economics | Digifesto

Arendt on social science

Despite my first (perhaps kneejerk) reaction to Arendt’s The Human Condition, as I read further I am finding it one of the most profoundly insightful books I’ve ever read.

It is difficult to summarize: not because it is written badly, but because it is written well. I feel every paragraph has real substance to it.

Here’s an example: Arendt’s take on the modern social sciences:

To gauge the extent of society’s victory in the modern age, its early substitution of behavior for action and its eventual substitution of bureaucracy, the rule of nobody, for personal rulership, it may be well to recall that its initial science of economics, which substitutes patterns of behavior only in this rather limited field of human activity, was finally followed by the all-comprehensive pretension of the social sciences which, as “behavioral sciences,” aim to reduce man as a whole, in all his activities, to the level of a conditioned and behaving animal. If economics is the science of society in its early stages, when it could impose its rules of behavior only on sections of the population and on parts of their activities, the rise of the “behavioral sciences” indicates clearly the final stage of this development, when mass society has devoured all strata of the nation and “social behavior” has become the standard for all regions of life.

To understand this paragraph, one has to know what Arendt means by society. She introduces the idea of society in contrast to the Ancient Greek polis, which is the sphere of life in Antiquity where the head of a household could meet with other heads of households to discuss public matters. Importantly for Arendt, all concerns relating to the basic maintenance and furthering of life–food, shelter, reproduction, etc.–were part of the private domain, not the polis. Participation in public affairs was for those who were otherwise self-sufficient. In their freedom, they would compete to outdo each other in acts and words that would resonate beyond their lifetime, deeds, through which they could aspire to immortality.

Society, in contrast, is what happens when the mass of people begin to organize themselves as if they were part of one household. The conditions of maintaining life are public. In modern society, people are defined by their job; even being the ruler is just another job. Deviation from ones role in society in an attempt to make a lasting change–deeds–are considered disruptive, and so are rejected by the norms of society.

From here, we get Arendt’s critique of the social sciences, which is essentially this: that is only possible to have a social science that finds regularities of people’s behavior when their behavior has been regularized by society. So the social sciences are not discovering a truth about people en masse that was not known before. The social sciences aren’t discovering things about people. They are rather reflecting the society as it is. The more that the masses are effectively ‘socialized’, the more pervasive a generalizing social science can be, because only under those conditions are there regularities there to be captured as knowledge and taught.

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March 31, 2015

Innovation, automation, and inequality

What is the economic relationship between innovation, automation, and inequality?

This is a recurring topic in the discussion of technology and the economy. It comes up when people are worried about a new innovation (such as data science) that threatens their livelihood. It also comes up in discussions of inequality, such as in Piketty’s Capital in the Twenty-First Century.

For technological pessimists, innovation implies automation, and automation suggests the transfer of surplus from many service providers to a technological monopolist providing a substitute service at greater scale (scale being one of the primary benefits of automation).

For Piketty, it’s the spread of innovation in the sense of the education of skilled labor that is primary force that counteracts capitalism’s tendency towards inequality and (he suggests) the implied instability. For the importance Piketty places on this process, he treats it hardly at all in his book.

Whether or not you buy Piketty’s analysis, the preceding discussion indicates how innovation can cut both for and against inequality. When there is innovation in capital goods, this increases inequality. When there is innovation in a kind of skilled technique that can be broadly taught, that decreases inequality by increasing the relative value of labor to capital (which is generally much more concentrated than labor).

I’m a software engineer in the Bay Area and realize that it’s easy to overestimate the importance of software in the economy at large. This is apparently an easy mistake for other people to make as well. Matthew Rognlie, the economist who has been declared Piketty’s latest and greatest challenger, thinks that software is an important new form of capital and draws certain conclusions based on this.

I agree that software is an important form of capital–exactly how important I cannot yet say. One reason why software is an especially interesting kind of capital is that it exists ambiguously as both a capital good and as a skilled technique. While naively one can consider software as an artifact in isolation from its social environment, in the dynamic information economy a piece of software is only as good as the sociotechnical system in which it is embedded. Hence, its value depends both on its affordances as a capital good and its role as an extension of labor technique. It is perhaps easiest to see the latter aspect of software by considering it a form of extended cognition on the part of the software developer. The human capital required to understand, reproduce, and maintain the software is attained by, for example, studying its source code and documentation.

All software is a form of innovation. All software automates something. There has been a lot written about the potential effects of software on inequality through its function in decision-making (for example: Solon Barocas, Andrew D. Selbst, “Big Data’s Disparate Impact” (link).) Much less has been said about the effects of software on inequality through its effects on industrial organization and the labor market. After having my antennas up for this for many reasons, I’ve come to a conclusion about why: it’s because the intersection between those who are concerned about inequality in society and those that can identify well enough with software engineers and other skilled laborers is quite small. As a result there is not a ready audience for this kind of analysis.

However unreceptive society may be to it, I think it’s still worth making the point that we already have a very common and robust compromise in the technology industry that recognizes software’s dual role as a capital good and labor technique. This compromise is open source software. Open source software can exist both as an unalienated extension of its developer’s cognition and as a capital good playing a role in a production process. Human capital tied to the software is liquid between the software’s users. Surplus due to open software innovations goes first to the software users, then second to the ecosystem of developers who sell services around it. Contrast this with the proprietary case, where surplus goes mainly to a singular entity that owns and sells the software rights as a monopolist. The former case is vastly better if one considers societal equality a positive outcome.

This has straightforward policy implications. As an alternative to Piketty’s proposed tax on capital, any policies that encourage open source software are ones that combat societal inequality. This includes procurement policies, which need not increase government spending. On the contrary, if governments procure primarily open software, that should lead to savings over time as their investment leads to a more competitive market for services. Equivalently, R&D funding to open science institutions results in more income equality than equivalent funding provided to private companies.

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February 22, 2015

Hirschman, Nigerian railroads, and poor open source user interfaces

Hirschman says he got the idea for Exit, Voice, and Loyalty when studying the failure of the Nigerian railroad system to improve quality despite the availability of trucking as a substitute for long-range shipping. Conventional wisdom among economists at the time was that the quality of a good would suffer when it was provisioned by a monopoly. But why would a business that faced healthy competition not undergo the management changes needed to improve quality?

Hirschman’s answer is that because the trucking option was so readily available as an alternative, there wasn’t a need for consumers to develop their capacity for voice. The railroads weren’t hearing the complaints about their service, they were just seeing a decline in use as their customers exited. Meanwhile, because it was a monopoly, loss in revenue wasn’t “of utmost gravity” to the railway managers either.

The upshot of this is that it’s only when customers are locked in that voice plays a critical role in the recuperation mechanism.

This is interesting for me because I’m interested in the role of lock-in in software development. In particular, one argument made in favor of open source software is that because it is not technology held by a single firm, users of the software are not locked-in. Their switching costs are reduced, making the market more liquid and, in theory favorable.

You can contrast this with proprietary enterprise software, where vendor lock-in is a principle part of the business model as this establishes the “installed base” and customer support armies are necessary for managing disgruntled customer voice. Or, in the case of social media such as Facebook, network effects create a kind of perceived consumer lock-in and consumer voice gets articulated by everybody from Twitter activists to journalists to high-profile academics.

As much as it pains me to admit it, this is one good explanation for why the user interfaces of a lot of open source software projects are so bad specifically if you combine this mechanism with the idea that user-centered design is important for user interfaces. Open source projects generally make it easy to complain about the software. If they know what they are doing at all, they make it clear how to engage the developers as a user. There is a kind of rumor out there that open source developers are unfriendly towards users and this is perhaps true when users are used to the kind of customer support that’s available on a product for which there is customer lock-in. It’s precisely this difference between exit culture and voice culture, driven by the fundamental economics of the industry, that creates this perception. Enterprise open source business models (I’m thinking about models like the Pentaho ‘beekeeper’) theoretically provide a corrective to this by being an intermediary between consumer voice and developer exit.

A testable hypothesis is whether and to what extent a software project’s responsiveness to tickets scales with the number of downstream dependent projects. In software development, technical architecture is a reasonable proxy for industrial organization. A widely used project has network effects that increasing switching costs for its downstream users. How do exit and voice work in this context?

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February 21, 2015

The node.js fork — something new to think about

For Classics we are reading Albert Hirschman’s Exit, Voice, and Loyalty. Oddly, though normally I hear about ‘voice’ as an action from within an organization, the first few chapters of the book (including the introduction of the Voice concept itselt), are preoccupied with elaborations on the neoclassical market mechanism. Not what I expected.

I’m looking for interesting research use cases for BigBang, which is about analyzing the sociotechnical dynamics of collaboration. I’m building it to better understand open source software development communities, primarily. This is because I want to create a harmonious sociotechnical superintelligence to take over the world.

For a while I’ve been interested in Hadoop’s interesting case of being one software project with two companies working together to build it. This is reminiscent (for me) of when we started GeoExt at OpenGeo and Camp2Camp. The economics of shared capital are fascinating and there are interesting questions about how human resources get organized in that sort of situation. In my experience, there becomes a tension between the needs of firms to differentiate their products and make good on their contracts and the needs of the developer community whose collective value is ultimately tied to the robustness of their technology.

Unfortunately, building out BigBang to integrate with various email, version control, and issue tracking backends is a lot of work and there’s only one of me right now to both build the infrastructure, do the research, and train new collaborators (who are starting to do some awesome work, so this is paying off.) While integrating with Apache’s infrastructure would have been a smart first move, instead I chose to focus on Mailman archives and git repositories. Google Groups and whatever Apache is using for their email lists do not publish their archives in .mbox format, which is pain for me. But luckily Google Takeout does export data from folks’ on-line inbox in .mbox format. This is great for BigBang because it means we can investigate email data from any project for which we know an insider willing to share their records.

Does a research ethics issue arise when you start working with email that is openly archived in a difficult format, then exported from somebody’s private email? Technically you get header information that wasn’t open before–perhaps it was ‘private’. But arguably this header information isn’t personal information. I think I’m still in the clear. Plus, IRB will be irrelevent when the robots take over.

All of this is a long way of getting around to talking about a new thing I’m wondering about, the Node.js fork. It’s interesting to think about open source software forks in light of Hirschman’s concepts of Exit and Voice since so much of the activity of open source development is open, virtual communication. While you might at first think a software fork is definitely a kind of Exit, it sounds like IO.js was perhaps a friendly fork of just somebody who wanted to hack around. In theory, code can be shared between forks–in fact this was the principle that GitHub’s forking system was founded on. So there are open questions (to me, who isn’t involved in the Node.js community at all and is just now beginning to wonder about it) along the lines of to what extent a fork is a real event in the history of the project, vs. to what extent it’s mythological, vs. to what extent it’s a reification of something that was already implicit in the project’s sociotechnical structure. There are probably other great questions here as well.

A friend on the inside tells me all the action on this happened (is happening?) on the GitHub issue tracker, which is definitely data we want to get BigBang connected with. Blissfully, there appear to be well supported Python libraries for working with the GitHub API. I expect the first big hurdle we hit here will be rate limiting.

Though we haven’t been able to make integration work yet, I’m still hoping there’s some way we can work with MetricsGrimoire. They’ve been a super inviting community so far. But our software stacks and architecture are just different enough, and the layers we’ve built so far thin enough, that it’s hard to see how to do the merge. A major difference is that while MetricsGrimoire tools are built to provide application interfaces around a MySQL data backend, since BigBang is foremost about scientific analysis our whole data pipeline is built to get things into Pandas dataframes. Both projects are in Python. This too is a weird microcosm of the larger sociotechnical ecosystem of software production, of which the “open” side is only one (important) part.

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January 25, 2015

The solution to Secular Stagnation is more gigantic stone monuments

Because I am very opinionated, I know what we should do about secular stagnation.

Secular stagnation is what economists are calling the problem of an economy that is growing incorrigibly slowly due to insufficient demand–low demand caused in part by high inequality. A consequence of this is that for the economy to maintain high levels of employment, real interest rates need to be negative. That is bad for people who have a lot of money and nothing to do with it. What, they must ask themselves in their sleepless nights, can we do with all this extra money, if not save it and earn interest?

History provides an answer for them. The great empires of the past that have had more money than they knew what to do with and lots of otherwise unemployed people built gigantic stone monuments. The Pyramids of Egypt. Angor Wat in Cambodia. Easter Island. Machu Pichu.

The great wonders of the world were all, in retrospect, enormous wastes of time and money. They also created full employment and will be considered amazing forever.

Chances like this do not come often in history.

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July 6, 2014

economic theory and intellectual property

I’ve started reading Piketty’s Capital. His introduction begins with an overview of the history of economic theory, starting with Ricardo and Marx.

Both these early theorists predicted the concentration of wealth into the hands of the owners of factors of production that are not labor. For Ricardo, land owners extract rents and dominate the economy. For Marx, capitalists–owners of private capital–accumulate capital and dominate the economy.

Since those of us with an eye on the tech sector are aware of a concentration of wealth in the hands of the owners of intellectual property, it’s a good question what kind of economic theory ought to apply to those cases.

One one sense, intellectual property is a kind of capital. It is a factor of production that is made through human labor.

On the other hand, we talk about ideas being ‘discovered’ like land is discovered, and we imagine that intellectual property can in principle be ‘shared’ like a ‘commons’. If we see intellectual property as a position in a space of ideas, it is not hard to think of it like land.

Like land, a piece of intellectual property is unique and gains in value due to further improvements–applications or innovations–built upon it. In a world where intellectual property ownership never expires and isn’t shared, you can imagine that whoever hold some critical early work in some field could extract rents for perpetuity. Owning a patent would be like owning a land estate.

Like capital, intellectual property is produced by workers and often owned by those investing in the workers with pre-existing capital. The produced capital is then owned by the initiating capitalist, and accumulates.

Open source software is an important exception to this pattern. This kind of intellectual property is unalienated from those that produce it.

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June 19, 2014

turns out network backbone markets in the US are competitive after all

I’ve been depressed lately about the oligopolistic control of telecommunications for a while now. There’s the Web We’ve Lost; there’s Snowden leaks; there’s the end of net neutrality. I’ll admit a lot of my moodiness about this has been just that–moodiness. But it was moodiness tied to a particular narrative.

In this narrative, power is transmitted via flows of information. Media is, if not determinative of public opinion, determinative of how that opinion is acted up. Surveillance is also an information flow. Broadly, mid-20th century telecommunications enabled mass culture due to the uniformity of media. The Internet’s protocols allowed it to support a different kind of culture–a more participatory one. But monetization and consolidation of the infrastructure has resulted in a society that’s fragmented but more tightly controlled.

There is still hope of counteracting that trend at the software/application layer, which is part of the reason why I’m doing research on open source software production. One of my colleagues, Nick Doty, studies the governance of Internet Standards, which is another piece of the puzzle.

But if the networking infrastructure itself is centrally controlled, then all bets are off. Democracy, in the sense of decentralized power with checks and balances, would be undermined.

Yesterday I learned something new from Ashwin Mathew, another colleague who studies Internet governance at the level of network administration. The man is deep in the process of finishing up his dissertation, but he looked up from his laptop for long enough to tell me that the network backbone market is in fact highly competitive at the moment. Apparently, there was a lot of dark fiberoptic cable (“dark fiber“–meaning, no light’s going through it) laid during the first dot-com boom, which has been laying fallow and getting bought up by many different companies. Since there are many routes from A to B and excess capacity, this market is highly competitive.

Phew! So why the perception of oligopolistic control of networks? Because the consumer-facing telecom end-points ARE an oligopoly. Here there’s the last-mile problem. When wire has to be laid to every house, the economies of scale are such that it’s hard to have competitive markets. Enter Comcast etc.

I can rest easier now, because I think that this means there’s various engineering solutions to this (like AirJaldi networks? though I think those still aren’t last mile…; mesh networks?) as well as political solutions (like a local government running its last mile network as a public utility).

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April 20, 2013

Ascendency and overhead in networked ecosystems

Ulanowicz (2000) proposes in information-theoretic terms several metrics for ecosystem health, where one models an ecosystem as a for example a trophic network. Principal among them ascendancy , which is a measure of the extent to which energy flows in the system are predictably structured weighted by the total energy of the system. He believes that systems tend towards greater ascendancy in expectation, and that this is predictive of ecological ‘succession’ (and to some extent ecological fitness). On the other hand, overhead, which is unpredictability (perhaps, inefficiency) in energy flows (“free energy”?), are important for the system’s resiliency towards external shocks.

At least in the papers I’ve read so far, Ulanowicz is not mathematically specific about the mechanism that leads to greater ascendancy, though he sketches some explanations. Autocatalytic cycles within the network reinforce their own positive perturbations and mutations, drawing in resources from external sources, crowding out and competing with them. These cycles become agents in themselves, exerting what Ulanwicz suggests is Aristotelian final or formal causal power on the lower level components. In this way, freely floating energy is drawn into structures of increasing magnificence and complexity.

I’m reminded on Bataille’s The Accursed Share, in which he attempts to account for societal differences and the arc of human history through the use of its excess energy. “The sexual act is in time what the tiger is in space,” he says, insightfully. The tiger, as an apex predator, is flame that clings brilliantly to the less glamorous ecosystem that supports it. That is why we adore them. And yet, their existence is fragile, as it depends on both the efficiency and stability of the rest of its network. When its environment is disturbed, it is the first to suffer.

Ulanowicz cites himself suggesting that a similar framework could be used to analyze computer networks. I have not read his account yet, though I anticipate several difficulties. He suggests that data flows in a computer network are analogous to energy flows within an ecosystem. That has intuitive appeal, but obscures the fact that some data is more valuable than others. A better analogy might be money as a substitute for energy. Or maybe there is a way to reduce both to a common currency, at least for modeling purposes.

Econophysics has been gaining steam, albeit controversially. Without knowing anything about it but based just on statistical hunches, I suspect that this comes down to using more complex models on the super duper complex phenomenon of the economy, and demonstrating their success there. In other words, I’m just guessing that the success of econophysics modeling is due to the greater degrees of freedom in the physics models compared to non-dynamic, structural equilibrium models. However, since ecology models the evolutionary dynamics of multiple competing agents (and systems of those agents), its possible that those models could capture quite a bit of what’s really going on and even be a source of strategic insight.

Indeed, economics already has a sense of stable versus unstable equilibria that resonate with the idea of stability of ecological succession. These ideas translate into game theoretic analysis as well. As we do more work with Strategic Bayesian Networks or other constructs to model equilibrium strategies in a networked, multi-agent system, I wonder if we can reproduce Ulanowicz’s results and use his ideas about ascendancy (which, I’ve got to say, are extraordinary and profound) to provide insight into the information economy.

I think that will require translating the ecosystem modeling into Judea Pearl’s framework for causal reasoning. Having been indoctrinated in Pearl’s framework in much of my training, I believe that it is general enough to subsume Ulanowicz’s results. But I have some doubt. In some of his later writings Ulanowicz refers explicitly to a “Hegelian dialectic” between order and disorder as a consequence of some of his theories, and between that and his insistence on his departure from mechanistic thinking over the course of his long career, I am worried that he may have transcended what it’s possible to do even with the modeling power of Bayesian networks. The question is: what then? It may be that once one’s work sublimates beyond our ability to model explicitly and intervene strategically, it becomes irrelevant. (I get the sense that in academia, Ulanwicz’s scientific philosophizing is a privilege reserved for someone tenured who late in their career is free to make his peace with the world in their own way) But reading his papers is so exhilarating to me. I’ve had no prior exposure to ecology before this, so his papers are packed with fresh ideas. So while I don’t know how to justify it to any of my mentors or colleagues, I think I just have to keep diving into it when I can, on the side.

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January 13, 2013

Pharmaceuticals, Patents, and the Media

I had an interesting conversation the other day with a health care professional. He was lamenting the relationship between doctors and pharmaceutical companies.

Pharmaceutical companies, he reported, put a lot of pressure on doctors to prescribe and sell drugs, giving them bonuses if they sell certain quotas. This provides an incentive for doctors to prescribe drugs that don’t cure patients. When you sell medicine and not health, why heal a patient?

I’ve long been skeptical about pharmaceutical companies for another reason: as an industry, they seem to be primarily responsible for use and abuse of the patent system. Big Pharma lobbies congress to keep patent laws strong, but then also games the patent system. For example, it’s common practice for pharmaceutical companies to make trivial changes to a drug formula in order to extend an existing patent past its normal legal term (14 years). The result is a de facto Sonny Bono law for patents.

The justification for strong patents is, of course, the problem of recouping fixed costs from research investment. So goes the argument: Pharmaceutical research is expensive, but pharmaceutical production is cheap. If firms can freely compete in the drug market, new firms will enter after a research phase and prices will drop so the original researching company makes no profit. Without that profit, they will never do the research in the first place.

This is a fair argument as far as it goes.

However, this economic argument provides a cover story that ignores other major parts of the pharmaceutical industry. Let’s talk about advertising. When Big Pharma puts out a $14 million dollar Super Bowl commercial, is that dipping into the research budget? Or is that part of a larger operating cost endured by these companies–the costs of making their brand a household name, of paying doctors to make subscriptions, and of lobbying for a congenial political climate?

A problem is that when pharmaceutical companies are not just researching and selling drugs but participating as juggernauts in the information economy, it’s very hard to tell how much of their revenue is necessary for innovation and how much funds bullying for unfair market advantage that hurts patients.

There are some possible solutions to this.

We can hope for real innovation in alternative business models for pharmaceutical research. Maybe we can advocate for more public funding through the university system. But that advocacy requires political will, which is difficult to foster without paid lobbyists or grassroots enthusiasm. Grassroots enthusiasm depends on the participating of the media.

Which gets us to the crux. Because if big media companies are cashing out from pharmaceutical advertising, what incentive do they have to disrupt the economic and political might of Big Pharma? It’s like the problem of relying on mass media to support campaign finance reform. Why would they shatter their own gravy train?

Lately, I’ve been seeing political problems more and more as aligned with centralization of the media. (Not a new observation by any means, but here I am late to the party). There are some major bottlenecks to worldwide information flow, and economic forces battle for these like mountain passes on ancient trade routes. Thankfully, this is also an area where there is a terrific amount of innovation and opportunity.

Here’s an interesting research question: how does one design a news dissemination network with mass appeal that both provides attractive content while minimizing potential for abuse by economic interests that are adversarial to the network users?

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October 9, 2012

telecom security and technonationalism

EDIT: This excellent article by Farhad Manjoo has changed my mind or at least my attitude about this issue. Except for the last paragraph, which I believe is a convergent truth.

Reading this report about the U.S. blocking Huawei telecom components in government networks is a bit chilling.

The U.S. invests a lot of money into research of anti-censorship technology that would, among other things, disrupt the autocratic control China maintains over its own network infrastructure.

So from the perspective of the military, telecommunications technology is a battlefield.

I think rightly so. The opacity and centrality of telecommunications and the difficulty of tracing cyber-security breaches make these into risky decisions.

The Economist’s line is:

So what is needed most is an international effort to develop standards governing the integrity and security of telecoms networks. Sadly, the House Intelligence Committee isn’t smart enough to see this.

That’s smug and doesn’t address the real security concerns, or the immense difficulty of establishing international standards on telecom security, let alone guaranteeing the implementation of those standards.

However, an easier solution than waiting for agreement among a standards body would be to develop an open hardware specification for the components that met the security standards and a system for verifying them. That would encourage a free market on secure telecom hardware, which Huawei and others could participate in if they liked.

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Category: economics