Tag: information economy

The paradox of ‘data markets’

We often hear that companies are “selling out data”, or that we are “paying for services” with our data. Data brokers literally buy and sell data about people. There are other forms of expensive data sources or data sets. There is, undoubtedly, one or more data markets.

We know that classically, perfect competition in markets depends on perfect information. Buyers and sellers on the market need to have equal and instantaneous access to information about utility curves and prices in order for the market to price things efficiently.

Since the bread and butter of the data market is information asymmetry, we know that data markets can never be perfectly competitive. If it was, the data market would cease to exist, because the perfect information condition would entail that there is nothing to buy and sell.

Data markets therefore have to be imperfectly competitive. But since these are the markets that perfect information in other markets might depend on, this imperfection is viral. The vicissitudes of the data market are the vicissitudes of the economy in general.

The upshot is that the challenges of information economics are not only those that appear in special sectors like insurance markets. They are at the heart of all economic activity, and there are no equilibrium guarantees.


Personal data property rights as privacy solution. Re: Cofone, 2017

I’m working my way through Ignacio Cofone’s “The Dynamic Effect of Information Privacy Law” (2017) (link), which is an economic analysis of privacy. Without doing justice to the full scope of the article, it must be said that it is a thorough discussion of previous information economics literature and a good case for property rights over personal data. In a nutshell, one can say that markets are good for efficient and socially desirable resource allocation, but they are only good at this when there are well crafted property rights to the goods involved. Personal data, like intellectual property, is a tricky case because of the idiosyncrasies of data–its has zero-ish marginal cost, it seems to get more valuable when it’s aggregated, etc. But like intellectual property, we should expect under normal economic rationality assumptions that the more we protect the property rights of those who create personal data, the more they will be incentivized to create it.

I am very warm to this kind of argument because I feel there’s been a dearth of good information economics in my own education, though I have been looking for it! I do believe there are economic laws and that they are relevant for public policy, let alone business strategy.

I have concerns about Cofone’s argument specifically, which are these:

First, I have my doubts that seeing data as a good in any classical economic sense is going to work. Ontologically, data is just too weird for a lot of earlier modeling methods. I have been working on a different way of modeling information flow economics that tries to capture how much of what we’re concerned with are information services, not information goods.

My other concern is that Cofone’s argument gives users/data subjects credit for being rational agents, capable of addressing the risks of privacy and acting accordingly. Hoofnagle and Urban (2014) show that this is empirically not the case. In fact, if you take the average person who is not that concerned about their privacy on-line and start telling them facts about how their data is being used by third-parties, etc., they start to freak out and get a lot more worried about privacy.

This throws a wrench in the argument that stronger personal data property rights would lead to more personal data creation, therefore (I guess it’s implied) more economic growth. People seem willing to create personal data and give it away, despite actual adverse economic incentives, because cat videos are just so damn appealing. Or something. It may generally be the case that economic modeling is used by information businesses but not information policy people because average users are just so unable to act rationally; it really is a domain better suited to behavioral economics and usability research.

I’m still holding out though. Just because big data subjects are not homo economicus doesn’t mean that an economic analysis of their activity is pointless. It just means we need to have a more sophisticated economic model, on that takes into account how there are many different classes of user that are differently informed. This kind of economic modeling, and empirically fitting it to data, is within our reach. We have the technology.


Cofone, Ignacio N. “The Dynamic Effect of Information Privacy Law.” Minn. JL Sci. & Tech. 18 (2017): 517.

Hoofnagle, Chris Jay, and Jennifer M. Urban. “Alan Westin’s privacy homo economicus.” (2014).

Notes on Posner’s “The Economics of Privacy” (1981)

Lately my academic research focus has been privacy engineering, the designing of information processing systems that preserve privacy of their users. I have been looking the problem particularly through the lens of Contextual Integrity, a theory of privacy developed by Helen Nissenbaum (2004, 2009). According to this theory, privacy is defined as appropriate information flow, where “appropriateness” is determined relative to social spheres (such as health, education, finance, etc.) that have evolved norms based on their purpose in society.

To my knowledge most existing scholarship on Contextual Integrity is comprised by applications of a heuristic process associated with Contextual Integrity that evaluates the privacy impact of new technology. In this process, one starts by identifying a social sphere (or context, but I will use the term social sphere as I think it’s less ambiguous) and its normative structure. For example, if one is evaluating the role of a new kind of education technology, one would identify the roles of the education sphere (teachers, students, guardians of students, administrators, etc.), the norms of information flow that hold in the sphere, and the disruptions to these norms the technology is likely to cause.

I’m coming at this from a slightly different direction. I have a background in enterprise software development, data science, and social theory. My concern is with the ways that technology is now part of the way social spheres are constituted. For technology to not just address existing norms but deal adequately with how it self-referentially changes how new norms develop, we need to focus on the parts of Contextual Integrity that have heretofore been in the background: the rich social and metaethical theory of how social spheres and their normative implications form.

Because the ultimate goal is the engineering of information systems, I am leaning towards mathematical modeling methods that trade well between social scientific inquiry and technical design. Mechanism design, in particular, is a powerful framework from mathematical economics that looks at how different kinds of structures change the outcomes for actors participating in “games” that involve strategy action and information flow. While mathematical economic modeling has been heavily critiqued over the years, for example on the basis that people do not act with the unbounded rationality such models can imply, these models can be a first step and valuable in a technical context especially as they establish the limits of a system’s manipulability by non-human actors such as AI. This latter standard makes this sort of model more relevant than it has ever been.

This is my roundabout way of beginning to investigate the fascinating field of privacy economics. I am a new entrant. So I found what looks like one of the earliest highly cited articles on the subject written by the prolific and venerable Richard Posner, “The Economics of Privacy”, from 1981.

Richard Posner, from Wikipedia

Wikipedia reminds me that Posner is politically conservative, though apparently he has changed his mind recently in support of gay marriage and, since the 2008 financial crisis, the laissez faire rational choice economic model that underlies his legal theory. As I have mainly learned about privacy scholarship from more left-wing sources, it was interesting reading an article that comes from a different perspective.

Posner’s opening position is that the most economically interesting aspect of privacy is the concealment of personal information, and that this is interesting mainly because privacy is bad for market efficiency. He raises examples of employers and employees searching for each other and potential spouses searching for each other. In these cases, “efficient sorting” is facilitated by perfect information on all sides. Privacy is foremost a way of hiding disqualifying information–such as criminal records–from potential business associates and spouses, leading to a market inefficiency. I do not know why Posner does not cite Akerlof (1970) on the “market for ‘lemons'” in this article, but it seems to me that this is the economic theory most reflective of this economic argument. The essential question raised by this line of argument is whether there’s any compelling reason why the market for employees should be any different from the market for used cars.

Posner raises and dismisses each objective he can find. One objection is that employers might heavily weight factors they should not, such as mental illness, gender, or homosexuality. He claims that there’s evidence to show that people are generally rational about these things and there’s no reason to think the market can’t make these decisions efficiently despite fear of bias. I assume this point has been hotly contested from the left since the article was written.

Posner then looks at the objection that privacy provides a kind of social insurance to those with “adverse personal characteristics” who would otherwise not be hired. He doesn’t like this argument because he sees it as allocating the costs of that person’s adverse qualities to a small group that has to work with that person, rather than spreading the cost very widely across society.

Whatever one thinks about whose interests Posner seems to side with and why, it is refreshing to read an article that at the very least establishes the trade offs around privacy somewhat clearly. Yes, discrimination of many kinds is economically inefficient. We can expect the best performing companies to have progressive hiring policies because that would allow them to find the best talent. That’s especially true if there are large social biases otherwise unfairly skewing hiring.

On the other hand, the whole idea of “efficient sorting” assumes a policy-making interest that I’m pretty sure logically cannot serve the interests of everyone so sorted. It implies a somewhat brutally Darwinist stratification of personnel. It’s quite possible that this is not healthy for an economy in the long term. On the other hand, in this article Posner seems open to other redistributive measures that would compensate for opportunities lost due to revelation of personal information.

There’s an empirical part of the paper in which Posner shows that percentage of black and Hispanic populations in a state are significantly correlated with existence of state level privacy statutes relating to credit, arrest, and employment history. He tries to spin this as an explanation for privacy statutes as the result of strongly organized black and Hispanic political organizations successfully continuing to lobby in their interest on top of existing anti-discrimination laws. I would say that the article does not provide enough evidence to strongly support this causal theory. It would be a stronger argument if the regression had taken into account the racial differences in credit, arrest, and employment state by state, rather than just assuming that this connection is so strong it supports this particular interpretation of the data. However, it is interesting that this variable ways more strongly correlated with the existence of privacy statutes than several other variables of interest. It was probably my own ignorance that made me not consider how strongly privacy statutes are part of a social justice agenda, broadly speaking. Considering that disparities in credit, arrest, and employment history could well be the result of other unjust biases, privacy winds up mitigating the anti-signal that these injustices have in the employment market. In other words, it’s not hard to get from Posner’s arguments to a pro-privacy position based of all things on market efficiency.

It would be nice to model that more explicitly, if it hasn’t been done yet already.

Posner is quite bullish on privacy tort, thinking that it is generally not so offensive from an economic perspective largely because it’s about preventing misinformation.

Overall, the paper is a valuable starting point for further study in economics of privacy. Posner’s economic lens swiftly and clearly puts the trade-offs around privacy statutes in the light. It’s impressively lucid work that surely bears directly on arguments about privacy and information processing systems today.


Akerlof, G. A. (1970). The market for” lemons”: Quality uncertainty and the market mechanism. The quarterly journal of economics, 488-500.

Nissenbaum, H. (2004). Privacy as contextual integrity. Wash. L. Rev., 79, 119.

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

Posner, R. A. (1981). The economics of privacy. The American economic review, 71(2), 405-409. (jstor)

Ascendency and overhead in networked ecosystems

Ulanowicz (2000) proposes in information-theoretic terms several metrics for ecosystem health, where one models an ecosystem as a for example a trophic network. Principal among them ascendancy , which is a measure of the extent to which energy flows in the system are predictably structured weighted by the total energy of the system. He believes that systems tend towards greater ascendancy in expectation, and that this is predictive of ecological ‘succession’ (and to some extent ecological fitness). On the other hand, overhead, which is unpredictability (perhaps, inefficiency) in energy flows (“free energy”?), are important for the system’s resiliency towards external shocks.
At least in the papers I’ve read so far, Ulanowicz is not mathematically specific about the mechanism that leads to greater ascendancy, though he sketches some explanations. Autocatalytic cycles within the network reinforce their own positive perturbations and mutations, drawing in resources from external sources, crowding out and competing with them. These cycles become agents in themselves, exerting what Ulanwicz suggests is Aristotelian final or formal causal power on the lower level components. In this way, freely floating energy is drawn into structures of increasing magnificence and complexity.

I’m reminded on Bataille’s The Accursed Share, in which he attempts to account for societal differences and the arc of human history through the use of its excess energy. “The sexual act is in time what the tiger is in space,” he says, insightfully. The tiger, as an apex predator, is flame that clings brilliantly to the less glamorous ecosystem that supports it. That is why we adore them. And yet, their existence is fragile, as it depends on both the efficiency and stability of the rest of its network. When its environment is disturbed, it is the first to suffer.
space tiger
Ulanowicz cites himself suggesting that a similar framework could be used to analyze computer networks. I have not read his account yet, though I anticipate several difficulties. He suggests that data flows in a computer network are analogous to energy flows within an ecosystem. That has intuitive appeal, but obscures the fact that some data is more valuable than others. A better analogy might be money as a substitute for energy. Or maybe there is a way to reduce both to a common currency, at least for modeling purposes.

Econophysics has been gaining steam, albeit controversially. Without knowing anything about it but based just on statistical hunches, I suspect that this comes down to using more complex models on the super duper complex phenomenon of the economy, and demonstrating their success there. In other words, I’m just guessing that the success of econophysics modeling is due to the greater degrees of freedom in the physics models compared to non-dynamic, structural equilibrium models. However, since ecology models the evolutionary dynamics of multiple competing agents (and systems of those agents), its possible that those models could capture quite a bit of what’s really going on and even be a source of strategic insight.

Indeed, economics already has a sense of stable versus unstable equilibria that resonate with the idea of stability of ecological succession. These ideas translate into game theoretic analysis as well. As we do more work with Strategic Bayesian Networks or other constructs to model equilibrium strategies in a networked, multi-agent system, I wonder if we can reproduce Ulanowicz’s results and use his ideas about ascendancy (which, I’ve got to say, are extraordinary and profound) to provide insight into the information economy.

I think that will require translating the ecosystem modeling into Judea Pearl’s framework for causal reasoning. Having been indoctrinated in Pearl’s framework in much of my training, I believe that it is general enough to subsume Ulanowicz’s results. But I have some doubt. In some of his later writings Ulanowicz refers explicitly to a “Hegelian dialectic” between order and disorder as a consequence of some of his theories, and between that and his insistence on his departure from mechanistic thinking over the course of his long career, I am worried that he may have transcended what it’s possible to do even with the modeling power of Bayesian networks. The question is: what then? It may be that once one’s work sublimates beyond our ability to model explicitly and intervene strategically, it becomes irrelevant. (I get the sense that in academia, Ulanwicz’s scientific philosophizing is a privilege reserved for someone tenured who late in their career is free to make his peace with the world in their own way) But reading his papers is so exhilarating to me. I’ve had no prior exposure to ecology before this, so his papers are packed with fresh ideas. So while I don’t know how to justify it to any of my mentors or colleagues, I think I just have to keep diving into it when I can, on the side.