Tag Archives: economics

Designer economies. How much freedom do we really have when imagining believable economic systems?

In November 2023, I was asked to keynote at something called the Space Economy Camp. The organizers were a diverse mix: the Complexity Economics Lab at Arizona State University; the 100 Years Starship Initiative, with its own literary prize; the Space Prize. The idea was this: 20 writers were selected via an open call, exposed to economics lectures, and put to work in small groups to imagine “sustainable, non-exploitative economies in space”.

I was one of the economists asked to give lectures. I decided to make it as practical as I could laying out some ideas from economics that, I thought, a sci-fi author might find useful in order to build fictional worlds with credible, if fictional, economies. Those ideas – useful or not, you be the judge of that – also applies to efforts of imagining economic systems outside of science fiction writing: for example to politics, or activism, or creating businesses or communities.

This post contains the editorialized notes from that lecture, reposted from Edgeryders.

1. Introduction and lecture outline

Worldbuilding is hard, as authors well know. In this lecture, we are going to take a look at the part of worldbuilding where you give your planet, eldritch dimension, fantasyland or post-climate change polity a believable, though obviously fictional, economy. In the time-honoured tradition, I have good news and bad news. The good news is that we have considerable latitude in designing your fictional economy, just as in inventing rituals, dress codes, weapons, and other technologies. The bad news is that coming up with a good design is nontrivial. But this is also good news, since overcoming difficulties with a creative act is what authors do, and it is perhaps the most fun humans can have.

In the lecture, We are going to reflect on some basic choices that we need to make when designing an economy. To help reflection, we invoke concepts from social sciences and economics.

Concept in real life Related concept in economics
Designing credible economies Incentive compatibility
“Human nature” Value theories
Institutions Economic anthropology
Plausible histories Subgame-perfect equilibria

2. Suspension of disbelief

I am no author, just a lowly reader. But, when I read, I take pleasure from diving into an immersive, textured world that I can explore. This pleasure is enhanced by suspension of disbelief, the psychological state of someone who, willingly, suspends certain functions of critical thinking in order to enjoy the narrative. Emphasis is on “certain”. If I read The Lord of The Rings, I can allow myself to get worried about Sauron’s armies eating what’s left of the free peoples of Middle-Earth (though there is no Middle-Earth: if I look out the window, Brussels is right there). But some parts of my critical thinking are harder to switch off. If, in order to prevail in the Battle of the Pelennor Fields, Gandalf had called in a drone strike on Mordor’s siege machines, that would have broken the suspension of disbelief, at least for me, and made my reading much less pleasant.

So, worldbuilding is a balancing act: the more space the author claims for imagining things that do not exist, the higher the potential entertainment value from the exoticism and mystery. But that space needs to be highly organised to avoid inconsistencies that puncture the bubble of the reader’s suspension of disbelief.

Careless depictions of the economy of fictional words can also rupture suspension of disbelief. A favourite example of mine is the use of coins cast in precious metal as currency in J.K. Rowling Harry Potter’s saga. The book makes it clear that the coins are precious and fungible (i.e. there is an incentive to steal them). They are kept in high-security vaults, guarded by goblins with great magical powers. This, however, does not quite gel: one imagines that, if the coins were really precious, wizards would simply magick them into existence. This would quickly lead to hyperinflation (depending on the magical cost of making new coins), which would drive the real value of those coins to near zero. I cannot justify those goblins.

Of course, this is a fantasy world, and you could always salvage it by inventing exceptions: for example, the wizards can magick up all sort of stuff, but not gold coins. Additionally, that no tracer spell can be put on coins so that they “know their master”, which would make them more like balances in a bank account, valuable but impossible to simply steal. But you see how this looks a bit contrived and “just so”.

Granted, readers can sometimes ignore the nagging feeling that something is off, and concentrate on the characters and the drama instead. This is easier in some subgenres than in others: readers of high fantasy novels are unlikely to pay close attention to economics, and indeed the Harry Potter’s saga is a huge success. So is Tolkien’s, and he got away with a complete disregard for anything to do with economics: who’s feeding, clothing and arming all these standing armies? Where are the breeders and stables to produce all those horses in Rohan, and would they not create some environmental damage? How is it that in besieged Minas Tirith there is no trace of a black market, like our grandparents experienced during World War 2 in most of Europe? On the other hand, if you are writing solarpunk sci-fi or cli-fi, alternative modes of organizing society are likely to be at the center of attention.

3. Incentive compatible mechanisms as a template for credibility

I propose that a good compass for figuring out whether a fictional economy is believable or not is its incentive compatibility. It goes through the concept of mechanism, associated with the names of Eric Maskin, Leonid Hurwicz and Roger Myerson. The basics are as follows:

  1. A mechanism is a manufactured environment for agents (normally people) to interact with one another (auctions, tax systems, social media…). Within the mechanisms, agents act according to their own objectives. [Maskin 2008]
  2. Designing a mechanism is about making its rules so that people in it spontaneously take the system in the direction that the mechanism designer wants to go.
  3. A mechanism is incentive compatible when every agent’s best strategy is to follow the rules, no matter what the other agents do [Hurwicz 1960].

In his Nobel lecture, Maskin asks three questions about mechanism design:

  1. When is it possible to design incentive-compatible mechanisms for attaining social goals?
  2. What form might these mechanisms take when they exist? Auctions, tax systems, elections, ritual combat, social media?
  3. When is finding such mechanisms ruled out theoretically?

They apply fairly directly to worldbuilding for science fiction authors! We can use them as a guide to imagine an economic system, for example that of a space economy. The authors can choose the system’s goals, then take on the mantle of the mechanism designer, and ask herself what form might incentive-compatible mechanisms take for those goals.

4. Incentives to do what? Introducing value theories

Incentive compatibility is about mechanisms being compatible with people’s incentives. Ok, but what are these incentives? What is it that people (be they humans, aliens, elves or robots) want? What do we value?

This is a philosophical question, and let us never forget that economics grew out of moral philosophy. Adam Smith was a professor of moral philosophy, and he authored a Theory of Moral Sentiments before The Wealth of Nations. The branch of economics dealing with it is called value theory. The most important thing to know about value theory is that it is inherently political and highly contested. Human communities in different times and places have adopted different value theories.

Consider, for example, the concept of production boundary, the imaginary line that divides activities that produce wealth to activities that merely redistribute it (I am borrowing this terminology from Mariana Mazzucato’s The Value of Everything). For example, imagine that Alice, a farmer, leases Bob’s land to grow wheat. Most economists would agree that Alice, when farming, produces wealth, whereas Bob, when collecting rent for the land, is simply redistributing to himself some of the wealth Alice has created.

Mazzucato’s point is that the production boundary has shifted over the centuries, amidst much debate and political battles. A common pattern has been that, when a class of people managed to attain some power, it fought to get legitimized as productive, declaring whatever it was doing as “production”. If what counts as value depends on who you ask, it follows that value is not some kind of universal measurable property, like mass. It’s a convention, that results from a political process.

An interesting question around value theories is this: who is supposed to be doing the valuing? The physiocrats and the classical economists disagreed about where the production boundary lay, but they agreed that value was an objective characteristic of things. Both schools believed that Alice the farmer creates value, while Bob the landlord does not. Furthermore, they believed this was just a fact, that depended on no one’s personal opinions and views. The objective nature of value implies that societies can and do make collective choices that they believe to lead to producing more value. If the value of care work (feminist economics) or nature (ecological economics) are objective, then surely we must reward their production, just as we reward the production of food and manufactured goods (you could state that value is, instead of objective, intersubjective and historically determined, and the argument would still hold).

Marginalist economics has a different idea. Starting in the second half of the 1800s, this current of economic thinking maintains that value is subjective. If anyone is willing to pay it for something, that something has value for thgat person. It posited that people wanted something called “utility”, and that different people would derive utility from different things. Its founders (Walras in France, Menger in Austria, Jevons and Marshall in the UK) had mathematical training and were keen to show that economics was “a real science” like physics. In order to make this idea of subjective preferences mathematically tractable, they assumed that, for each person, utility is an increasing function of her individual consumption of goods and services. Then, using calculus (invented in the 17th century), they could model individual choice as maximising an “utility function”, the arguments of which were individually consumed quantities of goods and services. Any collective dimension of value was discarded.

The founders of marginalism were well aware that theirs was a simplification, a thinking tool. Later, however, neoclassical economists started to think of this individualistic, subjective notion of value as true in itself. People, they thought, actually acted selfishly and in isolation: that was just human nature. Much sci-fi dystopia goes with the notion, and you can do so too. The main point of this lecture is that you don’t have to.

5. Underpinning the neoclassical theory of value: homo economicus

Neoclassical economists, then, posit that they have built a barebones model of human nature that (1) makes collective human behavior mathematically tractable and (2) despite its simplicity, captures the essence of how humans function. If both these claims hold, economists can build relatively simple models that will, nevertheless, capture the essence of human economic behavior and have explicative and predictive power. The idea of creating such a simplification for the purpose of doing economics goes all the way back to John Stuart Mill in 1836, and has later crystallized a model known as homo economicus. Looking at the conventional assumptions behind the economic theory taught in most university, homo economicus can be characterized precisely.

  • He is motivated by self-interest alone.
  • He has unlimited computational capacity (for example, computes the expected value of relevant stochastic variables).
  • This model underpins Pareto-efficient general equilibrium theory, in which the individual pursuit of self-interest by each economic agent achieves socially optimal outcomes. This is the philosophical foundation of Ayn Rand’s “Virtue of selfishness”, or Gordon Gekko’s “Greed is good”.

The history of economic thinking has seen several critiques of the homo economicus idea. Among others:

  • Most traditional societies are based on reciprocity (economic anthropology: Sahlins, Polany, Mauss…)
  • Bounded rationality (Veblen, Keynes, Simon…)
  • Inconsistent preferences for risk in investors (Tversky)
  • Unstable, poorly defined preferences (behavioral economics: Kahneman, Thaler, Knetsch…)
  • Not confirmed by experiments (experimental economics).
  • Not confirmed by experience in actual societies (Sen).

In general, homo economicus is not a good simplification of “human nature” on which to hang a theory of value. It creates more problems than it solves.

6. Underpinning other theories of value: group selection and the evidence from ancient societies

A more plausible theory emerged in the 2000s from the work of a a school of biologists interested in cultural evolution, the interaction between evolutionary pressure, the human genome, and human cultures. The main idea is that, unlike other species, humans are subject to evolutionary pressure on two fronts: the individual level (from Darwin’s natural selection, common to all species), and the group level (from group selection) [Henrich 2016]. The fitness of a human individual depends both on the individual’s own fitness (for example his or her resistance to pathogens) and on the success of the group to which he or she is a member.

It turns out that successful groups are groups that are good at cooperation, which is intuitive and confirmed by plenty of ethnographic and archaeological evidence. So, evolution pulls humans in two opposite directions: it wants us to be more competitive, to obtain a better position within the group; but it also wants us to be more cooperative, to benefit from the success of the group with respect to other groups.

The main driver of the group’s success is the scale at which it can manage cooperation [Wilson 2012]. Given cognitive limitations, this means the successful human in a successful group must be able to cooperate with complete strangers (unlike, for example, chimps: troops of chimps that encounter a lone chimp from a different troop will typically kill the stranger on sight). This poses the additional problem of free riding: non-cooperators in a cooperative group will reduce the group’s performance, because they benefit from the “public goods” created by the group without contributing to them. For this reason, humans appeared to have evolved methods to detect and expel the strangers in their midst. Experiments on infants as young as six months – and so untouched by education and value transmission – show that they react more favorably to others who speak their own dialect (even though they themselves have not yet learned to speak!). Biologists thinks that this “primary xenophobia” is innate, hardcoded into us at birth [Henrich 2016].

The late E. O. Wilson, perhaps the most accomplished scholar of cultural evolution, sums up his idea of “human nature” like this:

Individual selection is responsible for much of what we call sin, while group selection is responsible for the greater part of virtue. Together they have created the conflict between the poorer and the better angels of our nature.

So far the theory. Is it borne out by the data? An influential 2021 book by economic anthropologist David Graeber and archaeologist David Wengrow claims it is. The Davids combine ethnographic and archaeological evidence to confute the mainstream narrative about the invention of agriculture. Such mainstream narrative is associated to scholars such as, among others, Francis Fukuyama, Steven Pinker and most recently Yuval Harari, and goes like this: for a long time, humans lived in small hunting-gathering bands. These early societies were free and equal. Women were not oppressed. But then, ten thousands years ago, agriculture was invented. Early farming societies had a decisive advantage, as they could hoard stocks in good years to weather the bad ones. But farming meant inventing and enforcing property rights, which meant top-down management and therefore hierarchies. Stratified classes appeared, themselves allowing cooperation on a larger scale and giving farmers further advantages over hunters-gatherers. Patriarchy also ensued.

According to the Davids, this story is largely a fantasy, originated (much like Hardin’s tragedy of the commons story) by deductive thinking from philosophical premises originated in the Enlightenment. We have evidence of socially stratified hunting-gathering societies; egalitarian farming ones; societies taking up, then abandoning farming; even societies that farmed in the summer, and hunted-gathered in the winter, changing their leadership structure and political order with the season. The Davids refer back to Marcel Mauss’s 1903 studies of Inuit societies, which indeed changed their societal arrangements in sync with the season (hierarchical and patriarchal in the summers, egalitarian and free-love practicing in the winters). Given the harshness of living conditions in the Arctic regions, Mauss expected that these variations could be explained by the material advantages they brought, but he had to conclude that they could not. In the words of the Davids:

“Yet even in sub-Arctic conditions, Mauss calculated, physical considerations – availability of game, building materials and the like – explained at best 40 per cent of the picture […] To a large extent, he concluded, Inuit lived the way they did because they felt that’s how humans ought to live.”

7. Implications for authors of economic science fiction

So, where does this leave us? In a fascinating place. Powered by group selection, all kinds of societal and economic arrangements seem to be possible. In fact, very many are certainly possible, and we know that because we have tried them before. Remember Maskin’s definition of mechanism, “a manufactured environment for agents to make decisions”? That definition also describes a society. A society is a mechanism, because it is manufactured – via a political process – by its members. This gives authors a license to use their imagination to design new and fascinating economies we, the readers, can try on for size. It also gives them a library of arrangements that have been (or are still being) tried, to take inspiration from.

To conclude this lecture, I want to briefly point to some historical and contemporary examples.

Monastic economies

In the 6th century, St. Benedict of Nursia codified in his Rule the “protocol” overseeing the interactions among monks in a monastery (6th century). He did not found an order, but the Rule went viral and was adopted by the nascent monastic movement. People who used it were more likely to run a successful monastery than people who did not; and so, by the time of Charlemagne all Europe was infrastructured with successful monasteries running on the Rule.

Benedictine monasteries were units of production, because, in order to be effective places of devotion, they needed to be autonomous from the secular world. Benedict was aware of this, and his Rule contains some economic prescriptions:

  • Monks must price a little lower than seculars – doing otherwise would be avarice, a sin.
  • Everything monks do must be high quality. It is work, and work is dedicated to God and leads to Him.
  • Any profit you can make within these constraints is good, and you can use it to fund work that does not generate revenue.

This could not be more different from the neoclassical theory of labor supply, where a self-interested worker trades leisure for income; as well as from the theory of the profit-maximizing form. And yet, it worked extremely well. Monks made and ran inns; farmed the land; built water mills; created schools; copied and preserved manuscripts. At its peak, the famous Cluny Abbey served 10,000 warm meals a day to people in need. Also, this model is very stable, having been around (and prosperous) for 15 centuries straight. Even now, as a Benedictine superior told me, “we tend to to get prosperous, because monks work hard”.

Seasonal economies

These were described above in reference to the Inuit. The Davids again:

“In the summer […] property was possessively marked and patriarchs exercised coercive, sometimes even tyrannical power over their kin. But in the long winter months […] Inuit gathered together to build great meeting houses of wood, whale rib and stone; within these houses, virtues of equality, altruism and collective life prevailed.” [Graeber and Wengrow 2021]

Another traditional society with a similar arrangement are the Nambikwara in Northwest Brazil, studied by Lévi-Strauss in 1994.

Systems of cooperatives

Most economists, and most of the rest of us, think of the for-profit corporation as the “natural” form to organize economic activity. And yet, cooperatives are widespread all over the world. It is estimated that there are at least 280 million cooperators worldwide, and cooperatives have at least 27 million employees. Cooperatives lend themselves to self-organizing into “layers” to solve the problem of “make or buy”: a typical example is a group of farmers who grow grapes who join forces to commission a facility that will process the grapes of all of them into wine. This way, farmers can appropriate the added value of the transformation of their primary produce. One level above, you can find that the wine-making cooperatives of the same region can create a second-level coop to organize the distribution and marketing of the wine produced by all of them, and so on.

Europe has entire regions where most of the economy is cooperative. The most famous one is the Mondragon valley in Northern Spain, where an entire cooperative ecosystem of automotive manufacturing has come into being; several northern Italian regions are characterized by the prevalence of cooperatives in industries as diverse as agriculture, construction, insurance and banking.

Commons-based peer production

These are arrangements whereby non-hierarchical communities can maintain common resources (forests, fisheries, irrigation system) over time. They are very well documented: Elinor Ostrom won a Nobel for a 1990 book, Governing the Commons, where she not only looks in depth at case studies from Spain to Japan; she also comes up with 8 principles for designing the governance of a common resource. Principle 1 is “clearly define the group’s boundaries”, which goes back to Wilson and Henrich’s point about groups in competition needing to expel free riders. If you want to imagine a space economy, you could do worse than starting from here.

Notice that Ostrom’s book proves conclusively that “tragedies of the commons” do not always occur. Indeed, the 1968 paper by ecologist Garrett Hardin which introduced the “tragedy” concept was based not on evidence, but on deductive thinking: if homo economicus is a good model for human behavior, then tragedies of the commons should occur. Historically, the English commons (common lands) were eliminated not by “tragedies” of overconsumption, but by violent evictions and enclosures. Hardin himself was a white supremacist who cultivated a “lifeboat” vision of society.

War economies

A war economy is an economy the purpose of which is exogenous to the economy itself. Typically, this purpose is winning a war: the enemy is at the gate, and all economic efforts are directed to defeating them. War economies are field tested, and have proven to work extremely well: the most famous example is that of Germany during World War 1. Germany’s technocrat-in-chief, Walther Rathenau, pivoted the Empire’s economy in a matter of weeks as the war started [Scott 1998]. The state became the master planner, and, for many businesses, the main client and a sort of uber-CEO, with entire conglomerates strongarmed into pivoting overnight into new products.

This move was very successful in keeping the German army in the field and equipped, well after external observers had predicted its dissolution. And it was widely copied, which is why Rolls-Royce makes airplane engines as well as luxury cars.

Red plenty

This is more conjectural: the idea is that, as computing becomes cheaper and more powerful, central planning might emulate the efficiency of markets, while overcoming the latter’s blindness to externalities such as pollution or care work. We do know that the Soviet Union attempted cybernized central planning using linear programming techniques [Kantorovich 1939]. The centerpiece of the effort was the idea to use something called shadow prices (the marginal advantage of releasing a constraint) to simulate market prices. This fascinating story is told in a very strange (history? Fiction?) book by Francis Spufford, Red plenty.

8. Thinking up a fictional economy with subgame-perfect equilibria

In an attempt to grapple systematically with these ideas, a few years ago I was part of a group of people that were interested in the intersection between science fiction and economics, and called ourselves, cheekily, the Science Fiction Economics Lab. We started turning these things around in our heads, and came up with the idea of an open source world where we could explore these thoughts. We were doing worldbuilding, rather than writing actual sci-fi (though eventually some sci-fi stories set in our imaginary world did appear).

The overarching concept was that of a floating megacity, adrift in the oceans of a vaguely post-climate change Earth. We called it Witness. Witness had launched as a unitarian project, inspired by the floating city concept of UN Habitat, but then it had fractured along ideological lines. It was large enough to sustain several splinters, called Distrikts, each of them with its own economic system. We structured the work on Witness as a wiki (Witnesspedia), with major entries for the most important Distrikts: Libria, a hypercapitalist economy with minimal state intervention, reminiscent of much cyberpunk dystopias and, well, us. The Assembly, a cooperativist society with super-strong anti-monopoly culture. Hygge, a Nordic social-democracy on steroids; the Covenant, characterized by the presence of many monasteries and other religious institutions, with a strong manufacturing vocation (pun unintended).

This all is fun, but quite hard to take it down one level, to imagining institutions like markets, central banking, antitrust enforcement institutions, and indicators of economic performance (because, come on, what kind of self-respecting sci-fi piece of work would still be mentioning GDP? seriously). The incentive compatibility constrain kicks in. Perhaps the hardest nut to crack is the presence of trade across different systems: if Libria can trade freely with Hygge, will not its products – made with alienated labour – outcompete the fairer ones in Hygge? If not, why not? You find yourself designing policies that reproduce the economic systems you want – which is more or less what Graeber and Wengrow tell us real societies do.

In trying to make these imaginary economies credible, we felt the need to come up with an origin story. If these systems are in our future (which makes them more relatable) there should be an incentive-compatible path from here to there. So, maybe a Distrikt in Witness has robust trustbusting policies. Problem with monopolists is that they tend to capture their regulators, because they typically have more money and woman- or manpower than them. So, a society with strong anti-trust policies that never had any large monopoly is in equilibrium, because no firm becomes big enough to capture the regulators. But a society that starts with incumbent monopolies (like we do), and then somehow introduces antitrust policies, is not, because monopolists have the power to prevent effective regulation. To be credible at all, an imagined future needs to be connected to the present by an unbroken series of changes, each of these is incentive compatible. Game theory has a formalization of this concept, called a subgame-perfect equilibrium [Selten 1988].

Subgame-perfection is a test for the believability of the origin story of an economic system. To pass the test, the story needs to respect the incentive-compatibility constrain at each step of the way.

9. Coda: the role of science fiction in developing economic thinking

For the first time since I am intellectually active, late-stage capitalism is being seriously contested; and neoclassical economics with it. The battering ram of these contestations, alas, is not the economics profession, but climate change. But the profession is stirring, flexing muscles that had not been used for almost 100 years. New concepts are afoot: degrowth. Commons-based peer production. Universal basic services. Modern monetary theory. And some old concepts, like mutualism, and cooperativism, are making a comeback. They can inspire you as you build fictional economies in your head, and I hope you have as much fun as I did.

It is fun, but it also is important work. Like Cory Doctorow says, science fiction stories can function as architects renderings, making these models come alive, showing us what our lives would be like, if we lived in these systems. What would our jobs look like on a planet (in a galaxy far far away) that embraced degrowth? Our schools? Our romantic life? I am convinced that we need science fiction to inspire democratic debate on the urgent economic reforms that await. So, let’s get to it.

Essential bibliography

  1. D. Graeber and D. Wengrow, 2021. The dawn of everything: a new history of humanity. London: Allen Lane.
  2. J. P. Henrich, 2016. The secret of our success: how culture is driving human evolution, domesticating our species, and making us smarter. Princeton: Princeton University Press.
  3. L. Hurwicz, “Optimality and informational efficiency in resource allocation processes,” in Mathematical methods in the social sciences, K. Arrow, S. Karlin, and P. Suppes, Eds., Stanford: Stanford University Press.
  4. E. S. Maskin, 2008. “Mechanism Design: How to Implement Social Goals,” American Economic Review, vol. 98, no. 3, pp. 567–576, May 2008, doi: 10.1257/aer.98.3.567.
  5. M. Mazzucato, 2018. The value of everything: making and taking in the global economy. London, UK: Allen Lane, an imprint of Penguin Books.|
  6. E. Ostrom. Governing the Commons: The Evolution of Institutions for Collective Action, 1st ed. Cambridge University Press, 2015. doi: 10.1017/CBO9781316423936.|
  7. R. Selten, 1988. “Reexamination of the Perfectness Concept for Equilibrium Points in Extensive Games,” in Models of Strategic Rationality, vol. 2, in Theory and Decision Library C, vol. 2. , Dordrecht: Springer Netherlands, pp. 1–31. doi: 10.1007/978-94-015-7774-8_1.|
  8. E. O. Wilson, 2012. The social conquest of earth, 1st ed. New York: Liveright Pub. Corporation.|
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 this is now.

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


Thinking in networks: what it means for policy makers

Elegant, influential theories have a way to rewire your brain. In my formative years, it was not uncommon to joke that Marxist intellectuals could and would explain absolutely anything in terms of Marxist dialectics. For all our joking, exactly the same thing happened to me, as I dug deep into neoclassical economic theory. I did have access to non-neoclassical theories, but in the end it is the math that makes the difference. Mathematics gives you a grip on the model: by manipulating it, you can stretch it, adapt it, critique it, own it in a way that you can’t really any other way. In the end, the mathematical tools you use to think about the world become a default way to parse empirical data: when your only tool is a hammer, you see every problem as a nail and all that.

The hammer of neoclassical economics is functions. Not just any old function: convex, continuous, differentiable ones – designer functions with smooth hypersurfaces. If everything is a function of this kind, everything (say, your country’s economy) must have a maximum, because (bounded) continuous, convex and differentiable functions have exactly one max. This means there is a perfect (“optimal”) state of the world. You find it by calculus. You can then hack your way around the system with taxes, subsidies and interest rates until you push the economy to that maximum. If you are a consumer, or a worker, you also will be looking at a function, representing your well-being. Again, you can find its max, fine-tuning savings and consumptions, work and leisure into your personal sweet spot. There’s no such thing as unemployed: hey, the function is not discrete! What you are seeing is people that choose to allocate zero hours to work, given the existing wage rate (I exaggerate, but not much).

I spent the past five years learning how to use a new mathematical tool: networks. Going deep into the intuition of the math (as opposed to memorizing the equations) means, in the long run, a rewiring of your brain. What used to look like a nail suddenly makes much more sense as a screw. A good thing, since you are now the proud owner of a screwdriver! What I am seeing now as I consider public policies is this: I think of them as signals that the policy maker sends out. The interesting question is what carries the signal.

Traditional policy signals are broadcast: every agent in the economy receives the same message. Price signals (hence taxes and subsidies, too) are broadcast. So, in general, is regulation. Broadcast makes a lot of sense in an undifferentiated mean: if you want to reach a large number of recipients and they are all disconnected from each other, it’s a good technique. Just push that signal out in all directions, as loud as you can.

Once you really take networks on board, though, you start seeing them everywhere. And when you have all sorts of networks that could carry the signal for you, broadcast seems a blunt way to do things. Consider AIDS prevention policies. Broadcast policy sees that, as a category young people are more likely than old-timers to engage in unsafe sex, so it puts posters up in high schools. Since you can’t really be too graphic about it for political reasons, such posters tend to be quite bland, and immediately drowned by far stronger broadcasting signals that glorify sexual prowess and availability, those of commercial markets. Even if your average teen does become more careful, the epidemics still spreads through the very promiscuous few, who are unlikely to be impressed by a bland poster. All in all, near-zero impact is a good guess.

On the other hand, research has shown that networks of sexual partnerships are scale-free: a small number of individuals (not categories) have a very large number of sexual partners. These people are the main vector for the virus to spread. So here’s the networked version of AIDS prevention policy: go talk to the hubs. Dispatch researchers to identify them (it does not matter where you start, with scale-free networks it will take a small number of hops before you get to one); have one-on-one conversations with them. Spend time with them, they are important. Show them the data. Hire them, even. Should be cheap: it’s only a handful of people, who can have a disproportionate amount of impact on the epidemics by switching behavior. See the difference in approach?

In my talk at Policy Making 2.0 last week I tried to explore what it means, for policy makers, to think in terms of networks. I proposed that the gains from doing so are:

  1. impact: more bang for your taxpayer buck.
  2. reduced iatrogenics: policy becomes more surgical, so it causes less unintended damage.
  3. robustness to “too big to know”. Very simple network models exhibit sophisticated behavior. You can model several real-world phenomena without losing your grip on the intuition of the model, and therefore make more accountable decision.
  4. compassion. Networks owe their uncanny efficiency in carrying signals to large inequalities in the connectedness of nodes. Further, it is easy to build very simple models that produce inequalities even with identical nodes. This, at least for me, gets rid of the “underserving poor” rhetoric and fosters simpathy towards the smart and hard-working people out there that found themselves on the wrong side of system dynamics.
  5. measurability. Social interactions that happen online are now cheap to keep records of; you can use those record to build networks of interactions run quantitative analysis on them.

If you want to know more, you might find my (annotated) slides interesting. I am indebted, as ever, to the INSITE project and to all participants in Masters of Networks.