Digifesto

resisting the power of organizations

“From the day of his birth, the individual is made to feel there is only one way of getting along in this world–that of giving up hope in his ultimate self-realization. This he can achieve solely by imitation. He continuously responds to what he perceives about him, not only consciously but with his whole being, emulating the traits and attitudes represented by all the collectivities that enmesh him–his play group, his classmates, his athletic team, and all the other groups that, as has been pointed out, enforce a more strict conformity, a more radical surrender through complete assimilation, than any father or teacher in the nineteenth century could impose. By echoing, repeating, imitating his surroundings, by adapting himself to all the powerful groups to which he eventually belongs, by transforming himself from a human being into a member of organizations, by sacrificing his potentialities for the sake of readiness and ability to conform to and gain influence in such organizations, he manages to survive. It is survival achieved by the oldest biological means necessary, mimicry.” – Horkheimer, “Rise and Decline of the Individual”, Eclipse of Reason, 1947

Returning to Horkheimer‘s Eclipse of Reason (1947) after studying Beniger‘s Control Revolution (1986) serves to deepen ones respect for Horkheimer.

The two writers are for the most part in agreement as to the facts. It is a testament to their significance and honesty as writers that they are not quibbling about the nature of reality but rather are reflecting seriously upon it. But whereas maintains a purely pragmatic, unideological perspective, Horkheimer (forty years earlier) correctly attributes this pragmatic perspective to the class of business managers to whom Beniger’s work is directed.

Unlike more contemporary critiques, Horkheimer’s position is not to dismiss this perspective as ideological. He is not working within the postmodern context that sees all knowledge as contestable because it is situated. Rather, he is working with the mid-20th acknowledgment that objectivity is power. This is a necessary step in the criticality of the Frankfurt School, which is concerned largely with the way (real) power shapes society and identity.

It would be inaccurate to say that Beniger celebrates the organization. His history traces the development of social organization as evolving organism. Its expanding capacity for information processing is a result of the crisis of control unleashed by the integration of its energetic constituent components. Globalization (if we can extend Beniger’s story to include globalization) is the progressive organization of organizations of organization. It is interesting that this progression of organization is a strike against Weiner’s prediction of the need for society to arm itself against entropy. This conundrum is one we will need to address in later work.

For now, it is notable that Horkheimer appears to be responding to just the same historical developments later articulated by Beniger. Only Horkeimer is writing not as a descriptive scientist but as a philosopher engaged in the process of human meaning-making. This positions him to discuss the rise and decline of the individual in the era of increasingly powerful organizations.

Horkheimer sees the individual as positioned at the nexus of many powerful organizations to which he must adapt through mimicry for the sake of survival. His authentic identity is accomplished only when alone because submission to organizational norms is necessary for survival or the accumulation of organizational power. In an era where pragmatic ability to manipulate people, not spiritual ideals, qualifies one for organization power, the submissive man represses his indignation and rage at this condition and becomes an automoton of the system.

Which system? All systems. Part of the brilliance of both Horkheimer and Beniger is their ability to generalize over many systems to see their common effect on their constituents.

I have not read Horkheimer’s solution the individual’s problem of how to maintain his individuality despite the powerful organizations which demand mimicry of him. This is a pressing question when organizations are becoming ever more powerful by using the tools of data science. My own hypotheses, which is still in need of scientific validation, is that the solution lies in the intersecting agency implied by the complex topology of the organization of organizations.

software code as representation of knowledge

The reason why ubiquitous networked computing has changed how we represent knowledge is because the semantics of code are guaranteed by the mechnical implementations of its compilers.

This introduces a kind of discipline in the representation of knowledge as source code that is not present in natural language or even in formal mathematical notation, which must be interpreted by humans.

Evolutionarily, humanity’s innate capacity for natural language is well established. Literacy, however, is a trained skill that involves years of education. As Derrida points out in Of Grammatology, the transition from the understanding of language as speech or breath to the understanding of knowledge as text was a very significant change in the history of knowledge.

We have not yet adjusted institutionally to a world where knowledge is represented as code. Most of the institutions that run the world–the legal system, universities, etc.–still run on the basis of written language.

But the new institutions that are adapting to represent knowledge as data and software code to process it are becoming more powerful than these older institutions.

This power comes from these new institutions’ ability to assign the work of acting on their knowledge to computing machines that can work tirelessly and that integrate well with operations. These new institutions can process more information, gathered from more sources, than the old institutions. They are organizationally more intelligent than the older organizations. Because of this intelligence, they can accrue wealth and more power.

data science is not positivist, it’s power

Naively, we might assume that contemporary ‘data science’ is a form of positivist or post-positivist science. The scientist gathers data and subsumes it under logical formulae–models with fitted parameters. Indeed this is the case when data science is applied to natural phenomena, such as stars or the human genome.

The question of what kind of science ‘data science’ is becomes much more complex when we start to look at its application to social phenomena. This includes its application to the management of industrial and commercial technology–the so called “Internet of Things“. (Technology in general, and especially technology as situated socially, being a social phenomenon.)

There are (at least) two reasons why data science in these social domains is not strictly positivist.

The first is that, according to McKinsey’s Michael Chui, data science in the Internet of Things context is main about either real-time control or anomaly detection. Neither of these depends on the kind of nomothetic orientation that positivism requires. The former requires only an objective function over inputs to guide the steering of the dynamic system. The latter requires only the detection of deviation from historically observed patterns.

‘Data science’ applied in this context isn’t actually about the discovery of knowledge at all. It is not, strictly speaking, a science. Rather, it is a process through which the operations of existing technologies are related and improved by further technological interventions. Robust positivist engineering knowledge is applied to these cases. But however much the machines may ‘learn’, what they learn is not propositional.

Perhaps the best we can say is that ‘data science’ in this context is the science of techniques for making these kinds of interventions. As learning these techniques depends on mathematical rigor and empirical prototyping, we can say perhaps of the limited sense of ‘pure’ (not applied) data science that it is a positivist science.

But the second reason why data science is not positivist comes about as a result of its application. The problem is that when systems controlled by complex computational processes interact, the result is a more complex system. In adversarial cases, the interacting complex systems become the subject matter of cybersecurity research, towards which data science is one application. But as soon as on starts to study phenomena that are aware of the observer and can act in ways that respond to its presence, you get out of positivist territory.

A better way to think about data science might be to think of it in terms of perception. In, the visual system, data that comes in through the eye goes through many steps of preprocessing before it becomes the subject of attention. Visual representations feed into the control mechanisms of movement.

If we see data science not as a positivist attempt to discover natural laws, but rather as an extension of agency by expanding powers of perception and training skillful control, then we can get a picture of data science that’s consistent with theories of situated and embodied cognition.

These theories of situated and embodied cognition are perhaps the best contenders for what can displace the dominant paradigm as imagined by critics of cognitive science, economics, etc. Rather than being a rejection of explanatory power of naturalistic theories of information processing, these theories extend naive theories to embrace the complexity of how agents cognition is situated in a body in time, space, and society.

If we start to think of ‘data science’ not as a kind of natural science but as the techniques and tools for extending the information processing that is involved in ones individual or collective agency, then we can start to think about data science as what it really is: power.

is science ideological?

In a previous post, I argued that Beniger is an unideological social scientist because he grounds his social scientific theory in robust theory from the natural and formal sciences, like theory of computation and mathematical biology. Astute commenter mg has questioned this assertion.

Does firm scientific grounding absolve a theoretical inquiry from ideology – what about the ideological framework that the science itself has grown in and is embedded in? Can we ascribe such neutrality to science?

This is a good question.

To answer it, it would be good to have a working definition of ideology. I really like one suggested by this passage from Habermas, which I have used elsewhere.

The concept of knowledge-constitutive human interests already conjoins the two elements whose relation still has to be explained: knowledge and interest. From everyday experience we know that ideas serve often enough to furnish our actions with justifying motives in place of the real ones. What is called rationalization at this level is called ideology at the level of collective action. In both cases the manifest content of statements is falsified by consciousness’ unreflected tie to interests, despite its illusion of autonomy. The discipline of trained thought thus correctly aims at excluding such interests. In all the sciences routines have been developed that guard against the subjectivity of opinion, and a new discipline, the sociology of knowledge, has emerged to counter the uncontrolled influence of interests on a deeper level, which derive less from the individual than from the objective situation of social groups.

If we were to extract a definition of ideology from this passage, it would be something like this: an ideology is:

  1. an expression of motives that serves to justify collective action by a social group
  2. …that is false because it is unreflective of the social group’s real interests.

I maintain that the theories that Beniger uses to frame his history of technology are unideological because they are not expressions of motives. They are descriptive claims whose validity has been tested thoroughly be multiple independent social groups with conflicting interests. It’s this validity within and despite the contest of interests which gives scientific understanding its neutrality.

Related: Brookfield’s “Contesting Criticality: Epistemological and Practical Contradictions in Critical Reflection” (here), which I think is excellent, succinctly describes the intellectual history of criticality and how contemporary usage of it blends three distinct traditions:

  1. a Marxist view of ideology as the result of objectively true capitalistic social relations,
  2. a psychoanalytic view of ideology as a result of trauma or childhood,
  3. and a pragmatic/constructivist/postmodern view of all knowledge being situated.

Brookfield’s point is that an unreflective combination of these three perspectives is incoherent both theoretically and practically. That’s because while the first two schools of thought (which Habermas combines, above–later Frankfurt School writers deftly combined Marxism is psychoanalysis) both maintain an objectivist view of knowledge, the constructivists reject this in favor of a subjectivist view. Since discussion of “ideology” comes to us from the objectivist tradition, there is a contradiction in the view that all science is ideological. Calling something ‘ideological’ or ‘hegemonic’ requires that you take a stand on something, such as the possibility of an alternative social system.

Fascinated by Vijay Narayanan’s talk at #DataEDGE

As I write this I’m watching Vijay Narayanan’s, Director of Algorithms and Data Science Solutions at Microsoft, talk at the DataEDGE conference at UC Berkeley.

The talk is about “The Data Science Economy.” It began with a history of the evolution of the human centralized nervous system. He then went on to show the centralizing trend of the data economy. Data collection will be become more mobile, data processing will be done in the cloud. This data will be sifted by software and used to power a marketplace of services, which ultimately deliver intelligence to their users.

It was wonderful to see somebody so in the know reaffirming what has been a suspicion I’ve had since starting graduate school but have found little support for in the academic setting. The suspicion is that what’s needed to accurately model the data science economy is a synthesis of cognitive science and economics that can show the comparative market value and competitiveness of different services.

This is not out of the mainline of information technology, management science, computer science, and other associated disciplines that have been at the nexus of business and academia for 70 years. It’s an intellectual tradition that’s rooted in the 1940’s cybernetics vision of Norbert Wiener and was going strong in the social sciences as late as Beniger‘s The Control Revolution, which, like Narayanan, draws an explicit connection between information processing in the brain and information processing in the microprocessor–notably while acknowledging the intermediary step of bureaucracy as a large-scale information processing system.

There’s significant cross-pollination between engineering, economics, computer science, and cognitive psychology. I’ve read papers from, say, the Education field in the late 80’s and early 90’s that refers to this collectively as “the dominant paradigm”. At UC Berkeley today, it’s fascinating to see a departmental politics play out over ‘data science’ that echoes some of these concerns that a powerful alliance of ideas are getting mobilized by industry and governments while other disciplines are struggling to find relevance.

It’s possible that these specialized disciplinary discourses are important for the cultivation of thought that is important for its insight despite being fundamentally impractical. I’m coming to a different view: that maybe the ‘dominant paradigm’ is dominant because it is scientifically true, and that other disciplinary orientations are suffering because they are based on unsound theory. If disciplines that are ‘dominated’ by another paradigm are floundering because they are, to put it simply, wrong, then that is a very elegant explanation for what’s going on.

The ramification of this is that what’s needed is not a number of alternatives to ‘the dominant paradignm’. What’s needed is that scholars double down on the dominant paradigm and learn how to express in its logic the complexities and nuances that the other disciplines have been designed to capture. What we can hope for, in terms of intellectual continuity, is the preservation of what’s best of older ideas in a creative synthesis with the foundational principles of computer science and mathematical biology.

I really like Beniger

I’ve been a fan of Castells for some time but reading Ampuja and Koivisto’s critique of him is driving home my new appreciation of Beniger‘s The Control Revolution (1986).

One reason why I like Beniger is that his book is an account of social history and its relationship with technology that is firmly grounded in empirically and formally validated scientific theory. That is, rather than using as a baseline any political ideological framework, Beniger grounds his analysis in an understanding of the algorithm based in Church and Turing, and understanding of biological evolution grounded in biology, and so on.

This allows him to extend ideas about programming and control from DNA to culture to bureaucracy to computers in a way that is straightforward and plausible. His goal is, admirably, to get people to see the changes that technology drives in society as a continuation of a long regular process rather than a reason to be upset or a transformation to hype up.

I think there is something fundamentally correct about this approach. I mean that with the full force of the word correct. I want to go so far as to argue that Beniger (at least as of Chapter 3…) is an unideological theory of history and society that is grounded in generalizable and universally valid scientific theory.

I would be interested to read a substantive critique of Beniger arguing otherwise. Does anybody know if one exists?

intersecting agencies and cybersecurity #RSAC

I recurring theme in my reading lately (such as, Beniger‘s The Control Revolution, Horkheimer‘s Eclipse of Reason, and Norbert Wiener’s Cybernetics work) is the problem of two ways of reconciling explanations of how-things-came-to-be:

  • Natural selection. Here a number of autonomous, uncoordinated agents with some exogenously given variability encounter obstacles that limit their reproduction or survival. The fittest survive. Adaptation is due to random exploration at the level of the exogenous specification of the agent, if at all. In unconstrained cases, randomness rules and there is no logic to reality.
  • Purpose. Here there is a teleological explanation based on a goal some agent has “in mind”. The goal is coupled with a controlling mechanism that influences or steers outcomes towards that goal. Adaptation is part of the endogenous process of agency itself.

Reconciling these two kinds of description is not easy. A point Beniger makes is that differences between social theories in the 20th century can be read as differences in the divisions of where one demarcates agents within a larger system.


This week at the RSA Conference, Amit Yoran, President of RSA, gave a keynote speech about the change in mindset of security professionals. Just the day before I had attended a talk on “Security Basics” to reacquaint myself with the field. In it, there was a lot of discussion of how a security professional needs to establish “the perimeter” of their organization’s network. In this framing, a network is like the nervous system of the macro-agent that is an organization. The security professional’s role is to preserve the integrity of the organization’s information systems. Even in this talk on “the basics”, the speaker acknowledged that a determined attacker will always get into your network because of the limitations of the affordances of defense, the economic incentives of attackers, and the constantly “evolving” nature of the technology. I was struck in particular by this speaker’s detachment from the arms race of cybersecurity. The goal-driven adversariality of the agents involved in cybersecurity was taken as a given; as a consequence, the system evolves through a process of natural selection. The role of the security professional is to adapt to an exogenously-given ecosystem of threats in a purposeful way.

Amit Yoran’s proposed escape from the “Dark Ages” of cybersecurity got away from this framing in at least one way. For Yoran, thinking about the perimeter is obsolete. Because the attacker will always be able to infiltrate, the emphasis must be on monitoring normal behavior within your organization–say, which resources are accessed and how often–and detecting deviance through pervasive surveillance and fast computing. Yoran’s vision replaces the “perimeter” with an all-seeing eye. The organization that one can protect is the organization that one can survey as if it was exogenously given, so that changes within it can be detected and audited.

We can speculate about how an organization’s members will feel about such pervasive monitoring and auditing of activity. The interests of the individual members of a (sociotechnical) organization, the interests of the organization as a whole, and the interests of sub-organizations within an organization can be either in accord or in conflict. An “adversary” within an organization can be conceived of as an agent within a supervening organization that acts against the latter’s interests. Like a cancer.

But viewing organizations purely hierarchically like this leaves something out. Just as human beings are capable of more complex, high-dimensional, and conflicted motivations than any one of the organs or cells in our bodies, so too should we expect the interests of organizations to be wide and perhaps beyond the understanding of anyone within it. That includes the executives or the security professionals, which RSA Conference blogger Tony Kontzer suggests should be increasingly one and the same. (What security professional would disagree?)

What if the evolution of cybersecurity results in the evolution of a new kind of agency?

As we start to think of new strategies for information-sharing between cybersecurity-interested organizations, we have to consider how agents supervene on other agents in possibly surprising ways. An evolutionary mechanism may be a part of the very mechanism of purposive control used by a super-agent. For example, an executive might have two competing security teams and reward them separately. A nation might have an enormous ecosystem of security companies within its perimeter (…) that it plays off of each other to improve the robustness of its internal economy, providing for it the way kombucha drinkers foster their own vibrant ecosystem of gut fauna.

Still stranger, we might discover ways that purposive agents intersect at the neuronal level, like Siamese twins. Indeed, this is what happens when two companies share generic networking infrastructure. Such mereological complexity is sure to affect the incentives of everyone involved.

Here’s the rub: every seam in the topology of agency, at every level of abstraction, is another potential vector of attack. If our understanding of the organizational agent becomes more complex as we abandon the idea of the organizational perimeter, that complexity provides new ways to infiltrate. Or, to put it in the Enlightened terms more aligned with Yoran’s vision, the complexity of the system with it multitudinous and intersecting purposive agents will become harder and harder to watch for infiltrators.

If a security-driven agent is driven by its need to predict and audit activity within itself, then those agents will let a level complexity within themselves that is bounded by their own capacity to compute. This point was driven home clearly by Dana Wolf’s excellent talk on Monday, “Security Enforcement (re)Explained”. She outlined several ways that the computationally difficult cybersecurity functions–such as anti-virus and firewall technology–are being moved to the Cloud, where elasticity of compute resources theoretically makes it easier to cope with these resource demands. I’m left wondering: does the end-game of cybersecurity come down to the market dynamics of computational asymmetry?

This blog post has been written for research purposes associated with the Center for Long-Term Cybersecurity.

Beniger on anomie and technophobia

The School of Information Classics group has moved on to a new book: James Beniger’s 1986 The Control Revolution: Technological and Economic Origins of the Information Society. I’m just a few chapters in but already it is a lucid and compelling account of how the societal transformations due to information technology that are announced bewilderingly every decade are an extension of a process that began in the Industrial Revolution and just has not stopped.

It’s a dense book with a lot of interesting material in it. One early section discusses Durkheim’s ideas about the division of labor and its effect on society.

In a nutshell, the argument is that with industrialization, barriers to transportation and communication break down and local markets merge into national and global markets. This induces cycles of market disruption where because producers and consumers cannot communicate directly, producers need to “trust to chance” by embracing a potentially limitless market. This creates and unregulated economy prone to crisis. This sounds a little like venture capital fueled Silicon Valley.

The consequence of greater specialization and division of labor is a greater need for communication between the specialized components of society. This is the problem of integration, and it affects both the material and the social. The specifically, the magnitude and complexity of material flows result in a sharpening division of labor. When properly integrated, the different ‘organs’ of society gain in social solidarity. But if communication between the organs is insufficient, then the result is a pathological breakdown of norms and sense of social purpose: anomie.

The state of anomie is impossible wherever solidary organs are sufficiently in contact or sufficiently prolonged. In effect, being continguous, they are quickly warned, in each circumstance, of the need which they have of one another, and, consequently, they have a lively and continuous sentiment of their mutual dependence… But, on the contrary, if some opaque environment is interposed, then only stimuli of a certain intensity can be communicated from one organ to another. Relations, being rare, are not repeated enough to be determined; each time there ensues new groping. The lines of passage taken by the streams of movement cannot deepen because the streams themselves are too intermittent. If some rules do come to constitute them, they are, however, general and vague.

An interesting question is to what extent Beniger’s thinking about the control revolution extend to today and the future. An interesting sub-question is to what extent Durkheim’s thinking is relevant today or in the future. I’ll hazard a guess that’s informed partly by Adam Elkus’s interesting thoughts about pervasive information asymmetry.

An issue of increasing significance as communication technology improves is that the bottlenecks to communication become less technological and more about our limitations as human beings to sense, process, and emit information. These cognitive limitations are being overwhelmed by the technologically enabled access to information. Meanwhile, there is a division of labor between those that do the intellectually demanding work of creating and maintaining technology and those that do the intellectually demanding work of creating and maintaining cultural artifacts. As intellectual work demands the specialization of limited cognitive resources, this results in conflicts of professional identity due to anomie.

Long story short: Anomie is why academic politics are so bad. It’s also why conferences specializing in different intellectual functions can harbor a kind of latent animosity towards each other.

causal inference in networks is hard

I am trying to make statistically valid inferences about the mechanisms underlying observational networked data and it is really hard.

Here’s what I’m up against:

  • Even though my data set is a complete ecologically valid data set representing a lot of real human communication over time, it (tautologically) leaves out everything that it leaves out. I can’t even count all the latent variables.
  • The best methods for detecting causal mechanism, the potential outcomes framework for Rubin model, depends on the assumption that different members of the sample don’t interfere. But I’m working with networked data. Everything interferes with everything else, at least indirectly. That’s why it’s a network.
  • Did I mention that I’m working with communications data? What’s interesting about human communication is that it’s not really generated at random at all. It’s very deliberately created by people acting more or less intelligently all the time. If the phenomenon I’m studying is not more complex than the models I’m using to study it, then there is something seriously wrong with the people I’m studying.

I think I can deal with the first point here by gracefully ignoring it. It may be true that any apparent causal effect in my data is spurious and due to a common latent cause upstream. It may be true that the variance in the data is largely due to exogenous factors. Fine. That’s noise. I’m looking for a reliable endogenous signal. If there isn’t something there that would suggest that my entire data set is epiphenomal. But I know it’s not. So there’s got to be something there.

For the second point, there are apparently sophisticated methods for extending the potential outcomes framework to handling peer effects. These are gnarly and though I figure I could work with them, I don’t think they are going to be what I need because I’m not really looking for a causal relationship like a statistical relationship between treatment and outcome. I’m not after in the first instance what might be called type causation. I’m rather trying to demonstrate cases of token causation where causation is literally the transfer of information from object to another. And then I’m trying to show regularity in this underlying kind of causation in a layer of abstraction over it.

The best angle I can come up with on this so far is to use emergent properties of the network like degree assortativity to sort through potential mathematically defined graph generation algorithms. These algorithms can act as alternative hypotheses, and the observed emergent properties can theoretically be used to compute the likelihood of the observed data given the generation methods. Then all I need is a prior over graph generation methods! It’s perfectly Bayesian! I wonder if it is at all feasible to execute on. I will try.

It’s not 100% clear how you can take an algorithmically defined process and turn that into a hypothesis about causal mechanisms. Theoretically, as long as a causal network has computable conditional dependencies it can be represented by and algorithm. I believe that any algorithm (in the Church/Turing sense) can be represented as a causal network. Can this be done elegantly, so that the corresponding causal network represents something like what we’d expect from the scientific theory on the matter? This is unclear because, again, Pearl’s causal networks are great at representing type causation but not as expressive of token causation among a large population of uniquely positioned, generatively produced stuff. Pearl is not good at modeling life, I think.

The strategic activity of the actors is a modeling challenge but I think this is actually where there is substantive potential in this kind of research. If effective strategic actors are working in a way that is observably different from naive actors in some way that’s measurable in aggregate behavior, that’s a solid empirical result! I have some hypotheses around this that I think are worth checking. For example, probably the success of an open source community depends in part on whether members of the community act in ways that successfully bring new members in. Strategies that cultivate new members are going to look different from strategies that exclude newcomers or try to maintain a superior status. Based on some preliminary results, it looks like this difference between successful open source projects and most other social networks is observable in the data.

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 Picketty’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 Picketty, 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 Picketty places on this process, he treats it hardly at all in his book.

Whether or not you buy Picketty’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 Picketty’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 Picketty’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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