What counts as evidence in interdisciplinary research? Combining anthropology and network science (long)

Intro: why bother?

Over the past few years, it turns out, three of the books that most influenced my intellectual journey were written by anthropologists. This comes as something of a surprise, as I find myself in the final stages of a highly quantitative, data- and network science heavy Ph.D. programme. The better I become at constructing mathematical models and building quantitatively testable hypotheses around them, the more I find myself fascinated by the (usually un-quantitative) way of thinking great anthro research deploys.

This raises two questions. The first one is: why? What is calling to me from in there? The second one is: can I use it? Could one, at least in principle, see the human world simultaneously as a network scientist and as an anthropologist? Can I do it in practice?

The two questions are related at a deep level. The second one is hard, because the two disciplines simplify human issues in very different ways: they each filter out and zoom in to different things. Also, what counts as truth is different. Philosophers would say that network science and anthropology have different ontologies and different epistemologies. In other words, on paper, a bad match. The first one, of course is that this same difference makes for some kind of added value. Good anthro people see on a wavelength that I, as a network scientist, am blind to. And I long for it… but I do not want to lose my own discipline’s wavelength.

Before I attempt to answer these questions, I need to take a step back, and explain why I chose network science as my main tool to look at social and economic phenomena in the first place. I’m supposed to be an economist. Mainstream economists do not, in general, use networks much. They imagine that economic agents (consumers, firms, labourers, employers…) are faced with something called objective functions. For example, if you are a consumer, your objective is pleasure (“utility”). The argument of this function are things that give you pleasure, like holidays, concert tickets and strawberries. Your job is, given how much money you have, to figure our exactly which combination of concert tickets and strawberries will yield the most pleasure. The operative word is “most”: formally, you are maximising your pleasure function, subject to your budget constraint. The mathematical tool for maximising functions is calculus: and calculus is what most economists do best and trust the most.

This way of working is mathematically plastic. It allows scholars to build a consistent array of models covering just about any economic phenomenon. But it has a steep price: economic agents are cast as isolated. They do not interact with each other: instead, they explore their own objective functions, looking for maxima. Other economic agents are buried deep inside the picture, in that they influence the function’s parameters (not even its variables). Not good enough. The whole point of economic and social behaviour is that involves many people that coordinate, fight, trade, seduce each other in an eternal dance. The vision of isolated monads duly maximising functions just won’t cut it. Also, it flies in in the face of everything we know about cognition, and on decades of experimental psychology.

The networks revolution

You might ask how is it that economics insists on such a subpar theoretical framework. Colander and Kupers have a great reconstruction of the historical context in which this happened, and how it got locked in with university departments and policy makers. What matters to the present argument is this: I grasped at network science because it promised a radical fix to all this. Networks have their own branch of math: per se, they are no more relevant to the social world than calculus is. But in the 1930s, a Romanian psychiatrist called Jacob Moreno came up with the idea that the shape of relationships between people could be the object of systematic analysis. We now call this analysis social network analysis, or SNA.

Take a moment to consider the radicality and elegance of this intellectual move. Important information about a person is captured by the pattern of her relationships with others, whoever the people in question are. Does this mean, then, that individual differences are unimportant? It seems unlikely that Moreno, a practicing psychiatrist, could ever hold such a bizarre belief. A much more likely interpretation of social networks is that an individual’s pattern of linking to others, in a sense, is her identity. That’s what a person is.

Three considerations:

  1. The ontological implications of SNA are polar opposites of those of economics. Economists embrace methodological individualism: everything important in identity (individual preferences, for consumer theory; a firm's technology, in production theory) is given a priori with respect to economic activity. In sociometry, identity is constantly recreated by economic and social interaction.
  2. The SNA approach does not rule out the presence of irreducible differences across individuals. A few lines above I stated that an individual's pattern of linking to others, in a sense, is her identity. By "in a sense" I mean this: it is the part of the identity that is observable. This is a game changer: in economics, individual preferences are blackboxed. This introduces the risk of economic analysis becoming tautologic. If you observe an economic system that seems to plunge people into misery and anxiety, you can always claim this springs directly from people maximising their own objective functions because, after all, you can't know what they are. This kind of criticism is often levelled to neoliberal thinkers. But social networks? They are observable. They are data. No fooling around, no handwaving. And even though there remains an unobservable component of identity, modern statistical techniques like fixed effects estimation can make system-level inferences on what is observable (though they were invented after Moreno's times).
  3. Moreno's work is all the more impressive because the mathematical arsenal around networks was then in its infancy. The very first network paper was published by Euler in 1736, but it seems to have been considered a kind of amusing puzzle, and left brewing for over a century. In the times of Moreno there had been significant progress in the study of trees, a particular class of graphs used in chemistry. But basically Moreno relied on visual representation of his social networks, that he called sociograms, to draw systematic conclusions.
_By Martin Grandjean (Own work), strictly based on Moreno, 1934 [CC BY-SA 4.0 (http://creativecommons.org/licenses/by-sa/4.0)], via Wikimedia Commons_

With SNA, we have a way of looking at social and economic phenomena that is much more appealing than that of standard economics. It puts relationships, surely the main raw material of societies and economies, right under the spotlight. And it is just as mathematically plastic – more, in fact, because you can more legitimately make the assumption that all nodes in a social network are identical, except for the links connecting them to other nodes. I embraced it enthusiastically, and spent ten years teaching myself the new (to me) math and other relevant skills, like programming and agent-based modelling.

Understanding research methods in anthropology

As novel as networks science felt to me, anthropology is far stranger. From where I stand, it breaks off from scholarship as I was trained to understand it in three places. These are: how it treats individuals; how it treats questions; and what counts as legitimate answers.

Spotlight on individuals

A book written by an anthropologist is alive with actual people. It resonates with their voices, with plenty of quotations; the reader is constantly informed of their whereabouts and even names. David Graeber, for example, towards the beginning of Debt introduces a fictitious example of bartering deal between two men, Henry and Joshua; a hundred pages later he shows us a token of credit issued by an actual 17th century English shopkeeper, actually called Henry. This historical Henry did his business in a village called Stony Stratford, in Buckinghamshire. The token is there to make the case that the real Henry would do business in a completely different way than the fictional one (credit, not barter). 300 pages later (after sweeping over five millennia of economic, religious and cultural history in two continents) he informs us that Henry's last name was Coward, that he also engaged in some honourable money lending, and that he was held in high standing by his neighbours. To prove the case, he quotes the writing of one William Stout, a Quaker businessman from Lancashire, who started off his career as Henry's apprentice.

To an economist, this is theatrical, even bizarre. The author’s point is that it was normal for early modern trade in European villages to take place in credit, rather than cash. Why do we need to know this particular’s shopkeeper’s name and place of establishment, and the name and birthplace of his apprentice as well? Would the argument not be even stronger, if it applied to general trends, to the average shopkeeper, instead of this particular man?

I am not entirely sure what is going on here. But I think it is this: to build his case, the author had to enter in dialogue with real people, and make an effort to see things through their eyes. Ethnographers do this by actually spending time with living members of the groups they wish to study; in the case of works like Debt he appears to spend a great deal of time reading letters and diaries, and piecing things together (“Let me tell you how Cortés had gotten to be in that predicament…”). If the reader wishes to fully understand and appreciate the argument, she, too, needs to make that effort. And that means spending time with informants, even in the abridged form of reading the essay, and getting to know them. So, detailed descriptions of individual people are a device for empathy and understanding.

All this makes reading a good anthro book great fun. It also is the opposite of what network scientists do: we build models with identical agents to tease out the effect of the pattern of linking. Anthropologists zoom in on individual agents and make a point of keeping track of their unique trajectories and predicaments.

Asking big questions

Good anthropologists are ambitious, fearless. They zero in on big, hairy, super-relevant questions and lay siege to them. Look at James Scott:
I aim, in what follows, to provide a convincing account of the logic behind the failure of some of the great utopian social engineering schemes of the twentieth century.
That's a big claim right there. It means debugging the whole of development policies, most urban regeneration projects, villagization of agriculture schemes, and the building of utopian "model cities" like Kandahar or Brasilia. It means explaining why large, benevolent, evidence-based bureaucracies like the United Nations, the International Monetary Fund and the World Bank fail so often and so predictably. Yet Scott, in his magisterial Seeing Like a State, pushes on – and, as far as I am concerned, delivers the goods. Graeber's own ambition is in his book's title: Debt – The first 5,000 years.

Economists don’t do that anymore.You need to be very very senior (Nobel-grade, or close) to feel like you can tackle a big question. Researchers are encouraged to act as laser beams rather than searchlights, focusing tightly on well-defined problems. It was not always like that: Keynes’s masterpiece is immodestly titled The General Theory of Employment, Interest and Money. But that was then, and now it is.

What counts as "evidence"?

Ethnographic analysis – the main tool in the anthropologist's arsenal – is not exactly science. Science is about building a testable hypothesis, and then testing it. But testing implies reproducibility of experiments, and that is generally impossible for meso- and macroscale social phenomena, because they have no control group. You cannot re-run the Roman Empire 20 times to see what would have happened if Constantine had not embraced the christian faith. This kind of research is more like diagnosis in medicine: pathologies exist as mesoscale phenomena and studying them helps. But in the end each patient is different, and doctors want to get it right this time, to heal this patient.

How do you do rigorous analysis when you can’t do science? When I first became intrigued with ethnography, someone pointed me to Michael Agar’s The professional stranger. This book started out as a methodological treatise for anthropologists in the field; much later, Agar revisited it and added a long chapter to account for how the discipline had evolved since its original publication. This makes it a sort of meta-methodological guide. Much of Agar’s argument in the additional chapter is dedicated to cautiously suggesting that ethnographers can maintain some kind of a priori categories as they start their work. This, he claims, does not make an ethnographer a “hypothesis-testing researcher”, which would obviously be really bad. When I first read this expression, I did a double take: how could a researcher do anything else than test hypotheses? But no: a “hypothesis-testing researcher” is, to ethnographers, some kind of epistemological fascist. What they think of as good epistemology is to let patterns emerge from immersion in, and identification with, the world in which informants live. They are interested in finding out “what things look like from out here”.

It sounds pretty vague. And yet, good anthropologists get results. They make fantastic applied analysts, able to process diverse sources of evidence from archaeological remains to statistical data, and tie them up into deep, compelling arguments about what we are really looking at when we consider debt, or the metric system, or the particular pattern with which cypress trees are planted in certain areas. A hard-nosed scientist will scoff at many of the pieces (for example, Graeber writes things like “you can’t help feeling that there’s more to this story”. Good luck getting a sentence like that past my thesis supervisor), but those pieces make a very convincing whole. To anthropologists, evidence comes in many flavours. You might say they are epistemological opportunists: there is no one way to the truth. But there is a toolbox, from which the epistemology appropriate for the problem at hand can be drawn.

Coda: where does it all go?

You can see why interdisciplinary research is avoided like the plague by researchers who wish to publish a lot. Different disciplines see the world with very different eyes; combining them requires methodological innovation, with a high risk of displeasing practitioners of both.

But I have no particular need to publish, and remain fascinated by the potential of combining ethnography with network science for empirical research. I have a specific combination in mind: large scale online conversations, to be harvested with ethnographic analysis. Harvested content is then rendered as a type of graph called a semantic social network, and reduced and analysed via standard quantitative methods from network science. With some brilliant colleagues, we have outlined this vision in a paper (a second one is in the pipeline) so I won’t repeat it here.

I want, instead, to remark how this type of work is, to me, incredibly exciting. I see a potential to combine ethnography’s empathy and human centricity, anthropology’s fearlessness and network science’s exactness, scalability and emphasis on the mesoscale social system. The idea of “linking as identity” is a good example of methodological innovation: it reconciles the idea of identity as all-important with that of interdependence within the social context, and it enables simple® quantitative analysis. All this implies irreducible methodological tensions, but I think in most cases they can be managed (not solved) by paying attention to the context. The work is hard, but the rewards are substantial. For all the bumps in the road, I am delighted that I can walk this path, and look forward to what lies beyond the next turns.

Thanks @amelia for many conversations on the anthropologist’s point of view, and for the expression “what things look like from out here”

Photo credit: McTrent on flickr.com


@noemi, this relates to our conversation in Yerevan.

A long response to a long post (and a little more below than we’ve talked about before, @Alberto) I like your take on anthropology’s value (and I laughed aloud at this part: a “hypothesis-testing researcher” is, to ethnographer, some kind of epistemological fascist."). As well as the kind of language we use vs what your supervisor would approve of.

I’m particularly interested in the distinction between the way economists have conventionally conceived of individuals and their choices and network science’s. If you remember, one of the key questions we’ve been working through in Open Care involves this tension between the individual as an autonomous, thinking agent and the individual as an inextricable part of a community, who is nothing without other people.

This tension is something we grapple with in anthropology a lot. Anthropologists are not psychologists— we do not, as a rule, take the individual and their inner mental states as units of analysis. Instead, you’ll most commonly hear anthropologists say they study culture, communities, social groups (though psychology is important and we do draw on it). But really (as you touch on in your post) we study individuals AS community members— people who are constantly shaped by and shaping their human-saturated worlds. And they make hopelessly irrational decisions! But our premise, I think, is that human behaviour makes sense, if you’re looking hard enough and pulling in evidence from the different sources that you mention (if you’re engaging in deep hanging out with informants, if you have familiarized yourself with their history, with their economic condition, with their religious beliefs and how they apply them, and so on).

All this to say: I think that a useful question for us to pursue is how seeming individuals, with their own usernames and own experiences in the world, come together to form an Open Care community and get stuff done. What is this balance between the power of the individual to forge ahead and create solutions to problems they see in their world, and their dependency on a larger community (which, for most people, is a source of great joy)? I think one of the greatest potential contributions of our mixed methods is to put forth a richer picture of human decision-making processes as individuals IN a community, instead of seeing human actors as isolated units or as faceless members of a mass.

When I was at UCL I did a three month ethnography of a London household (5 young professionals living together in a flat). My goal was to study their communication patterns and their usage of digital technologies. It was good fun— I had them keep a digital diary for a week of every single communication they had, and discussed it with them via interviews. They also let me run some analytics on their social media and their email to generate maps of their social networks.

But I did something else as well. I sat down with them, gave them a piece of paper, and asked them to draw their social network for me. They reacted with a combination of amusement and horror! But after busily sketching with coloured pencils, we had a few long conversations about why they drew their social worlds the way they did. And then we compared it to the maps generated by the software (MIT’s immersion software and wolfram alpha’s facebook report, for those interested).

I learned some really fascinating things. To take one short example: from looking at the network map, it appeared that Raoul had a really close relationship with his sister---- they communicated across digital mediums very intensively. When I brought that up with him he laughed and said “No, we can’t stand each other! But it’s mum and dad’s 30th wedding anniversary this year and we’ve been tasked with throwing this huge party.” Time after time it became clear that if I’d relied only on the mapping to draw conclusions about these people’s social worlds, I’d be hopelessly incorrect. Win for ethnography, on the outset.

But another thing became very clear to me throughout this experience. Without these larger-scale maps of their social worlds, there are a thousand questions I wouldn’t have known to ask. I couldn’t have asked Raoul about his sister— I wouldn’t have known about their intensive communication. I wouldn’t have been able to find out that Sarah still has extended, intimate conversations with a group of friends totally unconnected from her main network, a group of Harry Potter fans she met online when she was 12 and has been talking to ever since, despite only having met in person one time. All of these constellations were illuminated through even the limited amount of network analysis available to me, and they lit up my ethnography.

Each of these individual stories has a life and character, and it offers a depth of knowledge unmatchable by more zoomed out studies. Once, a colleague and I were delivering a paper survey about health and social care services in our London borough when we were gently interrupted by a mother and her 23-year-old daughter, who was mobility impaired and had a learning disability. Elizabeth (my colleague) began to ask the mother the questions about housing needs in the city, and the mother responds “well, our housing completely depends on my daughter!” Elizabeth looked at the survey and then back up at the mother, and instead set it down to have an extended conversation with the two of them about resources for disabled youth. The daughter articulated that as a 23-year-old, she feels like she falls through the cracks. She has limited interactions with people her age and struggles to find spaces, now that she is out of school and doesn’t fit the category of child, that meet her social and educational needs. We chatted with them for an hour, gaining a robust idea of this intersectional problem from these two knowledgeable individuals.

She is an expert in her own world, and her contribution is overwhelmingly significant in terms of making an impact despite the fact that she is just one person. In the same way, each Open Carer’s story matters as a piece of evidence— deep, rich, informative. The Street Nurses know so much about homelessness in Brussels. Alex knows so much about helping refugees in Calais. They tell us what life looks like over there.

But we also theorise that together they can tell us even more. Alex in Calais talks to Aravella in Greece and we see more of the picture. Maybe it’s like the old metaphor of an elephant up close— Alex can see the trunk, Aravella can see the tail. The more people we talk to, the more of the elephant starts to come into view. The more we can make out its shape and understand what it is as a bigger thing. Maybe then we know how to…uh…move the elephant? ok the metaphor is failing me here, but you see where I’m going. We understand something bigger by stitching together these worldviews and stories, and we can mobilise. Stories are not just stories of, they are stories for.

Marilyn Strathern once said that no one lives a generalised life. And at the same time none of us lives life alone. We grew up in a culture, with other people, and we move through a saturated world. As anthropologists we hold these two things in the balance — that we are constellations of individuals knit together, who move between the general and particular to make sense of our lives every day.

Here’s my hope. That because Bridget and Yannick and Brady and Alex have talked about art and mental health, some policy maker somewhere will see a connection that they didn’t before and build resources in their city that they otherwise wouldn’t have. Or maybe an international art initiative will take place, lead by these people. Or maybe a group of people will walk away from the conversation they had on the platform enriched with new ideas, or emboldened to make their world better. The form doesn’t matter much. The point is that something was made visible and shared. We saw what it looked like from somewhere else, and we learned.


This is also why semantic social networks are so exciting. To take the above example, the way that we do coding in Open Care would have illuminated what Raoul and his sister were talking about. Even at a zoomed out level, we would see that they were interacting around party planning, and that the subgroup off to the side were chatting about Harry Potter :slight_smile: cool stuff


@amelia thanks so much for these thoughts, they are incredibly valuable to me. Couple of take-home points:

  • “Identity as linking” seems like a powerful operational concept to stitch together netsci and anthro, because: [quote=“amelia, post:3, topic:7160”]
    we study individuals AS community members— people who are constantly shaped by and shaping their human-saturated worlds

  • The epistemological agility of ethnographers and anthropologists has to stay out of the network model, of course. I have in mind Elizabeth creating on the fly an extra space for this mother-and-daughter pair to express themselves. But it does enrich its interpretation, provide context and ask questions.

  • The story with Raoul and his sister can be read as the product of data scarcity. The strong relationship between the two happened because your sample was so limited in time. Data scarcity is largely resolved when you do SNA is scalable contexts like online platforms: edgeryders.eu, for example, can map almost six years of interactions across its community. Shocks like the party mentioned by Raoul regress to the mean over such a long sample.

  • But good point: SSNA can also solve that elegantly. Stuff that does not pertain to the current analysis is discarded automatically.

True about the Raoul story, but the unfortunate reality is that in a lot of disciplines 3 months would be considered a long amount of engagement. I too would consider it scarce :slight_smile:

Still, though, if you saw periods of intense conversation and lulls over time, you wouldn’t necessarily interpret that as them not actually liking each other without the semantic data attached.

It’s interesting that you used the words make an “extra space for” re Elizabeth, because one of the key themes in my thesis was that there was all this rich data but nowhere for them to put it to send it on to the overseeing NHS body, because the system in which they were meant to input the information was very rigid. So the problem of scaling that kind of data was not solved efficiently (in fact, it was so mangled by data transfer that the original information was basically rendered unrecognizable).