Category Archives: economy

Yesterday, Google introduced a new feature, which represents a substantial extension to how their search engine presents information and marks a significant departure from some of the principles that have underpinned their conceptual and technological approach since 1998. The “knowledge graph” basically adds a layer to the search engine that is based on formal knowledge modelling rather than word statistics (relevance measures) and link analysis (authority measures). As the title of the post on Google’s search blog aptly points out, the new features work by searching “things not strings”, because what they call the knowledge graph is simply a – very large – ontology, a formal description of objects in the world. Unfortunately, the roll-out is progressive and I have not yet been able to access the new features, but the descriptions, pictures, and video paint a rather clear picture of what product manager Johanna Wright calls the move “from an information engine to a knowledge engine”. In terms of the DIKW model (Data-Information-Knowledge-Wisdom), the new feature proposes to move up a layer by adding a box of factual information on a recognized object (the examples Google uses are the Taj Mahal, Marie Curie, Matt Groening, etc.) next to the search results. From the presentation, we can gather that the 500 million objects already referenced will include a large variety of things, such as movies, events, organizations, ideas, and so on.

This is really a very significant extension to the current logic and although we’ll need more time to try things out and get a better understanding of what this actually means, there are a couple of things that we can already single out:

  • On a feature level, the fact box brings Google closer to “knowledge engines” such as Wolfram Alpha and as we learn from the explanatory video, this explicitly includes semantic or computational queries, such as “how many women won the Nobel Prize?” type of questions.
  • If we consider Wikipedia to be a similar “description layer”, the fact box can also be seen as a competitor to everybody’s favorite encyclopedia, which is a further step into the direction of bringing information directly to the surface of the results page instead of simply referring to a location. This means that users do not have to leave the Google garden to find a quick answer. It will be interesting to see whether this will actually show up in Wikipedia traffic stats.
  • The introduction of an ontology layer is a significant departure from the largely statistical and graph theoretical methods favored by Google in the past. While features based on knowledge modelling have proliferated around the margins (e.g. in Google Maps and Local Search), the company is now bringing them to the center stage. From what I understand, the selection of “facts” to display will be largely driven by user statistics but the facts themselves come from places like Freebase, which Google bought in 2010. While large scale ontologies were prohibitive in the past, a combination of the availability of crowd-sourced databases (Wikipedia, etc.), the open data movement, better knowledge extraction mechanisms, and simply the resources to hire people to do manual repairs has apparently made them a viable option for a company of Google’s size.
  • Competing with the dominant search engine has just become a lot harder (again). If users like the new feature, the threshold for market entry moves up because this is not a trivial technical gimmick that can be easily replicated.
  • The knowledge graph will most certainly spread out into many other services (it’s already implemented in the new Google Docs research bar), further boosting the company’s economies of scale and enhancing cross-navigation between the different services.
  • If the fact box – and the features that may follow – becomes a pervasive and popular feature, Google’s participation in making information and knowledge accessible, in defining its shape, scope, and relevance, will be further extended. This is a reason to worry a bit more, not because the Google tools as such are a danger, but simply because of the levels of institutional and economic concentration the Internet has enabled. The company has become what Michel Callon calls an “obligatory passage point” in our relation to the Web and beyond; the knowledge graph has the potential to exacerbate the situation even further.

This is a development that looks like another element in the war for dominance on the Web that is currently fought at a frenetic pace. Since the introduction of actions into Facebook’s social graph, it has become clear that approaches based on ontologies and concept modelling will play an increasing role in this. In a world mediated by screens, the technological control of meaning – the one true metamedium – is the new battleground. I guess that this is not what Berners-Lee had in mind for the Semantic Web…

Google is an interesting company and not only because it has superbig data centers and mighty algorithms. It is also interesting because it controls a pretty big pie of the Internet advertisement market and uses a fascinating auction system to sell ad space. Need traffic? You can either SEO your site to the max or just buy some advertisement. Google apparently has good prices and good results. At least Microsoft seems to think that:  Yes, this is an ad for Bing in the first line. What is even more wondrous though is why Google would advertise (right column, third from the top) on the query “search engines” on their own site. AdWords must be very effective indeed.

Over the last couple of weeks, things have heated up considerably for Google – on the mobile side with the start of a patent war, but also in the search area, the core of the company’s business. Led by Senator Mike Lee (a Utah Republican), the US Senate’s Antitrust Subcommittee has started to probe into certain aspects of Google’s ranking mechanisms and potential cases of abuse and manipulation.

In a hearing on Wednesday, Lee confronted Eric Schmidt with accusations of tampering with results and the evidence the Senator presented was in fact very interesting because it raises the question of how to show or even prove that a highly complex algorithmic procedure “has been tampered with”. As you can see in this video, a scatter-plot from an “independent study” that compares the search ranking for three price comparison sites (Nextag, Pricegrabber, and Shopper) with Google Price Search using 650 shopping related queries. What we can see on the graph is that while there is considerable variation in ranking for the competitors (a site shows up first for one query and way down for another), Google’s site seems to consistently stick to place three. Lee makes this astounding difference the core of his argument and directly asks Schmidt: “These results are in fact the result of the same algorithm as the rankings for the other comparison sites?” The answer is interesting in itself as Schmidt argues that Google’s service is not a product comparison site but a “product site” and that the study basically compares apples to oranges (“they are different animals”). Lee then homes in on the “uncanny” statistical regularity and says “I don’t know whether you call this a separate algorithm or whether you’re reverse engineered a single algorithm, but either way, you’ve cooked it!” to which Schmidt replies “I can assure you that we haven’t cooked anything.”

According to this LA Times article, Schmidt’s testimony did not satisfy the senators and there’s open talk about bias and conflict of interest. I would like to add to add three things here:

1) The debate shows a real mismatch between 20th century concepts of both bias and technology and the 21st century challenge to both of these question that comes in the form of Google. For the senator, bias is something very blatant and obvious, a malicious individual going to the server room at night, tempering with the machinery, transforming the pure technological objectivity into travesty by inserting a line of code that puts Google to third place most of the time. The problem with this view is of course that it makes a clear and strong distinction between a “biased” and an “unbiased” algorithm and clearly misses the point that every ranking procedure implies a bias. If Schmidt says “We haven’t cooked anything!”, who has written the algorithm? If it comes to an audit of Google’s code, I am certain that no “smoking gun” in the form of a primitive and obvious “manipulation” will be found. If Google wants to favor its own services, there are much more subtle and efficient ways to do so – the company does have the best SEO team one could possibly imagine after all. There is simply no need to “cook” anything if you are the one who specifies the features of the algorithm.

2) The research method applied in the mentioned study however is really quite interesting and I am curious to see how far the Senate committee will be able to take the argument. The statistical regularity shown is certainly astounding and if the hearings attain a deeper level of technological expertise, Google may be forced to detail a significant portion of its ranking procedures to show how something like this can happen. It would, of course, be extremely simple to break the pattern by introducing some random element that does not affect the average rank but adds variation. That’s also the reason why I think that Lee’s argument will ultimately fizzle.

3) The core of the problem, I would argue, is not so much the question of manipulation but the fact that by branching into more and more commercial areas, Google finds itself in a market configuration where conflicts of interest are popping up everywhere they turn. As both a search business and an actor on many of the markets that are, at least in part, ordered by the visibility layering in search results, there is a fundamental and structural problem that cannot be solved by any kind of imagined technical neutrality. Even if there is no “in house SEO” going on, the mere fact that Google search prominently links to other company services could already be seen as problematic. In a sense, Senator Lee’s argument actually creates a potentially useful “way out”: if there is no evil line of code written in the dark of night, no “smoking gun”, then everything is fine. The systematic conflict of interest persists however, and I do not believe that more subtle forms of bias towards Google services could be proven or even be seriously debated in a court of law. This level of technicality, I would argue, is no longer (fully) in reach for this kind of causal demonstration. Not so much because of the complexity of the algorithms, but rather because the “state” of the machine includes the full structure of the dataset it is working on, which means the full index in this case. To understand what Google’s algorithms actually do, looking at these algorithms without the data is no longer enough. And the data is big. Very big.

As you can see, I am quite pessimistic about the possibility to bring the kind of argumentation presented by Senator Lee to a real conclusion. If the case against Microsoft is an indicator, I would argue that this pessimism is warranted.

I do believe that we need to concentrate much more on the principal conflicts of interest rather than actual cases of abuse that may be simply too difficult to prove. The fundamental question is really how far a search company that controls such a large portion of the global market should be allowed to be active in other markets. And, really, should a single company control the search market in the first place? Limiting the very potential for abuse is, in my view, the road that legislators and regulators should take, rather than picking a fight over technological issues that they simply cannot win in the long run.

EDIT: Google has compiled its own Guide to the Hearing. Interesting.

German publisher Heise Verlag is an international curiosity. It publishes a small number of highly influential computer-related magazines that give a voice to a tech ethos that is at the same time extremely competent in the subject matter (I’ve been a steady subscriber to c’t magazin for over 15 years now, and I am still baffled sometimes just how good it is) and very much aware of the social and political implications of computing (their online magazine Telepolis testifies to that).

Data protection and privacy are long-standing concerns of the heise editors and true to a spirit of society-oriented design, they have introduced a concept as well as a technical implementation of a two-step “like” button. Such buttons, by Facebook or other companies, have of course become a major vector of user-tracking on the Web. By using an iframe, every button loads some code from Facebook’s server and sends the referring url (e.g. as an information. The iframe being hosted on the domain, cross-site privacy protections can be circumvented, the url information connected to an identifier cookie and, consequently, to a user account. Plugins like the Priv3 project block these mechanisms but a) users have to have a heightened level of awareness to even consider installing something like this and b) the plugin interferes with convenient functions like Google search preferences.

Heise’s suggestion, which they already implemented on their own sites, is simple: websites can download a small bit of code that implements a two-step procedure: the “like” button is greyed out after the page first loads and there is no tracking happening. A first click on the button loads the “real” Facebook code, and the second click provides the usual functionality. The solution is very simple to implement and really a very minor inconvenience. Independently from the debate whether “like” buttons and such add any real value to the Web, this example shows that “social” features like these can be designed in a way that does not necessarily lead to pervasive user tracking.

The echo to this initiative has been very strong (check the Slashdot discussion here), especially in Germany, where privacy (or rather Datenschutz, a concept less centered on the individual but rather on the role of data in society) is an intensely debated issue, due to obvious historical reasons. Facebook apparently threatened to blacklist at a point, but has since then backpedaled. After all, c’t magazin prints around 600.000 issues of every number and is extremely influential in the German (and Dutch!) computer landscape. I am very curious to see how this story unfolds, because let’s be clear: Facebook’s earning potential is closely tied to its capacity to capture, enrich, and analyze user data.

This initiative – and the Heise ethos in general – underscores that a “respectable” and sober engineering culture does not exclude an explicit normative stance on social and political issues. And is shows that this stance can be translated into technical models, implemented, and shared, both as an idea and as code.

While riding my bike today, I listened to a very thought-provoking and enjoyable talk (LSE site / YouTube) given back in may at the LSE by Harvard law professor Gerald Frug, entitled  “The Architecture of Governance”. The argument basically revolves around the actual “design” or “architecture” of governance/government structures and, more precisely, the complicated relationship between local and central governments. While this is not a talk about technology, there is much to learn concerning how to think about the design of (political) systems – mechanisms for organizing collective decision-making – beyond the petty moralizing and finger-pointing that seems to have taken hold of large parts of public debate today in much of the Western world. What I find quite intriguing is that Krug pays so much attention to the particularities of how seemingly consensual ideas (“power to the local”) can be implemented with rather different potential outcomes. In that sense, “parameter details” and fine-print may have a much larger impact than one might think and it’s worth-while to talk about them and not just the grand questions of “participation” vs. “representation”, and so on. Good fun!

The entertaining platform for technology and design, TED, posted a talk by Area/Code chairman and co-Founder Kevin Slavin, entitled “How algorithms shape our world”:

There are a couple of interesting examples and ideas in there and the analogy between finance algorithms and the larger processing of “culture” is well argued. A fun 15 minutes – there’s even explosions in there!

In August 2010, Edinburgh Sociologist Donald MacKenzie (whose book An Engine, not a Camera is an outstanding piece of scholarship) wrote an article in the Financial Times titled Unlocking the Language of Structured Securities where he discusses a software suite for financial analysis called Intex and compares it to a language that allows to see and interact with the world in certain ways rather than others. MacKenzie describes his first encounter with Intex as a moment of revelation that quickly turned into doubt:

The psychological effect was striking: for the first time, I felt I could understand mortgage-backed securities. Of course, my new-found confidence was spurious. The reliability of Intex’s output depends entirely on the validity of the user’s assumptions about prepayment, default and severity. Nevertheless, it is interesting to speculate whether some of the pre-crisis vogue for mortgage-backed securities resulted from having a system that enabled neophytes such as myself to feel they understood them.

While MacKenzie does not go as far as imputing the recent financial crisis to a piece of software, he points out that Intex is not recursive in its mode of analysis: when evaluating a complex financial asset, for example one of the now (in)famous CDOs that are made up of other assets, themselves combining further values, and so on, Intex does not follow the trail down to the basic entities (the individual mortgage) but calculates risk only from the rating of the asset in question. MacKenzie argues that Goldman-Sachs’ 2006 decision to basically get out of mortgage-based securities may well be a result of their commitment to go beyond available tools by implementing a (very costly) “bottom-up” approach that builds its evaluation of an asset by calculating up from the basic units of value. The card-house character of these financial instruments could become visible by changing tools and thereby changing perspective or language. Software makes it possible to implement very different practices or languages and to make them pervasive; but how does a company chose one strategy over another? What are the organizational and “cultural” factors that lead Goldman-Sachs to change its approach? These may be the truly challenging questions here, although they may never get answered. But they lead to a methodological lesson.

The particular strength of systems like Intex lies in their capacity to black-box evaluation strategies behind a neat interface that allows users to immediately operate on the underlying models, weaving these models into their decisions and practices. Conceptually, we understand the ways in which software shapes action better and better but the empirical complexity of concrete settings is positively daunting even outside of the realm of financial markets. What I take from MacKenzie’s work is that in order to understand the role of software, we have to be very familiar with the specific terrain a system is embedded in, instead of bringing overarching assumptions to the table. Software is a means for building structure and this building is always happening in particular organizational settings that are certainly caught up in larger trends but also full of local challenges, politics, and knowledge. Programs are at the same time structuring backdrop practice and part of a strategic repertoire that actors dispose of.

The case of financial software indicates that market behavior standardizes around available tools which leads to the systemic delegation of certain decision processes to software makers. This may result in a particular type of herd behavior and potentially in imbalance and crisis. Somewhat ironically, it is Goldman-Sachs that showed the potential of going against the grain by questioning programmed wisdom. That the company recently paid $550M in fines for abusing their analytical advantage by betting against a CDO they were selling to customers as an investment indicates that ethics and cunning are unfortunately two pair of shoes…

When it comes to scrutinizing companies for their actions and policies concerning control over information, privacy issues, and market dominance in areas related to public debate, large media conglomerates have been the traditional objects of analysis. More recently, Internet giants such as Google and Facebook have been critically examined and when the hype levels off, Twitter will probably be the next on the list. Malcolm Gladwell’s recent piece in The New Yorker may very well be an indicator of things to come.

Whether the issues related to “social media” are important or not, I have the feeling that the debate overshadows questions and problem fields that may in fact be much more important. The most obvious case, in my view, is the debate on privacy on Facebook. While the matter is not irrelevant, I think that e.g. present and future state-run information systems such as the french EDVIGE, a central police database that assembles all kinds of personal information concerning select persons “of interest”, have been overshadowed by debate on whether your employer can see the pictures that document your drinking binges after somebody (you?) put them on the ‘Book. There is a certain disequilibrium in how Internet researchers and critics distribute their attention that has allowed all kinds of things to pass below the radar. But there is one event that has really shook me up recently, both because of its importance and the lack of outcry it garnered, at least in my echo chamber: the acquisition of the Reuters group by the Thomson corporation in 2008 and the creation of Thomson Reuters, an information giant second to none.

Thomson Reuters market divisions

Thomson Reuters market divisions

I have stumbled upon Thomson Reuters a couple of times over the last years: first, when I researched the history of citation indexing, I learned that Thomson Scientific had bought the Institute of Scientific Information (and their Web of Science citation index megabase from which things like the notorious Impact Factor are calculated) in 1992; then again when I noticed that the ClearForest API for term extraction had be renamed, remodeled, and rebranded as OpenCalais after Reuters bought the company in 2007; finally, last year, when I noticed that the Reuters video platform appeared more and more often in articles and links. When I finally started to look a little closer (NYSE:TRI) I was astounded to find a company with a market cap of $31B, annual revenues of $13B, and 55K+ employees all over the world. Yes, this is no Apple big, but still very, very big for a company that sells information.

I knew Reuters from my studies in communication science as the world’s biggest news agency (with roughly one and a half competitors: Associated Press and Agence France Presse) but I had never consciously registered the Thomson company – a Canadian Family business that went from the media (owning the London Times at one point) to publishing before transforming itself in a rather risky move into a digital information broker for all kinds of special fields (legal, health, finance, etc.). Reuters was a perfect match and I really wonder how that merger went through without too much hassle from the different regulatory bodies. Even more so when I found out that Reuters actually had devised a very spicy regulatory clause when it made its IPO in 1984: to avoid control over such a central source of information, no  single shareholder would be allowed to hold more than 15% of the companies stocks. Apparently, that clause was enacted at least once when Murdoch’s News Corporation (already holding 15%) bought a competitor that also owned a piece of Reuters and consequently had to shed stock to stay below the threshold. The merger effectively brought the new Reuters Thomson under full control (53%) of The Woodbridge Company, a private holding that represents the Thomson family.

Such control over a news agency (and the many more specialized services that are part of the giant’s portfolio) should give us pause in the best of times when media companies are swimming in resources, are able to pay good money for good journalism, and keep their own network of correspondents. But recent years have seen nothing but cost cutting in journalism, which has led to an even greater reliance on news agencies. I wager that Google News would work a lot less well if people actually started to write their own copy instead of remodeling Reuters’ and AP send outs.

But despite these rather traditional – but nonetheless crucial – concerns over media ownership and control, there is a second point that is somewhat closer to my area of expertise. I have recently been thinking a lot about how to best phrase criticism of the assumption that digital networks necessarily lead to decentralization. Thomson Reuters – but also other information giants such as Google and Facebook – is a great example for how digital technologies can lead to quite impressive cost reductions for economies of scale and, consequently, market concentration. These arguments should be taken into account:

  • While the barriers of entry to the Internet are really low (you can have your own blog in minutes), scaling up to millions of visitors is a real challenge. Building your own datacenter is a real bump in the learning curve and to get over it, you need  to make certain investments. But once you pass that bump, scaling suddenly becomes cheaper again because you have the knowledge ressources and experience that can now be applied to make the datacenter grow. One of Google’s strengths lies in this area and this immensely facilitates branching out into new information ventures. The same goes for Thomson Reuters: they master platform technology and distribution technologies for all kinds of contents and they can build on that mastery to add new things to serve information to a globalized planet. To use the language of the long tail: there may be more special interest information that can find an audience with shelve space becoming effectively unlimited; but there is also no longer a need for more than one shelve.
  • The same goes for a more elusive matter: the mastery of information. The database techniques and indexing tools we use to store information – as well as the search and data-mining algorithms – can be very easily transported from one domain to the next. While it may be (very) difficult to create useful search tools for medical information, once you have built them it is rather easy to adapt these tools to, let’s say the legal domain. Again, this is what makes Google strong: basic search technology can be applied to advertising, books, mail, product prices, and even video if you can do automatic transcription. With the acquisition of ClearForest, Thomson Reuters has class-leading in-house data-mining and this is not something you can get by simply posting a couple of job ads in the local newspaper. Data-mining is extremely useful in areas where fast decision-making is crucial but also when it comes to building powerful search tools. Again, these techniques can be applied to any number of fields and once you have the basics right you can just add new domains with very little cost.

These two points go a far way in explaining why the Internet has seen the lightning fast emergence of network giants over the last couple of years. I really don’t want to postulate yet another “law” of the Net but I believe that there is something to this idea of the bump: it’s easy to have a basic presence on the Web but it’s hard to scale up to a large audience and to use advanced computational techniques; but one you pass the bump, the economies of scale kick in and from there it seems like there are no barriers to growth. The Thomsons have certainly made that bet when they acquired Reuters and so far, it seems to work out quite nicely for them.

I hope we can find a means to extend critique from questions of ownership into the heart of the (informational) beast and come up with better ways to understand how the still ongoing shift to exclusively digital information affords new means of handling and exploiting that information – with organizational, economic, and political consequences. While that work is starting to take shape for consumer companies like Google that are in the spotlight, there is surprisingly little on invisible network giants like Thomson Reuters that cater mostly to professional clients.