Category: science

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.

Considering the Endless Frontier Act

As a scientist/research engineer, I am pretty excited about the Endless Frontier Act. Nothing would make my life easier than a big new pile of government money for basic research and technological prototypes awarded to people with PhDs. I’m absolutely all for it and applaud the bipartisan coalition moving it forward.

I am somewhat concerned, however, that the motivation for it is the U.S.’s fear of technological inferiority with respect to China. I’ll take the statement of Dr. Reif, President of MIT, at face value, which is probably foolish given the political acumen and moral flexibility of academic administrators. But look at this:

The COVID-19 pandemic is intensifying U.S. concerns about China’s technological strength. Unfortunately, much of the resulting policy debate has centered on ways to limit China’s capacities — when what we need most is a systematic approach to strengthening our own.

Very straightforward. This is what it’s about. Ok. I get it. You have to sell it to the Trump administration. It’s a slam dunk. But then why write this:

The aim of the new directorate is to support fundamental scientific research — with specific goals in mind. This is not about solving incremental technical problems. As one example, in artificial intelligence, the focus would not be on further refining current algorithms, but rather on developing profoundly new approaches that would enable machines to “learn” using much smaller data sets — a fundamental advance that would eliminate the need to access immense data sets, an area where China holds an immense advantage. Success in this work would have a double benefit: seeding economic benefits for the U.S. while reducing the pressure to weaken privacy and civil liberties in pursuit of more “training” data.

This sounds totally dubious to me. There are well known mathematical theorems addressing why learning without data is impossible. The troublesome fact nodded to is that is because of the political economy of China, it is possible to collect “immense data sets”–specifically about people–without civil liberties issues getting in the way. This presumes that the civil liberties problem with AI is the collection of data from data subjects, not the use of machine learning on those data subjects. But even if you could magically learn about data subjects without collecting data from them, you wouldn’t bypass the civil liberties concerns. Rather, you would have a nightmare world where even sans data collection you could act with godly foresight in one’s interventions on polity. This is a weird fantasy and I’m pretty sure the only reason it’s written this way is to sell the idea superficially to uncritical readers trying to reconcile the various narratives around U.S., technology, and foreign policy which are incoherent.

What it’s really all about, of course, is neoliberalism. Dr. Reif is not shy about this:

The bill would also encourage universities to experiment with new ways to help accelerate the process of bringing innovative ideas to the marketplace, either via established companies or startups. At MIT we started The Engine, an independent entity that provides private-sector funding, work space and technical assistance to start-ups that are developing technologies with enormous potential but that require more extensive technical development than typical VCs will fund, from fusion energy to a fast, inexpensive test for COVID-19. Other models may suit other institutions — but the nation needs to encourage many more such efforts, across the country, to reap the full benefits of our federal investment in science.

The implication here is that unless the results of federal investment in the sciences can be privatized, the country does not “reap the full benefits” of the federal investment. This makes the whole idea of a massively expanded federal government program make a lot more sense, politically, because it’s a massive redistribution of funds to, ultimately, Big Tech, who can buy up any successful ‘startups’ without any downside investment risk. And Big Tech now runs the country and has found a way to equate its global market share with national security such that these things are now indistinguishable in any statement of U.S. policy.

This would all be fine I guess if not for the fact that science is different from technology in that science is, cannot be, a private endeavor. The only way science works is if you have an open vetting process that is constantly arguing with itself and forcing the scientists to reproduce results. This global competition for scientific prestige through the conference and journal systems is what “keeps it honest”, which is precisely what allows it to be credible. (Bourdieu, Science of Science, 2004)

A U.S. strategy since basically the end of World War II has been to lead the scientific field, get first mover advantage on any discoveries, and reap the benefit of being the center of education for global scientific talent through foreign tuition fees and talented immigrants. Then it wields technology transfer as a magic wand for development.

Now this is backfiring a bit because Chinese science students are returning to China to be entrepreneurial there and also work for the government. The U.S. is discovering that science, being an open system, allows others countries to free ride and this is perhaps bothersome to it. The current administration seems to hate the idea of anybody free-riding off of something the U.S. is doing, though in the past those spillover effects (another name for them!) would have been the basis of U.S. leadership. You can’t really have it both ways.

So the renaming of the NSF to the NSTF–with “technology” next to “science”–is concerning because “technology” investment need not be openly vetted. Rather, given the emphasis on go-to-market strategy, it suggests that the scientific norms of reproducibility will be secondary to privatization through intellectual property laws, including trade secrecy. The could be quite bad, because without a disinterested community of people vetting the results, what you’ll probably get is a lot of industrially pre-captured bullshit.

Let’s acknowledge for a minute that the success of most startups little to do with the quality of the technology made and much to do with path dependency in network growth, marketing, and regulatory arbitrage. If the government starts a VC fund run by engineers with no upside then that money goes into a bunch of startups which then compete for creative destruction of each other until one, large enough based on its cannibalizing of the others, gets consumed by by FAANG company. It will, in other words, look like Silicon Valley today, which is not terribly efficient at discovery because success is measured by the market. I.e., because (as Dr. Reif suggests) the return on investment is realized as capital accumulation.

This is all pretty backwards if what you’re trying to do is maintain scientific superiority. Scientific progress requires a functional economy of symbolic capital among scientists operating with intellectual integrity that is “for its own sake”, not operating at the behest of market conquest. The spillover effects and freeriding in science is a feature, not a bug, and it’s difficult to reconcile it with a foreign policy that is paranoid about technology transfer to its competitors. Indeed, this is one reason why scientists are often aligned with humanitarian causes, world peace, etc.

Science is a good social structure with a lot going for it. I hope the new bill pours more money into it without messing it up too much.

social structure and the private sector

The Human Cell

Academic social scientists leaning towards the public intellectual end of the spectrum love to talk about social norms.

This is perhaps motivated by the fact that these intellectual figures are prominent in the public sphere. The public sphere is where these norms are supposed to solidify, and these intellectuals would like to emphasize their own importance.

I don’t exclude myself from this category of persons. A lot of my work has been about social norms and technology design (Benthall, 2014; Benthall, Gürses and Nissenbaum, 2017)

But I also work in the private sector, and it’s striking how differently things look from that perspective. It’s natural for academics who participate more in the public sphere than the private sector to be biased in their view of social structure. From the perspective of being able to accurately understand what’s going on, you have to think about both at once.

That’s challenging for a lot of reasons, one of which is that the private sector is a lot less transparent than the public sphere. In general the internals of actors in the private sector are not open to the scrutiny of commentariat onlookers. Information is one of the many resources traded in pairwise interactions; when it is divulged, it is divulged strategically, introducing bias. So it’s hard to get a general picture of the private sector, even though accounts for a much larger proportion of the social structure that’s available than the public sphere. In other words, public spheres are highly over-represented in analysis of social structure due to the available of public data about them. That is worrisome from an analytic perspective.

It’s well worth making the point that the public/private dichotomy is problematic. Contextual integrity theory (Nissenbaum, 2009) argues that modern society is differentiated among many distinct spheres, each bound by its own social norms. Nissenbaum actually has a quite different notion of norm formation from, say, Habermas. For Nissenbaum, norms evolve over social history, but may be implicit. Contrast this with Habermas’s view that norms are the result of communicative rationality, which is an explicit and linguistically mediated process. The public sphere is a big deal for Habermas. Nissenbaum, a scholar of privacy, reject’s the idea of the ‘public sphere’ simpliciter. Rather, social spheres self-regulate and privacy, which she defines as appropriate information flow, is maintained when information flows according to these multiple self-regulatory regimes.

I believe Nissenbaum is correct on this point of societal differentiation and norm formation. This nuanced understanding of privacy as the differentiated management of information flow challenges any simplistic notion of the public sphere. Does it challenge a simplistic notion of the private sector?

Naturally, the private sector doesn’t exist in a vacuum. In the modern economy, companies are accountable to the law, especially contract law. They have to pay their taxes. They have to deal with public relations and are regulated as to how they manage information flows internally. Employees can sue their employers, etc. So just as the ‘public sphere’ doesn’t permit a total free-for-all of information flow (some kinds of information flow in public are against social norms!), so too does the ‘private sector’ not involve complete secrecy from the public.

As a hypothesis, we can posit that what makes the private sector different is that the relevant social structures are less open in their relations with each other than they are in the public sphere. We can imagine an autonomous social entity like a biological cell. Internally it may have a lot of interesting structure and organelles. Its membrane prevents this complexity leaking out into the aether, or plasma, or whatever it is that human cells float around in. Indeed, this membrane is necessary for the proper functioning of the organelles, which in turn allows the cell to interact properly with other cells to form a larger organism. Echoes of Francisco Varela.

It’s interesting that this may actually be a quantifiable difference. One way of modeling the difference between the internal and external-facing complexity of an entity is using information theory. The more complex internal state of the entity has higher entropy than the membrane. The fact that the membrane causally mediates interactions between the internals and the environment prevents information flow between them; this is captured by the Data Processing Inequality. The lack of information flow between the system internals and externals is quantified as lower mutual information between the two domains. At zero mutual information, the two domains are statistically independent of each other.

I haven’t worked out all the implications of this.


Benthall, Sebastian. (2015) Designing Networked Publics for Communicative Action. Jenny Davis & Nathan Jurgenson (eds.) Theorizing the Web 2014 [Special Issue]. Interface 1.1. (link)

Sebastian Benthall, Seda Gürses and Helen Nissenbaum (2017), “Contextual Integrity through the Lens of Computer Science”, Foundations and Trends® in Privacy and Security: Vol. 2: No. 1, pp 1-69. http://dx.doi.org/10.1561/3300000016

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

“To be great is to be misunderstood.”

A foolish consistency is the hobgoblin of little minds, adored by little statesmen and philosophers and divines. With consistency a great soul has simply nothing to do. He may as well concern himself with his shadow on the wall. Speak what you think now in hard words, and to-morrow speak what to-morrow thinks in hard words again, though it contradict every thing you said to-day. — `Ah, so you shall be sure to be misunderstood.’ — Is it so bad, then, to be misunderstood? Pythagoras was misunderstood, and Socrates, and Jesus, and Luther, and Copernicus, and Galileo, and Newton, and every pure and wise spirit that ever took flesh. To be great is to be misunderstood. –
Emerson, Self-Reliance

Lately in my serious scientific work again I’ve found myself bumping up against the limits of intelligibility. This time, it is intelligibility from within a technical community: one group of scientists who are, I’ve been advised, unfamiliar with another, different technical formalism. As a new entrant, I believe the latter would be useful to understand the domain of the former. But to do this, especially in the context of funders (who need to explain things to their own bosses in very concrete terms), would be unproductive, a waste of precious time.

Reminded by recent traffic of some notes I wrote long ago in frustration at Hannah Arendt, I found something apt about her comments. Science in the mode of what Kuhn calls “normal science” must be intelligible to itself and its benefactors. But that is all. It need not be generally intelligible to other scientists; it need not understand other scientists. It need only be a specialized and self-sustaining practice, a discipline.

Programming (which I still study) is actually quite different from science in this respect. Because software code is a medium used for communication by programmers, and software code is foremost interpreted by a compiler, one relates as a programmer to other programmers differently than the way scientists relate to other scientists. To some extent the productive formal work has moved over into software, leaving science to be less formal and more empirical. This is, in my anecdotal experience, now true even in the fields of computer science, which were once one of the bastions of formalism.

Arendt’s criticism of scientists, that should be politically distrusted because “they move in a world where speech has lost its power”, is therefore not precisely true because scientific operations are, certainly, mediated by language.

But this is normal science. Perhaps the scientists who Arendt distrusted politically were not normal scientists, but rather those sorts of scientists that were responsible for scientific revolutions. These scientist must not have used language that was readily understood by their peers, at least initially, because they were creating new concepts, new ideas.

Perhaps these kinds of scientists are better served by existentialism, as in Nietzsche’s brand, as an alternative to politics. Or by Emerson’s transcendentalism, which Sloterdijk sees as very spiritually kindred to Nietzsche but more balanced.

Three possibilities of political agency in an economy of control

I wrote earlier about three modes of social explanation: functionality, which explains a social phenomenon in terms of what it optimizes; politics, which explains a social phenomenon in terms of multiple agents working to optimize different goals; and chaos, which explains a social phenomenon in terms of the happenings of chance, independent of the will of any agent.

A couple notes on this before I go on. First, this view of social explanation is intentionally aligned with mathematical theories of agency widely used in what is broadly considered ‘artificial intelligence’ research and even more broadly  acknowledged under the rubrics of economics, cognitive science, multi-agent systems research, and the like. I am willfully opting into the hegemonic paradigm here. If years in graduate school at Berkeley have taught me one pearl of wisdom, it’s this: it’s hegemonic for a reason.

A second note is that when I say “social explanation”, what I really mean is “sociotechnical explanation”. This is awkward, because the only reason I have to make this point is because of an artificial distinction between technology and society that exists much more as a social distinction between technologists and–what should one call them?–socialites than as an actual ontological distinction. Engineers can, must, and do constantly engage societal pressures; they must bracket of these pressures in some aspects of their work to achieve the specific demands of engineering. Socialites can, must, and do adopt and use technologies in every aspect of their lives; they must bracket these technologies in some aspects of their lives in order to achieve the specific demands of mastering social fashions. The social scientist, qua socialite who masters specific social rituals, and the technologist, qua engineer who masters a specific aspect of nature, naturally advertise their mastery as autonomous and complete. The social scholar of technology, qua socialite engaged in arbitrage between communities of socialites and communities of technologists, naturally advertises their mastery as an enlightened view over and above the advertisements of the technologists. To the extent this is all mere advertising, it is all mere nonsense. Currency, for example, is surely a technology; it is also surely an artifact of socialization as much if not more than it is a material artifact. Since the truly ancient invention of currency and its pervasiveness through the fabric of social life, there has been no society that is not sociotechnical, and there has been no technology that is is not sociotechnical. A better word for the sociotechnical would be one that indicates its triviality, how it actually carries no specific meaning at all. It signals only that one has matured to the point that one disbelieves advertisements. We are speaking scientifically now.

With that out of the way…I have proposed three modes of explanation: functionality, politics, and chaos. They refer to specific distributions of control throughout a social system. The first refers to the capacity of the system for self-control. The second refers to the capacity of the components of the system for self-control. The third refers to the absence of control.

I’ve written elsewhere about my interest in the economy of control, or in economies of control, plurally. Perhaps the best way to go about studying this would be an in depth review of the available literature on information economics. Sadly, I am at this point a bit removed from this literature, having gone down a number of other rabbit holes. In as much as intellectual progress can be made by blazing novel trails through the wilderness of ideas, I’m intent on documenting my path back to the rationalistic homeland from which I’ve wandered. Perhaps I bring spices. Perhaps I bring disease.

One of the questions I bring with me is the question of political agency. Is there a mathematical operationalization of this concept? I don’t know it. What I do know is that it is associated most with the political mode of explanation, because this mode of explanation allows for the existence of politics, by which I mean agents engaged in complex interactions for their individual and sometimes collective gain. Perhaps it is the emerging dynamics of the individual’s shifting constitution as collectives that captures best what is interesting about politics. These collectives serve functions, surely, but what function? Is it a function with any permanence or real agency? Or is it a specious functionality, only a compromise of the agents that compose it, ready to be sabotaged by a defector at any moment?

Another question I’m interested in is how chaos plays a role in such an economy of control. There is plenty of evidence to suggest that entropy in society, far from being a purely natural consequence of thermodynamics, is a deliberate consequence of political activity. Brunton and Nissenbaum have recently given the name obfuscation to some kinds of political activity that are designed to mislead and misdirect. I believe this is not the only reason why agents in the economy of control work actively to undermine each others control. To some extent, the distribution of control over social outcomes is zero sum. It is certainly so at the Pareto boundary of such distributions. But I posit that part of what makes economies of control interesting is that they have a non-Euclidean geometry that confounds the simple aggregations that make Pareto optimality a useful concept within it. Whether this hunch can be put persuasively remains to be seen.

What I may be able to say now is this: there is a sense in which political agency in an economy of control is self-referential, in that what is at stake for each agent is not utility defined exogenously to the economy, but rather agency defined endogenously to the economy. This gives economic activity within it a particularly political character. For purposes of explanation, this enables us to consider three different modes of political agency (or should I say political action), corresponding to the three modes of social explanation outlined above.

A political agent may concern itself with seizing control. It may take actions which are intended to direct the functional orientation of the total social system of which it is a part to be responsive to its own functional orientation. One might see this narrowly as adapting the total system’s utility function to be in line with one’s own, but this is to partially miss the point. It is to align the agency of the total system with one’s one, or to make the total system a subsidiary to one’s agency.  (This demands further formalization.)

A political agent may instead be concerned with interaction with other agents in a less commanding way. I’ll call this negotiation for now. The autonomy of other agents is respected, but the political agent attempts a coordination between itself and others for the purpose of advancing its own interests (its own agency, its own utility). This is not a coup d’etat. It’s business as usual.

A political agent can also attempt to actively introduce chaos into its own social system. This is sabotage. It is an essentially disruptive maneuver. It is action aimed to cause the death of function and bring about instead emergence, which is the more positive way of characterizing the outcomes of chaos.

Varela’s modes of explanation and the teleonomic

I’m now diving deep into Francisco Varela’s Principles of Biological Autonomy (1979). Chapter 8 draws on his paper with Maturana, “Mechanism and biological explanation” (1972) (html). Chapter 9 draws heavily from his paper, “Describing the Logic of the Living: adequacies and limitations of the idea of autopoiesis” (1978) (html).

I am finding this work very enlightening. Somehow it bridges between my interests in philosophy of science right into my current work on privacy by design. I think I will find a way to work this into my dissertation after all.

Varela has a theory of different modes of explanation of phenomena.

One form of explanation is operational explanation. The categories used in these explanations are assumed to be components in the system that generated the phenomena. The components are related to each other in a causal and lawful (nomic) way. These explanations are valued by science because they are designed so that observers can best predict and control the phenomena under study. This corresponds roughly to what Habermas identifies as technical knowledge in Knowledge and Human Interests. In an operational explanation, the ideas of purpose or function have no explanatory value; rather the observer is free to employ the system for whatever purpose he or she wishes.

Another form of explanation is symbolic explanation, which is a more subtle and difficulty idea. It is perhaps better associated with phenomenology and social scientific methods that build on it, such as ethnomethodology. Symbolic explanations, Varela argues, are complementary to operational explanations and are necessary for a complete description of “living phenomenology”, which I believe Varela imagines as a kind of observer-inclusive science of biology.

To build up to his idea of the symbolic explanation, Varela first discusses an earlier form of explanation, now out of fashion: teleological explanation. Teleological explanations do not support manipulation, but rather “understanding, communication of intelligible perspective in regard to a phenomenal domain”. Understanding the “what for” of a phenomenon, what its purpose is, does not tell you how to control the phenomenon. While it may help regulate ones expectations, Varela does not see this as its primary purpose. Communicability motivates teleological explanation. This resonates with Habermas’s idea of hermeneutic knowledge, what is accomplished through intersubjective understanding.

Varela does not see these modes of explanation as exclusive. Operational explanations assume that “phenomena occur through a network of nomic (lawlike) relationships that follow one another. In the symbolic, communicative explanation the fundamental assumption is that phenomena occur through a certain order or pattern, but the fundamental focus of attention is on certain moments of such an order, relative to the inquiring community.” But these modes of explanation are fundamentally compatible.

“If we can provide a nomic basis to a phenomenon, an operational description, then a teleological explanation only consists of putting in parenthesis or conceptually abbreviating the intermediate steps of a chain of causal events, and concentrating on those patterns that are particularly interesting to the inquiring community. Accordingly, Pittendrich introduced the term teleonomic to designate those teleological explanations that assume a nomic structure in the phenomena, but choose to ignore intermediate steps in order to concentrate on certain events (Ayala, 1970). Such teleologic explanations introduce finalistic terms in an explanation while assuming their dependence in some nomic network, hence the name teleo-nomic.”

A symbolic explanation that is consistent with operational theory, therefore, is a teleonomic explanation: it chooses to ignore some of the operations in order to focus on relationships that are important to the observer. There are coherent patterns of behavior which the observer chooses to pay attention to. Varela does not use the word ‘abstraction’, as a computer scientist I am tempted to. But Varela’s domains of interest, however, are complex physical systems often represented as dynamic systems, not the kind of well-defined chains of logical operations familiar from computer programming. In fact, one of the upshots of Varela’s theory of the symbolic explanation is a criticism of naive uses of “information” in causal explanations that are typical of computer scientists.

“This is typical in computer science and systems engineering, where information and information processing are in the same category as matter and energy. This attitude has its roots in the fact that systems ideas and cybernetics grew in a technological atmosphere that acknowledged the insufficiency of the purely causalistic paradigm (who would think of handling a computer through the field equations of thousands of integrated circuits?), but had no awareness of the need to make explicit the change in perspective taken by the inquiring community. To the extent that the engineering field is prescriptive (by design), this kind of epistemological blunder is still workable. However, it becomes unbearable and useless when exported from the domain of prescription to that of description of natural systems, in living systems and human affairs.”

This form of critique makes its way into a criticism of artificial intelligence by Winograd and Flores, presumabley through the Chilean connection.

equilibrium representation

We must keep in mind not only the capacity of state simplifications to transform the world but also the capacity of the society to modify, subvert, block, and even overturn the categories imposed upon it. Here is it useful to distinguish what might be called facts on paper from facts on the ground…. Land invasions, squatting, and poaching, if successful, represent the exercise of de facto property rights which are not represented on paper. Certain land taxes and tithes have been evaded or defied to the point where they have become dead letters. The gulf between land tenure facts on paper and facts on the ground is probably greatest at moments of social turmoil and revolt. But even in more tranquil times, there will always be a shadow land-tenure system lurking beside and beneath the official account in the land-records office. We must never assume that local practice conforms with state theory. – Scott, Seeing Like a State, 1998

I’m continuing to read Seeing Like a State and am finding in it a compelling statement of a state of affairs that is coded elsewhere into the methodological differences between social science disciplines. In my experience, much of the tension between the social sciences can be explained in terms of the differently interested uses of social science. Among these uses are the development of what Scott calls “state theory” and the articulation, recognition, and transmission of “local practice”. Contrast neoclassical economics with the anthropology of Jean Lave as examples of what I’m talking about. Most scholars are willing to stop here: they choose their side and engage in a sophisticated form of class warfare.

This is disappointing from the perspective of science per se, as a pursuit of truth. To see where there’s a place for such work in the social sciences, we only have to the very book in front of us, Seeing Like a State, which stands outside of both state theory and local practices to explain a perspective that is neither but rather informed by a study of both.

In terms of the ways that knowledge is used in support of human interests, in the Habermasian sense (see some other blog posts), we can talk about Scott’s “state theory” as a form of technical knowledge, aimed at facilitating power over the social and natural world. What he discusses is the limitation of technical knowledge in mastering the social, due to complexity and differentiation in local practice. So much of this complexity is due to the politicization of language and representation that occurs in local practice. Standard units of measurement and standard terminology are tools of state power; efforts to guarantee them are confounded again and again in local interest. This disagreement is a rejection of the possibility of hermeneutic knowledge, which is to say linguistic agreement about norms.

In other words, Scott is pointing to a phenomenon where because of the interests of different parties at different levels of power, there’s a strategic local rejection of inter-subjective agreement. Implicitly, agreeing even on how to talk with somebody with power over you is conceding their power. The alternative is refusal in some sense. A second order effect of the complexity caused by this strategic disagreement is the confounding of technical mastery over the social. In Scott’s terminology, a society that is full of strategic lexical disagreement is not legible.

These are generalizations reflecting tendencies in society across history. Nevertheless, merely by asserting them I am arguing that they have a kind of special status that is not itself caught up in the strategic subversions of discourse that make other forms of expertise foolish. There must be some forms of representation that persist despite the verbal disagreements and differently motivated parties that use them.

I’d like to call these kinds of representations, which somehow are technically valid enough to be useful and robust to disagreement, even politicized disagreement, as equilibrium representations. The idea here is that despite a lot of cultural and epistemic churn, there are still attractor states in the complex system of knowledge production. At equilibrium, these representations will be stable and serve as the basis for communication between different parties.

I’ve posited equilibrium representations hypothetically, without having a proof or example yet on one that actually exists. My point is to have a useful concept that acknowledges the kinds of epistemic complexities raised by Scott but that acknowledges the conditions for which a modernist epistemology could prevail despite those complexities.


Seeing Like a State: problems facing the code rural

I’ve been reading James C. Scott’s Seeing Like a State: How Certain Schemes to Improve the Human Condition Have Failed for, once again, Classics. It’s just as good as everyone says it is, and in many ways the counterpoint to James Beniger’s The Control Revolution that I’ve been looking for. It’s also highly relevant to work I’m doing on contextual integrity in privacy.

Here’s a passage I read on the subway this morning that talks about the resistance to codification of rural land use customs in Napoleonic France.

In the end, no postrevolutionary rural code attracted a winning coalition, even amid a flurry of Napoleonic codes in nearly all other realms. For our purposes, the history of the stalemate is instructive. The first proposal for a code, which was drafted in 1803 and 1807, would have swept away most traditional rights (such as common pasturage and free passage through others’ property) and essentially recast rural property relations in the light of bourgeois property rights and freedom of contract. Although the proposed code pefigured certain modern French practices, many revolutionaries blocked it because they feared that its hands-off liberalism would allow large landholders to recreate the subordination of feudalism in a new guise.

A reexamination of the issue was then ordered by Napoleon and presided over by Joseph Verneilh Puyrasseau. Concurrently, Depute Lalouette proposed to do precisely what I supposed, in the hypothetical example, was impossible. That is, he undertook to systematically gather information about all local practices, to classify and codify them, and then to sanction them by decree. The decree in question would become the code rural. Two problems undid this charming scheme to present the rural poplace with a rural code that simply reflected its own practices. The first difficulty was in deciding which aspects of the literally “infinite diversity” or rural production relations were to be represented and codified. Even if a particular locality, practices varied greatly from farm to farm over time; any codification would be partly arbitrary and artificially static. To codify local practices was thus a profoundly political act. Local notables would be able to sanction their preferences with the mantle of law, whereas others would lose customary rights that they depended on. The second difficulty was that Lalouette’s plan was a mortal threat to all state centralizers and economic modernizers for whom a legible, national property regime was the procondition of progress. As Serge Aberdam notes, “The Lalouette project would have brought about exactly what Merlin de Douai and the bourgeois, revolutionary jurists always sought ot avoid.” Neither Lalouette nor Verneilh’s proposed code was ever passed, because they, like their predecessor in 1807, seemed to be designed to strengthen the hand of the landowners.

(Emphasis mine.)

The moral of the story is that just as the codification of a land map will be inaccurate and politically contested for its biases, so too a codification of customs and norms will suffer the same fate. As Borges’ fable On Exactitude in Science mocks the ambition of physical science, we might see the French attempts at code rural to be a mockery of the ambition of computational social science.

On the other hand, Napoleonic France did not have the sweet ML we have today. So all bets are off.

three kinds of social explanation: functionalism, politics, and chaos

Roughly speaking, I think there are three kinds of social explanation. I mean “explanation” in a very thick sense; an explanation is an account of why some phenomenon is the way it is, grounded in some kind of theory that could be used to explain other phenomena as well. To say there are three kinds of social explanation is roughly equivalent to saying there are three ways to model social processes.

The first of these kind of social explanation is functionalism. This explains some social phenomenon in terms of the purpose that it serves. Generally speaking, fulfilling this purpose is seen as necessary for the survival or continuation of the phenomenon. Maybe it simply is the continued survival of the social organism that is its purpose. A kind of agency, though probably very limited, is ascribed to the entire social process. The activity internal to the process is then explained by the purpose that it serves.

The second kind of social explanation is politics. Political explanations focus on the agencies of the participants within the social system and reject the unifying agency of the whole. Explanations based on class conflict or personal ambition are political explanations. Political explanations of social organization make it out to be the result of a complex of incentives and activity. Where there is social regularity, it is because of the political interests of some of its participants in the continuation of the organization.

The third kind of social explanation is hardly an explanation at all. It is explanation by chaos. This sort of explanation is quite rare, as it does not provide much of the psychological satisfaction we like from explanations. I mention it here because I think it is an underutilized mode of explanation. In large populations, much of the activity that happens will do so by chance. Even large organizations may form according to stochastic principles that do not depend on any real kind of coordinated or purposeful effort.

It is important to consider chaotic explanation of social processes when we consider the limits of political expertise. If we have a low opinion of any particular person’s ability to understand their social environment and act strategically, then we must accept that much of their “politically” motivated actions will be based on misconceptions and therefore be, in an objective sense, random. At this point political explanations become facile, and social regularity has to be explained either in terms of the ability of social organizations qua organizations to survive, or the organization must be explained in a deflationary way: i.e., that the organization is not really there, but just in the eye of the beholder.