We need a theory of collective agency to guide data intermediary design

Last week Jake Goldenfein and I presented some work-in-progress to the Centre for Artificial Intelligence and Digital Ethics (CAIDE) at the University of Melbourne. The title of the event was “Data science and the need for collective law and ethics”; perhaps masked by that title is the shift we’re taking to dive into the problem of data intermediaries. I wanted to write a bit about how we’re thinking about these issues.

This work builds on our work “Data Science and the Decline of Liberal Law and Ethics“, which was accepted by a conference that was then canceled due to COVID-19. In retrospect, it’s perhaps for the best that the conference was canceled. The “decline of liberalism” theme fit the political moment when we wrote the piece, when Trump and Sanders were contenders for the presidency of the U.S, and authoritarian regimes appeared to be providing a new paradigm for governance. Now, Biden is the victor and it doesn’t look like liberalism is going anywhere. We must suppose that our project will take place in a (neo)liberal context.

Our argument in that work was that many of the ideas animating the (especially Anglophone) liberalism we see in the U.S., the U.K., and Australia legal systems have been inadequate to meaningfully regulate artificial intelligence. This is because liberalism imagines a society of rational individuals appropriating private property through exchanges on a public market and acting autonomously, whereas today we have a wide range of agents with varying levels of bounded rationality, many of which are “artificial” in Herbert Simon’s sense of being computer-enabled firms, tied together in networks of control, not least of these being privately owned markets (the platforms). Essentially, loopholes in liberalism have allowed a quite different form of sociotechnical ordering to emerge because that political theory did not take into account a number of rather recently discovered scientific truths about information, computing, and control. Our project is to tackle this disconnect between theory and actuality, and to try to discover what’s next in terms of a properly cybernetic political theory that advances the goal of human emancipation.

Picking up where our first paper left off, this has gotten us looking at data intermediaries. This is an area where there has been a lot of work! We were particularly inspired by Mozilla’s Data Futures review of different forms of data intermediary institutions, including data coops, data trusts, data marketplaces, and so on. There is a wide range of ongoing experiments with alternative forms of “data stewardship” or “data governance”.

Our approach has been to try to frame and narrow down the options based on normative principles, legal options, and technical expertise. Rather than asking empirically what forms of data governance have been attempted, we are wondering: what ought the goals of a data intermediary be, given the facts about cybernetic agency in the world we live? How could such an institution accomplish what has been lost by the inadequacies of liberalism?

Our thinking has led us to the position that what has prevented liberalism from regulating the digital economy is its emphasis on individual autonomy. We draw on the new consensus in privacy scholarship that individual “notice and choice” is an ineffective way to guarantee consumer protection in the digital economy. Not only are bounded rationality constraints on consumers preventing them from understanding what they are agreeing to, but also the ability of firms to control consumer’s choice architecture has dwarfed the meaningfulness of whatever rationality individuals do have. Meanwhile, it is now well understood (perhaps most recently by Pistor (2020)) that personal data is valuable only when it is cleaned and aggregated. This makes the locus of economic agency around personal data necessarily a collective one.

This line of inquiry leads us to a deep question to which we do not yet have a ready answer, which is “What is collective emancipation in the paradigm of control?” Meaning, given what we know about the “sciences of the artificial”, control theory, theory of computation and information, etc., with all of its challenges to the historical idea of the autonomous liberal agent, what does it mean for a collective of individuals to be free and autonomous?

We got a lot of good feedback on our talk, especially from discussant Seth Lazar, who pointed out that there are many communitarian strands of liberalism that we could look to for normative guides. He mentioned, for example, Elizabeth Anderson’s relational egalitarianism. We asked Seth whether he thought that the kind of institution that guaranteed the collective autonomy of its members would have to be a state, and he pointed out that that was a question of whether or not such a system would be entitled to use coercion.

There’s a lot to do on this project. While it is quite heady and philosophical, I do not think that it is necessarily only an abstract or speculative project. In a recent presentation by Vincent Southerland, he proposed that one solution to the problematic use of algorithms in criminal sentencing would be if “the community” of those advocating for equity in the criminal justice system operated their own automated decision systems. This raises an important question: how could and should a community govern its own a technical systems, in order to support what in Southerland’s case is an abolitionist agenda. I see this as a very aligned project.

There is also a technical component to the problem. Because of economies of scale and the legal climate, more and more computation is moving onto proprietary cloud systems. Most software now is provided “as a service”. It’s unclear what this means for organizations that would try to engage in self-governance, even when these organizations are autonomous state entities such as municipalities. In some conversations, we have considered what modifications of the technical ideas of the “user agent”, security firewalls and local networks, and hybrid cloud infrastructure would enable collective self-governance. This is the pragmatic “how?” that follows our normative “what?” and “why?” question but it is no less important to implementing a prototype solution.


Benthall, Sebastian and Goldenfein, Jake, Data Science and the Decline of Liberal Law and Ethics (June 22, 2020). Available at SSRN: https://ssrn.com/abstract=3632577 or http://dx.doi.org/10.2139/ssrn.3632577

Narayanan, A., Toubiana, V., Barocas, S., Nissenbaum, H., & Boneh, D. (2012). A critical look at decentralized personal data architectures. arXiv preprint arXiv:1202.4503.

Pistor, K. (2020). Rule by data: The end of markets?. Law & Contemp. Probs.83, 101.

Regulating infoglut?

In the 20’s, many people were attracted for the first time in investing in the stock market. It was a time when fortunes were made and lost, but made more than they were lost, and so on average investors saw large returns. However, the growth in value of stocks was driven in part, and especially in the later half of the decade, by debt. The U.S. Federal Reserve chose to lower interest rates, making it easier to borrow money. When the interest rates on loans were lower than the rates of return on stocks, everybody from households to brokers began to take on debt to reinvest in the stock market. (Brooks, 1999)

After the crash of ’29, which left the economy decimated, there was a reckoning, leading to the Securities Act of 1933 and the Securities Exchange Act of 1934. The latter established the Securities and Exchange Commission (SEC), and established the groundwork for the more trusted financial institutions we have today.

Cohen (2016) writes about a more current economic issue. As the economy changes from being centered on industrial capitalism to informational capitalism, the infrastructural affordances of modern computing and networking have invalidated the background logic of how many regulations are supposed to work. For example, anti-discrimination regulation is designed to prevent decisions from being made based on protected or sensitive attributes of individuals. However, those regulations made most sense when personal information was relatively scarce. Today, when individual activity is highly instrumented by pervasive computing infrastructure, we suffer from infoglut — more information than is good for us, either as individuals or as a society. As a consequence, proxies of protected attributes are readily available for decision-makers and indeed are difficult to weed out of a machine learning system even when market actors fully intend to do so (see Datta et al., 2017). In other words, the structural conditions that enable infoglut erode rights that we took for granted in the absence of today’s network and computing systems.

In an ongoing project with Salome Viljoen, we are examining the parallels between the financial economy and the data economy. These economies are, of course, not fully distinct. However, they are distinguished in part by how they are regulated: the financial economy has over a century of matured regulations defining it and reducing system risks such as those resulting from a debt-financed speculative bubble; the data economy has emerged only recently as a major source of profit with perhaps unforeseen systemic risks.

We have an intuition that we would like to pin down more carefully as we work through these comparisons: that there is something similar about the speculative bubbles that led to the Great Depression and today’s infoglut. In a similar vein to prior work looking that uses regulatory analogy to motivate new thinking about data regulation (Hirsch, 2013; Froomkin, 2015) and professional codes (Stark and Hoffman, 2019), we are interested in how financial regulation may be a precedent for regulation of the data economy.

However, we have reason to believe that the connections between finance and personal data are not merely metaphorical. Indeed, finance is an area with well-developed sectoral privacy laws that guarantee the confidentiality of personal data (Swire, 2003); it is also the case that financial institutions are one of the many ways personal data originating from non-financial contexts is monetized. We do not have to get poetic to see how these assets are connected; they are related as a matter of fact.

What is more elusive, and at this point only a hypothesis, is that there is valid sense in which the systemic risks of infoglut can be conceptually understood using tools similar to those that are used to understand financial risk. Here I maintain an ambition: that systemic risk due to infoglut may be understood using the tools of macroeconomics and hence internalized via technocratic regulatory mechanisms. This would be a departure from Cohen (2016), who gestures more favorably towards “uncertainty” based regulation that does not attempt probabilistic expectation but rather involves tools such as threat modeling, as used in some cybersecurity practices.


Brooks, J. (1999). Once in Golconda: A true drama of Wall Street 1920-1938. John Wiley & Sons.

Cohen, J. E. (2016). The regulatory state in the information age. Theoretical Inquiries in Law17(2), 369-414.

Datta, A., Fredrikson, M., Ko, G., Mardziel, P., & Sen, S. (2017, October). Use privacy in data-driven systems: Theory and experiments with machine learnt programs. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (pp. 1193-1210).

Froomkin, A. M. (2015). Regulating Mass Surveillance as Privacy Pollution: Learning from Environmental Impact Statements. U. Ill. L. Rev., 1713.

Hirsch, D. D. (2013). The glass house effect: Big Data, the new oil, and the power of analogy. Me. L. Rev.66, 373.

Stark, L., & Hoffmann, A. L. (2019). Data is the new what? Popular metaphors & professional ethics in emerging data culture.

Swire, P. P. (2003). Efficient confidentiality for privacy, security, and confidential business information. Brookings-Wharton Papers on Financial Services2003(1), 273-310.

Surden, H. (2007). Structural rights in privacy. SMUL Rev.60, 1605.

double contingency and technology

One of the best ideas to come out of the social sciences is “double contingency”: the fact that two people engaged in communication are in a sense unpredictable to each other. That mutual unpredictability is an element of what it means to be in communication with another.

The most recent articulation of this idea is from Luhmann, who was interested in society as a system of communication. Luhmann is not focused on the phenomenology of the participants in a social system; in as sense, he looks like social systems the way an analyst might look at communications data from a social media site. The social system is the set of messages. Luhmann is an interesting figure in intellectual history in part because he is the one who made the work of Maturana and Varela officially part of German philosophical canon. That’s a big deal, as Maturana and Varela’s intellectual contributions–around the idea of autopoiesis, for example–were tremendously original, powerful, and good.

“Double contingency” was also discussed, one reads, by Talcott Parsons. This does not come up often because at some point the discipline of Sociology just decided to bury Parsons.

Double contingency comes up in interesting ways in European legal scholarship about technology. Luhmann, a dense German writer, is not read much in the United States, despite his being essentially right about things. Hildebrandt (2019) uses double contingency in her perhaps perplexingly framed argument for the “incomputability” of human personhood. Teubner (2006) makes a somewhat different but related argument about agency, double contingency, and electronic agents.

Hildebrandt and Teubner make for an interesting contrast. Hildebrandt is interested in the sanctity of humanity qua humanity, and in particular of privacy defined as the freedom to be unpredictable. This is an interesting inversion for European phenomenological philosophy. Recall that originally in European phenomenology human dignity was tied to autonomy, but autonomy depended on universalized rationality, with the implication that the most important thing about human dignity was that one followed universal moral rules (Kant). Hildebrandt is almost staking out an opposite position: that Arendtian natality, the unpredictableness of being an original being at birth, is the source of one’s dignity. Paradoxically, Hildebrandt argues that it humanity has this natality essentially and so claims that predictive technology might truly know the data subject are hubris, but also that the use of these predictive technologies is threat to natality unless their use is limited by data protection laws that ensure contestability of automated decisions.

Teubner (2006) takes a somewhat broader and, in my view, more self-consistent view. Grounding his argument firmly in Luhmann and Latour, Teubner is interested in the grounds of legally recognized (as opposed to ontologically, philosophically sanctified) personhood. And, he finds, the conditions of personhood can apply to many things besides humans! “Black box, double contingency, and addressability”, three fictions on which the idea of personhood depend, can apply to corporations and electronic agents as well as humans individually. This provides a kind of consistency and rationale for why we allow these kinds of entities to engage in legal contracts with each other. The contract, it is theorized, is a way of managing uncertainty, reducing the amount of contingency in the inherent “double contingency”-laden relationship.

Something of the old Kantian position comes through in Teubner, in that contracts and the law are regulatory. However, Teubner, like Nissenbaum, is ultimately a pluralist. Teubner writes about multiple “ecologies” in which the subject is engaged, and to which they are accountable in different modalities. So, the person, qua economic agent, is addressed in terms of their preferences. But the person, qua legal institutions, is addressed in terms of their embodiment of norms. The “whole person” does not appear in any singular ecology.

I’m sympathetic with the Teubnerian view here, perhaps in contrast with Hildebrandt’s view, the the following sense: while there may indeed be some intrinsic indeterminacy to an individual, this indeterminacy is meaningless unless it is also situated in (some) social ecology. However, what makes a person contingent visa vie one ecology is precisely that only a fragment of them is available to that ecology. The contingency to the first ecology is a consequence of their simultaneous presence within other ecologies. The person is autonomous, and hence also unpredictable, because of this multiplied, fragmented identity. Teubner, I think correctly, concludes that there is a limited form of personhood to non-human agents, but as these agents will be even more fragmented than humans, they are only persons in an attenuated sense.

I’d argue that Teubner helpfully backfills how personhood is socially constructed and accomplished, as opposed to guaranteed from birth, in a way that complements Hildebrandt nicely. In the 2019 article cited here, Hildebrandt argues for contestability of automated decisions as a means of preserving privacy. Teubner’s theory suggests that personhood–as participant in double contingency, as a black box–is threatened rather by context collapse, or the subverting of the various distinct social ecologies into a single platform in which data is shared ubiquitously between services. This provides a normative a universalist defense of keeping contexts separate (which in a different article Hildebrandt connects to purpose binding in the GDPR) which is never quite accomplished in, for example, Nissenbaum’s contextual integrity.


Hildebrandt, Mireille. “Privacy as protection of the incomputable self: From agnostic to agonistic machine learning.” Theoretical Inquiries in Law 20.1 (2019): 83-121.

Teubner, Gunther. “Rights of non‐humans? Electronic agents and animals as new actors in politics and law.” Journal of Law and Society 33.4 (2006): 497-521.

System 2 hegemony and its discontents

Recent conversations have brought me back to the third rail of different modalities of knowledge and their implications for academic disciplines. God help me. The chain leading up to this is: a reminder of how frustrating it was trying to work with social scientists who methodologically reject the explanatory power of statistics, an intellectual encounter with a 20th century “complex systems” theorist who also didn’t seem to understand statistics, and the slow realization that’s been bubbling up for me over the years that I probably need to write an article or book about the phenomenology of probability, because I can’t find anything satisfying about it.

The hypothesis I am now entertaining is that probabilistic or statistical reasoning is the intellectual crux, disciplinarily. What we now call “STEM” is all happy to embrace statistics as its main mode of empirical verification. This includes the use of mathematical proof for “exact” or a priori verification of methods. Sometimes the use of statistics is delayed or implicit; there is qualitative research that is totally consistent with statistical methods. But the key to this whole approach is that the fields, in combination, are striving for consistency.

But not everybody is on board with statistics! Why is that?

One reason may be because statistics is difficult to learn and execute. Doing probabilistic reasoning correctly is at times counter-intuitive. That means that quite literally it can make your head hurt to think about it.

There is a lot of very famous empirical cognitive psychology that has explored this topic in depth. The heuristics and biases research program of Kahneman and Tversky was critical for showing that human behavior rarely accords with decision-theoretic models of mathematical, probabilistic rationality. An intuitive, “fast”, prereflective form of thinking, (“System 1”) is capable of making snap judgments but is prone to biases such as the availability heuristic and the representativeness heuristic.

A couple general comments can be made about System 1. (These are taken from Tetlock’s review of this material in Superforecasting). First, a hallmark of System 1 is that it takes whatever evidence it is working with as given; it never second-guesses it or questions its validity. Second, System 1 is fantastic at provided verbal rationalizations and justifications of anything that it encounters, even when these can be shown to be disconnected from reality. Many colorful studies of split brain cases, but also many other lab experiments, show the willingness people have to make of stories to explain anything, and their unwillingness to say, “this could be due to one of a hundred different reasons, or a mix of them, and so I don’t know.”

The cognitive psychologists will also describe a System 2 cognitive process that is more deliberate and reflective. Presumably, this is the system that is sometimes capable of statistical or otherwise logical reasons. And a big part of statistical reasoning is questioning the source of your evidence. A robust application of System 2 reasoning is capable of overcoming System 1’s biases. At the level of institutional knowledge creation, the statistical sciences are comprised mainly of formalized, shared results of System 2 reasoning.

Tetlock’s work, from Expert Political Judgment and on, is remarkable for showing that deference to one or the other cognitive system is to some extent a robust personality trait. Famously, those of the “hedgehog” cognitive style, who apply System 1 and a simplistic theory of the world to interpret everything they experience, are especially bad at predicting the outcomes of political events (what are certainly the results of ‘complex systems’), whereas the “fox” cognitive style, which is more cautious about considering evidence and coming to judgments, outperforms them. It seems that Tetlock’s analysis weighs in favor of System 2 as a way of navigating complex systems.

I would argue that there are academic disciplines, especially those grounded in Heideggerian phenomenology, that see the “dominance” of institutions (such as academic disciplines) that are based around accumulations of System 2 knowledge as a problem or threat.

This reaction has several different guises:

  • A simple rejection of cognitive psychology, which has exposed the System 1/System 2 distinction, as “behaviorism”. (This obscures the way cognitive psychology was a major break away from behaviorism in the 50’s.)
  • A call for more “authentic experience”, couched in language suggesting ownership or the true subject of one’s experience, contrasting this with the more alienated forms of knowing that rely on scientific consensus.
  • An appeal to originality: System 2 tends to converge; my System 1 methods can come up with an exciting new idea!
  • The interpretivist methodological mandate for anthropological sensitivity to “emic”, or directly “lived experience”, of research subjects. This mandate sometimes blurs several individually valid motivations, such as: when emic experience is the subject matter in its own right, but (crucially) with the caveat that the results are not generalizable; when emic sensitivity is identified via the researcher’s reflexivity as a condition for research access; or when the purpose of the work is to surface or represent otherwise underrepresented views.

There are ways to qualify or limit these kinds of methodologies or commitments that makes them entirely above reproach. However, under these limits, their conclusions are always fragile. According to the hegemonic logic of System 2 institutions, a consensus of those thoroughly considering the statistical evidence can always supercede the “lived experience” of some group or individual. This is, at the methodological level, simply the idea that while we may make theory-laden observations, when those theories are disproved, those observations are invalidated as being influenced by erronenous theory. Indeed, mainstream scientific institutions take as their duty this kind of procedural objectivity. There is no such thing as science unless a lot of people are often being proven wrong.

This provokes a great deal of grievance. “Who made scientists, an unrepresentative class of people and machines disconnected from authentic experience, the arbiter of the real? Who are they to tell me I am wrong, or my experiences invalid?” And this is where we start to find trouble.

Perhaps most troubling is how this plays out at the level of psychodynamic politics. To have one’s lived experiences rejected, especially those lived experiences of trauma, and especially when those experiences are rejected wrongly, is deeply disturbing. One of the more mighty political tendencies of recent years has been the idea that whole classes of people are systematically subject to this treatment. This is one reason, among others, for influential calls for recalibrating the weight given to the experiences of otherwise marginalized people. This is what Furedi calls the therapeutic ethos of the Left. This is slightly different from, though often conflated with, the idea that recalibration is necessary to allow in more relevant data that was being otherwise excluded from consideration. This latter consideration comes up in a more managerialist discussion of creating technology that satisfies diverse stakeholders (…customers) through “participatory” design methods. The ambiguity of the term “bias”–does it mean a statistical error, or does it mean any tendency of an inferential system at all?–is sometimes leveraged to accomplish this conflation.

It is in practice very difficult to disentangle the different psychological motivations here. This is partly because they are deeply personal and mixed even at the level of the individual. (Highlighting this is why I have framed this in terms of the cognitive science literature). It is also partly because these issues are highly political as well. Being proven right, or wrong, has material consequences–sometimes. I’d argue: perhaps not as often as it should. But sometimes. And so there’s always a political interest, especially among those disinclined towards System 2 thinking, in maintaining a right to be wrong.

So it is hypothesized (perhaps going back to Lyotard) that at an institutional level there’s a persistent heterodox movement that rejects the ideal of communal intellectual integrity. Rather, it maintains that the field of authoritative knowledge must contain contradictions and disturbances of statistical scientific consensus. In Lyotard’s formulation, this heterodoxy seeks “legitimation by paralogy”, which suggests that its telos is at best a kind of creative intellectual emancipation from restrictive logics, generative of new ideas, but perhaps at worst a heterodoxy for its own sake.

This tendency has an uneasy relationship with the sociopolitical motive of a more integrated and representative society, which is often associated with the goal of social justice. If I understand these arguments directly, the idea is that, in practice, legitimized paralogy is a way of giving the underrepresented a platform. This has the benefits of increasing, visibly, representation. Here, paralogy is legitimized as a means of affirmative action, but not as a means improving system performance objectively.

This is a source of persistent difficulty and unease, as the paralogical tendency is never capable of truly emancipating itself, but rather, in its recuperated form, is always-already embedded in a hierarchy that it must deny to its initiates. Authenticity is subsumed, via agonism, to a procedural objectivity that proves it wrong.

Looking for references: phenomenology of probability

A number of lines of inquiry have all been pointing in the same direction for me. I now have a question and I’m on the lookout for scholarly references on it. I haven’t been able to find anything useful through my ordinary means.

I’m looking for a phenomenology of probability.

Hopefully the following paragraphs will make it clearer what I mean.

By phenomenology, I mean a systematic account (-ology) of lived experience (phenomen-). I’m looking for references especially in the “cone” of influences on Merleau-Ponty, and the “cone” of those influenced by Merleau-Ponty.

By probability, I mean the whole gestalt of uncertainty, expectation, and realization that is normally covered by the mathematical subject. The simplest example is the experience of tossing a coin. But there are countless others; this is a ubiquitous mode of phenomenon.

There is at least some indication that this phenomenon is difficult to provide a systematic account for. Probabilistic reasoning is not a very common skill. Perhaps the best account of this that I can think of is in Philip Tetlock’s Superforecasting, in which he reports that a large proportion of people are able to intuit only two kinds of uncertainty (“probably will happen” or “probably won’t happen”), another portion can reason in three (“probably will”, “probably won’t”, and “I don’t know”). For some people, asking for graded expectations (“I think there’s a 30% chance it will happen”) is more or less meaningless.

Nevertheless, all the major quantitative institutions–finance, telecom, digital services, insurance, the hard sciences, etc.–thrive on probabilistic calculations. Perhaps there’s a concentration here.

The other consideration leading towards the question of phenomenology of probability is the question of the interpretation of mathematical probability theory. As is well known, the same mathematics can be interpreted in multiple ways. There is an ‘objective’, frequentist interpretation, according to which probability is the frequency of events in the world. But with the rise of machine learning ‘subjectivist’ or Bayesian interpretations became much more popular. Bayesian probability is a calculus of rational subjective expectations, and transformation of those expectations, according to new evidence.

So far in my studies and research, I’ve never encountered a synthesis of Merleau-Pontean phenomenology with the subjectivist intepretation of probability. This is somewhat troubling.

Is there a treatment of this anywhere?

On Cilliers on “complex systems”

Mireille Hildebrandt has been surfacing the work of Cilliers on complex systems.

I’ve had a longstanding interest in the modeling of what are variously called “complex systems” or “complex adaptive systems” to study the formation of social structure, particularly as it might inform technical and policy design. I’m thrilled to be working on projects along these lines now, at last.

So naturally I’m intrigued by Hildebrandt’s use of Cillier’s to, it seems, humble if not delegitimize the aspirations of complex systems modeling. But what, precisely, is Cillier’s argument? Let’s look at the accessible “What can we learn from a theory of complexity?“.

First, what is a “complex system” to Cilliers?

I will not provide a detailed description of complexity here, but only summarize the general characteristics of complex systems as I see them.

1. Complex systems consist of a large number of elements that in
themselves can be simple.
2. The elements interact dynamically by exchanging energy or information. These interactions are rich. Even if specific elements only interact with a few others, the effects of these interactions are propagated throughout the system. The interactions are nonlinear.
3. There are many direct and indirect feedback loops.
4. Complex systems are open systems—they exchange energy or information with their environment—and operate at conditions far from
5. Complex systems have memory, not located at a specific place, but
distributed throughout the system. Any complex system thus has a
history, and the history is of cardinal importance to the behavior of the
6. The behavior of the system is determined by the nature of the interactions, not by what is contained within the components. Since the
interactions are rich, dynamic, fed back, and, above all, nonlinear, the
behavior of the system as a whole cannot be predicted from an inspection of its components. The notion of “emergence” is used to describe this aspect. The presence of emergent properties does not provide an argument against causality, only against deterministic forms of prediction.
7. Complex systems are adaptive. They can (re)organize their internal
structure without the intervention of an external agent.

Certain systems may display some of these characteristics more prominently than others. These characteristics are not offered as a definition of complexity, but rather as a general, low-level, qualitative description. If we accept this description (which from the literature on complexity theory appears to be reasonable), we can investigate the implications it would have for social or organizational systems.

This all looks quite standard at first glance except for point (6), which pointedly contains not only a description (the system exhibits emergent properties) but also a point about “deterministic forms of prediction”.

Cilliers hedges against actually defining his terms. This it seems, consistent with his position (expressed later).

The next section then presents some thematic, but tentative, consequences of thinking of organizations as complex systems. Presumably, this is all material he justifies elsewhere, as these are very loose arguments.

The point which Hildebrandt is gesturing towards is in the section “WHAT WE CANNOT LEARN FROM A THEORY OF COMPLEXITY”. This is Cilliers’s Negative Argument. There is indeed an argument here:

Looking at the positive aspects we discussed above, you will notice that none is specific. They are all heuristic, in the sense that they provide a general set of guidelines or constraints. Perhaps the best way of putting it is to say that a theory of complexity cannot help us to take in specific positions, to make accurate predictions. This conclusion follows inevitably from the basic characteristics discussed above.

At this point, Cilliers has twice (here, and in point (6) of the definition earlier) mentioned “prediction”, which is a very mathematically understood concept within the field of statistics. There is a red flag here. It is the use of the term “accurate predictions” or “deterministic forms of prediction”. What do these mean? Would a statistician say that any prediction is, strictly speaking, accurate or deterministic? Likely not. They could provide a confidence interval, or a Bayesian posterior odds–how much they would be willing to bet on an outcome. But these may not be what Cilliers means by “prediction”. There is a folk theoretic, qualitative sense of “prediction” which is sometimes used talismanicly by those who engage in foresight but eschew statistical prediction–scenario planners for example.

This is a different semantic world. On the formal modeling side, when one constructs a model and, as one does today, runs it as a simulation, what one does is run it multiple times over with different input parameters in order to get a sense of the probability distribution of outcomes. “Prediction”, to the formal science community, is the discovery of this distribution, not the pinpointing of a particular outcome. Very often, this distribution might have a high variance–meaning, a wide range of possible values, and therefore a very small amount of confidence that it will land on any particular. This is nevertheless considered a satisfactory outcome for a model. The model “predicts” that the result will be from a high-variance distribution.

For example, consider the toss of a single six-sided die. Nobody can predict the outcome deterministically. The “prediction” one can make is that it will land on 1, 2, 3, 4, 5, or 6, with equal odds.

So, already, we see Cilliers disconnected from mainstream statistical practice. If “deterministic prediction” is not what statisticians mean by prediction in any case, even simple ones, then they certainly do not believe they could make such a prediction about a complex system.

This is not the only time when Cilliers appears unfamiliar with the mathematical grounds that his argument gestures at. The following paragraph is quite disturbing:

In order to predict the behavior of a system accurately, we need a detailed understanding of that system, i.e., a model. Since the nature of a complex system is the result of the relationships distributed all over the system, such a model will have to reflect all these relationships. Since they are nonlinear, no set of interactions can be represented by a set smaller than the set itself—superposition does not hold. This is one way of saying that complexity is not compressible. Moreover, we cannot accurately determine the boundaries of the system, because it is open. In order to model a system precisely, we therefore have to model each and every interaction in the system, each and every interaction with the environment—which is of course also complex—as well as each and every interaction in the history of the system. In short, we will have to model life, the universe and everything. There is no practical way of doing this.

This paragraph’s validity hinges on its notion of “precision” which, as I’ve explained, is not something any statistically informed modeler is going for. A few comments on the terminology here:

  • In formal algorithmic information theory (which has, through Solomonoff induction and the minimum description length principle, a close underlying connection with statistical inference) “compressibility” refers to whether or not a representation of some kind–such as a written description or set of data–can be, given some some language of interpretation or computation, represented in a briefer or more compressed form. When one writes a computational description of a model, that is likely its most compressed form. (If not, it is not written very well). When the model is executed, it simulates the relations of objects within the system dynamically. These relations may well be non-linear. All of this is very conventional. If the model is stochastic–meaning, containing randomized behavior, as many are–then there will of course be a broad distribution of outcomes. And it’s true that any particular distribution will not be compressible to the source code alone: the compression will need to include also all the random draws used in making the stochastic decisions of the simulation. However, only the source code and the random draws will be needed. So it is still quite possible for the specific state of the model to be compressible to something much less than a full description of the state!
  • Typically, a model of a complex system will designate some objects and relations as endogenous, meaning driven by the internals of the model, and other factors as exogenous, meaning coming from outside of the boundary of the model. If the exogenous factors are unknown, and they almost always are, then they will be modeled as a set of all possible inputs, possibly with a probability distribution of some kind. This distribution can be attained through, for example, random sampling as well as adjusting it to take into account what is unknown. (Probability distributions that express that we don’t know about something are sometimes called “maximum entropy distributions”, for reasons that are clear from information theory.)

So in this paragraph, which seems to reflect the core part of Cilliers’s argument, it’s frankly, not clear that he knows what he’s talking about. The most charitable interpretation, I believe, is this: Cilliers is not satisfied with probabilistic prediction, as almost anybody else doing computational modeling of complex systems is bound to be. Rather, he believes the kind of prediction that matters is the prediction of a specific outcome with absolute certainty. This, truly, is not possible to get for a complex enough system. Indeed, even simple stochastic systems cannot be predicted in this way.

Let’s call this Cilliers’s Best Negative Argument. What are the implications of it?

What does this amount to in practice? It means that we have to make decisions without having a model or a method that can predict the exact outcome of those decisions. … Does this mean we should avoid decisions, hoping that they will make themselves? Most definitely not. … Not to make a decision is of course also a decision. What, then, are the nature of our decisions? Because we cannot base them on calculation only—calculation would eliminate the need for choice—we have to acknowledge that our decisions have an ethical nature.

This argument is, to a scientist, weird. Suppose, as is likely the case, that we can never make exact predictions but only, at best, probabilistic predictions. In other words, suppose all of our decisions are decisions under uncertainty, which is a claim anybody trained in, say, machine learning is bound to agree with for reasons that have nothing to do with Cilliers. Does this mean that: (a) these decisions cannot be based on calculation, and (b) these decisions have an ethical nature?

Prima facie, (a) is false. Decisions under uncertainty are made based on calculation all the time. This is what decision theory, a well-developed branch of mathematics and philosophy used in economics, for example, is all about. Simply: one calculates the action that maximizes the expected value of the action, meaning the value of the possible outcomes weighted according to their probability.

It is surprising that somebody writing about “complex systems” in the year 2000 working in the field of management science would not address this point, as the von Neumann-Morgenstern theory of utility was developed in 1947 and is not at all a secret.

Perhaps, then, Cilliers is downplaying this because his real mission is to revitalize the ethical. So far, it seems he is saying: decision-making under uncertainty is always, unlike decision-making under conditions of certainty, ethical in some sense. Is that what he’s saying?

I do not take it to mean being nice or being altruistic. It has nothing to do with middleclass values, nor can it be reduced to some interpretation of current social norms. I use the word in a rather lean sense: it refers to the inevitability of choices that cannot be backed up scientifically or objectively.

…. What?

Why call it ethics? First, because the nature of the system or organization in question is determined by the collection of choices made in it.

Ok, this looks fine.

Secondly, since there is no final objective or calculable ground for our decisions, we cannot shift the responsibility for the decision on to something else—“Don’t blame me, the genetic algorithm said we should sell!” We know that all of our choices to some extent, even if only in a small way, incorporate a step in the dark. Therefore we cannot but be responsible for them.

We are getting to the crux of the argument. Decision-making under uncertainty, Cilliers argues, carries responsibility.

There are two parts to this argument. The first is, I find, the most interesting. Decision-making within a complex system is much more difficult and existentially defining than decision-making about a complex system. And precisely because it is existentially defining, I could see how it would carry special responsibility, or ethical weight.

However, for the aforementioned reasons, his hinging this argument on calculability is confusing and uncompelling. There may be many situations where the most responsible or ethical decision is one based on calculated expected results. For example, consider the decision to implement an economic lockdown policy in response to a pandemic. One could listen to political interests of various stripes and appease one or the other. But perhaps in such a situation is it most responsible to calculate, to the best of one’s ability, the probably outcome of one’s choices before implementing them.

And it seems like Cilliers would agree with this:

It may appear at this stage as if I am arguing against any kind of calculation, that I am dismissing the importance of modeling complex systems. Nothing is further from the truth. The important point I want to make is that calculation will never be sufficient. The last thing this could mean is that calculation is unnecessary. On the contrary, we have to do all the calculation we possibly can. That is the first part of our responsibility as scientists and managers. Calculation and modeling will provide us with a great deal of vital information.

This is a point of happy agreement!

It will just not provide us with all the information.

This is a truism nobody doing computational modeling work would argue with.

The problem would remain, however, that this information has to be
All the models we construct—whether they are formal, mathematical models, or qualitative, descriptive models—have to be limited. We cannot model life, the universe, and everything. There may not be any
explicit ethical component contained within the model itself, but ethics (in the sense in which I use the term) has already played its part when the limits of the model were determined, when the selection was made of what would be included in the frame of the investigation. The results produced by the model can never be interpreted independently of that frame. This is no revelation, it is something every scientist knows, or at least should know. Unfortunately, less scrupulous people, often the popularizers of some scientific idea or technique, extend the field of applicability of that idea way beyond the framework that gives it sense and meaning.

Well, this is quite frustrating. It turns out Cilliers is not writing this article for scientists working on computational modeling of complex systems. He’s writing this article, I guess, to warn people off of listening to charlatans. This is a worthy goal. But then why would he write in a way that is so misleading about the nature of computational decision-making? Once again, the insight Cilliers is missing is that the difference between a deterministic model and a probabilistic model is not a difference that makes the latter less “calculable” or “objective” or “scientific”, even though it may (quantitatively) have less information about the system it describes.

Cilliers goes on:

My position could be interpreted as an argument that contains some mystical or metaphysical component, slipped in under the name “ethics.” In order to forestall such an interpretation, I will digress briefly. It is often useful to distinguish between the notions “complex” and “complicated.” A jumbo jet is complicated, a mayonnaise is complex (a least for the French). A complicated system is something we can model accurately (at least in principle). Following this line of thought, one may argue that the notion “complex” is merely a term we use for something we cannot yet model. I have much sympathy for this argument. If one maintains that there is nothing metaphysical about a complex system, and that the notion of causality has to be retained, then perhaps a complex system is ultimately nothing more than extremely complicated. It should therefore be possible to model complex systems in principle, even though it may not be practical.

In conversations about this material, it seems that some are under the impression that a difference between a “complicated” and a “complex” system is a difference in kind. It is clear from this paragraph that for Cilliers, this is not the case. This would accord with all the mathematical theory of complexity which would identify how levels of complexity can be quantitatively measured. Missing from this paragraph, still, is any notion of probability or statistical accuracy. Which is too bad.

In the end, my assessment is that Cilliers is making a good try here and if he’s influential, as I suppose he might by in South Africa, then he’s done so by popularizing some of the work of mathematicians, physicists, etc. But because of some key omissions, his argument is confusing if not misleading. In particular, it is prone to be misinterpreted, as it does not deal with precision about the underlying technical material. I would not rely on it to teach students about complex systems and the limitations of modeling them. I would certainly not draw any clear, ontological lines between “complicated” and “complex” systems as Cilliers does not do this himself.


Cilliers, Paul (2000). “What can we learn from a theory of complexity?” (PDF). Emergence2.1: 23-33. doi:10.1207/S15327000EM0201_03.

Schumpeter on Marx as Prophet and Sociologist

Continuing to read Schumpeter’s Capitalism, Socialism, and Democracy (1942) As I mentioned in a previous post, I was surprised to find that, in a book I thought would tell me about the currently prevailing theory of platform monopoly and competition, Schumpeter’s first few chapters are devoted entirely to a consideration of Karl Marx.

Schumpeter’s treatment of Marx is the epitome of respectful disagreement. Each chapter in his treatment, “Marx the Prophet”, “Marx the Sociologist”, “Marx the Economist”, and “Marx the Teacher”, is brimming with praise and reverence for the intellectual accomplishments of Marx. Schumpeter is particularly sensitive to the value of Marx’s contributions in their historical context: they exceeded what came before it and introduced many critical new ideas and questions.

Contextualizing it thus, Schumpeter then engages in a deep intellectual critique of Marx, pointing out many inconsistencies and omissions of the doctrine and of the contemporary Marxist or Marxian tendencies of his time.

Marx the Prophet”

Schumpeter’s first chapter on Marx addresses the question of why Marx has such a devoted following, one that exceeds that of any other social scientist or economist. He does not find it plausible that Marx has been so attractive because of his purely intellectual analysis. The popular following of Marx far exceeds those who have engaged deeply with Marx’s work. So Schumpeter’s analysis is about the emotional power of Marxian thought. This is a humanistic discussion foremost. It is a discussion, quite literally and unmetaphorically, of religion.

In one important sense, Marxism is a religion. To the believer it presents, first, a system of ultimate ends that embody the meaning of life and are absolute standards by which to judge events and actions; and, secondly, a guide to those ends which implies a plan to salvation and the indication of the evil from which mankind, or a chosen section of mankind, is to be saved. We may specify further: Marxist socialism also belongs to that subgroup which promises paradise on this side of the grave.

Schumpeter believes that Marxism is successful because at some necessary points Marx sacrificed logical integrity for what was in effect good marketing to a audience that had an emotional need for his message.

This need came about in part because, with the success of bourgeois capitalism, other religions had begun to wane in influence. “Faith in any real sense was rapidly falling away from all classes of society”, leaving “the workman” literally hopeless. “Now, to millions of human hearts the Marxian message of the terrestrial paradise of socialism meant a new ray of light and a new meaning of life.” This acknowledgement of the emotional power of Marxism is not meant to be dismissive; on the contrary, what made it successful was that “the message was framed and conveyed in such a way as to be acceptable to the positivistic mind of its time.” He “formulat[ed] with unsurpassed force that feeling of being thwarted and ill treated which is the auto-therapeutic attitude of the unsuccessful many, and, on the other hand, by proclaiming that socialistic deliverance from these ills was a certainty amenable to rational proof.”

Marxism offered certainty of one’s course of action for people who were otherwise despairing, all in a form that seemed consistent with dominant rationalist and scientific modes of thought.

The religious quality of Marxism also explains the characteristic attitude of the orthodox Marxist towards opponents. To him, as any believer in a Faith, the opponent is not merely in error but in sin. Dissent is disapproved of not only intellectually but also morally. There cannot be any excuse for it once the Message has been revealed.

Schumpeter does find an intellectual weakness in Marxism here. He argues that Marxism excites individual feelings and attempts to direct them towards class consciousmess, an idea that depends on theoretical assumptions about the logic of social evolution. Schumpeter is doubtful about this logic of class formation. He writes that “the true psychology of the workman… centers in the wish to become a small bourgeois and to be helped to that status by political force.” This question of the structure of social classes is addressed in the next chapter.


Religion is a difficult topic for mainstream research and scholarship today, especially in the United States. For applications for University lecturer positions in the United Kingdom, there is commonly a section devoted to the applicant’s experience with “pastoral care”. The history of universities in the UK is tied up with religious history, and this resonates through the expectation that part of the role of a professor is to tend to the spiritual needs, in the broadest possible sense, of the students.

The equivalent section in applications in the United States is a section asking the applicant to discuss their experiences fostering or representing diversity, equity, and inclusion. These terms have had many meanings, but it requires a special amount of mental density to not read this as relating to the representation and treatment of minorities. This is a striking indication that the prevailing “religion” of education institutions in the U.S. is indeed a form of political progressivism.

There is a wide variance of opinion how much contemporary progressive ideals are aligned with or indebted to Marxian ones. I’m not sure I can add to that debate. Here, I am simply noting that both Marxism and progressivism have some of these religious traits in common, including perhaps the promise of a new material world order and a certain amount of epistemic closure.

Naturally there are more and less intellectual approaches to both Marxism and contemporary progressivism. There are priests and lay people of every good religion. Schumpeter’s analysis proceeds as intellectual critique.

Marx the Sociologist”

Schumpeter next addresses the sociological content of Marx. He argues that though Marx was a neo-Hegelian and that these philosophical themes permeate his work, they do not dominate it. “Nowhere did he betray positive science to metaphysics.” He brought an powerful comprehensive of contemporary social facts to his work, and used the persuasively in his arguments in a way that raised the standard of empiricism in the scholarship of his time. And the result of this empiricism is Marx’s Economic Intepretation of History, according to which economic conditions shape and account for the rise and fall of the world of ideas: religions, metaphysics, schools of art, political volitions, etc. Ideas and values “had in the social engine the role of transmission belts.” This is as opposed to a vulgar interpretation that would assume all individual motives can be reduced to individual economic motivates; this is a misrepresentation.

Schumpeter views the term “materialism”, as applied to Marx, as meaningless, and mentions in a footnote his encounters with Catholic radicals who “declared themselves Marxists in everything except in matters related to their faith” with perfect consistency.

Schumpeter instead condenses Marx’s view of history into two statements:

  • “The forms or conditions of production are the fundamental determinant of social structures. “[T]he “hand mill” creates feudal, and the “steam-mill,” capitalist societies. Technology thus becomes a driving factor of social change, though technology is understood in its fullness as situated sociotechnical process.
  • The forms of production have a logic of their own. The hand-mill and steam-mill each create social orders which ultimately outgrow their own frame and lead to the practical necessity of the next technological advance.

This smacks of “technological determinism” which is full-throatedly rejected by more contemporary sociological and anthropological scholars. And Schumpeter points out this weakness as well, in a particular operational form: he notes that many social structures are quite durable, persisting past the technological context of their origins. This is a weakness of Marx’s work. There are historical facts, such as the emergence of feudal landlordism in the sixth century, which run counter to Marx’s analysis. The implication is that _unless_ one is taking Marx _religiously_, one would take his arguments seriously enough to engage them as positive science, and then refine one’s views in light of contradictory evidence. This all can be done with ample respect for Marx’s work. Schumpeter is warning against a fundamentalist use of Marx.

This is a buildup to his analysis of the next major sociological theme of Marx, the Theory of Social Classes. Schumpeter credits Marx with the introduction of the important idea of social class. The important claim made by Marx is that a social class is not simple a set of individuals that have something in common. Rather, they are theorized as a social form, “live entities that exist as such”, emergent beings with their own causal force. Marxism rejects methodological individualism.

Once we understand social classes to be social forms in themselves, it becomes sensible to discuss “class struggle”, an important Marxist idea. Schumpeter seems to believe that the strongest form of the idea of class struggle is incorrect, but a weaker version, “the proposition that historical events may often be interpreted in terms of class interests and class attitudes and that existing class structures are always an important factor in historical interpretation”, is a valuable contribution.

“Clearly, success on the line of advance opened up by the principle of class struggle depends upon the validity of the particular theory of classes we make our own. Our picture of history and all our interpretations of cultural patterns and the mechanism of social change will differ according to whether we choose, for instance, the racial theory of classes and like Gobineau reduce human history to the history of the struggle of races or, say, the division of labor theory of classes in the fashion of Schmoller or of Durkheim and resolve class antagonisms into antagonisms between the interests of vocational groups. Nor is the range of possible differences in analysis confined to the problem of the nature of classes. Whatever view we may hold about it, different interpretations will result from different definitions of class interest and from different opinions about how class action manifests itself. The subject is a hotbed of prejudice to this day, and as yet hardly in its scientific stage.”

Schumpeter sees Marx’s own theory of the nature and action of social classes as incomplete and under-specified. “The theory of his chief associate, Engels, was of the division of labor type and essentially un-Marxian in its implications.” Finding Marx’s true theory of social classes is, in Schumpeter’s view, a delicate task of piecing together disjoint parts of Das Kapital.

“The basic idea is clear enough, however. The stratifying principle consists in the ownership, or exclusion from ownership, of means of production such as factory buildings, machinery, raw materials and the consumers’ goods that enter in the workman’s budget. We have thus, fundamentally, two and only two classes, those owners, the capitalists, and those have-nots, who are compelled to sell their labor, the laboring class or proletariat. The existence of intermediate groups, such as are formed by farmers or artisans who employ labor but also do manual work, by clerks and by the professions is of course not denied; but they are treated as anomalies which tend to disappear in the course of the capitalist process.”

This sets up the most fundamental antagonism as that over the private control over the means to produce. The very nature of this relation is strife, or class war.

The crucial question raised by this framing is the question of primitive accumulation, “that is to say, how capitalists came to be capitalists in the first instance.” Here, Schumpeter calls shenanigans on Marx. For while Marx rejects wholesale the idea that some people became capitalists rather than others due to superior intelligence, work ethic, and saving. Schumpeter believes this ‘children’s tale’, “whale far from telling the whole truth, yet tells a good deal of it’. Schumpeter is a believer in entrepreneurial wit, energy, and frugality as the accounting for “the founding of industrial positions in nine cases out of ten.” And yet, he agrees that saving alone, as perhaps implied by classical economics predating Marx, does not account for capital accumulation.

Here, Schumpeter begins to work with some economics facts. Some people save. But saving does not in general turn one into a capitalist. Rather, typically an enterprise is begun by borrowing other people’s savings. Banks arise as the intermediary between household savings and entrepreneurial capital investments.

Schumpeter attributes to Marx a bad faith or at least simplistic rejection of this theory–a popularly applauded “guffaw”–that paves the way for an alternative theory: that primitive accumulation was the result of force or robbery. This is a popular theory. But Schumpeter argues that is begs the question. For how is it that “some people acquire the power to subjugate and rob”? Marx’s answer to this is a historical argument: feudalism was a classist regime of force. Feudal inequality gave way to capitalist inequality. This core logic of this idea is considered, skeptically, in several footnotes. For example, in one, Schumpeter asks whether it is more likely that control over cannons gives one power, or if power gives one control over cannons.

Schumpeter remains incredulous, as he sees Marx’s theory of primative accumulation as avoidant of the main phenomenon that it undertakes to explain. He points to the phenomenon of medium-sized owner-managed firms. Where do they come from? Class positions, he argues, are more often the cause of economic conditions than the other way around, as “business achievement is obviously not everywhere the only avenue to social eminence and only where it is can ownership of means of production causally determine group’s position in the social structure.” Schumpeter also questions the implied hereditary nature of Marx’s theory of social class, as he sees class mobility (both upward and downward) as a historical fact. For Schumpeter, the empirical counterarguments to Marx here are all “obvious”.

Schumpeter then places the value of Marxist theory instead in the propagandist joining of the Economic Theory of History and his theory of Social Classes, which together have more tightly deterministic implications than either do individually. Here Schumpeter makes all kinds of heretical points. For example, socialism, “which in reality has nothing to do with the presence or absence of social classes”, became, for Marx, the only possible kind of classless society. Why? It is so by virtue of the tautology given the definitions Marxist theory provides. But this begins to crumble once the strict binary of social classes is eroded into something more realistic. Schumpeter argues that, contra Marx, in normal times, the relationship between labor and capital is “primarily one of cooperation and that any theory to the contrary must draw largely on pathological cases for verification.” You wouldn’t have the grounds for antagonism at all, he points out, if you didn’t have some much cooperation to work with; indeed, in Schumpeter’s view the two are inseperable.

Ultimately, Schumpeter believes Marx’s theory of social classes depends on this economic theory, grounded in economic facts. The sociological theory of social classes is compelling to many in its own right, but does not hold up to scrutiny in itself. “Marx the Economist” is the subject of the next chapter.


Schumpeter is treating Marx dialectically, attempting to lay out the scope of his argument in its popularly understood, schematic form and showing how, while tautological in its structure, it depends ultimately on some more nuanced theories of economics which will no doubt be questioned in the next chapter.

Comparing Schumpeter’s analysis of Marx with the contemporary economy, we see all sorts of confusions that seem to violate that Marxian class binary. There are multiple social classes, many of whom seem to have a far more ambiguous relationship to capital than either the proletariat or capitalists. The relatively modern idea that one’s savings, however they are earned, should be invested directly into the stock market (a market for ownership over capital) rather than into a bank that then lends to companies has, it’s been said, given “everyone” with substantial savings a stake in the capitalist economy. What does this mean for Marx?

Economic sociology, such as that of Bourdieu, has since developed a far more nuanced analysis of social classes. It is an empirical question, truly, what sociological theory of social classes is most valid; it is unlikely to be anything simple, given how richly textured the social field is in fact. On the other hand, there is much to be learned from a theory of history that gives weight to economic forces, especially economic forces broadly construed. Schumpeter is asking us to try a little harder to understand what actually happens historically, including the plurality of explanations for a large aggregate social fact, rather than fall for the emotional potency and simplistic tautology Marx provides.

instrumental realism and reproducibility in science and society

In Instrumental Realism, Ihde does a complimentary treatment of Ackerman’s Data, Instruments, and Theory (1985), which is positioned as a rebuttal to Kuhn. It is a defense of the idea of scientific progress, which is so disliked by critical scholarship. The key issue is are relativistic attacks on scientific progression that point out, for example, the ways in which theory shapes observation, which undermines the objectivity of observation. Ackerman’s rebuttal is that science does not progress through advance of theory, but rather through advance of instrumentation. Instruments allow data to be collected independently of theory. This creates and bounds “data domains”–fields of “data text” that can then be the site of scientific controversy and resolution.

The paradigmatic scientific instruments in Ackerman’s analysis are the telescope and the microscope. But it’s worthwhile thinking about what this means for the computational tools of “data science”.

Certainly, there has been a great amount of work done on the design and standardization of computational tools, and these tools work with ever increasing speed and robustness.

One of the most controversial points made in research today is the idea that the design and/or of these computational tools encodes some kind of bias that threatens the objectivity of their results.

One story, perhaps a straw man, for how this can happen is this: the creators of these tools have (perhaps unconscious) theoretical presuppositions that are the psychological encoding of political power dynamics. These psychological biases impact their judgment as they use tools. This sociotechnical system is therefore biased as the people in it are biased.

Ackerman’s line of argument suggests that the tools, if well designed, will create a “data domain” that might be interpeted in a biased way, but that this concern is separable from the design of the tools themselves.

A stronger (but then perhaps even harder to defend) argument would be that the tools themselves are designed in such a way that the data domain is biased.

Notably, the question of scientific objectivity depends on a rather complex and therefore obscure supply chain of hardware and software. Locating the bias in it must be extraordinarily difficult. In general, the solution to handling this complexity must be modularity and standardization: each component is responsible for something small and well understood, which provides a “data domain” available for downstream use. This is indeed what the API design of software packages is doing. The individual components are tested for reproducible performance and indeed are so robust that, like most infrastructure, we take them for granted.

The push for “reproducibility” in computational science is a further example of refinement of scientific instruments. Today, we see the effort to provide duplicable computational environments with Docker containers, with preserved random seeds, and appropriately versioned dependencies, so that the results of a particular scientific project are maintained despite the constant churn of software, hardware, and networks that undergird scientific communication and practice (let alone all the other communication and practice it undergirds).

The fetishization of technology today has many searching for the location of societal ills within the modules of this great machine. If society, running on this machine, has a problem, there must be a bug in it somewhere! But the modules are all very well tested. It is far more likely that the bug is in their composition. An integration error.

The solution (if there is a solution, and if there isn’t, why bother?) has to be to instrument the integration.

Sources of the interdisciplinary hierarchy

Lyotard’s 1979 treatise The Postmodern Condition tells a prescient story about the transformation of the university. He discusses two “metanarratives” used for the organization of universities: the German Humboldt model of philosophy as the central discipline, with all other fields of knowledge radiating out from it; and the French model of the university as the basis of education of the modern democratic citizen. Lyotard argues (perhaps speciously) that because of what the late Wittgenstein had to say about the autonomy of language games (there are no facts; there are only social rules) and because of cybernetics (the amalgamation of exact and applied sciences that had been turned so effectively towards control of human and machine), the metanarratives had lost their legitimacy. There was only “legitimation by performativity”, knowledge proving itself by virtue of its (technical) power, and “legitimiation by paralogy”, knowledge legitimizing itself through semantic disruption, creating pools of confusion in which one could still exist though out-of-alignment with prevailing cybernetic logics.

This duality–between cybernetics and paralogy–excludes a middle term identified in Habermas’s 1968 Knowledge and the Structure of Human Interests. Habermas identifies three “human interests” that motivate knowledge: the technical interest (corresponding to cybernetic performativity), the emancipatory interest (perhaps corresponding to the paralogic turn away from cybernetic performativity), and, thirdly, the hermeneutic interest. The latter is the interest in collective understanding that allows for collective understanding. As Habermas’s work matures, this interest emerges as the deliberative, consensual basis of law.

These frameworks for understanding knowledge and the university share an underlying pragmatism. Both Lyotard and Habermas seem to agree about the death of the Humboldt model: knowledge for its own sake is a deceased metanarrative. Knowledge for democratic citizens, the purportedly French model in Lyotard, was knowledge of shared historical narratives and agreement about norms for Habermas. Lyotard was pessimistic about the resilience of these kinds of norms under the pressure of cybernetics. Indeed, this tension between “smart technology” and “rule of law” remains today, expressed in the work of Hildebrandt. The question of whether technical knowledge threatens or delegitimizes legal/hermeneutic knowledge is still with us today.

These intellectual debates are perhaps ultimately about university politics and academic disciplines. If they are truly _ultimately_ about that, that marks their limitation. For what the pragmatist orientation towards knowledge implies is that knowledge does not exist for its own sake, but rather, in most cases, for its application. Philosophers can therefore only achieve so much by appealing to generalized interests. All real applications are contextualized.

Two questions unanswered by these sources (at least in what is assuredly this impoverished schematic of their arguments) are:

  • Whence the interests and applications that motivate the university as socially and economically situated?
  • What accounts for the tensions between the technical/performative disciplines and the hermeneutic and emancipatory ones?

In 1979, the same publication year of The Postmodern Condition, Pierre Bourdieu published Distinction: A Social Critique of the Judgement of Taste. While not in itself an epistemology, Bourdeiu’s method and conclusions provide a foundation for later studies of science, journalism, and the university. Bourdieu’s insight is that aesthetic taste–in art, in design, in hobbies, etc.–is a manifestation of socioeconomic class understood in terms of a multidimensional matrix of forms of capital–such as economic wealth, but also social status and prestigue, and social capital in knowledge and skills. Those with lots of wealth and low cultural capital–the nouveau riche–will value expensive, conspicuous consumption. Those with low wealth and high cultural capital–academics, perhaps–will value intricate works that require time and training to understand and so on. But these preferences exist to maintain the social structures of (multiply defined) capital accumulation.

A key figure in Bourdieu’s story is that of the petit bourgeoisie, the transitional middle class that has specialized their labor, created perhaps a small business, but has not accumulated capital in a way that secures them in the situation where they aspire to be. In today’s economy, these might include the entrepreneurs–those who would, by their labor, aspirationally transform themselves from laborers into capitalists. They would do this by the creation of technology–the means of productions, capital. Unlike labor applied directly to the creation of goods and services as commodities, capital technologies, commodified through the institution of intellectual property, have the potential to scale in use well beyond the effort of their creation and, through Schumpeterian disruption, make their creators wealthy enough to change their class position. On the other hand, there are those who prefer the academic lifestyle, who luxuriate in the study of literature and critique. Through the institutions of critical academia, these are also jobs that can be won through the accumulation of, in this case social and cultural, capital. By design, these are fields of knowledge that exist for their own sake. There are also, of course, law and social scientific disciplines that are helpful for the cultural formation of politicians, legislators, and government workers of various kinds.

Viewed in this way, we can start to see “human interests” not merely as transcendental features the general human condition, but rather as the expression of class and capital interests. This makes sense given the practical reality of universities getting most of their income through tuition. Students attend universities in order to prepare themselves for careers. The promise of a professional career allows universities to charge higher tuition. Where in the upper classes people choose to compete on intangible cultural capital rather than economic capital, universities maintain specialized disciplinary tracks in the humanities.

Notably, the emancipatory role of the humanities, lauded by Habermas, subtly lampooned (parhaps) by Lyotard, is in other works more closely connected to leisure. As early as 1947, Horkheimer, in Eclipse of Reason, points out that the kind of objective reason he sees as essential to the moral grounding of society that has been otherwise derailed by capitalism relies on leisure time that this a difficult class attainment. In perhaps cynical Bourdieusian terms, the ability to reflect on the world and decide, beyond the restrictions of material demands, on an independent or transcendent system of values is itself a form of cultural accumulation of the most rarified kind. However, as this form of cultural attainment is not connected directly to any means of production, it is perhaps a mystery what grounds it pragmatically.

There’s an answer. It’s philanthropy. The arts and humanities, the idealistic independent policy think tanks, and so on, are funded by those who, having accumulated economic capital and the capacity for leisurely thinking about the potential for a better word, have allocated some portion of their wealth towards “causes”. The competition for legitimacy between and among philanthropic causes is today a major site of politics and ideology. Most obviously, political parties and candidacy run on donations, which is in a sense a form of values-driven philanthropy. The appropriation of state funds, or not, for particular causes becomes a battlefield of all forms of capital at the end of the day

This is all understandable from the perspective that is now truly at the center of the modern university: the perspective of business administration. Ever since Herbert Simon, it has been widely known that the managerialist discipline and computational and cybernetic sciences are closely aligned. The economic sociology of Bourdieu is notable in that it is a successor to the sociology of Marx, but also a successor to the phenomenological approach of Kant, and yet is ultimately consistent with the managerialist view of institutions relying on skilled capital management. Disciplines or sub-disciplines that are peripheral to these core skillsets by virtue of their position in the network of capital flows are marginal by definition.

This accounts for much of interdisciplinary politics and grievance. The social structures described here account for the teleological dependency structure of different forms of knowledge: what it is possible to motivate, and with what. To the extent that a discipline as a matter of methodological commitment is unable to account for this social structure, it will be dependent on its own ability to perpetuate itself autonomously though the stupefication of its students.

There is another form of disciplinary dependency worth mentioning. It cuts the other way: it is the dependency that arises from the infrastructural needs of the knowledge institutions. This instrumental dependency is where this line of reasoning connects with Ihde’s instrumental realism as a philosophy of science. Here, too, there are disciplines that are blind to themselves. To the extent that a discipline is unable to account for the scientific advances necessary for its own work, it survives through the heroics of performative contradiction. There may be cases where an institution has developed enough teleological autonomy to reject the knowledge behind its own instrumentation, but in these cases we must be tempted to consider the knowledge claims of the former to be specious and pretensious. What purpose does fashionable nonsense have, if it rejects the authority of those that it depends on materially? “Those” here referring to those classes that embody the relevant infrastructural knowledge.

The answer is perhaps best addressed using the Bourdieusian insights already addressed: an autonomous field of discourse that denies its own infrastructure is a cultural market designed to establish a distinct form of capital, an expression of leisure. The rejection of performativity, or tenuous and ambiguous connection to it, becomes a class marker; synecdochal with leisure itself, which can then be held up as an esteemable goal. Through Lyotard’s analysis, we can see how a field so constructed might be successful through the rhetorical power of its own paralogic.

What has been lost, through this process, is the metanarrative of the university, most especially of the university as an anchor of knowledge in itself. The pragmatist cybernetic knowledge orientation entails that the university is subsumed to wider systems of capital flows, and the only true guarantee of its autonomy is philanthropic endowment which might perpetuate its ability to develop a form of capital that serves its own sake.

TikTok, Essential Infrastructure, and Imperial Regulation by Golden Share

As Microsoft considers acquiring TikTok’s American operations, President Trump has asked that the Federal Treasury should own a significant share. This move is entirely consistent with this administration’s technology regulation principles, which sees profitable telecommunications and digital services companies as both a cybersecurity attack surface and a prized form of capital that must be “American owned”. Surprising, perhaps, is the idea of partial government ownership. However, this idea has been floated recently by a number of scholars and think tanks. Something like Treasury ownership of company shares could set up institutions that serve not just American economic, but also civic interests.

Jake Goldenfein and I have recently published a piece in Phenomenal World, Essential Infrastructures“. It takes as its cue the recent shift of many “in person” activities onto Zoom during COVID-19 lockdowns to review the prevailing regulatory regimes governing telecommunications infrastructure and digital services. We trace the history of Obama-era net neutrality, grounded in an idea of the Internet as a public utility or essential facility. We then show how in the Trump administration, a new regime based on national and economic security directed Federal policy. We then go into some policy recommendations moving forward.

A significant turning point during the Trump administration has been the shift away from the emphasis on domestic and foreign provision of open Internet in order to provide a competitive market for digital services, and towards the idea that telecom infrastructure and digital services are powerful behemoths that, as critical infrastructure, are vulnerable attack surfaces of the nation but also perhaps the primary form of wealth and source of rents. As any analysis of the stock market, especially since the COVID-19 lockdowns, would tell you, Big Tech has been carrrying the U.S. stock market while other businesses crumble. These new developments continue the trend of the past several years of corporate concentration, and show some of the prescience of the lately hyperactive CFIUS regulatory group, preventing foreign investment in these “critical infrastructure”. This is a defense of American information from foreign investors; it is also a defense of American wealth from competition over otherwise publicly traded assets.

Under the current conditions of markets and corporate structure, which Jake and I analyze in our other recent academic paper, we have to stop looking at “AI” and “data science” as technologies and start looking at them as forms of capital. That is how CFIUS is looking at them. That is how their investors and owners look at them. Many of the well-intentioned debates about “AI ethics” and “technology politics” are eager to keep the conversation in more academically accessible and perhaps less cynical terms. By doing so, they miss the point.

In the “Essential Infrastructures” article, we are struggling with this confluence of the moral/political and the economic. Jake and I are both very influenced by Helen Nissenbaum, who would be quick to point out that when social activities that normally depend on the information affordances of in-person communication go on-line, there is ample reason to suspect that norms will be violated and that the social fabric will undergo an uncomfortable transformation. We draw attention to some of the most personal aspects of life–dating/intimacy, family, religion, and more broadly civil society–which have not depended as much on private capital as infrastructure as they do now. Of course, this is all relative and society has been trending this way for a long time. But COVID lockdowns have brought this condition to a new extreme.

There will be those that argue that there is nothing alarming about every aspect of human life being dependent on private capital infrastructure designed to extract value from them for their corporate owners. Some may find this inevitable, tolerable, even desirable. We wonder who would make a sincere, full-throated defense of this future world. We take a different view. We take as an assumption that the market is one sphere among many and that maintenance of the autonomy of some of the more personal spheres is of moral importance.

Given (because we see no other way about it) that these infrastructures are a form of capital, how can the autonomy of the spheres that depend on them be preserved? In our article, our proposal is that the democratic state provides additional oversight and control over this capital. Recognizing that the state is always an imperfect representative of individuals in their personal domains, it is better than nothing.

We propose that states can engage with infrastructure-as-capital directly as owners and investors, just as other actors interact with it. This proposal accords with other similar proposals for how states might innovate in their creation and maintenance of sovereign wealth since COVID. The editorial for our piece was thorough. Since we drafted it, we have found others who have articulated the logic of the general value of this approach better than we have.

The Berggruen Institute’s Gilman and Feygin (20202) have been active this year in publishing new policy research that is consistent with what we’re proposing. Their proposals for a “mutualist economy” wherein a “national endowment” is built from public investment in technology and intellectual property, which is then either distributed to citizens as Universal Basic Capital or used as a source of wealth by the state, is cool. The Berggruen Institute’s Noema magazine has published the thoughts of Ray Dalio and Joseph Stiglitz about using this approach for corporate bailouts in response to COVID.

These are all good ideas. Our proposal differs only slightly. If national endowment is built from shares in companies that are bailed out during COVID, then the national endowment is unlikely to include those successful FANG companies that are so successfully disrupting and eating the lunch of the companies that are getting bailed out. It would be too bad if the national endowment included only those companies that are failing, while the tech giants on which civil society and state are increasingly dependent attract all the real value to be had.

In our article, we are really proposing that governments–whether federal, state, or even municipal–get themselves a piece of Amazon, Google, and Verizon. The point here is not simply to get more of the profit generated by these firms into democratic coffers. Rather, the point is to shift the balance of power. Our proposal is perhaps more aligned with Hockett and Omarova’s (2017) proposal for a National Investment Authority, and more specifically Omarova’s proposal of a “golden share approach” (2016). Recall that much of the recent activity of CFIUS has been motivated by the understanding that significant shareholders in a private corporation have rights to access information within it. This is why blocking foreign investment in companies has been motivated under a “cybersecurity” rationale. If a foreign owner of, say, Grinder, could extract compromising information from the company in order to blackmail U.S. military personnel, then it could be more difficult to enforce the illegality of that move.

In the United States, there is a legal gap in the regulation of technology companies domestically given their power over personal and civic life. In a different article (2020, June), we argued that technology law and ethics needs to deal with technology as a corporation, rather than a network or assemblage of artifacts and individuals. This is difficult, as these corporations are powerful, directed by shareholders to whom they have a fiduciary duty to maximize profits, and very secretive about their operations. “Sovereign investment”–or, barring that, something similar on a state or local level–would give governments a legal way to review the goings-on in companies that it has a share in. This information access alone could enable further civic oversight and regulatory moves by the government.

When we wrote our article, we did not imagine that soon after it was published the Trump administration would recommend a similar policy for the acquisition of foreign-owned companies that it is threatening to boot off the continent. However, this is one way to get leverage on the problem of how the government can acquire, at low cost, something that is already profitable.

This will likely scare foreign-owned technology companies off of doing business in the U.S. And a U.S.-owned company is likely to fall afoul of other national markets. However, since the Snowden revelations, U.S. companies have been seen, overseas, as extensions of the U.S. state. Schrems II solidifies that view in Europe. Technology markets are already global power-led spheres of influence.


Benthall, S., & Goldenfein, J. (2020, June). Data Science and the Decline of Liberal Law and Ethics. In Ethics of Data Science Conference-Sydney.

Gilman, N. and Feygin, Y. (April, 2020), “The Mutualist Economy: A New Deal for Ownership” Whitepaper. Berggruen Institute.

Gilman, N. and Feygin, Y. (June, 2020) “Building Blocks of a National Endowment” Whitepaper. Berggruen Institute.

Hockett, R. C., & Omarova, S. T. (2017). Private Wealth and Public Goods: A Case for a National Investment Authority. J. Corp. L.43, 437.

Nissenbaum, H. (2009). Privacy in context: Technology, policy, and the integrity of social life. Stanford University Press.

Omarova, S. T. (2016). Bank Governance and Systemic Stability: The Golden Share Approach. Ala. L. Rev.68, 1029.