that at least implicitly has the characteristic of a model. There is a deductive side to even the most inductive approach. Data is made not given and every projection of that data is oriented. To quote Fernando Pereira:
[W]ithout well-chosen constraints — from scientific theories — all that number crunching will just memorize the experimental data.
As Jonah points out, Anderson’s article is probably a straw man argument whose sole purpose is to attract attention but it points to something that is really important: too many people think that mathematical methods for knowledge discovery (datamining that is) are neutral and objective tools that will find what’s really there and show the world as it is without the stain of human intentionality; these algorithms are therefore not seen as objects of political inquiry. In this view statistics is all about counting facts and only higher layers of abstraction (models, theories,…) can have a political dimension. But it matters what we count and how we count.
In the end, Anderson’s piece is little more than the habitual prostration before the altar of emergence and self-organization. Just exchange the invisible hand for the invisible brain and you’ll get pop epistemology for hive minds…