A note towards formal modeling of informational capitalism

Cohen’s Between Truth and Power (2019) is enormously clarifying on all issues of the politics of AI, etc.

“The data refinery is only secondarily an apparatus for producing knowledge; it is principally an apparatus for producing wealth.”

– Julie Cohen, Between Truth and Power, 2019

Cohen lays out the logic of informational capitalism in comprehensive detail. Among her authoritatively argued points is that scholarly consideration of platforms, privacy, data science, etc. has focused on the scientific and technical accomplishments undergirding the new information economy, but that really its key institutions, the platform and the data refinery, are first and foremost legal and economic institutions. They exist as businesses; they are designed to “extract surplus”.

I am deeply sympathetic to this view. I’ve argued before that the ethical and political questions around AI are best looked at by considering computational institutions (1, 2). I think getting to the heart of the economic logic is the best way to understand the political and moral concerns raised by information capitalism. Many have argued that there is something institutionally amiss about informational capitalism (e.g. Strandburg, 2013); a recent CfP went so far as to say that the current market for data and AI is not “functional or sustainable.”

As far as I’m concerned, Cohen (2019) is the new gold standard for qualitative analysis of these issues. It is thorough. It is, as far as I can tell, correct. It is a dense and formidable work; I’m not through it yet. So while it may contain all the answers, I haven’t read them yet. This leaves me free to continue to think about how I would go about solving them.

My perspective is this: it will require social scientific progress to crack the right institutional design to settle informational capitalism in a satisfying way. Because computation is really at the heart of the activity of economic institutions, computation will need to be included within the social scientific models in question. But this is not something particularly new; rather, it’s implicitly already how things are done in many “hard” social science disciplines. Epstein (2006) draws the connections between classical game theoretic modeling and agent-based simulation, arguing that “The Computer is not the point”: rather, the point is that the models are defined in terms of mathematical equations, which are by foundational laws of computing amenable to being simulated or solved through computation. Hence, we have already seen a convergence of methods from “AI” into computational economics (Carroll, 2006) and sociology (Castelfranchi, 2001).

This position is entirely consistent with Abebe et al.’s analysis of “roles for computing in social change” (2020). In that paper, the authors are concerned with “social problems of justice and equity”, loosely defined, which can be potentially be addressed through “social change”. They defend the use of technical analysis and modeling as playing a positive role even according to the politics the Fairness, Accountability, and Transparency research community, which are particular. Abebe et al. address backlashes against uses of formalism such as that of Selbst et al. (2019); this rebuttal was necessary given the disciplinary fraughtness of the tech policy discourse.

What I am proposing in this note is something ever so slightly different. First, I am aiming at a different political problematic than the “social problems of justice and equity”. I’m trying to address the economic problems raised by Cohen’s analysis, such as the dysfunctionality of the data market. Second, I’d like to distinguish between “computing” in the method of solving mathematical model equations and “computing” as an element of the object of study, the computational institution (or platform, or data refinery, etc.) Indeed, it is the wonder and power of computation that it is possible to model one computational process within another. This point may be confusing for lawyers and anthropologists, but it should be clear to computational social scientists when we are talking about one or other, though our scientific language has not settled on a lexicon for this yet.

The next step for my own research here is to draw up a mathematical description of informational capitalism, or the stylized facts about it implied by Cohen’s arguments. This is made paradoxically both easier and more difficult by the fact that much of this work has already been done. A simple search of literature on “search costs”, “network effects”, “switching costs”, and so on, brings up a lot of fine work. The economists have not been asleep all this time. But then why has it taken so long for the policy critiques around informational capitalism, including those around informational capitalism and algorithmic opacity, to emerge?

I have two conflicting hypotheses, one quite gloomy and the other exciting. The gloomy view is that I’m simply in the wrong conversation. The correct conversation, the one that has adequately captured the nuances of the data economy already, is elsewhere–maybe in an economics conference in Zurich or something, and this discursive field of lawyers and computer scientists and ethicists is just effectively twiddling its thumbs and working on poorly framed problems because it hasn’t and can’t catch up with the other discourse.

The exciting view is that the problem of synthesizing the fragments of a solution from the various economists literatures with the most insight legal analyses is an unsolved problem ripe for attention.

Edit: It took me a few days, but I’ve found the correct conversation. It is Ross Anderson’s Workshop on Economics and Information Security. That makes perfect sense: Ross Anderson is a brilliant thinker in that arena. Naturally, as one finds, all the major results in this space are 10-20 years old. Quite probably, if I had found this one web page a couple years ago, my dissertation would have been written much differently–not so amateurishly.

It is supremely ironic to me how, in an economy characterized by a reduction in search costs, the search for the answers I’ve been looking for in information economics has been so costly for me.

References

Abebe, R., Barocas, S., Kleinberg, J., Levy, K., Raghavan, M., & Robinson, D. G. (2020, January). Roles for computing in social change. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (pp. 252-260).

Castelfranchi, C. (2001). The theory of social functions: challenges for computational social science and multi-agent learning. Cognitive Systems Research2(1), 5-38.

Carroll, C. D. (2006). The method of endogenous gridpoints for solving dynamic stochastic optimization problems. Economics letters91(3), 312-320.

Cohen, J. E. (2019). Between Truth and Power: The Legal Constructions of Informational Capitalism. Oxford University Press, USA.

Epstein, Joshua M. Generative social science: Studies in agent-based computational modeling. Princeton University Press, 2006.

Fraser, N. (2017). The end of progressive neoliberalism. Dissent2(1), 2017.

Selbst, A. D., Boyd, D., Friedler, S. A., Venkatasubramanian, S., & Vertesi, J. (2019, January). Fairness and abstraction in sociotechnical systems. In Proceedings of the Conference on Fairness, Accountability, and Transparency (pp. 59-68).

Strandburg, K. J. (2013). Free fall: The online market’s consumer preference disconnect. U. Chi. Legal F., 95.