Category Archives: complexity economics

Albert O. Hirschman

Exit vs. voice: reacting to decline in 2017

So you are worried, restless, outraged. In a little over a year, you have watched cynical, myopic politicians manipulate electorates into making disastrous choices. Meanwhile, precious attention is being diverted from issues that really matter, like climate change, privacy, inequality and regulating AI. You know you will personally have to bear some of these costs, even though you had no part in those choices. You are seeing your rights reduced (Brexit), your national prestige sinking (Trump), the effectiveness of your government curtailed (German elections), your non-white or wrong-surname friends and family members being humiliated and made feel unwelcome (all of the above). Now what?

One of the heroes of my youth, Albert Hirschman, has an answer to that. You can do one of three things:

  1. Exit. Refuse to touch the problem, and leave your nation or community behind to deal with it. They made the mess, let them clean up.
  2. Voice. Engage with the status quo. State your concern and grievance, and try to change it through applying some kind of pressure.
  3. Loyalty. Suck it up, and live with it. It’s not that bad after all, at least not for you personally.

Loyalty is, of course, far and away the most popular method. This is because most things in human affairs work reasonably well. With your attention absorbed by corporate greed, climate change and the refugee crisis it is easy to overlook that traffic laws and the drinking water infrastructure work quite well, at least in my corner of the world. Most people will simply stay loyal and move on. But what if you can’t live with it? Do you exit or voice? and how?

In 2017, as I pondered these questions in relations to my own life and work, I stumbled onto two new books that argued different corners of the question. 

The first one is Lobbying for change, by my countryman and co-conspirator Alberto Alemanno. Alberto, a legal scholar, makes a resounding case for voice. His experience as an activist and campaigner taught him that decision makers are often much more open to take on board ideas and suggestions than you might think. This, he argues, is especially true when ideas and suggestions come from citizens, because a citizen’s economic interests are thought to align with that of society as a whole, or at least of large groups within society.

Alberto’s main intuition is this: citizen lobbying is fuelled by discontent, but it ends up producing greater societal cohesion. This is because lobbyists are by definition not themselves decision makers. So, nothing they do will work if it does not channel that discontent into a proposal that benefits them, and that the other interested parties can at least live with. In order to protect her interest or argue her cause, the citizen lobbyist cannot but help the common good. The book then proceeds to plot a course for anybody willing to be a citizen lobbyist to become an effective one. In a way, it’s a user’s manual for Hirschmanian voice.

An aside is in order. Of course, in the lobbying game “interested parties” are only those who sit at the table and argue for themselves. Those who don’t (because they are politically weak, like the Roma in Europe, or because they are not human, like the climate) are fair game, and they have generally not fared well even under advanced democracies. This is, however, a problem with voice in general, not with Alberto’s contribution. In fact, citizen lobbying is meant to be cheap enough that weaker and even non-human parties can find at least some voice.

The second book is Walkaway, by science fiction author and Boing Boing editor Cory Doctorow. It is firmly in the exit camp – even in its title. Doctorow is an interesting author, one of a small milieu of SF writers who write honest-to-God philosophical fiction. It’s a bit like reading Gulliver’s Travels: characters spend a lot of time explaining each other the economic, philosophical and technological foundation of the imaginary societies they live in. Doctorow stands apart from other authors in this group in that he is by far the most concerned with economics : in fact he himself claims that Walkaway is really about the Coase Theorem.

Walkaway imagines a near future society where open source technology is advanced enough that people can drop out of mainstream societies in a relatively order manner, and live off the land and dirt-cheap open tech. And they do: the combined effect of automation and mounting income and wealth inequalities make it so that meaningful employment is almost impossible (unless one enjoys serving the only healthy market, that of really rich people). People are knee deep in student debt. Most of humanity has simply nothing to offer to “the economy” and the society it supports. So they walk away, and provide to their needs with the same logic that Wikipedians build Wikipedia. Initially this gives rise to a sort of dual economy, with a large fringe (maybe 5% of the population?) living in Walkaway. Later on, the mainstream economy kind of eats itself, so that the share of humans in Walkaway rises significantly. Walkaway itself becomes a sort of mainstream, with super-rich people and their minions continuing to run places like London and Singapore, but not much else.

Walkaway argues for exit because it lays out a political strategy that leads people to winning by refusing to engage. Cannot get a decent job and pay your student loan? Walk away. The police evacuates the open source compound where you live? Walk away, rebuild it 50 kilometers down the road. They take that down too? Walk away. In the book, walkaway “wins”, but that’s not even the point. The point is that you can make a better life for yourself by not engaging with the system.

Like most people reading this, I am very dissatisfied with some of the things that are going on in the world right now. I can not live with them. Which one is it going to be, voice or exit? I have been a voice guy all my life, but now I wonder. I can see a game – theoretical argument for exit: if you commit to voice, the powers that be can stall you forever, while you exhaust your energy for change in endless negotiations. Exit – walkaway – has a key advantage: if you execute well, its results do not depend on your opponent. If you believe Doctorow’s intuition, if enough people exit your opponent are going to be badly hit. That gives you potentially significant clout as you walk away.

So, I guess, at a minimum, voice should not be taken for granted. Engagement and participation should be economised, and always be underpinned by an ever present, credible, threat of exit. “Credibility” in this sense means building as much autonomy as you can. This is what we are looking into at Edgeryders And you? Are you closer to the voice or to the exit camp?

Photo credit: McTrent on flickr.com

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

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. 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. David Graeber’s own ambition is in the 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.

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(r) 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.

Photo credit: McTrent on flickr.com

 

Photo credit: Gerald Grote on flickr.com

Complexity and public policy: a very short reading list

I have a new talk out, sort of. So far I have delivered it only in Italian (slides with notes), and it’s still work in progress. But getting there. It addresses the following question: can we reform government by making it more open and smart? If so, how?

I know. It sounds like something from a B-list TEDx event. You can almost picture some eager junior civil servant talking about “innovation” and “design” and “disruption”, the sort of disruption that does not destroy anyone’s job, civil rights, or democratic institutions. What could possibly go wrong?

It turns out to be much more difficult than that. Even talking about it is difficult. To even address the question, I had to ask myself: what is government? Why did it come into existence? Whhich evolutionary pressures now constrain its evolution? Doing so set me on a strange journey. I have been on it for about ten years now. It led me to uncover relevant stuff in many disciplines: history, economics, anthropology, networks science, sociology, math, philosophy, archaeology, experimental psychology, biology (lots of biology). It does not look like it’ll be over any time soon.

I still don’t know if and how we can make government more open and smarter. But I did get something in return for ten years of hard thinking: my brain is now rewired. I now look at administrative action in a perspective borrowed from complexity science. I draw most of my metaphors from biology. I have (somewhat) learned to look for emergence and self-organisation, and I can’t unsee it. I have become (somewhat) aware of my own psychological biases and cognitive limits. This transformation has been so profound that I can barely discuss with my former war buddies anymore.

And what I see is not cute. It’s strong stuff, inebriating and scary. So: last week I did this talk to open the School of Civic Technologies in Torino, and some students asked me for a reading list. Here it is, but don’t say you have not been warned. This is a red pill-blue pill situation. “There’s no turning back.”

So, here’s a barebones reading list in chronological order. If your interests center on public policy, start from the end. If you are more curious about complexity science, skip Ostrom, read Waldrop first and work your way up. Whatever you do, read Scott.

  1. Elinor Ostrom, 1990, Governing the Commons. People can and do steward common resources over the long run, with no central control and no definition of property rights. Great example, solid theorizing.
  2. Mitchell Waldrop, 1992, Complexity: the Emerging Science at the Edge of Order and Chaos. Still the best account of the story of the Santa Fe Institute in the early days. Functions as an introduction to the main intuitions behind complexity science.
  3. James Scott, 1998, Seeing Like a State: How Certain Schemes to Improve the Human Condition Have Failed. Essential reading. It shows how statecraft and legibility are tightly coupled. Casts a dark light on the emphasis on “evidence based”  and “data driven” when the guy speaking these words is also the guy with the gun.
  4. David Graeber, 2011, Debt: The First 5,000 Years. A long-term history of money and debt (it turns out they are the same thing). The book is very rich, and most of its value is not in its main thesis. For my purposes, the main teaching lies in the incredible value brought to the table by disciplines apparently quite far removed from policy issues – and, conversely, of the intellectual danger of not being interdisciplinary.
  5. Duncan Watts, 2011, Everything is obvious (when you know the answer). One of my favourite networks scientists sets out an ambitious (but achievable) research plan for the social sciences. Its take on what constitutes “data” and “evidence”, and of their limits, are typical of complexity science. Vanilla policy people tend to not understand data even minimally crunched.
  6. David Colander and Roland Kupers, 2014, Complexity and the Art of Public Policy:
    Solving Society’s Problems from the Bottom Up. An account of what public policies would look like if both the government and the governed knew complexity science, and were prepared to take it seriously. Review, in English and Italian.
  7. Beth Noveck, 2015, Smart Citizens, Smarter State. An authoritative take on why open government is failing. My favourite part is the treatment of how government became professionalized (and therefore exclusionary) in the USA. Review, in English and Italian.