Digifesto

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 computational really at the heart of the active 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, where simulation 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 implies 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 is 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.

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.

Big tech surveillance and human rights

I’ve been alarmed by two articles to cross my radar today.

  • Bloomberg Law has given a roundup on the contributions Google and Facebook have given to tech policy advocacy groups. Long story short: they give a lot of money, and while these groups say they are not influenced by the donations, they tend to favor privacy policies that do not interfere with the business models of these Big Tech companies.
  • Amnesty International has put out a report arguing that the business models of Google and Facebook are “an unprecedented danger to human rights”.

Surveillance Giants lays out how the surveillance-based business model of Facebook and Google is inherently incompatible with the right to privacy and poses a systemic threat to a range of other rights including freedom of opinion and expression, freedom of thought, and the right to equality and non-discrimination.

Amnesty International

Until today, I never had a reason to question the judgment of Amnesty International. I have taken seriously their perspective as an independent watchdog group looking out for human rights. Could it be that Google and Facebook have, all this time, been violating human rights left and right? Have I been a victim of human rights abuses from the social media sites I’ve used since college?

This is a troubling thought, especially as an academic researcher who has invested a great deal of time studying technology policy. While in graduate school, the most lauded technology policy think tanks, those that were considered most prestigious and genuine, such as the Center for Democracy and Technology (CDT), are precisely those listed by the Bloomberg Law article as having been in essence supporting the business models of Google and Facebook all along. Now I’m in moral doubt. Amnesty International has condemned Google of human rights violations for the sake of profit, with CDT (for example) as an ideological mouthpiece.

Elsewhere in my academic work it’s come to light that what is an increasingly popular, arguably increasingly consensus view of technology policy is a direct contradiction of the business model and incentives of companies like Google and Facebook. The other day colleagues and I did a close read of the New York Privacy Act (NYPA), which is not under consideration. New York State’s answer to the CCPA is notable in that it foregrounds Jack Balkin’s notion of an information fiduciary. According to the current draft, data controllers (it uses this EU-inspired language) would have a fiduciary duty to consumers, who are natural persons (but not independent contractors, such as Uber drivers) whose data is being collected. This bill, in its current form, requires that the data controller put its care and responsibility of the consumer over and above its fiduciary duty to its shareholders. Since Google and Facebook are (at least) two-sided markets, with consumers making up only one side, this (if taken seriously) has major implications for how these Big Tech companies operate with respect to New York residents. Arguably, it would require these companies to put the interests of the consumers that are their users ahead of the interests of their real customers, the advertisers–which pay the revenue that goes to shareholders.

If all data controllers were information fiduciaries, that would almost certainly settle the human rights issues raised by Amnesty International. But how likely is this strong language to survive the legislative process in New York?

There are two questions on my mind after considering all this. The first is what the limits of Silicon Valley self-regulation are. I’m reminded of an article by Mulligan and Griffin about Google’s search engine results. For a time, when a user queried “Did the holocaust happen?” the first search results would deny the holocaust. This prompted the Mulligan and Griffin article about what principles could be used to guide search engine behavior besides the ones used to design the search engine initially. Their conclusion is that human rights, as recognized and international experts, could provide those principles.

The essay concludes by offering a way forward grounded in developments in business and human rights. The emerging soft law requirement that businesses respect and remedy human rights violations entangled in their business operations provides a normative basis for rescripting search. The final section of this essay argues that the “right to truth,” increasingly recognized in human rights law as both an individual and collective right in the wake of human rights atrocities, is directly affected by Google and other search engine providers’ search script. Returning accurate information about human rights atrocities— specifically, historical facts established by a court of law or a truth commission established to document and remedy serious and systematic human rights violations—in response to queries about those human rights atrocities would make good on search engine providers’ obligations to respect human rights but keep adjudications of truth with politically legitimate expert decision makers. At the same time, the right to freedom of expression and access to information provides a basis for rejecting many other demands to deviate from the script of search. Thus, the business and human rights framework provides a moral and legal basis for rescripting search and for cabining that rescription.

Mulligan and Griffin, 2018

Google now returns different results when asked “Did the holocaust happen?”. The first hit is the Wikipedia page for “Holocaust denial”, which states clearly that the views of Holocaust deniers are false. The moral case on this issue has been won.

Is it realistic to think that the moral case will be won when the moral case directly contradicts the core business model of these companies? That is perhaps akin to believing that medical insurance companies in the U.S. will cave to moral pressure and change the health care system in recognition of the human right to health.

These are the extreme views available at the moment:

  • Privacy is a human right, and our rights are being trod on by Google and Facebook. The ideology that has enabled this has been propagated by non-profit advocacy groups and educational institutions funded by those companies. The human rights of consumers suffer under unchecked corporate control.
  • Privacy, as imagined by Amnesty International, is not a human right. They have overstated their moral case. Google and Facebook are intelligent consumer services that operate unproblematically in a broad commercial marketplace for web services. There’s nothing to see here, or worry about.

I’m inclined towards the latter view, if only because the “business model as a human rights violation” angle seems to ignore how services like Google and Facebook add value for users. They do this by lowering search costs, which requires personalized search and data collection. There seem to be some necessary trade-offs between lowering search costs broadly–especially search costs when what’s being searched for is people–and autonomy. But unless these complex trade-offs are untangled, the normative case will be unclear and business will proceed simply as usual.

References

Mulligan, D. K., & Griffin, D. (2018). Rescripting Search to Respect the Right to Truth.

Autonomy as link between privacy and cybersecurity

A key aspect of the European approach to privacy and data protection regulation is that it’s rooted in the idea of an individual’s autonomy. Unlike an American view of privacy which suggests that privacy is important only because it implies some kind of substantive harm—such as reputational loss or discrimination–in European law it’s understood that personal data matters because of its relevance to a person’s self-control.

Autonomy etymologically is “self-law”. It is traditionally associated with the concept of rationality and the ability to commit oneself to duty. My colleague Jake Goldenfein argues that autonomy is the principle that one has the power to express one’s own narrative about oneself, and for that narrative to have power. Uninterpretable and unaccountable surveillance, “nudging”, manipulation, profiling, social sorting, and so on are all in a sense an attack on autonomy. They interfere with the individual’s capacity to self-rule.

It is more rare to connect the idea of autonomy to cybersecurity, though here the etymology of the words also weighs in favor of it. Cyber- has its root in in Greek kybernetes, for steersman, governor, pilot, or rudder. To be secure means to be free from threat. So cybersecurity for a person or organization is the freedom of their (self-control) from external threat. Cybersecurity is the condition of being free to control oneself–to be autonomous.

Understood in this way, privacy is just one kind of cybersecurity: the cybersecurity of the individual person. We can speak additionally of the cybersecurity of a infrastructure, such as a power grid, or of an organization, such as a bank, or of a device, such as a smartphone. What both the privacy and cybersecurity discussions implicate are questions of the ontology of the entities involved and their ability to control themselves and control each other.

Open Source Software (OSS) and regulation by algorithms

It has long been argued that technology, especially built infrastructure, has political implications (Winner, 1980). With the rise of the Internet as the dominating form of technological infrastructure, Lessig (1999), among others, argued that software code is a regulating force parallel to the law. By extension of this argument, we would expect open source software to be a regulating force in society.

This is not the case. There is a lot of open source software and much of it is very important. But there’s no evidence to say that open source software, in and of itself, regulates society except in the narrow sense in that the communities that build and maintain it are necessarily constrained by its technical properties.

On the other hand, there are countless platforms and institutions that deploy open source software as part of their activity, which does have a regulating force on society. The Big Tech companies that are so powerful that they seem to rival some states are largely built on an “open core” of software. Likewise for smaller organizations. OSS is simply part of the the contemporary software production process, and it is ubiquitous.

Most widely used programming languages are open source. Perhaps a good analogy for OSS is that it is a collection of languages, and literatures in those languages. These languages and much of their literature are effectively in the public domain. We might say the same thing about English or Chinese or German or Hindi.

Law, as we know it in the modern state, is a particular expression of language with purposeful meaning. It represents, at its best, a kind of institutional agreement that constraints behavior based on its repetition and appeals to its internal logic. The Rule of Law, as we know it, depends on the social reproduction of this linguistic community. Law Schools are the main means of socializing new lawyers, who are then credentialed to participate in and maintain the system which regulates society. Lawyers are typically good with words, and their practice is in a sense constrained by their language, but only in the widest of Sapir-Whorf senses. Law is constrained more the question of which language is institutionally recognized; indeed, those words and phrases that have been institutionally ratified are the law.

Let’s consider again the generative question of whether law could be written in software code. I will leave aside for a moment whether or not this would be desirable. I will entertain the idea in part because I believe that it is inevitable, because of how the algorithm is the form of modern rationality (Totaro and Ninno, 2014) and the evolutionary power of rationality.

A law written in software would need to be written in a programming language and this would all but entail that it is written on an “open core” of software. Concretely: one might write laws in Python.

The specific code in the software law might or might not be open. There might one day be a secretive authoritarian state with software laws that are not transparent or widely known. Nothing rules that out.

We could imagine a more democratic outcome as well. It would be natural, in a more liberal kind of state, for the software laws to be open on principle. The definitions here become a bit tricky: the designation of “open source software” is one within the schema of copyright and licensing. Could copyright laws and license be written in software? In other words, could the ‘openness’ of the software laws be guaranteed by their own form? This is an interesting puzzle for computer scientists and logicians.

For the sake of argument, suppose that something like this is accomplished. Perhaps it is accomplished merely by tradition: the institution that ratifies software laws publishes these on purpose, in order to facilitate healthy democratic debate about the law.

Even with all this in place, we still don’t have regulation. We have now discussed software legislation, but not software execution and enforcement. If software is only as powerful as the expression of a language. A deployed system, running that software, is an active force in the world. Such a system implicates a great many things beyond the software itself. It requires computers and and networking infrastructure. It requires databases full of data specific to the applications for which its ran.

The software dictates the internal logic by which a system operates. But that logic is only meaningful when coupled with an external societal situation. The membrane between the technical system and the society in which it participates is of fundamental importance to understanding the possibility of technical regulation, just as the membrane between the Rule of Law and society–which we might say includes elections and the courts in the U.S.–is of fundamental importance to understanding the possibility of linguistic regulation.

References

Lessig, L. (1999). Code is law. The Industry Standard18.

Hildebrandt, M. (2015). Smart technologies and the end (s) of law: Novel entanglements of law and technology. Edward Elgar Publishing.

Totaro, P., & Ninno, D. (2014). The concept of algorithm as an interpretative key of modern rationality. Theory, Culture & Society31(4), 29-49.

Winner, L. (1980). Do artifacts have politics?. Daedalus, 121-136.

The diverging philosophical roots of U.S. and E.U. privacy regimes

For those in the privacy scholarship community, there is an awkward truth that European data protection law is going to a different direction from U.S. Federal privacy law. A thorough realpolitical analysis of how the current U.S. regime regarding personal data has been constructed over time to advantage large technology companies can be found in Cohen’s Between Truth and Power (2019). There is, to be sure, a corresponding story to be told about EU data protection law.

Adjacent, somehow, to the operations of political power are the normative arguments leveraged both in the U.S. and in Europe for their respective regimes. Legal scholarship, however remote from actual policy change, remains as a form of moral inquiry. It is possible, still, that through professional training of lawyers and policy-makers, some form of ethical imperative can take root. Democratic interventions into the operations of power, while unlikely, are still in principle possible: but only if education stays true to principle and does not succumb to mere ideology.

This is not easy for educational institutions to accomplish. Higher education certainly is vulnerable to politics. A stark example of this was the purging of Marxist intellectuals from American academic institutions under McCarthyism. Intellectual diversity in the United States has suffered ever since. However, this was only possible because Marxism as a philosophical movement is extraneous to the legal structure of the United States. It was never embedded at a legal level in U.S. institutions.

There is a simply historical reason for this. The U.S. legal system was founded under a different set of philosophical principles; that philosophical lineage still impacts us today. The Founding Fathers were primarily influenced by John Locke. Locke rose to prominence in Britain when the Whigs, a new bourgeois class of Parliamentarian merchant leaders, rose to power, contesting the earlier monarchy. Locke’s political contributions were a treatise pointing out the absurdity of the Divine Right of Kings, the prevailing political ideology of the time, and a second treatise arguing for a natural right to property based on the appropriation of nature. This latter political philosophy was very well aligned with Britain’s new national project of colonialist expansion. With the founding of the United States, it was enshrined into the Constitution. The liberal system of rights that we enjoy in the U.S. are founded in the Lockean tradition.

Intellectual progress in Europe did not halt with Locke. Locke’s ideas were taken up by David Hume, whose introduced arguments that were so agitating that they famously woke Immanuel Kant, in Germany, from his “dogmatic slumber”, leading him to develop a new highly systematic system of morality and epistemology. Among the innovations in this work was the idea that human freedom is grounded in the dignity of being an autonomous person. The source of dignity is not based in a natural process such as the tilling of land. It is rather based in on transcendental facts about what it means to be human. The key to morality is treating people like ends, not means; in other words, not using people as tools to other aims, but as aims in themselves.

If this sound overly lofty to an American audience, it’s because this philosophical tradition has never taken hold in American education. In both the United Kingdom and Britain, Kantian philosophy has always been outside the mainstream. The tradition of Locke, through Hume, has continued on in what philosophers will call “analytic philosophy”. This philosophy has taken on the empiricist view that the only source of knowledge is individual experience. It has transformed over centuries but continues to orbit around the individual and their rights, grounded in pragmatic considerations, and learning normative rules using the case-by-case approach of Common Law.

From Kant, a different “continental philosophy” tradition produced Hegel, who produced Marx. We can trace from Kant’s original arguments about how morality is based on the transcendental dignity of the individual to the moralistic critique that Marx made against capitalism. Capitalism, Marx argued, impugns the dignity of labor because it treats it like a means, not an end. No such argument could take root in a Lockean system, because Lockean ethics has no such prescription against treating others instrumentally.

Germany lost its way at the start of the 20th century. But the post-war regime, funded by the Marshall plan, directed by U.S. constitutional scholars as well as repatriating German intellectuals, had the opportunity to rewrite their system of governance. They did so along Kantian lines: with statutory law, reflecting a priori rational inquiry, instead of empiricist Common Law. They were able to enshrine into their system the Kantian basis of ethics, with its focus on autonomy.

Many of the intellectuals influencing the creation of the new German state were “Marxist” in the loose sense that they were educated in the German continental intellectual tradition which, at that time, included Marx as one of its key figures. By the mid-20th century they had naturally surpassed this ideological view. However, as a consequence, the McCarthyist attack on Marxism had the effect of also purging some of the philosophical connection between German and U.S. legal education. Kantian notions of autonomy are still quite foreign to American jurisprudence. Legal arguments in the United States draw instead on a vast collection of other tools based on a much older and more piecemeal way of establishing rights. But are any of these tools up to the task of protecting human dignity?

The EU is very much influenced by Germany and the German legal system. The EU has the Kantian autonomy ethic at the heart of its conception of human rights. This philosophical commitment has recently expressed itself in the EU’s assertion of data protection law through the GDPR, whose transnational enforcement clauses have brought this centuries-old philosophical fight into contemporary legal debate in legal jurisdictions that predate the neo-Kantian legal innovations of Continental states.

The puzzle facing American legal scholars is this: while industrial advocates and representatives tend to disagree with the strength of the GDPR, arguing that it is unworkable and/or based on poorly defined principle, the data protections that it offer seem so far to be compelling to users, and the shifting expectations around privacy in part induced by it are having effects on democratic outcomes (such as the CCPA). American legal scholars now have to try to make sense of the GDPR’s rules and find a normative basis for them. How can these expansive ideas of data protection, which some have had the audacity to argue is a new right (Hildebrandt, 2015), be grafted onto the the Common Law, empiricist legal system in a way that gives it the legitimacy of being an authentically American project? Is there a way to explain data protection law that does not require the transcendental philosophical apparatus which, if adopted, would force the American mind to reconsider in a fundamental way the relationship between individuals and the collective, labor and capital, and other cornerstones of American ideology?

There may or may not be. Time will tell. My own view is that the corporate powers, which flourished under the Lockean judicial system because of the weaknesses in that philosophical model of the individual and her rights, will instinctively fight what is in fact a threatening conception of the person as autonomous by virtue of their transcendental similarity with other people. American corporate power will not bother to make a philosophical case at all; it will operate in the domain of realpolitic so well documented by Cohen. Even if this is so, it is notable that so much intellectual and economic energy is now being exerted in the friction around a poweful an idea.

References

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

Hildebrandt, M. (2015). Smart technologies and the end (s) of law: Novel entanglements of law and technology. Edward Elgar Publishing.

Notes on Krussell & Smith, 1998 and macroeconomic theory

I’m orienting towards a new field through my work on HARK. A key paper in this field is Krusell and Smith, 1998 “Income and wealth heterogeneity in the macroeconomy.” The learning curve here is quite steep. These are, as usual, my notes as I work with this new material.

Krusell and Smith are approaching the problem of macroeconomic modeling on a broad foundation. Within this paradigm, the economy is imagined as a large collection of people/households/consumers/laborers. These exist at a high level of abstraction and are imagined to be intergenerationally linked. A household might be an immortal dynasty.

There is only one good: capital. Capital works in an interesting way in the model. It is produced every time period by a combination of labor and other capital. It is distributed to the households, apportioned as both a return on household capital and as a wage for labor. It is also consumed each period, for the utility of the households. So all the capital that exists does so because it was created by labor in a prior period, but then saved from immediate consumption, then reinvested.

In other words, capital in this case is essentially money. All other “goods” are abstracted way into this single form of capital. The key thing about money is that it can be saved and reinvested, or consumed for immediate utility.

Households also can labor, when they have a job. There is an unemployment rate and in the model households are uniformly likely to be employed or not, no matter how much money they have. The wage return on labor is determined by an aggregate economic productivity function. There are good and bad economic periods. These are determine exogenously and randomly. There are good times and bad times; employment rates are determined accordingly. One major impetus for saving is insurance for bad times.

The problem raised by Krusell and Smith in this, what they call their ‘baseline model’, is that because all households are the same, the equilibrium distribution of wealth is far too even compared with realistic data. It’s more normally distributed than log-normally distributed. This is implicitly a critique at all prior macroeconomics, which had used the “representative agent” assumption. All agents were represented by one agent. So all agents are approximately as wealthy as all others.

Obviously, this is not the case. This work was done in the late 90’s, when the topic of wealth inequality was not nearly as front-and-center as it is in, say, today’s election cycle. It’s interesting that one reason why it might have not been front and center was because prior to 1998, mainstream macroeconomic theory didn’t have an account of how there could be such inequality.

The Krusell-Smith model’s explanation for inequality is, it must be said, a politically conservative one. They introduce minute differences in utility discount factor. The discount factor is how much you discount future utility compared to today’s utility. If you have a big discount factor, you’re going to want to consume more today. If you have a small discount factor, you’re more willing to save for tomorrow.

Krussell and Smith show that teeny tiny differences in discount factor, even if they are subject to a random walk around a mean with some persistence within households, leads to huge wealth disparities. Their conclusion is that “Poor households are poor because they’ve chosen to be poor”, by not saving more for the future.

I’ve heard, like one does, all kinds of critiques of Economics as an ideological discipline. It’s striking to read a landmark paper in the field with this conclusion. It strikes directly against other mainstream political narratives. For example, there is no accounting of “privilege” or inter-generational transfer of social capital in this model. And while they acknowledge that in other papers there is the discussion of whether having larger amounts of household capital leads to larger rates of return, Kruselll and Smith sidestep this and make it about household saving.

The tools and methods in the paper are quite fascinating. I’m looking forward to more work in this domain.

References

Krusell, P., & Smith, Jr, A. A. (1998). Income and wealth heterogeneity in the macroeconomy. Journal of political Economy106(5), 867-896.

Herbert Simon and the missing science of interagency

Few have ever written about the transformation of organizations by information technology with the clarity of Herbert Simon. Simon worked at a time when disciplines were being reconstructed and a shift was taking place. Older models of economic actors as profit maximizing agents able to find their optimal action were giving way as both practical experience and the exact sciences told a different story.

The rationality employed by firms today is not the capacity to choose the best action–what Simon calls substantive rationality. It is the capacity to engage in steps to discover better ways of acting–procedural rationality.

So we proceed step by step from the simple caricature of the firm depicted in textbooks to the complexities of real firms in the real world of business. At each step towards realism, the problem gradually changes from choosing the right course of action (substantive rationality) to finding way of calculating, very approximately, where a good course of action lies (procedural rationality). With this shift, the theory of the firm becomes a theory of estimation under uncertainty and a theory of computation.

Simon goes on to briefly describe the fields that he believes are poised to drive the strategic behavior of firms. These are Operations Research (OR) and artificial intelligence (AI). The goal of both these fields is to translate problems into mathematical specifications that can be executed by computers. There is some variation within these fields as to whether they aim at satisficing solutions or perfect answers to combinatorial problems, but for the purposes to this article they are the same–certainly the fields have cross-pollinated much since 1969.

Simon’s analysis was prescient. The impact of OR and AI on organizations simply can’t be understated. My purpose in writing this is to point to the still unsolved analytical problems of this paradigm. Simon notes that the computational techniques he refers to percolate only so far up the corporate ladder.

OR and AI have been applied mainly to business decisions at the middle levels of management. A vast range of top management decisions (e..g. strategic decisions about investment, R&D, specialization and diversification, recruitment, development, and retention of managerial talent) are still mostly handled traditionally, that is, by experienced executives’ exercise of judgment.

Simon’s proposal for how to make these kinds of decisions more scientific is the paradigm of “expert systems”, which did not, as far as I know, take off. However, these were early days, and indeed at large firms AI techniques are used to make these kinds of executive decisions. Though perhaps equally, executives defend their own prerogative for human judgment, for better or for worse.

The unsolved scientific problem that I find very motivating is based on a subtle divergence of how the intellectual fields have proceeded. Surely economic value and consequences of business activities are wrapped up not in the behavior of an individual firm, but of many firms. Even a single firm contains many agents. While in the past the need for mathematical tractability led to assumptions of perfect rationality for these agents, we are now far past that and “the theory of the firm becomes a theory of estimation under uncertainty and a theory of computation.” But the theory of decision-making under uncertainty and the theory of computation are largely poised to address problems of the solving a single agent’s specific task. The OR or AI system fulfills a specific function of middle management; it does not, by and large, oversee the interactions between departments, and so on. The complexity of what is widely called “politics” is not captured yet within the paradigms of AI, though anybody with an ounce of practical experience would note that politics is part of almost any organizational life.

How can these kinds of problems be addressed scientifically? What’s needed is a formal, computational framework for modeling the interaction of heterogeneous agents, and a systematic method of comparing the validity of these models. Interagential activity is necessarily quite complex; this is complexity that does not fit well into any available machine learning paradigm.

References

Simon, H. A. (1969). The sciences of the artificial. Cambridge, MA.