philosophy of science

July 31, 2020

Instrumental realism — a few key points

Continuing my reading of Ihde (1991), I’m getting to the meat of his argument where he compares and constrasts his instrumental realist position with two contemporaries: Heelan (1989), whom Ihde points out is a double doctorate in physics and philosophy and so might be especially capable of philosophizing about physics praxis, and Hacking (1983), who is from my perspective the most famous of the three.

Ihde argues that he, Hacking, and Heelan are all more or less instrumental realists, but that Ihde and Heelan draw more from the phenomenological tradition, which emphasizes embodied perception and action, whereas Hacking is more in the Anglo-American ‘analytic’ tradition of starting from analysis of language. Ihde’s broader argument in the book is one of convergence: he uses the fact that many different schools of thought have arrived at similar conclusions to support the idea that those conclusions are true. That makes perfect sense to me.

Broadly speaking, instrumental realism is a position that unites philosophy of science with philosophy of technology to argue that:

That science is able to grasp, understand, theorize the real
That this reality is based on embodied perception and praxis. Or, in the more analytic framing, on observation and experiment.
That scientific perception and praxis is able to go “beyond” normal, every-day perception and praxis because of its use of scientific instruments, of which the microscope is a canonical example.
This position counters many simple relativistic threats to scientific objectivity and integrity, but does so by placing emphasis on scientific tooling. Science advances, mainly, by means of the technologies and infrastructures that it employs.
This position is explicitly embodied and materialist, counter to many claims that scientific realism depends on its being disembodied or transcendental.

This is all very promising though there are nuances to work out. Ihde’s study of his contemporaries is telling.

Ihde paints Heelan as a compelling thinker on this topic, though a bit blinkered by his emphasis on physics as the true or first science. Heelean’s view of scientific perception is that it is always both perception and measurement. Being what Ihde calls a “Euro-American” (which I think is quite funny), Ihde can describe him as therefore saying that scientific observation is both a matter of perception-praxis and a matter of hermeneutics–by which I mean the studying of a text in community with others or, to use the more Foucauldean term, “discourse”. Measurement, somewhat implicitly here is a kind of standardized way of “reading”. Ihde makes a big deal out of the subtle differences between “seeing” and “reading”.

To the extent that “discourse”, “hermeneutics”, “reading”, etc. imply a weakness of the scientific standpoint, they weigh against the ‘realism’ of instrumental realism. However, the term measurement is telling in that the difference between, say, different units of measurement of length, mass, time, etc. does not challenge the veracity of the claim “there are 24 hours in a day” because translating between different units is trivial.

Ihde characterizes Hacking as a fellow traveler, converging on instrumental realism when he breaks from his own analytic tradition to point out that experiment is one of the most important features of science, and that experiment depends on and is advanced by instrumentation. Ihde writes that Hacking is quite concerned about “(a) how an instrument is made, particularly with respect to theory-driven design, and (b) the physical processes entailed in the “how” or conditions of use.” Which makes perfect sense to me–that’s exactly what you’d want to scrutinize if you’d taking the ‘realism’ in instrumental realism seriously.

Ihde’s positions here, as the positions of his contemporaries, seem perfectly reasonable to me. I’m quite happy to adopt this view; it corresponds to conclusions I’ve reached in my own reading and practice and it’s nice to have a solid reference and term for it.

The questions that come up next are how instrumental realism applies to today’s controversies about science and technology. Just a handful of notes here:

I work quite a bit with scientific sofware. It’s quite clear to me that scientific software development is a major field of scientific instrumentation today. Scientists “see” and “do” via computers and software controls. This has made “data science” a core aspect of 21st century science in general, as it’s the part of science that is closest to the instrumentation. This confirms my long-held view that scientific software communities are the groups to study if you’re trying to understand sociology of science today.
On the other hand, it’s becoming increasingly clear in scientific practice that you can’t do software-driven science without the Internet and digital services, and these are now controlled by an oligopoly of digital services conglomerates. The hardware infrastructure–data centers, caching services, telecom broadly speaking, cloud computing hubs–goes far beyond the scientific libraries. Scientific instrumentation depends critically now on mass corporate IT.
These issues are compounded by how Internet infrastructure–now privately owned and controlled for all intents and purposes–is also the instrument of so much social science research. Don’t get me started on social media platforms as research tools. For me, the best resource on this is Tufekci, 2014.
The most hot-button, politically charged critique in the philosophy of science space is that science and/or data science and/or AI as it is currently constituted is biased because of who is represented in these research communities. The position being contested is the idea that AI/data science/computational social science etc. is objective because it is designed in a way that aligns with mathematical theory.
- I would be very interested to read something connecting postcolonial, critical race, and feminist AI/data science practices to instrumental realism directly. I think these groups ought to be able to speak to each other easily, since the instrumental realists from the start are interested in the situated embodiment of the observer.
- On the other hand, I think it would be difficult for the critical scholars to find fault in the “hard core” of data science/computing/AI technologies/instruments because, truly, they are designed according to mathematical theory that is totally general. This is what I think people mean when they say AI is objective because it’s “just math”. AI/data science praxis makes you sensitive to what aspects of the tooling are part of the core (libraries of algorithms, based on vetted mathematical theorems) and what are more incidental (training data sets, for example, or particular parameterizations of the general algorithms). If critical scholars focused on these parts of the scientific “stack”, and didn’t make sweeping comments that sound like they implicate the “core”, which we have every reason to believe is quite solid, they would probably get less resistance.
- On the other hand, if science is both a matter of perception-praxis and hermeneutics, then maybe the representational concerns are best left on the hermeneutic side of the equation.

References

Hacking, I. (1983). Representing and Intervening: Introductory Topics in the Philosophy of Natural Science.

Heelan, P. A. (1989). Space-perception and the philosophy of science. Univ of California Press.

Ihde, D. (1991). Instrumental realism: The interface between philosophy of science and philosophy of technology (Vol. 626). Indiana University Press.

Tufekci, Z. (2014, May). Big questions for social media big data: Representativeness, validity and other methodological pitfalls. In Eighth International AAAI Conference on Weblogs and Social Media.

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April 23, 2017

Process theory; generative epistemology; configurative ontology: notes on Cederman, part 1

I’ve recently had recommended to me the work of L.E. Cederman, who I’ve come to understand is a well-respected and significant figure in computational social science, especially agent based modeling. In particular, I’ve been referred to this paper on the theoretical foundations of computational sociology:

Cederman, L.E., 2005. Computational models of social forms: Advancing generative process theory 1. American Journal of Sociology, 110(4), pp.864-893. (link)

This is a paper I wish I had encountered years ago. I’ve written much here about my struggles with “interdisciplinary” research. In short: I’ve been trying to study social phenomena with scientific rigor. This is a very old problem fraught with division. On top of that, there’s been, it seems, an epistemological upset because of advances in data collection and processing that poses a practical challenge to a lot of established disciplines. On top of this, the social phenomena I’m interested in most tend to involve the interaction between people and technology, which brings with it an association with disciplines specialized to that domain (HCI, STS) that for me have not made my research any more straightforward. After trying for some time to do the work I wanted to do under the new heading of data science, I did not find what I was looking intellectually in that emerging field, however important the practical skill-set involved has been to me.

Computational social science, I’ve convinced myself if not others, is where the answers lie. My hope for it is that as a new discipline, it’s able to break away from dogmas that limited other disciplines and trapped their ambitions in endless methodological debates. What is being offered, I’ve imagined, in computational social science is the possibility of a new paradigm, or at least a viable alternative one. Cederman’s 2005 paper holds out the promise for just that.

Let me address for now some highlights of his vision of social science and how they relate to the other. I hope to come to the rest in a later post.

Sociological process theory. This is a position in sociological theory that Cederman attributes to 19th century sociologist Georg Simmel. The core of this position is that social reality is not fixed, but rather result of an ongoing process of social interactions that give rise to social forms.

“The large systems and the super-individual organizations that customarily come to mind when we think of society, are nothing but immediate interactions that occur among men constantly every minute, but that have become crystallized as permanent fields, as autonomous phenomena.” (Simmel quoted in Wolf 1950, quoted in Cederman 2005)

There is a lot to this claim. If one is coming from the field of Human Computer Interaction (HCI), what may seem most striking about it is how well it resonates with a scholarly tradition that is most frequently positioned as a countercurrent to an unthinking positivism in design. Lucy Suchman, Etienne Wenger, and Jean Lave are scholars that come to mind as representative of this way of thinking. Much of the intellectual thrust of Simmel can be found in Paul Dourish’s criticism of positivist understandings of “context” in HCI.

For Dourish, the intellectual ground of this position is phenomenological social science, often associated with ethnomethodology. Simmel predates phenomenology but is a neo-Kantian, a contemporary of Weber, and a critic of the positivism of his day (the original positivism). As a social scientific tradition, it has had its successors (maybe most notably George Herbert Mead) but has submerged under other theoretical traditions. From Cederman’s analysis, one gathers that this is largely due to process theory’s inability to ground itself in rigorous method. Its early proponents were fond of metaphorical writing in a way that didn’t age well. Cederman pays homage to the sociological process theory’s origins, but quickly moves to discuss an epistemological position that complements it. Notably, this position is neither positivist, nor phenomenological, nor critical (in the Frankfurt School sense), but something else: generative epistemology.

Generative epistemology. Cederman positions generative epistemology primarily in opposition to positivism and particularly a facet of positivism that he calls “nomothetic explanation”: explanation in terms of laws and regularities. The latter is considered the gold standard of natural science and the social sciences that attempt to mimic them. This tendency is independent of whether the inquiry is qualitative or quantitative. Both comparative analysis and statistical control look for a conjunction of factors that is regularly predictive of some outcome. (Cederman’s sources on this: (Gary) King, Keohane, and Verba (1994), and Goldthorpe, 1997. The Gary King cited is I assume the same Gary King who goes on to run Harvard’s IQSS; I hope to return to this question of positivism in computational social science in later writing. I tend to disagree with the idea that ‘data science’ or ‘big data’ has primarily a positivist tendency.)

Cederman describes the ‘process theorist’s’ alternative as based on abduction, not induction. Recall that ‘abduction’ was Peirce’s term for ‘inference to the best explanation’. The goal is to take an observed sociological phenomenon and explain its generation by accounting for how it is socially produced. The preference for generative explanation, in Simmel, comes in part from a pessimism about isolating regularities in complex social systems. Through this theorization, knowledge is gained; the knowledge gained is a theoretical advance that makes a social phenomenon less ‘puzzling’.

“The construction of generative explanations based on abductive inference is an inherently theoretical endeavor (McMullin, 1964). Instead of subsuming observations under laws, the main explanatory goal is to make a puzzling phenomenon less puzzling, something that inevitably requires the introduction of new knowledge through theoretical innovation.”

The specifics of the associated method are less clear than the motivation for this epistemology. Many early process theorists resorted to metaphors. But where all this is going is into the construction of models, and especially computational models, as a way of presenting and testing generative theories. Models generate forms through logical operations based on a number of parameters. A comparison between the logical form and the empirical form is made. If it favorable, then the empirical form can be characterized as the result of a process described by the variables and model. (Barth, 1981)

Cederman draws from Barth (1981) and Thomas Fararo (1989) to ally himself with ‘realist’ social science. The term is clarified later: ‘realism’ is opposed to ‘instrumentalism’, a reference that cuts to one of core epistemological debates in computational methods. An instrumental method, such as a machine learning ensemble, may provide a very instrumental model for purposes of prediction and control that nevertheless does not capture what’s really going on in the underlying process. Realist mathematical sociology, on the other hand, attempts to capture the reality of the process generating the social phenomenon in the precise language of processing, mathematics/computation. The underlying metaphysical point is one that many people would rather not attend to. For now, we will follow Cederman’s logic to a different ontological point.

Configurative ontology. Sociological process theory requires explanations to be specify the process that generates the social form observed. The entities, relations, and mechanisms may be unobserved or even unobservable. Postivists, Cederman argues, will often take the social forms to be variables themselves and undertheorize how the variables have been generated, since they care only about predicting actual outcomes. Whereas positivists study ‘correlations’ among elements, Simmel studies ‘sociations’, the interactions that result in those elements. The ontology, then, is that social forms are “configurations of social interactions and actors that together constitute the structures in which they are embedded.”

In this view, variables, such as would be used in some more positivist social scientific study, “merely measure dimensions of social forms; they cannot represent the forms themselves except in very simple cases.” While a variable based analysis detaches a social phenomenon from space and time, “social forms always possess a duration in time and an extension in space.

Aside from a deep resonance with Dourish’s critique of ‘contextual computing’ (noted above), this argument once again recalls much of what now comes under the expansive notion of ‘criticism’ of social sciences. Ethnomethodology and ethnography more general are now often raised as an alternative to simplistic positivist methods. In my experience at Berkeley and exposure so far to the important academic debates, the most noisy contest is between allegedly positivist or instrumentalist (they are different, surely) quantitative methods and phenomenological ethnographic methods. Indeed, it is the latter who more often now claim the mantle of ‘realism’. What is different about Cederman’s case in this paper is that he is setting up a foundation for realist sociology that is nevertheless mathematized and computational.

What I am looking for in this paper, and haven’t found yet, is an account of how these ‘realist’ models of social processes are tested for their correspondence to empirical social form. Here is where I believe there is an opportunity that I have not yet seen fully engaged.

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September 10, 2015

de Beauvoir on science as human freedom

I appear to be unable to stop writing blog posts about philosophers who wrote in the 1940’s. I’ve been attempting a kind of survey. After a lot of reading, I have to say that my favorite–the one I think is most correct–is Simone de Beauvoir.

Much like “bourgeois”, “de Beauvoir” is something I find it impossible to remember how to spell. Therefore I am setting myself up for embarrassment by beginning to write about her work, The Ethics of Ambiguity. On the other hand, it’s nice to come full circle. In a notebook I was scribbling in when I first showed up in graduate school I was enthusiastic about using de Beauvoir to explicate what’s interesting about open source software development. Perhaps now is the right time to indulge the impulse.

de Beauvoir is generally not considered to be a philosopher of science. That’s too bad, because she said some of the most brilliant things about science ever said. If you can get past just a little bit of existentialist jargon, there’s a lot there.

Here’s a passage. The Marxists have put this entire book on the Internet, making it easy to read.

To will freedom and to will to disclose being are one and the same choice; hence, freedom takes a positive and constructive step which causes being to pass to existence in a movement which is constantly surpassed. Science, technics, art, and philosophy are indefinite conquests of existence over being; it is by assuming themselves as such that they take on their genuine aspect; it is in the light of this assumption that the word progress finds its veridical meaning. It is not a matter of approaching a fixed limit: absolute Knowledge or the happiness of man or the perfection of beauty; all human effort would then be doomed to failure, for with each step forward the horizon recedes a step; for man it is a matter of pursuing the expansion of his existence and of retrieving this very effort as an absolute.

de Beauvoir’s project in The Ethics of Ambiguity is to take seriously the antimonies of society and the individual, of nature and the subject, which Horkheimer only gets around to stating at the conclusion of contemporary analysis. Rather than cry from wounds of getting skewered by the horns of the antinomy, de Beauvoir turns that ambiguity inherent in the antinomy into a realistic, situated ethics.

If de Beauvoir’s ethics have a telos or purpose, it is to expand human freedom and potential indefinitely. Through a terrific dialectical argument, she reasons out why this project is in a sense the only honest one for somebody in the human condition, despite its transcendence over individual interest.

Science, then, becomes one of several activities which one undertakes to expand this human potential.

Science condemns itself to failure when, yielding to the infatuation of the serious, it aspires to attain being, to contain it, and to possess it; but it finds its truth if it considers itself as a free engagement of thought in the given, aiming, at each discovery, not at fusion with the thing, but at the possibility of new discoveries; what the mind then projects is the concrete accomplishment of its freedom.

Science is the process of free inquiry, not the product of a particular discovery. The finest scientific discoveries open up new discoveries.

What about technics?

The attempt is sometimes made to find an objective justification of science in technics; but ordinarily the mathematician is concerned with mathematics and the physicist with physics, and not with their applications. And, furthermore, technics itself is not objectively justified; if it sets up as absolute goals the saving of time and work which it enables us to realize and the comfort and luxury which it enables us to have access to, then it appears useless and absurd, for the time that one gains can not be accumulated in a store house; it is contradictory to want to save up existence, which, the fact is, exists only by being spent, and there is a good case for showing that airplanes, machines, the telephone, and the radio do not make men of today happier than those of former times.

Here we have in just a couple sentences dismissal of instrumentality as the basis for science. Science is not primarily for acceleration; this is absurd.

But actually it is not a question of giving men time and happiness, it is not a question of stopping the movement of life: it is a question of fulfilling it. If technics is attempting to make up for this lack, which is at the very heart of existence, it fails radically; but it escapes all criticism if one admits that, through it, existence, far from wishing to repose in the security of being, thrusts itself ahead of itself in order to thrust itself still farther ahead, that it aims at an indefinite disclosure of being by the transformation of the thing into an instrument and at the opening of ever new possibilities for man.

For de Beauvoir, science (as well as all the other “constructive activities of man” including art, etc.) should be about the disclosure of new possibilities.

Succinct and unarguable.

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July 18, 2015

Hannah Arendt on the apoliticality of science

The next book for the Berkeley School of Information’s Classics reading group is Hannah Arendt’s The Human Condition, 1958. We are reading this as a follow-up to Sennett’s The Craftsman, working backwards through his intellectual lineage. We have the option to read other Arendt. I’m intrigued by her monograph On Violence, because it’s about the relationship between violence and power (which is an important thing to think about) and also because it’s comparatively short (~100 pages). But I’ve begun dipping into The Human Condition today only to find an analysis of the role of science in society. Of course I could not resist writing about it here.

Arendt opens the book with a prologue discussing the cultural significance of the Apollo mission. She muses at shift in human ambition that has lead to its seeking to leave Earth. Having rejected Heavenly God as Father, she sees this as a rejection of Earth as Mother. Poetic stuff–Arendt is a lucid writer, prose radiating wisdom.

Then Arendt begins to discuss The Problems with Science (emphasis mine):

While such possibilities [of space travel, and of artificial extension of human life and capabilities] still may lie in a distant future, the first boomerang effects of science’s great triumphs have made themselves felt in a crisis within the natural sciences themselves. The trouble concerns the fact that the “truths” of the modern scientific world view, though they can be demonstrated in mathematical formulas and proved technologically, will no longer lend themselves to normal expression in speech and thought. The moment these “truths” are spoken of conceptually and coherently, the resulting statements will be “perhaps not as meaningless as a ‘triangular circle,’ but much more so than a ‘winged lion'” (Erwin Schödinger). We do not yet know whether this situation is final. But it could be that we, who are earth-bound creatures and have begun to act as though we are dwellers of the universe, will forever be unable to unable to understand, that is, to think and speak about the things which nevertheless we are able to do. In this case, it would be as though our brain, which constitutes the physical, material condition of our thoughts, were unable to follow what we do, so that from now on we would indeed need artificial machines to do our thinking and speaking. If it should turn out to be true that knowledge (in the sense of know-how) and thought have parted company for good, then we would indeed become the helpless slaves, not so much of our machines as of our know-how, thoughtless creatures at the mercy of every gadget which is technically possible, no matter how murderous it is.

We can read into Arendt a Heideggerian concern about man’s enslavement of himself through technology, and equally a distrust mathematical formalism that one can also find in Horkheimer‘s Eclipse of Reason. It’s fair to say that the theme of technological menace haunted the 20th century; this is indeed the premise of Beniger‘s The Control Revolution, whose less loaded account described how the advance of technical control could be seen as nothing less or more than the continuing process of life’s self-organization.

What is striking to me about Arendt’s concerns, especially after having attended SciPy 2015, a conference full of people discussing their software code as a representation of scientific knowledge, ~~is how ignorant Arendt is about how mathematics is used by scientists.~~ (EDIT: The error here is mine. A skimming of the book past the prologue (always a good idea before judging the content of a book or its author…) makes it clear that this comment about mathematical formalism is not a throwaway statement at the beginning of the book to motivate a discussion of political action, but rather something derived from her analysis of political action and the history of science. Ironically, I’ve read her “speech” and interpreted it politically (in the narrow sense of implicating identities of “the scientist”, a term which she does seem to use disparagingly or distancingly elsewhere, when another more charitable reading (one that was more sensitive to how she is “technically” defining her terms (though I expect she would deny this usage)–“speech” being rather specialized for Arendt, not being merely ‘utterances’–wouldn’t be as objectionable. I’m agitated by the bluntness of my first reading, and encouraged to read further.)

On the one hand, Arendt wisely situates mathematics as an expression of know-how, and sees technology as an extension of human capacity not as something autonomous from it. But it’s strange to read her argue essentially that mathematics and technology is not something that can be discussed. This ignores the daily practice of scientists, mathematicians, and their intellectual heirs, software engineers, which involves lots of discussion about about technology. Often these discussions are about the political impact of technical decisions.

As an example, I had the pleasure of attending a meeting of the NumPy community at SciPy. NumPy is one of the core packages for scientific computing in Python which implements computationally efficient array operations. Much of the discussion hinged on whether and to what extent changes to the technical interface would break downstream implementations using the library, angering their user base. This political conflict, among other events, lead to the creation of sempervirens, a tool for collecting data about how people are using the library. This data will hopefully inform decisions about when to change the technical design.

Despite the facts of active discourse about technology in the mathematized language of technology, Arendt maintains that it is the inarticulateness of science that makes it politically dangerous.

However, even apart from these last and yet uncertain consequences, the situation created by the sciences is of great political significance. Wherever the relevance of speech is at stake, matters become political by definition, for speech is what makes man a political being. If we would follow the advice, so frequently urged upon us, to adjust our cultural attitudes to the present status of scientific achievement, we would in all earnest adopt a way of life in which speech is no longer meaningful. For the sciences today have been forced to adopt a “language” of mathematical symbols which, though it was originally meant only as an abbreviation for spoken statements, now contains statements that in no way can be translated back into speech. The reason why it may be wise to distrust the political judgment of scientists qua scientists is not primarily their lack of “character”–that they did not refuse to develop atomic weapons–or their naivete–that they did not understand that once these weapons were developed they would be the last to be consulted about their use–but precisely the fact that they move in a world where speech has lost its power. And whatever men do or know or experience can make sense only to the extent that it can be spoken about. There may be truths beyond speech, and they may be of great relevance to man in the singular, that is, to man in so far as he is not a political being, whatever else he may be. Men in the plural, that is, men in so far as they live and move and act in this world, can experience meaningfulness only because they can talk with and make sense to each other and to themselves.

There is an element of truth to this analysis. But there is also a ~~deep misunderstanding~~ of the scientific process as one that somehow does not involve true speech. Here we find another root of a much more contemporary debate about technology in society reflected in recent concern about the power of ‘algorithms’. (EDIT: Again, after consideration, shallowly accusing Arendt of a “deep misunderstanding” at this stage is hubris. Though there does seem to be a connection between some of the contemporary debate about algorithms to Arendt’s view, it’s wrong to project historically backwards sixty years when The Human Condition is an analysis of the shifting conditions over the preceding two millennia.

Arendt claims early on that the most dramatic change in the human condition that she can anticipate is humanity’s leaving the earth to populate the universe. I want to argue that the creation of the Internet has been transformative of the human condition in a different way.)

I think it would be fair to say that Arendt, beloved a writer though she is, ~~doesn’t know what she’s talking about when she’s talking about mathematical formalism~~. (EDIT: Again, a blunt conclusion. However, the role of formalism in, say, economics (though much debated) stands as a counterexample to Arendt in other ways.) And perhaps this is the real problem. When, for almost a century, theorists have tried to malign the role of scientific understanding in politics, it has been (incoherently) either on the grounds that it is secretly ideological in ways that have gone unstated, or (as for Arendt) that it is ~~cognitively defective~~ in a way that prevents it from participating in politics proper. (EDIT: This is a misreading of Arendt. It appears that what makes mathematical science apolitical for Arendt is precisely its universality, and hence its inability to be part of discussion about the different situations of political actors. Still, something seems quite wrong about Arendt’s views here. How would she think about Dwork’s “Fairness through awareness“?

The frustration for a politically motivated scientist is this: Political writers will sometimes mistake their own inability to speak or understand mathematical truths for their general intelligibility. On grounds of this alleged intelligibility they dismiss scientists from political discussion. They then find themselves apolitically enslaved by technology they don’t understand, and angry about it. Rather than blame their own ignorance of the subject matter, they blame scientists for being unintelligible. This is despite scientists intelligibility to each other.

An analysis of the politics of science will be incomplete without a clear picture of how scientists and non-scientists relate to each other and communicate. As far as I can tell, such an analysis is almost impossible politically speaking because of the power dynamic of the relation. Professional non-scientific intellects are loathe to credit scientists with an intellectual authority that they feel that they are not able to themselves attain, and scientific practice requires adhering to standards of rigor which give one greater intellectual authority; these standards by their nature require ahistorical analysis, dismissal of folk theorizing, etc. It has become politically impossible to ground an explanation of a social phenomenon on the basis that one population is “smarter” than another, despite this being a ready first approximation and one that is used in practice by the vast majority of people in private. Hence, the continuation of the tradition of treatises putting science in its place.

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May 14, 2015

data science is not positivist, it’s power

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

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

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

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

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

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

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

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

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

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

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

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May 9, 2015

is science ideological?

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

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

This is a good question.

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

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

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

an expression of motives that serves to justify collective action by a social group

…that is false because it is unreflective of the social group’s real interests.

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

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

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

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

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December 15, 2014

Some research questions

Last week was so interesting. Some weeks you just get exposed to so many different ideas that it’s trouble to integrate them. I tried to articulate what’s been coming up as a result. It’s several difficult questions.

Assuming trust is necessary for effective context management, how does one organize sociotechnical systems to provide social equity in a sustainable way?
Assuming an ecology of scientific practices, what are appropriate selection mechanisms (or criteria)? Are they transcendent or immanent?
Given the contradictory character of emotional reality, how can psychic integration occur without rendering one dead or at least very boring?
Are there limitations of the computational paradigm imposed by data science as an emerging pan-constructivist practice coextensive with the limits of cognitive or phenomenological primitives?

Some notes:

I think that two or three of these questions above may be in essence the same question. In that they can be formalized into the same mathematical problem, and the solution is the same in each case.
I really do have to read Isabelle Stengers and Nancy Nersessian. Based on the signals I’m getting, they seem to be the people most on top of their game in terms of understanding how science happens.
I’ve been assuming that trust relations are interpersonal but I suppose they can be interorganizational as well, or between a person and an organization. This gets back to a problem I struggle with in a recurring way: how do you account for causal relationships between a macro-organism (like an organization or company) and a micro-organism? I think it’s when there are entanglements between these kinds of entities that we are inclined to call something an “ecosystem”, though I learned recently that this use of the term bothers actual ecologists (no surprise there). The only things I know about ecology are from reading Ulanowicz papers, but those have been so on point and beautiful that I feel I can proceed with confidence anyway.
I don’t think there’s any way to get around having at least a psychological model to work with when looking at these sorts of things. A recurring an promising angle is that of psychic integration. Carl Jung, who has inspired clinical practices that I can personally vouch for, and Gregory Bateson both understood the goal of personal growth to be integration of disparate elements. I’ve learned recently from Turner’s The Democratic Surround that Bateson was a more significant historical figure than I thought, unless Turner’s account of history is a glorification of intellectuals that appeal to him, which is entirely possible. Perhaps more importantly to me, Bateson inspired Ulanowicz, and so these theories are compatible; Bateson was also a cyberneticist following Wiener, who was prescient and either foundational to contemporary data science or a good articulator of its roots. But there is also a tie-in to constructivist epistemology. DiSessa’s epistemology, building on Piaget but embracing what he calls the computational metaphor, understands the learning of math and physics as the integration of phenomenological primitives.
The purpose of all this is ultimately protocol design.
This does not pertain directly to my dissertation, though I think it’s useful orienting context.

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Digifesto

Tag: philosophy of science

July 31, 2020

Instrumental realism — a few key points

September 10, 2015

de Beauvoir on science as human freedom

July 18, 2015

Hannah Arendt on the apoliticality of science

May 14, 2015

data science is not positivist, it’s power

May 9, 2015

is science ideological?

December 15, 2014

Some research questions