Digifesto

Category: economics

December 4, 2018

the make or buy decision (TCE) in the software and cybersecurity

The paradigmatic case of transaction cost economics (TCE) is the make-or-buy decision. A firm, F, needs something, C. Do they make it in-house or do they buy it from somewhere else?

If the firm makes it in-house, they will incur some bureaucratic overhead costs in addition to the costs of production. But they will also be able to specialize C for their purposes. They can institute their own internal quality controls. And so on.

If the firm buys it on the open market from some other firm, say, G, they don’t pay the overhead costs. They do lose the benefits of specialization, and the quality controls are only those based on economic competitive pressure on suppliers.

There is an intermediate option, which is a contract between F and G which establishes an ongoing relationship between the two firms. This contract creates a field in which C can be specialized for F, and there can be assurances of quality, while the overhead is distributed efficiently between F and G.

This situation is both extremely common in business practice and not well handled by neoclassical, orthodox economics. It’s the case that TCE is tremendously preoccupied with.

My background and research is in the software industry, which is rife with cases like these.

Developers are constantly faced with a decision to make-or-buy software components. In principle, they can developer any component themselves. In practice, this is rarely cost-effective.

In software, open source software components are a prevalent solution to this problem. This can be thought of as a very strange market where all the prices are zero. The most popular open source libraries are very generic , having little “asset specificity” in TCE terms.

The lack of contract between developers and open source components/communities is sometimes seen as a source of hazard in using open source components. The recent event-stream hack, where an upstream component was injected with malicious code by a developer who had taken over maintaining the package, illustrates the problems of outsourcing technical dependencies without a contract. In this case, the quality problem is manifest as a supply chain cybersecurity problem.

In Williamson’s analysis, these kinds of hazards are what drive firms away from purchasing on spot markets and towards contracting or in-house development. In practice, the role of open source support companies fills the role of being a responsible entity G that firm F can build a relationship with.

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December 3, 2018

Williamson on four injunctions for good economics

Williamson (2008) (pdf) concludes with a description of four injunctions for doing good economics, which I will quote verbatim.

Robert Solow’s prescription for doing good economics is set out in three injunctions: keep it simple; get it right; make it plausible (2001, p. 111). Keeping it simple entails stripping away the inessentials and going for the main case (the jugular). Getting it right “includes translating economic concepts into accurate
mathematics (or diagrams, or words) and making sure that further logical operations are correctly performed and verified” (Solow, 2001, p. 112). Making it plausible entails describing human actors in (reasonably) veridical ways and maintaining meaningful contact with the phenomena of interest (contractual or otherwise).

To this, moreover, I would add a fourth injunction: derive refutable implications to which the relevant (often microanalytic) data are brought to bear. Nicholas Georgescu-Roegen has a felicitous way of putting it: “The purpose of science in general is not prediction, but knowledge for its own sake,” yet prediction is “the touchstone of scientific knowledge” (1971, p. 37).

Why the fourth injunction? This is necessitated by the need to choose among alternative theories that purport to deal with the same phenomenon—say vertical integration—and (more or less) satisfy the first three injunctions. Thus assume that all of the models are tractable, that the logic of each hangs together, and that agreement cannot be reached as to what constitutes veridicality and meaningful contact with the phenomena. Does each candidate theory then have equal claimsfor our attention? Or should we be more demanding? This is where refutable implications and empirical testing come in: ask each would-be theory to stand up and be counted.

Why more economists are not insistent upon deriving refutable implications and submitting these to empirical tests is a puzzle. One possibility is that the world of theory is set apart and has a life of its own. A second possibility is that some economists do not agree that refutable implications and testing are
important. Another is that some theories are truly fanciful and their protagonists would be discomfited by disclosure. A fourth is that the refutable implications of favored theories are contradicted by the data. And perhaps there are still other reasons. Be that as it may, a multiplicity of theories, some of which are
vacuous and others of which are fanciful, is an embarrassment to the pragmatically oriented members of the tribe. Among this subset, insistence upon the fourth injunction—derive refutable implications and submit these to the data—is growing.

References

Williamson, Oliver E. “Transaction cost economics.” Handbook of new institutional economics. Springer, Berlin, Heidelberg, 2008. 41-65.

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December 2, 2018

Discovering transaction cost economics (TCE)

I’m in the process of discovering transaction cost economics (TCE), the branch of economics devoted to the study of transaction costs, which include bargaining and search costs. Oliver Williamson, who is a professor at UC Berkeley, won the Nobel Prize for his work on TCE in 2009. I’m starting with the Williamson, 2008 article (in the References) which seems like a late-stage overview of what is a large body of work.

Personally, this is yet another time when I’ve discovered that the answers or proper theoretical language for understanding something I am struggling with has simply been Somewhere Else all alone. Delight and frustration are pretty much evening each other out at this point.

Why is TCE so critical (to me)?

  • I think the real story about how the Internet and AI have changed things, which is the topic constantly reiterated in so many policy and HCI studies about platforms, is that they reduced search costs. However, it’s hard to make the case for that without a respectable theorization of search costs and how they matter to the economy. This, I think, what transaction cost economics are about.
  • You may recall I wrote my doctoral dissertation about “data economics” on the presumption (which was, truly, presumptuous) that a proper treatment of the role of data in the economy had not yet been done. This was due mainly to the deficiencies of the discussion of information in neoclassical economic theory. But perhaps I was a fool, because it may be that this missing-link work on information economics has been in transaction cost economics all along! Interestingly, Pat Bajari, who is Chief Economist at Amazon, has done some TCE work, suggesting that like Hal Varian’s economics, this is stuff that actually works in a business context, which is more or less the epistemic standard you want economics to meet. (I would argue that economics should be seen, foremost, as a discipline of social engineering.)
  • A whole other line of research I’ve worked on over the years has been trying to understand the software supply chain, especially with respect to open source software (Benthall 2016; Benthall, 2017). That’s a tricky topic because the idea of “supply” and “chain” in that domain are both highly metaphorical and essentially inaccurate. Yet there are clearly profound questions about the relationships between sociotechnical organizations, their internal and external complexity, and so on to be found there, along with (and this is really what’s exciting about it) ample empirical basis to support arguments about it, just by the nature of it. Well, it turns out that the paradigmatic case for transaction cost economics is vertical integration, or the “make-or-buy” decision wherein a firm decides to (A) purchase it from an open market, (D) produce something in-house, or (C) (and this is the case that transaction cost economics really tries to develop) engage with the supplier in a contract which creates an ongoing and secure relationship between them. Labor contracts are all, for reasons that I may go into later, of this (C) kind.

So, here comes TCE, with its firm roots in organization theory, Hayekian theories of the market, Coase’s and other theories of the firm, and firm emphasis on the supply chain relation between sociotechnical organizations. And I HAVEN’T STUDIED IT. There is even solid work on its relation to privacy done by Whittington and Hoofnagle (2011; 2013). How did I not know about this? Again, if I were not so delighted, I would be livid.

Please expect a long series of posts as I read through the literature on TCE and try to apply it to various cases of interest.

References

Benthall, S. (2017) Assessing Software Supply Chain Risk Using Public Data. IEEE STC 2017 Software Technology Conference.

Benthall, S., Pinney, T., Herz, J., Plummer, K. (2016) An Ecological Approach to Software Supply Chain Risk Management. Proceedings of the 15th Python in Science Conference. p. 136-142. Ed. Sebastian Benthall and Scott Rostrup.

Hoofnagle, Chris Jay, and Jan Whittington. “Free: accounting for the costs of the internet’s most popular price.” UCLA L. Rev. 61 (2013): 606.

Whittington, Jan, and Chris Jay Hoofnagle. “Unpacking Privacy’s Price.” NCL Rev. 90 (2011): 1327.

Williamson, Oliver E. “Transaction cost economics.” Handbook of new institutional economics. Springer, Berlin, Heidelberg, 2008. 41-65.

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November 25, 2018

Is competition good for cybersecurity?

A question that keeps coming up in various forms, but for example in response to recent events around the ‘trade war’ between the U.S. and China and its impact on technology companies, is whether or not market competition is good or bad for cyber-security.

Here is a simple argument for why competition could be good for cyber-security: The security of technical products is a positive quality of them, something that consumers would like. Market competition is what gets producers to make higher quality products at lower cost. Therefore, competition is good for security.

Here is an argument for why competition could be bad for cyber-security: Security is a hard thing for any consumer to understand; since most won’t, we have an information asymmetry here and therefore a ‘market for lemons’ kind of market failure. Therefore, competition is bad for security. It would be better to have a well-regulated monopoly.

This argument echoes, though it doesn’t exactly parallel, some of the arguments in Pasquale’s work on Hamiltonian’s and Jeffersonian’s in technology platform regulation.

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November 18, 2018

“the privatization of public functions”

An emerging theme from the conference on Trade Secrets and Algorithmic Systems was that legal scholars have become concerned about the privatization of public functions. For example, the use of proprietary risk assessment tools instead of the discretion of judges who are supposed to be publicly accountable is a problem. More generally, use of “trade secrecy” in court settings to prevent inquiry into software systems is bogus and moves more societal control into the realm of private ordering.

Many remedies were proposed. Most involved some kind of disclosure and audit to experts. The most extreme form of disclosure is making the software and, where it’s a matter of public record, training data publicly available.

It is striking to me to be encountering the call for government use of open source systems because…this is not a new issue. The conversation about federal use of open source software was alive and well over five years ago. Then, the arguments were about vendor lock-in; now, they are about accountability of AI. But the essential problem of whether core governing logic should be available to public scrutiny, and the effects of its privatization, have been the same.

If we are concerned with the reliability of a closed and large-scale decision-making process of any kind, we are dealing with problems of credibility, opacity, and complexity. The prospects of an efficient market for these kinds of systems are dim. These market conditions are the conditions of sustainability of open source infrastructure. Failures in sustainability are manifest as software vulnerabilities, which are one of the key reasons why governments are warned against OSS now, though the process of measurement and evaluation of OSS software vulnerability versus proprietary vulnerabilities is methodologically highly fraught.

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November 16, 2018

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.

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November 15, 2018

The Crevasse: a meditation on accountability of firms in the face of opacity as the complexity of scale

To recap:

(A1) Beneath corporate secrecy and user technical illiteracy, a fundamental source of opacity in “algorithms” and “machine learning” is the complexity of scale, especially scale of data inputs. (Burrell, 2016)

(A2) The opacity of the operation of companies using consumer data makes those consumers unable to engage with them as informed market actors. The consequence has been a “free fall” of market failure (Strandburg, 2013).

(A3) Ironically, this “free” fall has been “free” (zero price) for consumers; they appear to get something for nothing without knowing what has been given up or changed as a consequence (Hoofnagle and Whittington, 2013).

Comments:

(B1) The above line of argument conflates “algorithms”, “machine learning”, “data”, and “tech companies”, as is common in the broad discourse. That this conflation is possible speaks to the ignorance of the scholarly position on these topics, and ignorance that is implied by corporate secrecy, technical illiteracy, and complexity of scale simultaneously. We can, if we choose, distinguish between these factors analytically. But because, from the standpoint of the discourse, the internals are unknown, the general indication of a ‘black box’ organization is intuitively compelling.

(B1a) Giving in to the lazy conflation is an error because it prevents informed and effective praxis. If we do not distinguish between a corporate entity and its multiple internal human departments and technical subsystems, then we may confuse ourselves into thinking that a fair and interpretable algorithm can give us a fair and interpretable tech company. Nothing about the former guarantees the latter because tech companies operate in a larger operational field.

(B2) The opacity as the complexity of scale, a property of the functioning of machine learning algorithms, is also a property of the functioning of sociotechnical organizations more broadly. Universities, for example, are often opaque to themselves, because of their own internal complexity and scale. This is because the mathematics governing opacity as a function of complexity and scale are the same in both technical and sociotechnical systems (Benthall, 2016).

(B3) If we discuss the complexity of firms, as opposed the the complexity of algorithms, we should conclude that firms that are complex due to scale of operations and data inputs (including number of customers) will be opaque and therefore have strategic advantage in the market against less complex market actors (consumers) with stiffer bounds on rationality.

(B4) In other words, big, complex, data rich firms will be smarter than individual consumers and outmaneuver them in the market. That’s not just “tech companies”. It’s part of the MO of every firm to do this. Corporate entities are “artificial general intelligences” and they compete in a complex ecosystem in which consumers are a small and vulnerable part.

Twist:

(C1) Another source of opacity in data is that the meaning of data come from the causal context that generates it. (Benthall, 2018)

(C2) Learning causal structure from observational data is hard, both in terms of being data-intensive and being computationally complex (NP). (c.f. Friedman et al., 1998)

(C3) Internal complexity, for a firm, is not sufficient to be “all-knowing” about the data that is coming it; the firm has epistemic challenges of secrecy, illiteracy, and scale with respect to external complexity.

(C4) This is why many applications of machine learning are overrated and so many “AI” products kind of suck.

(C5) There is, in fact, an epistemic crevasse between all autonomous entities, each containing its own complexity and constituting a larger ecological field that is the external/being/environment for any other autonomy.

To do:

The most promising direction based on this analysis is a deeper read into transaction cost economics as a ‘theory of the firm’. This is where the formalization of the idea that what the Internet changed most are search costs (a kind of transaction cost) should be.

It would be nice if those insights could be expressed in the mathematics of “AI”.

There’s still a deep idea in here that I haven’t yet found the articulation for, something to do with autopoeisis.

References

Benthall, Sebastian. (2016) The Human is the Data Science. Workshop on Developing a Research Agenda for Human-Centered Data Science. Computer Supported Cooperative Work 2016. (link)

Sebastian Benthall. Context, Causality, and Information Flow: Implications for Privacy Engineering, Security, and Data Economics. Ph.D. dissertation. Advisors: John Chuang and Deirdre Mulligan. University of California, Berkeley. 2018.

Burrell, Jenna. “How the machine ‘thinks’: Understanding opacity in machine learning algorithms.” Big Data & Society 3.1 (2016): 2053951715622512.

Friedman, Nir, Kevin Murphy, and Stuart Russell. “Learning the structure of dynamic probabilistic networks.” Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence. Morgan Kaufmann Publishers Inc., 1998.

Hoofnagle, Chris Jay, and Jan Whittington. “Free: accounting for the costs of the internet’s most popular price.” UCLA L. Rev. 61 (2013): 606.

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

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November 7, 2018

the resilience of agonistic control centers of global trade

This post is merely notes; I’m fairly confident that I don’t know what I’m writing about. However, I want to learn more. Please recommend anything that could fill me in about this! I owe most of this to discussion with a colleague who I’m not sure would like to be acknowledged.

Following the logic of James Beniger, an increasingly integrated global economy requires more points of information integration and control.

Bourgeois (in the sense of ‘capitalist’) legal institutions exist precisely for the purpose of arbitrating between merchants.

Hence, on the one hand we would expect international trade law to be Habermasian. However, international trade need not rest on a foundation of German idealism (which increasingly strikes me as the core of European law). Rather, it is an evolved mechanism.

A key part of this mechanism, as I’ve heard, is that it is decentered. Multiple countries compete to be the sites of transnational arbitration, much like multiple nations compete to be tax havens. Sovereignty and discretion are factors of production in the economy of control.

This means, effectively, that one cannot defeat capitalism by chopping off its head. It is rather much more like a hydra: the “heads” are the creation of two-sided markets. These heads have no internalized sense of the public good. Rather, they are optimized to be attractive to the transnational corporations in bilateral negotiation. The plaintiffs and defendants in these cases are corporations and states–social forms and institutions of complexity far beyond that of any individual person. This is where, so to speak, the AI’s clash.

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October 23, 2018

For a more ethical Silicon Valley, we need a wiser economics of data

Kara Swisher’s NYT op-ed about the dubious ethics of Silicon Valley and Nitasha Tiku’s WIRED article reviewing books with alternative (and perhaps more cynical than otherwise stated) stories about the rise of Silicon Valley has generated discussion and buzz among the tech commentariat.

One point of debate is whether the focus should be on “ethics” or on something more substantively defined, such as human rights. Another point is whether the emphasis should be on “ethics” or on something more substantively enforced, like laws which impose penalties between 1% and 4% of profits, referring of course to the GDPR.

While I’m sympathetic to the European approach (laws enforcing human rights with real teeth), I think there is something naive about it. We have not yet seen whether it’s ever really possible to comply with the GDPR could wind up being a kind of heavy tax on Big Tech companies operating in the EU, but one that doesn’t truly wind up changing how people’s data are used. In any case, the broad principles of European privacy are based on individual human dignity, and so they do not take into account the ways that corporations are social structures, i.e. sociotechnical organizations that transcend individual people. The European regulations address the problem of individual privacy while leaving mystified the question of why the current corporate organization of the world’s personal information is what it is. This sets up the fight over ‘technology ethics’ to be a political conflict between different kinds of actors whose positions are defined as much by their social habitus as by their intellectual reasons.

My own (unpopular!) view is that the solution to our problems of technology ethics are going to have to rely on a better adapted technology economics. We often forget today that economics was originally a branch of moral philosophy. Adam Smith wrote The Theory of Moral Sentiments (1759) before An Inquiry into the Nature and Causes of the Wealth of Nations (1776). Since then the main purpose of economics has been to intellectually grasp the major changes to society due to production, trade, markets, and so on in order to better steer policy and business strategy towards more fruitful equilibria. The discipline has a bad reputation among many “critical” scholars due to its role in supporting neoliberal ideology and policies, but it must be noted that this ideology and policy work is not entirely cynical; it was a successful centrist hegemony for some time. Now that it is under threat, partly due to the successes of the big tech companies that benefited under its regime, it’s worth considering what new lessons we have to learn to steer the economy in an improved direction.

The difference between an economic approach to the problems of the tech economy and either an ‘ethics’ or a ‘law’ based approach is that it inherently acknowledges that there are a wide variety of strategic actors co-creating social outcomes. Individual “ethics” will not be able to settle the outcomes of the economy because the outcomes depend on collective and uncoordinated actions. A fundamentally decent person may still do harm to others due to their own bounded rationality; “the road to hell is paved with good intentions”. Meanwhile, regulatory law is not the same as command; it is at best a way of setting the rules of a game that will be played, faithfully or not, by many others. Putting regulations in place without a good sense of how the game will play out differently because of them is just as irresponsible as implementing a sweeping business practice without thinking through the results, if not more so because the relationship between the state and citizens is coercive, not voluntary as the relationship between businesses and customers is.

Perhaps the biggest obstacle to shifting the debate about technology ethics to one about technology economics is that it requires a change in register. It drains the conversation of the pathos which is so instrumental in surfacing it as an important political topic. Sound analysis often ruins parties like this. Nevertheless, it must be done if we are to progress towards a more just solution to the crises technology gives us today.

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September 3, 2018

How trade protection can increase labor wages (the Stolper-Samuelson theorem)

I’m continuing a look into trade policy 8/08/30/trade-policy-and-income-distribution-effects/”>using Corden’s (1997) book on the topic.

Picking up where the last post left off, I’m operating on the assumption that any reader is familiar with the arguments for free trade that are an extension of those arguments of laissez-faire markets. I will assume that these arguments are true as far as they go: that the economy grows with free trade, that tariffs create a dead weight loss, that subsidies are expensive, but that both tariffs and subsidies do shift the market towards imports.

The question raised by Corden is why, despite its deleterious effects on the economy as a whole, protectionism enjoys political support by some sectors of the economy. He hints, earlier in Chapter 5, that this may be due to income distribution effects. He clarifies this with reference to an answer to this question that was given as early as 1941 by Stolper and Samuelson; their result is now celebrated as the Stolper-Samuelson theorem.

The mathematics of the theorem can be read in many places. Like any economic model, it depends on some assumptions that may or may not be the case. Its main advantage is that it articulates how it is possible for protectionism to benefit a class of the population, and not just in relative but in absolute terms. It does this by modeling the returns to different factors of production, which classically have been labor, land, and capital.

Roughly, the argument goes like this. Suppose and economy has two commodities, one for import and one for export. Suppose that the imported good is produced with a higher labor to land ratio than the export good. Suppose a protectionist policy increases the amount of the import good produced relative to the export good. Then the return on labor will increase (because more labor is used in supply), and the return on land will decrease (because less land is used in supply). Wages will increase and rent on land will decrease.

These breakdowns of the economy into “factors of production” feels very old school. You rarely read economists discuss the economy in these terms now, which is itself interesting. One reason why (and I am only speculating here) is that these models clarify how laborers, land-owners, and capital-owners have different political interests in economic intervention, and that can lead to the kind of thinking that was flushed out of the American academy during the McCarthy era. Another reason may be that “capital” has changed meaning from being about ownership of machine goods into being about having liquid funds available for financial investment.

I’m interested in these kinds of models today partly because I’m interested in the political interests in various policies, and also because I’m interested in particular in the economics of supply chain logistics. The “factors of production” approach is a crude way to model the ‘supply chain’ in a broad sense, but one that has proven to be an effective source of insights in the past.

References

Corden, W. Max. “Trade policy and economic welfare.” OUP Catalogue (1997).

Stolper, Wolfgang F., and Paul A. Samuelson. “Protection and real wages.” The Review of Economic Studies 9.1 (1941): 58-73.

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