However bizarre these analogies might seem at first sight, they have been applied successfully to finance, where the concept of “herding behaviour” denotes cases in which a majority of traders adopt a shared, or very similar, pathway. It induces lower diversification of portfolios, thereby generating systemic risk (put differently, all people put all their eggs in the same basket, which is a bad choice if it turns out that it was the wrong basket).

The question, then, is whether something similar is going on among economists too. Herding behaviour may result from the institutional structure and incentives, which may make it a suitable strategy for an individual to always swim with the current; yet risk may arise from limiting the insurance function that diversity of research could play, especially in connection with policy recommendations. In this sense, the scholarly community of economists may come to resemble the financial community (and sheep and fish communities too, for that matter).

I discussed this question last Friday at a thought-provoking session at the annual ASSA meetings (the single most importance economics conference in the world!) in Chicago. The idea is from an INET-funded project led by John Davis and Wade Hands, and co-sponsored by the Journal of Economic Methodology which will devote a special issue to it. It follows a two-day workshop on the topic we had last month at Duke University. The other panelists were two distinguished contributors to the reflection on and around the discipline, notably David Colander and Mark Thoma.

Wade and John started outlining their original idea -an interesting part of which is the idea of doing the “economics of economics”, or the study of economists as we study any other economic agents. David insisted on a skewed structure of incentives, which has institutional origins and may lead the profession astray; and recommended, as he has often done recently, a humbler view in which economists would see themselves not as “scientists” in search of big truths, but as “engineers” in charge of sorting out practical problems. Mark exposed the risks due to excessive insularity of the discipline and stressed the important role of blogs (he is a most effective blogger himself!) as bridges between the ivory tower of professional economists and the wider public.

My specific contribution was on the “lifecycle” of the economics community, with a constant flow of leavers (those who retire or more extremely, die) and joiners (new PhD economists going on the market). Does turnover contribute to renewal in the discipline -”funeral by funeral”, as Paul Samuelson famously said? Starting from an initial consensus around the key foundations taught in graduate schools, does a regular flow of newcomers bring in some degree of change or diversification? (I can tell for now that results are mixed and point toward something I would call “cautious change” -but I’ll write more on the topic in the next few months).

Overall, the notion of herding itself seemed more complex and perhaps, even more challenging than we initially thought -but still, an extremely fertile subject of discussion that stimulated insightful comments from the audience. More will come in the next months…

Here’s an excerpt:

A New York City subway train holds 1,200 people. This blog was viewed about 7,900 times in 2011. If it were a NYC subway train, it would take about 7 trips to carry that many people.

Click here to see the complete report.

Of all the economic bubbles … few have burst more spectacularly than the reputation of economics itself.

So wrote The Economist in July 2009, commenting on the financial crisis. In the last few years, many have pointed their fingers at the discipline and its incapacity to predict the crisis, let alone to devise remedies for it when it burst. Even top figures in the profession, most prominently Nobel Prize Paul Krugman, have painted a bleak picture of the role of economists in the crisis. To be sure, it is primarily macro and financial economics that are under attack; yet other areas of economics are not immune either. One reason is that, in the view of many, the use (or abuse) of mathematics in economics is at least partly to blame.

When the Queen visited the London School of Economics and famously asked why no economists had sounded the alarm bell, the official response of a committee of economists supported by the British Academy timidly suggested that mathematical expertise may not have been up to expectations:

Some of the best mathematical minds … frequently lost sight of the bigger picture.

Other allegations were more explicit: in their counter-letter to the Queen, a small group of British dissident economists claimed that:

Preference for mathematical technique over real-world substance diverted many economists from looking at the vital whole.

And Krugman was widely cited when he wrote in The New York Times that:

Economists, as a group, mistook beauty, clad in impressive-looking mathematics, for truth.

even though he later explained that rather than the use of mathematics in economics in general, he was criticising the tendency to identify good math with good work; and concluded:

So by all means let’s have math in economics — but as our servant, not our master.

More radical voices did attack the use of mathematics in economics in general, though: in Tony Lawson’s words,

The more fundamental problem … is the emphasis on mathematical deductivist modelling per se.

Interestingly, none of these criticisms is new. Many of today’s opposers of mathematics cite a 1991 report of an AEA Commission on Graduate Education in Economics pointing out that:

Graduate programs may be turning out a generation with too many idiot savants skilled in technique but innocent of real economic issues.

Many critical voices were heard at the time, including in the popular press. A 1996 article in The New Yorker stated:

Economics has been transformed into an abstruse discipline that often appears to resemble a branch of mathematics.

And in 2000, the petition that started the so-called post-autistic movement started with:

We wish to escape from imaginary worlds…We oppose the uncontrolled use of mathematics!

Does this mean that nothing has changed in the last twenty years? Or instead, that today’s critics have simply failed to notice? That they are just reiterating older arguments, taking advantage of the crisis to bash those economic theories that have always dominated over theirs?

The reality, in my view, is a mix of the two. Economics has changed, and significantly so. In the last ten years, it has tackled a growing range of social behaviours, from divorce and child obesity to AIDS, aging, social networking, and crime. It has relaxed many of its older assumptions, including individual rationality, in favour of more eclectic and realistic approaches. Abstract mathematics has been largely replaced by empirically oriented (though still highly numerate) methods, primarily econometrics but also experiments, computer simulation and even brain scans. The discipline is now “defined neither by its subject matter nor by its method“.  If part of this strand of research may seem irrelevant, though clever (think about sumo wrestlers in Freakonomics), other work has deserved widespread praise for addressing some of today’s most pressing social problems: the contribution of Esther Duflo and co-authors on poverty is often mentioned in this respect.

Yet rigidities remain. Economics still ostensibly ignores all forms of qualitative research, regardless of the insight they bring to neighbouring disciplines. Similarly, fieldwork is a rarely, if ever, practised. Even though economists do use empirical data, the fact they they do not collect the data themselves (or disregard any qualitative evidence in the rare cases in which they do), introduces a wedge between the data and the observer. This may reinstate a bias towards excessive abstraction and lack of realism.

True, some progress is being done: think about field experiments and randomised controlled trials, such as those of Duflo. Yet it is very limited in scope and scale, and economists still largely lack awareness of, and fail to engage in reflection on, how to use field information at best, and how to integrate it rigorously into their analysis. Thinking of qualitative evidence and fieldwork experience as relevant requires a step forward from the traditional, mathematically informed mindframe that they have inherited from the past.

For pretty much the same reasons, economists are often wary of cross-disciplinary exchanges, especially with the social sciences deemed as “soft” —anthropology, sociology, political science. A particularly striking case is social network analysis, to which economists are contributing largely in isolation from an otherwise multi-disciplinary scholarly community. This attitude may backfire, by deterring exchanges with possible sources of new ideas.

In short, the discipline is moving ahead, and has done much to overcome its old criticisms. But at the moment, not all of them can be easily dismissed, and further steps need to be taken.

Indeed, at the upcoming Winter School on Analytical Software at the University of Greenwich, I will offer a new Introduction to SNA workshop (13th January 2011). As the first one, it is aimed at those researchers and post-graduate students who are new to the field, and would like to better understand whether and how they can use it to enhance their own research programmes. All social science backgrounds are welcome, and participants are assumed not to have any previous  knowledge of SNA. The goal of the workshop is to provide attendees with basic insight into social network analysis, and how it can be used in social science research, policy and business applications. There will be no use of computers, and familiarity with analytical or statistical software is not required.

The workshop will be structured similarly to the July one, though I plan to increase the number of examples, illustrations and practical applications with respect to the theory and techniques, and to make it more interactive. More formally, I would say that: 

Participants will learn:

• Fundamental principles of social network analysis and their grounding in social theories;
• The use of network-based reasoning to draw social policy and business recommendations;
• Some essential technicalities: format, collection method and properties of network data;
• Opportunities and challenges arising from the increasing availability of social network data from the internet;
• Use of visualisation tools and how they support network data interpretation;
• Basic measures of network composition and structure – how they can be used to uncover important aspects of the social phenomenon under study and how they can be represented graphically;
• A sense of the usefulness of more complex statistical models of networks such as ERGM and Siena;
• How SNA can be successfully integrated into social science research, business applications and policy advice using various sources.

Requirements

Participants may come from all social sciences backgrounds and are assumed to have no previous knowledge of Social Network Analysis.

Learning Outcomes

The workshop will provide attendees with a basic insight into social network analysis and how it can be used in their social science, business and policy research as well as applications.

More information and registration forms are available from the Winter School’s website.

Our slides are available here.

A social networks approach would be most helpful here to disentangle these factors and better understand their dynamics. Social interactions and influences are not just some determinants of rioting behaviour among others, and cannot just be placed on the same list as values and poverty. Interactions are the very channels through which the underlying factors (which do include values and norms as well as deprivation and unemployment) may or may not be activated and inform action. Similarly, social media are to be seen as potential accelerators of the effects of these underlying factors, which may have strengthened their impact; they may also have channeled counter-influences, though, thus inducing some to refrain from illicit action.

All in all, there is a need to see unrest as an emergent complex phenomenon depending on group dynamics, intertwined social influences, and the structures and evolution of social networks in which individual behaviours are embedded.

Further discussion is needed of course, and I look forward to reading this new report more carefully -but in all cases, I already see the interest of combining its findings with the insight that might come from a network-based approach.

It’s more of a work-in-progress now —we plan to build an upgraded version that includes both theoretical refinements and some validation through empirical data. As one would usually do in a longer-term perspective, we are now seeking collaborations, funding, ethical approval, etc. We hope to build a research programme of some breadth and duration on this basis.

Still, the experience of last August has been a most fruitful and gratifying one. Being able to engage as researchers in a public debate, involving other researchers but also a wider range of audiences, from software engineers to political activists, journalists, and community volunteers, has been exciting and encouraging. It has also been a lesson learned on how to leverage the potential of social media to enhance the impact of research.

Though further improvements in substantive insight, methods, and public engagement are certainly possible, I think this was an interesting first step, on which to build in future.

Today’s slides are available here.

Update as of 5th November 2011: my co-author Antonio A. Casilli is currently delivering a French-language presentation on this topic at a seminar on “The digital civil war”, organised by association Ars Industrialis at Centre Pompidou, Paris. His speech is even more pointing to the future developments we now have in mind. His slides (in French) are here.

Co-organised by Institut Télécom and EHESS, two major higher education institutions in France and bringing together PhD students from a variety of disciplines, cultural and national backgrounds, it was a great experience.

I was in charge, with a nice and well-motivated team, of coordinating students’ tutorials and it was great to see how small but sound projects could be set up in such a short time, by the sheer effect of collaboration and exchange!

I also gave two lectures, a general methodological introduction to social network analysis, available here, and a presentation of the (now well-known!) paper I did this summer with Antonio A. Casilli on Internet censorship and the riots (here!).

Update as of 22nd September: Videos from the lectures and presentations (all in French, though some speakers had slides in English!) are now available on the School’s website!

Update as of 23rd September: Antonio Casilli has also published a post on the School (in French). Débora Pereira has translated it into Portuguese here.

The below word cloud has been built –just for fun!– from lecture summaries, available on the Summer School’s website (with the Wordle Java application):

Official data, let’s clarify the terminology, are those produced by Eurostat, National Statistical Institutes and public-sector agencies. They are in high (and growing) demand by social science researchers because of their quality and reliability, and because the topics they cover are closely related to economic and social policy issues on which many researchers aim to provide informed advice. There has been a historical tension, though, between researchers’ hunger for data and statistical agencies’ pledge of confidentiality, or the promise that they make to respondents that they won’t reveal any of their private information: it is this very promise that supports the trust that the public places in such agencies and ensures their continuing existence. As a result statistical institutes, often in conjunction with academic institutions (including data centres and archives such as the members of the CESSDA network in Europe) have developed a wide range of increasingly sophisticated tools and methods that enables delivery of data for scientific research without jeopardising confidentiality protection. These methods are technical, statistical, legal and educational, and the amount of expertise existing in this area is amazing.

Today, however, a data revolution is ongoing. A whole new amount of data is being created and becoming available, mainly through the Internet, at extremely low cost. Most of these data are from actual usages rather than surveys or questionnaires and therefore, eliminate all inaccuracies that may derive from respondents’ errors, misunderstandings or deliberate lies. These data are also available in very large volumes and therefore, allow statistical and analytical treatments that would not be possible with smaller-scale studies. The attractiveness of these data for research (and not only: think of public administration, company management, marketing, and legal disputes, to name but a few) is enormous. These data raise equally enormous new challenges: in terms of privacy and confidentiality protection, in particular, it is often questionable whether legal provisions are fully met (think of Facebook-related debates…). Other issues are just as opaque, undecided or  even unaddressed: for example, what about archiving, formats, documentation, or preservation? A key issue is that these data aren’t handled by the organisations that have the most comprehensive expertise in data management.

There indeed seems to be a neat separation between the “old” data community, which has much of the expertise and experience needed, and the more diverse set of producers and users of the “new” Internet data. The former is taking little notice of these new developments, in my view, and may risk to be superseded by all that is happening on the web –thereby jeopardising, once and for all, all the efforts made so far to ensure a proper and well-organised recording of  life in our societies whilst also abiding by freedom-of-information principles, and supporting social science research. The latter is also taking risks, though, to the extent that it is not (or not always) taking privacy-protection legal provisions seriously enough, it is hardly developing any methodologies to ensure appropriate data curation and preservation, and it is not even giving much thought to data analysis and interpretation issues.    

There is an urgent need, I believe, to unify what we know in terms of data, old and new, and bring together the different communities that have been involved in these areas so far –for mutual benefit.

The event prompts me to think more about my latest post with Antonio A. Casilli, where we argued that sheer data do not speak by themselves (specifically, about the ethnic origins of participants in the recent UK riots): data need to be carefully selected to extract the relevant information, and then need to be interpreted. Without theories, data just don’t make any sense.

Attending such a large statistics conference did not change my mind -quite the contrary. In a comment to the Financial Times (also referring to the conference!) of last Wednesday, the economist John Kay warned young scholars about the interpretation and use of data. Don’t just believe figures, he said -what does it mean that “on average, men think about sex every seven seconds”? Ask where these responses come from, who collected/elaborated the data, and what were the original questions that such numbers were meant to answer. Computer programmes help us manipulate larger amounts of data much faster than in the past -but do not remove the need for interpretation. New findings remain just as unlikely as they used to -he added, “When I discover something surprising in data, the most common explanation is that I made a mistake”.

At the conference, I picked up a copy of the journal “Significance” (March 2011 issue) featuring an interview with Hal Varian, chief economist at Google. He is quoted to have said “The sexy profession of the next decade will be statistician”. Said by someone who works for one of the largest data handlers of the world, this sentence first sounded worrying -but no. Varian explains that the massive amounts of data available today can be as useless as they are cheap. Tools and techniques are needed to extract the right information from the data -otherwise they can be of little use. And in some cases, more information can be obtained from a smaller sample, cleverly selected and analysed, than a larger dataset. What he meant by qualifying statisticians as “sexy” (apart from pleasing the journal’s readers, one could argue!) is in fact best translated by his opposition between statisticians and computer scientists: the latter “use vast datasets and unstructured models”, while the former “have complex models and smaller datasets”. He was in fact, calling for more theory and thinking, now that the data are abundant: again, data by themselves are not enough.

In this sense, I would add that other professions may also become sexy -not just statistics but also the social sciences, to the extent that they also provide theories, tools and methods that make sense of all these data. Enthusiasms for what some call “data deluge” has led many to forget this other aspect -yet slowly, perhaps too slowly, some are starting to realize we cannot do without theories, after all.