I have been reading and writing for a long time. Every once in a while I have thought, “I should write a book”. I starting thinking about the scope of the book, get overwhelmed, realize that I don’t have the time, and give up.
Not too long ago, some PhD students I know suggested that I should write a book, which is very encouraging — somebody might actually read the book!
I thought about it again recently and settled on some fundamentals:
I will write multiple books, and two books I’ve tried to write before (about ethics and metaphysics) are going to come later, after the first book.
I am nowhere near where I want to be to write a book about politics and economics; that’s my next research agenda with the computational economics and multi-agent systems tools.
What I have written about quite a bit already is epistemology and scientific method. That’s a real passion of mine, and it’s one where I feel my views have solidified.
This is probably also a topic where I’ve got a somewhat original and deep perspective, given my interdisciplinary romping, relative to what others have done.
Because this isn’t what I’m interested in researching next, and because it’s a topic that I think everybody should know about, I’m free to write this as a (aspirationally) popular book, trying to walk the reader persuasively through interesting arguments and history, rather than trying to establish my expertise among a crowded field of other experts.
Since this problem is never going away, and indeed only gets more confusing with, e.g., AI and other technical infrastructure, I’m not in a rush to finish it.
This is a good setup for my future research, since in many ways it’s a public-facing account of why my (now much more narrow and difficult to get into) research makes sense to do.
So, there we go!
A few surprising things about getting started. First, GenAI tools are useful, or at least seem useful, when outlining a book. Rather than force somebody else to listen to me rattle off ideas for what to include, I can rattle off my ideas to a CLI tool, and it slots those ideas into the outline and draft materials for me. Cool!
Second, I feel, philosophically, in good shape. There are good reasons for believing in things, good tests for things. There’s no need to be ‘post-truth’ about anything. The anti-science side of the science wars, and various skeptical positions along the way, have robustly lost their argument and I can explain why. Critical counter-epistemologies can be incorporated back into a robust constructivist epistemology without sacrificing objectivity. I’m bullish about objective human knowledge and its continued accumulation.
Third, it’s very comforting looking at historical epistemology, because it never changes. I’m going to start with Socrates. But there’s also a surprising amount of material to add based on developments in the last 20 years, and a lot of this has to do with technology and statistical methods. I think what might be useful is explicitly connecting the problem of epistemology to the mathematizations of knowledge that are at the heart of knowledge infrastructure. This is useful to do, but not located in any discipline that I know of.
Fourth, I think this work can be helpful as “AI for science” as well as “AI-powered misinformation” take off. A lot of the book will be about technology and institutions and their recursive impact on knowledge. The book will have implications for how to navigate the world that could otherwise be confusing for the next generation of people who have to live in it. I have my children in mind when I work on this — I want to be able to efficiently convey a lot of stuff I’ve learned to them. That’s motivating.
Fifth, I’m actually qualified to write this book. My official degrees are related to this field. I’m working on adjacent areas. No imposter syndrome!
Now, it’s only been a few days and the book outline already spans 2500 years of history and a bunch of fields of study. It is quite probably “too ambitous” and maybe it will never get written or read. But… the project feels compelling, and it’s a fine “first book” attempt.
I had a wonderful time at FAccT 2026 in Montreal. I was at the first FAT* in 2018 and thought it was doing something important — creating an intellectual space for serious thinking about powerful socio-technical systems in a way that’s consistent with computational thinking. Prior to that conference, I didn’t know where to publish work along those lines. This year was my third time publishing at it, second time presenting. This year, I presented a paper that was all about causal modeling in service of privacy-by-design, which brings closure on a long engagement with trying to mathematize social norms. I’m satisfied by how we did this, and the approach is very extensible. I intend to build on it in various ways for the rest of my career.
At one moment of the conference, in one of the socials, there was a bit of a philosophical exchange. I discovered that I am not the only person in the world that subscribes to both enactivist and transcendental pragmatist principles, and in fact these approaches seemed to be an increasingly good fit for the direction of the field, though I did hear Agre and postmodernism mentioned a few times.
So, happily, I’m feeling settled in what have been my two greatest intellectual preoccupations: the question of what is true, and the question of what is right or good. Provisionally — and I suppose this sort of thing is always provisional — I feel well educated on these matters, and have tested several theories, and been wrong enough times, to have a rough idea of the right methods for approaching these problems. I’ve made a good deal of progress by accepting, with some humility, that there is maybe not so much room for innovation in these areas as one might have hoped in earlier years. The conclusions (so far) of the process of inquiry are mundane, not spectacular or ‘radical’. However, it is comforting to have greater confidence in common sense than I once did.
To try to put it briefly, I think the following is the case. We are individually and collectively alive, and this is a condition for our knowledge and relationships with others. Knowledge, and cognition more generally, is a process of adaptation whereby a living system responds to change and maintains viability over time, as it is structurally coupled with their environment. An individual’s mental habits are those that survive experience and inquiry and, above all, maintain the life of the individual. The ‘contents’ of the mental states of the individual are those representations, coupled bodily with the environment, which capture useful sensorimotor regularities of their local world. In this sense, knowledge is embodied, personal, and particular, at the individual, ‘ontogenic’ level. (This is all ‘enactivism’ in a nutshell; it is also ‘pragmatism’ in Peirce’s sense; and something borrowed from traditional philosophy of mind.)
However, no individual exists alone. We are born with chromosomes from two parents. We are raised, live, and die in society. The collective social organism is also alive, and must maintain its viability over time. The development of language, institutions, communication systems — including the legal system, property laws, and markets — are a kind of ‘cognition’ at the collective, or ‘phylogenic” level. It is at this level ‘objectivity’ is constructed; knowledge can be ‘transcendent’ of individual bodies because its meaning comes from its use in social forms. As a specific accomplishment of certain kinds of social systems, we get general representations of the world that are reliably ‘true’. And, as a specific accomplishment of certain other kinds of social systems, we can get representations of social behavior that are reliably ‘good’ or ‘right’, mainly because they interactively maintain the viability of the collective, and the individual, dependent on it, within it. Considering society decomposed into separate mutually supportive but distinctly personal spheres of activity, we get Contextual Integrity, and the implied norms of personal information flow. Considering society in its broadest sense, we get Dewey’s notions of social democracy, and ultimately Habermasian notions of legal legitimacy.
Thus construed, we have lots of room to improve ourselves in both knowledge and wisdom. This is primarily achieved by the improvement of institutions and social forms, as these are the harnesses which distinctly embodied agents create objective representations. Computational social science remains a worthy pursuit as a means to these ends. The ultimate end is survival and flourishing of humanity — one’s descendants, one’s society, one’s ecosystem — which is an intrinsic motivation which grounds the whole process transcendentally.
I’m grateful to Christoph Salge for working with me on a paper about enactivism and the legal responsibility of AI systems, which has refined and improved my confidence in these points.
The above is, I suppose, all well and good, but it’s incomplete in at least two respects, which I find bothersome. These two issues are at the heart of many of today’s deepest political conflicts, and I suppose they reflect inherent instabilities in the characterization of truth and goodness described above.
The first is the problem of tribes, or nations. Individuals can to some extent choose which collectives they are loyal to, and social collectives come into conflict. Universalist ethics are a longstanding ideal, but frequently come into conflict with particularist loyalties to tribe, race, state, etc. Some amount of loyalty to family seems essential for viability and is broadly good. When tribal conflict leads to war or subjugation, that is often not good. There is both cooperation and defection by tribes; tribal boundaries are unstable; tribes have their own local knowledge and customs and these can be in conflict with universals. A great deal has been written about this in various fields and I’m finding it so intractable that it has become tedious.
The second is the problem, or solution, of wealth. I’ll admit that I wish I had much more to say about this than I currently do. For one, while I believe I have a pretty good idea of what truth and ethics are, I do not have a similarly grounded concept of what wealth is. And yet, the distribution of wealth is the single greatest political and moral concern of our society, besides, maybe, the relations between tribes. Wealth cannot be the same thing as truth, and it cannot be the same thing as ethics — the tension between the three is obvious; and yet it seems connected to both in intrinsic ways. Some amount of wealth is necessary for the survival and flourishing of the individual and the collective. Wealth is sensitive to institutions — property and liability regimes, markets, and so on — but what makes these institutions proper or desirable? The assumptions of the prevailing theory often seem weak on inspection.
This latter issue is the subject of economics, but I have not yet been able to find economic theory that is on enactivist or systems-theoretic grounding. I have come to the conclusion that it doesn’t exist yet in a convincing or generalizable way, and it’s a problem my work is pointing at. A big methodological gap is in computational methods that are a good fit for the problem. This is what I’m most interested in advancing in the short term.
One thing that’s become clear to me lately is that I now believe that ethics can be naturalized. I also believe that there is in fact a form of ‘natural law’. By this I mean that that there are rights and values that are inherent to human nature. Real legal systems can either lie up to natural law, or not.
This is not the only position that it’s possible to take on these topics.
One different position, that I do not have, is that ethics depends on the supernatural. I bring this up because religion is once again very politically salient in the United States. Abrahamic religions ground ethics and morality in a covenant between humans and a supernatural God. Divine power authorizes the ethical code. In some cases this is explicitly stated law, in others it is a set of principles. Beyond divine articulation, this position maintains that ethics are supernaturally enforced through reward and punishment. I don’t think this is how things work.
Another position I don’t have is that there is that ethics are opinion or cultural construction, full stop. Certainly there’s a wide diversity of opinions on ethics and cultural attitudes. Legal systems vary from place to place. This diversity is sometimes used as evidence that there aren’t truths about ethics or law to be had. But that is, taken alone, a silly argument. Lots of people and legal systems are simply wrong. Moreover, moral and ethical truths can take contingency and variety into account, and they probably should. It can be true that laws should be well-adapted to some otherwise arbitrary social expectations or material conditions. And so on.
There has historically been hemming and hawing about the fact/value dichotomy. If there’s no supernatural guarantor of ethics, is the natural world sufficient to produce values beyond our animal passions? This increasingly feels like an argument from a previous century. Adequate solutions to this problem have been offered by philosophers over time. They tend to involve some form of rational or reflective process, and aggregation over the needs and opinions of people in heterogeneous circumstances. Habermas comes to mind as a one of the synthesizers of a new definition of naturalized law and ethics.
For some reason, I’ve encountered so much resistance to this form of ethical or moral realism over the years. But looking back on it, I can’t recall a convincing argument for it. I can recall many claims that the idea of ethical and moral truth are somehow politically dangerous, but that this not the same thing.
There is something teleological about most viable definitions of naturalized ethics and natural law. They are would would hypothetically be decided on by interlocutors in an idealized but not yet realized circumstance. A corollary to my position is that ethical and moral facts exist, but many have not yet been discovered. A scientific process is needed to find them. This process is necessarily a social scientific process, since ethical and moral truths are truths about social systems and how they work.
It would be very fortunate, I think, if some academic department, discipline, or research institution were to take up my position. At present, we seem to have a few different political positions available to us in the United States:
A conservative rejection of the university of being insufficiently moral because of its abandonment of God
A postmodern rejection of ethical and moral truths that relativizes everything
A positivist rejection of normativity as the object of social science because of the fact/value dichotomy
Politicized disciplines that presume a political agenda and then perform research aligned with that agenda
Explicitly normative disciplines that are discursive and humanistic but not inclined towards rigorous analysis of the salient natural facts
None of these is conducive to a scientific study of what ethics and morals should be. There are exceptions, of course, and many brilliant people in many corners who make great contributions towards this goal. But they seem scattered at the margins of the various disciplines, rather than consolidated into a thriving body of intellect. At a moment where we see profound improvements (yes, improvements!) in our capacity for reasoning and scientific exploration, why hasn’t something like this emerged? It would be an improvement over the status quo.
Complex systems theory is a way of thinking about systems with many interacting parts and functions. It draws on physics and the science of modeling dynamic systems. It’s a trans-disciplinary, quantitative science of everything. It often and increasingly gets applied to social systems, often through the methods of agent-based modeling (ABM). ABM has a long history in computational sociology. More recently, it has made inroads into economics and finance (Axtell and Farmer, 2022). That’s important intellectual territory to win over because, of course, it is vitally important for both private and public interests. Its progress there is gradual but steady. ABM and complex systems methods have no dogma besides mathematical and computational essentials. Their eventual triumph is more or less assured. As I’ve argued, ABM and complex systems theory are thus an exciting frontier for legal theory (Benthall and Strandburg, 2021). For these reasons, one line of my research is involved in developing computational frameworks (i.e. software libraries, mathematical scaffolding) for computational social scientific modeling.
Contextual Integrity (CI) is an ethical theory developed by Helen Nissenbaum. It is especially applicable to questions of the ethics of information technology and computation. Central to the theory is the idea of “appropriate information flow”, or flows of (personal) information which conform with “information norms”. According to CI, information norms are legitimized by a balance of societal values, contextual purposes, and individual ends. The work of the CI ethicist is to wrestle with the alignments and contradictions between these alignments, purposes, and ends to identify the most legitimate norms for a given context,. When the legitimate norms are identified, it is then in principle possible to design and deploy technology in accordance with these norms.
CI is a philosophy grounded in social theory. It has never been robustly quantified and many people think this is impossible to do. I’m not among these people. In fact, much of my work is about trying to quantify or model CI. It should come as no surprise, then, that I now see CI in terms of complexity theory. It has struck me recently that what this amounts to, more or less, is a computational social theory of ethics! This idea is exciting to me, and one day I’ll want to write it down in detail. For now, I have some nice diagrams and notes from a recent presentation I wanted to share.
CI is a theory of ethics that is ultimately concerned with the way that values, purposes, and ends legitimize socially understood practices. The ethicist’s job, for CI, is to design legitimate institutions. A problem for the ethicist is that some institutions can be legitimate, but utopian, in that they are not stable behavioral patterns for the sociotechnical system. Complex systems theory, as a descriptive science, is well adapted to modeling systems and identifying the regular behaviors within them, under varying conditions. Borrowing a notion from physics, a system can exhibit many regular behavioral states, which we might call phases. For example, it is well known that water has many different phases, depending on the temperature: ice, the liquid water, steam, etc.
Norms have both descriptive and (ahem) normative dimensions. (This confusing jargon is part of why it’s so hard to make progress in this area.) In other words, for there to be an actually existing norm, it has to be both regular and, to be ethical according to CI, legitimate.
There are critics of CI who argue that one problem with it is that it assumes an apolitical consensus of information norms without addressing how norms might be distorted by, e.g., power in society. This is not terribly fair to Nissenbaum’s broader corpus of work, which certainly acknowledges political complexity (see for example the recent Nissenbaum, 2024). Suffice it to say here that not all individual end up being ‘legitimized’ when ethicists assess things, and that legitimization is always political. Moreover, individual ends and politics can, of course, often be the driver of system behavior away from legitimate institutions. We can’t always have nice things.
Nevertheless, it remains useful to consider how and under what conditions a system could remain legitimate despite technological change. This is what the original CI design heuristic is: a procedure for evaluating what to do when a new technology creates a disruptive change in societal information flows.
Ideally, for CI, when a new technology destabilizes the sociotechnical system’s behavior and threatens it with illegitimate practices, society reacts (through journalism, through ethics, through a political process, through private choices and actions, etc.) and returns the system to a regular behavioral pattern that is legitimate. This might not be the same behavior as the system started with. It might be even better. And that’s OK.
What’s bad, for CI, is if the system gets stuck in an illegitimate but still robust phase.
While there are some applications of CI that serve anodyne ends of parsing and implementing uncontroversial privacy rules, there are other uses of CI as a radical critique of the status quo. This is well exemplified by Ido Sivan-Sevilla et al.’s comments on the FTC ANPR on Commercial Surveillance and Lax Data Security Practices (2022), which is a succinct and to-the-point condemnation of the “notice and consent” practices in commercial surveillance. We live in a world in which standard, even ubiquitous, technology norms depend on “laughable legal fictions” such as the idea that users of web services are legitimate parties to contracts with vendors. It is well documented how these fictions have been enshrined into law by decades of pressure by the technology sector in courts and government (Cohen, 2019).
Together, CI and complex systems theory can show how society can be a winner, or loser, beyond the sum of individual outcomes. There are certainly those that have argued that, essentially, “there is no such thing as society”, and that voluntary, binary transactions between parties are all there is. An anarchic, libertarian, or laissez-faire system certainly serves the individual ends of some, and is to some extent stable until the lords of anarchy create new systems of rules that are in their interest. It is difficult to analyze the social costs of these political changes in terms of “individual harms”, because the true marginal cost is not measurable at the level of the individual, but rather at the level of the phase transition. A complex systems theory allows for this broader view of what is at stake.
This approach also, I think, helps convey the fragility of legitimate institutions. Nothing guarantees legitimacy. Legitimate institutions typically constrain the behavior of some actors in ways that they individually do not enjoy. There are social processes which can steer a system towards a more legitimate phase, but these will meet with resistance, sometimes fail, and can be coopted by bad faith actors serving their own ends.
Indeed, there are those who would say we do not live in a legitimate system and have not lived in one for a long time. “Legitimate for whom?” Even if this is so, CI invites us to have a productive dialog about what legitimacy would entail, by sorting out different motivations and looking at the options for balancing them out. This good faith search for resolutions is often thankless and unrewarded, but certainly we would be worse off without it. On the other hand, arguments about legitimate institutions that are divorced from realistic understandings of sociotechnical processes are easily deployed as propaganda and ideology to cover illegitimate behavior. Ethics requires a science of sociotechnical systems; sociotechnical systems are complex; complex systems theory is a solid foundation for such a science.
References
Axtell, R. L., & Farmer, J. D. (2022). Agent-based modeling in economics and finance: Past, present, and future. Journal of Economic Literature, 1-101.
Benthall, S., & Strandburg, K. J. (2021). Agent-based modeling as a legal theory tool. Frontiers in Physics, 9, 666386.
Cohen, J. E. (2019). Between truth and power. Oxford University Press.
Nissenbaum, H. (2024). AI Safety: A Poisoned Chalice?. IEEE Security & Privacy, 22(2), 94-96.
Through a few new emerging projects and opportunities, I’ve had reason to circle back to the topic of Artificial Intelligence and ethics. I wanted to jot down a few notes as some recent reading and conversations have been clarifying some ideas here.
In my work with Jake Goldenfein on this topic (published 2021), we framed the ethical problem of AI in terms of its challenge to liberalism, which we characterize in terms of individual rights (namely, property and privacy rights), a theory of why the free public market makes the guarantees of these rights sufficient for many social goods, and a more recent progressive or egalitarian tendency. We then discuss how AI technologies challenge liberalism and require us to think about post-liberal configurations of society and computation.
A natural reaction to this paper, especially given the political climate in the United States, is “aren’t the alternatives to liberalism even worse?” and it’s true that we do not in that paper outline an alternative to liberalism which a world with AI might aspire to.
John Mearsheimer’s The Great Delusion: Liberal Dreams and International Realities (2018) is a clearly written treatise on political theory. Mearsheimer rose to infamy in 2022 after the Russian invasion of Ukraine because of widely circulated videos of a lecture in 2015 in which he argued that the fault for Russia’s invasion of Crimea in 2014 was due to U.S. foreign policy. It is because of that infamy that I’ve decided to read The Great Delusion, which was a Financial Times Best Book of 2018. The Financial Times editorials have since turned on Mearsheimer. We’ll see what they say about him in another four years. However politically unpopular he may be, I found his points interesting and have decided to look at his more scholarly work. I have not been disappointed, and find that he clearly articulates political philosophy I will use these articulations. I won’t analyze his international relations theory here.
Putting Mearsheimer’s international relations theories entirely aside for now, I’ve been pleased to find The Great Delusion to be a thorough treatise on political theory, and it goes to lengths in Chapter 3 to describe liberalism as a political theory (which will be its target). Mearsheimer distinguished between four different political ideologies, citing many of their key intellectual proponents.
Modus vivendi liberalism. (Locke, Smith, Hayek) A theory committed to individual negative rights, such as private property and privacy, against the impositions by the state. The state should be minimal, a “night watchman”. This can involve skepticism about the ability of reason to achieve consensus about the nature of the good life; political toleration of differences is implied by the guarantee of negative rights.
Progressive liberalism. (Rawls) A theory committed to individual rights, including both negative rights and positive rights, which can be in tension. An example positive right is equal opportunity, which requires interventions by the state in order to guarantee. So the state must play a stronger role. Progressive liberalism involves more faith in reason to achieve consensus about the good life, as progressivism is a positive moral view imposed on others.
Utilitarianism. (Bentham, Mill) A theory committed to the greatest happiness for the greatest number. Not committed to individual rights, and therefore not a liberalism per se. Utilitarian analysis can argue for tradeoffs of rights to achieve greater happiness, and is collectivist, not in individualist, in the sense that it is concerned with utility in aggregate.
Liberal idealism. (Hobson, Dewey) A theory committed to the realization of an ideal society as an organic unity of functioning subsystem. Not committed to individual rights primarily, so not a liberalism, though individual rights can be justified on ideal grounds. Influenced by Hegelian views about the unity of the state. Sometimes connected to a positive view of nationalism.
This is a highly useful breakdown of ideas, which we can bring back to discussions of AI ethics.
Jake Goldenfein and I wrote about ‘liberalism’ in a way that, I’m glad to say, is consistent with Mearsheimer. We too identity right- and left- wing strands of liberalism. I believe our argument about AI’s challenge to liberal assumptions still holds water.
Utilitarianism is the foundation of one of the most prominent versions of AI ethics today: Effective Altruism. Much has been written about Effective Altruism and its relationship to AI Safety research. I have expressed some thoughts. Suffice it to say here that there is a utilitarian argument that ‘ethics’ should be about prioritizing the prevention of existential risk to humanity, because existential catastrophe would prevent the high-utility outcome of humanity-as-joyous-galaxy-colonizers. AI is seen, for various reasons, to be a potential source of catastrophic risk, and so AI ethics is about preventing these outcomes. Not everybody agrees with this view.
For now, it’s worth mentioning that there is a connection between liberalism and utilitarianism through theories of economics. While some liberals are committed to individual rights for their own sake, or because of negative views about the possibility of rational agreement about more positive political claims, others have argued that negative rights and lack of government intervention lead to better collective outcomes. Neoclassical economics has produced theories and ‘proofs’ to this effect, which rely on mathematical utility theory, which is a successor to philosophical utilitarianism in some respects.
It is also the case that a great deal of AI technology and technical practice is oriented around the vaguely utilitarian goals of ‘utility maximization’, though this is more about the mathematical operationalization of instrumental reason and less about a social commitment to utility as a political goal. AI practice and neoclassical economics are quite aligned in this way. If I were to put the point precisely, I’d say that the reality of AI, by exposing bounded rationality and its role in society, shows that arguments that negative rights are sufficient for utility-maximizing outcomes are naive, and so are a disappointment for liberals.
I was pleased that Mearsheimer brought up what he calls ‘liberal idealism’ in his book, despite it being perhaps a digression from his broader points. I have wondered how to place my own work, which draws heavily on Helen Nissenbaum’s theory of Contextual Integrity (CI), which is heavily influenced by the work of Michael Walzer. CI is based on a view of a society composed of separable spheres, which distinct functions and internally meaningful social goods, which should not be directly exchanged or compared. Walzer has been called a communitarian. I suggest that CI might be best seen as a variation of liberal idealism, in that it orients ethics towards a view of society as an idealized organic unity.
If the present reality of AI is so disappointing, then we must try to imagine a better ideal, and work our way towards it. I’ve found myself reading more and more work, such as by Felix Adler and Alain Badiou, that advocate for the need for an ideal model of society. What we currently are missing is a good computational model of such a society which could do for idealism what neoclassical economics did for liberalism. Which is, namely, to create a blueprint for a policy and science of its realization. If we were to apply AI to the problem of ethics, it would be good to use it this way.
Most of the great historical philosophers did not have children.
I can understand why. For much of my life, I’ve been propelled by a desire to understand certain theoretical fundamentals of knowledge, ethics, and the universe. No doubt this has led me to become the scientist I am today. Since becoming a father, I have less time for these questions. I find myself involved in more mundane details of life, and find myself beginning to envy those in what I had previously considered the most banal professions. Fatherhood involves a practical responsibility that comes front-and-center, displacing youthful ideals and speculations.
I’m quite proud to now be working on what are for me rather applied problems. But these problems have deep philosophical roots and I enjoy the thought that I will one day be able to write a mature philosophy as a much older man some time later. For now, I would like to jot down a few notes about how my philosophy has changed.
I write this now because my work is now intersecting with other research done by folks I know are profoundly ethically motivated people. My work on what is prosaically called “technology policy” is crossing into theoretical territory currently occupied by AI Safety researchers of the rationalist or Effective Altruist vein. I’ve encountered these folks before and respect their philosophical rigor, though I’ve never quite found myself in agreement with them. I continue to work on problems in legal theory as well, which always involves straddling the gap between consequentialism and deontological ethics. My more critical colleagues may be skeptical of my move towards quantitative economic methods, as the latter are associated with a politics that has been accused of lacking integrity. In short, I have several reasons to want to explain, to myself at least, why I’m working on the problems I’ve chosen, at least as a matter of my own philosophical trajectory.
So first, a point about logic. The principle of non-contradiction imposes a certain consistency and rigor on thought and encourages a form of universalism of theory and ethics. The internal consistency of the Kantian transcendental subject is the first foundation for deontological ethics. However, for what are essentially limitations of bounded rationality, this gives way in later theory to Habermasian discourse ethics. The internal consistency of the mind is replaced with the condition that to be involved in communicative action is to strive for agreement. Norms form from disinterested communications that collect and transcend the perspectival limits of the deliberators. In theory.
In practice, disinterested communication is all but impossible, and communicative competence is hard to find. At the time of this writing, my son does not yet know how to talk. But he communicates, and we do settle on norms, however transitory. The other day we established that he is not allowed to remove dirt from the big pot with the ficus elastica and deposit in other rooms of the house. This is a small accomplishment, but it highlights how unequal rationality, competence, and authority is not a secondary social aberration. It is a primary condition of life.
So much for deontology. Consequential ethics does not fare much better. Utility has always been a weakly theorized construct. In modern theory, it has been mathematized into something substantively meaningless. It serves mainly to describe behavior, rather than to explain it; it provides little except a just-so-story for a consumerist society which is, sure enough, best at consuming itself. Attempts to link utility to something like psychological pleasure, as was done in the olden days, have bizarre conclusions. Parents are not as happy, studies say, as those without children. So why bother?
Nietzsche was a fierce critic of both Kantian deontological ethics and facile British utilitarianism. He argued that in the face of the absurdity of both systems, the philosopher had to derive new values from the one principle that they could not, logically, deny: life itself. He believed that a new ethics could be derived from the conditions of life, which for him was a process of overcoming resistance in pursuit of other (perhaps arbitrary) goals. Suffering, for Nietzsche, was not a blemish on life; rather, life is sacred enough to justify monstrous amounts of suffering.
Nietzsche went insane and died before he could finish his moral project. He didn’t have kids. If he had, maybe he would have come to some new conclusions about the basis for ethics.
In my humble opinion and limited experience thus far, fatherhood is largely about working to maintain the conditions of life for one’s family. Any attempt at universalism that does not extend to one’s own offspring is a practical contradiction when one considers how one was once a child. The biological chain of being is direct, immediate, and resource intensive in a way too little acknowledged in philosophical theory.
In lieu of individual utility, the reality of family highlights the priority of viability, or the capacity of a complex, living system to maintain itself and its autonomy over time. The theory of viability was developed in the 20th century through the field of cybernetics — for example, by Stafford Beer — though it was never quite successfully formulated or integrated into the now hegemonic STEM disciplines. Nevertheless, viability provides a scientific criterion by which to evaluate social meaning and ethics. I believe that there is still tremendous potential in cybernetics as an answer to longstanding philosophical quandaries, though to truly capture this value certain mathematical claims need to be fleshed out.
However, an admission of the biological connection between human beings cannot eclipse economic realities that, like it or not, have structured human life for thousands of years. And indeed, in these early days of child-rearing, I find myself ill-equipped to address all of my son’s biological needs relative to my wife and instead have a comparative advantage in the economic aspects of his, our, lives. And so my current work, which involves computational macroeconomics and the governance of technology, is in fact profoundly personal and of essential ethical importance. Economics has a reputation today for being a technical and politically compromised discipline. We forget that it was originally, and maybe still is, a branch of moral philosophy deeply engaged with questions of justice precisely because it addresses the conditions of life. This ethical imperative persists despite, or indeed because of, its technical complexity. It may be where STEM can address questions of ethics directly. If only it had the right tools.
In summary, I see promise in the possibility of computational economics, if inspired by some currently marginalized ideas from cybernetics, in satisfactorily addressing some perplexing philosophical questions. My thirsting curiosity, at the very least, is slaked by daily progress along this path. I find in it the mathematical rigor I require. At the same time, there is space in this work for grappling with the troublingly political, including the politics of gender and race, which are both of course inexorably tangled with the reality of families. What does it mean, for the politics of knowledge, if the central philosophical unit and subject of knowledge is not the individual, or the state, or the market, but the family? I have not encountered even the beginning of an answer in all my years of study.
I’ve tried to piece together double contingency before, and am finding myself re-encountering these ideas in several projects. I just now happened on this very succinct account of double contingency in Hildebrandt (2013), which I wanted to reproduce here.
Parsons was less interested in personal identity than in the construction of social institutions as proxies for the coordination of human interaction. His point is that the uncertainty that is inherent in the double contingency requires the emergence of social structures that develop a certain autonomy and provide a more stable object for the coordination of human interaction. The circularity that comes with the double contingency is thus resolved in the consensus that is consolidated in sociological institutions that are typical for a particular culture. Consensus on the norms and values that regulate human interaction is Parsons’s solution to the problem of double contingency, and thus explains the existence of social institutions. As could be expected, Parsons’s focus on consensus and his urge to resolve the contingency have been criticized for its ‘past-oriented, objectivist and reified concept of culture’, and for its implicitly negative understanding of the double contingency.
This paragraph says a lot, both about “the problem” posed by “the double contingency”, the possibility of solution through consensus around norms and values, and the rejection of Parsons. It is striking that in the first pages of this article, Hildebrandt begins by challenging “contextual integrity” as a paradigm for privacy (a nod, if not a direct reference, to Nissenbaum (2009)), astutely pointing out that this paradigm makes privacy a matter of delinking data so that it is not reused across contexts. Nissenbaum’s contextual integrity theory depends rather critically on consensus around norms and values; the appropriateness of information norms is a feature of sociological institutions accountable ultimately to shared values. The aim of Parsons, and to some extent also Nissenbaum, is to remove the contingency by establishing reliable institutions.
The criticism of Parsons as being ‘past-oriented, objectivist and reified’ is striking. It opens the question whether Parsons’s concept of culture is too past-oriented, or if some cultures, more than others, may be more past-oriented, rigid, or reified. Consider a continuum of sociological institutions ranging from the rigid, formal, bureaucratized, and traditional to the flexible, casual, improvisational, and innovative. One extreme of these cultures is better conceptualized as “past-oriented” than the other. Furthermore, when cultural evolution becomes embedded in infrastructure, no doubt that culture is more “reified” not just conceptually, but actually, via its transformation into durable and material form. That Hildebrandt offers this criticism of Parsons perhaps foreshadows her later work about the problems of smart information communication infrastructure (Hildebrandt, 2015). Smart infrastructure poses, to those which this orientation, a problem in that it reduces double contingency by being, in fact, a reification of sociological institutions.
“Reification” is a pejorative word in sociology. It refers to a kind of ideological category error with unfortunate social consequences. The more positive view of this kind of durable, even material, culture would be found in Habermas, who would locate legitimacy precisely in the process of consensus. For Habermas, the ideals of legitimate consensus through discursively rational communicative actions finds its imperfect realization in the sociological institution of deliberative democratic law. This is the intellectual inheritor of Kant’s ideal of “perpetual peace”. It is, like the European Union, supposed to be a good thing.
So what about Brexit, so to speak?
Double contingency returns with a vengeance in Luhmann, who famously “debated” Habermas (a more true follower of Parsons), and probably won that debate. Hildebrandt (2013) discusses:
A more productive understanding of double contingency may come from Luhmann (1995), who takes a broader view of contingency; instead of merely defining it in terms of dependency he points to the different options open to subjects who can never be sure how their actions will be interpreted. The uncertainty presents not merely a problem but also a chance; not merely a constraint but also a measure of freedom. The freedom to act meaningfully is constraint [sic] by earlier interactions, because they indicate how one’s actions have been interpreted in the past and thus may be interpreted in the future. Earlier interactions weave into Luhmann’s (1995) emergent social systems, gaining a measure of autonomy — or resistance — with regard to individual participants. Ultimately, however, social systems are still rooted in double contingency of face-to-face communication. The constraints presented by earlier interactions and their uptake in a social system can be rejected and renegotiated in the process of anticipation. By figuring out how one’s actions are mapped by the other, or by social systems in which one participates, room is created to falsify expectations and to disrupt anticipations. This will not necessarily breed anomy, chaos or anarchy, but may instead provide spaces for contestation, self-definition in defiance of labels provided by the expectations of others, and the beginnings of novel or transformed social institutions. As such, the uncertainty inherent in the double contingency defines human autonomy and human identity as relational and even ephemeral, always requiring vigilance and creative invention in the face of unexpected or unreasonably constraining expectations.
Whereas Nissenbaum’s theory of privacy is “admitted conservative”, Hildebrandt’s is grounded in a defense of freedom, invention, and transformation. If either Nissenbaum or Hildebrandt were more inclined to contest each other directly, this may be privacy scholarship’s equivalent of the Habermas/Luhmann debate. However, this is unlikely to occur because the two scholars operate in different legal systems, reducing the stakes of the debate.
We must assume that Hildebrandt, in 2013, would have approved of Brexit, the ultimate defiance of labels and expectations against a Habermasian bureaucratic consensus. Perhaps she also, as would be consistent with this view, has misgivings about the extraterritorial enforcement of the GDPR. Or maybe she would prefer a a global bureaucratic consensus that agreed with Luhmann; but this is a contradiction. This psychologistic speculation is no doubt unproductive.
What is more productive is the pursuit of a synthesis between these poles. As a liberal society, we would like our allocation of autonomy; we often find ourselves in tension with the the bureaucratic systems that, according to rough consensus and running code, are designed to deliver to us our measure of autonomy. Those that overstep their allocation of autonomy, such as those that participated in the most recent Capitol insurrection, are put in prison. Freedom cooexists with law and even order in sometimes uncomfortable ways. There are contests; they are often ugly at the time however much they are glorified retrospectively by their winners as a form of past-oriented validation of the status quo.
References
Hildebrandt, M. (2013). Profile transparency by design?: Re-enabling double contingency. Privacy, due process and the computational turn: The philosophy of law meets the philosophy of technology, 221-46.
Hildebrandt, M. (2015). Smart technologies and the end (s) of law: novel entanglements of law and technology. Edward Elgar Publishing.
Nissenbaum, H. (2009). Privacy in context: Technology, policy, and the integrity of social life. Stanford University Press.
One of the best ideas to come out of the social sciences is “double contingency”: the fact that two people engaged in communication are in a sense unpredictable to each other. That mutual unpredictability is an element of what it means to be in communication with another.
The most recent articulation of this idea is from Luhmann, who was interested in society as a system of communication. Luhmann is not focused on the phenomenology of the participants in a social system; in as sense, he looks like social systems the way an analyst might look at communications data from a social media site. The social system is the set of messages. Luhmann is an interesting figure in intellectual history in part because he is the one who made the work of Maturana and Varela officially part of German philosophical canon. That’s a big deal, as Maturana and Varela’s intellectual contributions–around the idea of autopoiesis, for example–were tremendously original, powerful, and good.
“Double contingency” was also discussed, one reads, by Talcott Parsons. This does not come up often because at some point the discipline of Sociology just decided to bury Parsons.
Double contingency comes up in interesting ways in European legal scholarship about technology. Luhmann, a dense German writer, is not read much in the United States, despite his being essentially right about things. Hildebrandt (2019) uses double contingency in her perhaps perplexingly framed argument for the “incomputability” of human personhood. Teubner (2006) makes a somewhat different but related argument about agency, double contingency, and electronic agents.
Hildebrandt and Teubner make for an interesting contrast. Hildebrandt is interested in the sanctity of humanity qua humanity, and in particular of privacy defined as the freedom to be unpredictable. This is an interesting inversion for European phenomenological philosophy. Recall that originally in European phenomenology human dignity was tied to autonomy, but autonomy depended on universalized rationality, with the implication that the most important thing about human dignity was that one followed universal moral rules (Kant). Hildebrandt is almost staking out an opposite position: that Arendtian natality, the unpredictableness of being an original being at birth, is the source of one’s dignity. Paradoxically, Hildebrandt argues that it humanity has this natality essentially and so claims that predictive technology might truly know the data subject are hubris, but also that the use of these predictive technologies is threat to natality unless their use is limited by data protection laws that ensure contestability of automated decisions.
Teubner (2006) takes a somewhat broader and, in my view, more self-consistent view. Grounding his argument firmly in Luhmann and Latour, Teubner is interested in the grounds of legally recognized (as opposed to ontologically, philosophically sanctified) personhood. And, he finds, the conditions of personhood can apply to many things besides humans! “Black box, double contingency, and addressability”, three fictions on which the idea of personhood depend, can apply to corporations and electronic agents as well as humans individually. This provides a kind of consistency and rationale for why we allow these kinds of entities to engage in legal contracts with each other. The contract, it is theorized, is a way of managing uncertainty, reducing the amount of contingency in the inherent “double contingency”-laden relationship.
Something of the old Kantian position comes through in Teubner, in that contracts and the law are regulatory. However, Teubner, like Nissenbaum, is ultimately a pluralist. Teubner writes about multiple “ecologies” in which the subject is engaged, and to which they are accountable in different modalities. So, the person, qua economic agent, is addressed in terms of their preferences. But the person, qua legal institutions, is addressed in terms of their embodiment of norms. The “whole person” does not appear in any singular ecology.
I’m sympathetic with the Teubnerian view here, perhaps in contrast with Hildebrandt’s view, the the following sense: while there may indeed be some intrinsic indeterminacy to an individual, this indeterminacy is meaningless unless it is also situated in (some) social ecology. However, what makes a person contingent visa vie one ecology is precisely that only a fragment of them is available to that ecology. The contingency to the first ecology is a consequence of their simultaneous presence within other ecologies. The person is autonomous, and hence also unpredictable, because of this multiplied, fragmented identity. Teubner, I think correctly, concludes that there is a limited form of personhood to non-human agents, but as these agents will be even more fragmented than humans, they are only persons in an attenuated sense.
I’d argue that Teubner helpfully backfills how personhood is socially constructed and accomplished, as opposed to guaranteed from birth, in a way that complements Hildebrandt nicely. In the 2019 article cited here, Hildebrandt argues for contestability of automated decisions as a means of preserving privacy. Teubner’s theory suggests that personhood–as participant in double contingency, as a black box–is threatened rather by context collapse, or the subverting of the various distinct social ecologies into a single platform in which data is shared ubiquitously between services. This provides a normative a universalist defense of keeping contexts separate (which in a different article Hildebrandt connects to purpose binding in the GDPR) which is never quite accomplished in, for example, Nissenbaum’s contextual integrity.
References
Hildebrandt, Mireille. “Privacy as protection of the incomputable self: From agnostic to agonistic machine learning.” Theoretical Inquiries in Law 20.1 (2019): 83-121.
Teubner, Gunther. “Rights of non‐humans? Electronic agents and animals as new actors in politics and law.” Journal of Law and Society 33.4 (2006): 497-521.
Recent conversations have brought me back to the third rail of different modalities of knowledge and their implications for academic disciplines. God help me. The chain leading up to this is: a reminder of how frustrating it was trying to work with social scientists who methodologically reject the explanatory power of statistics, an intellectual encounter with a 20th century “complex systems” theorist who also didn’t seem to understand statistics, and the slow realization that’s been bubbling up for me over the years that I probably need to write an article or book about the phenomenology of probability, because I can’t find anything satisfying about it.
The hypothesis I am now entertaining is that probabilistic or statistical reasoning is the intellectual crux, disciplinarily. What we now call “STEM” is all happy to embrace statistics as its main mode of empirical verification. This includes the use of mathematical proof for “exact” or a priori verification of methods. Sometimes the use of statistics is delayed or implicit; there is qualitative research that is totally consistent with statistical methods. But the key to this whole approach is that the fields, in combination, are striving for consistency.
But not everybody is on board with statistics! Why is that?
One reason may be because statistics is difficult to learn and execute. Doing probabilistic reasoning correctly is at times counter-intuitive. That means that quite literally it can make your head hurt to think about it.
There is a lot of very famous empirical cognitive psychology that has explored this topic in depth. The heuristics and biases research program of Kahneman and Tversky was critical for showing that human behavior rarely accords with decision-theoretic models of mathematical, probabilistic rationality. An intuitive, “fast”, prereflective form of thinking, (“System 1”) is capable of making snap judgments but is prone to biases such as the availability heuristic and the representativeness heuristic.
A couple general comments can be made about System 1. (These are taken from Tetlock’s review of this material in Superforecasting). First, a hallmark of System 1 is that it takes whatever evidence it is working with as given; it never second-guesses it or questions its validity. Second, System 1 is fantastic at provided verbal rationalizations and justifications of anything that it encounters, even when these can be shown to be disconnected from reality. Many colorful studies of split brain cases, but also many other lab experiments, show the willingness people have to make of stories to explain anything, and their unwillingness to say, “this could be due to one of a hundred different reasons, or a mix of them, and so I don’t know.”
The cognitive psychologists will also describe a System 2 cognitive process that is more deliberate and reflective. Presumably, this is the system that is sometimes capable of statistical or otherwise logical reasons. And a big part of statistical reasoning is questioning the source of your evidence. A robust application of System 2 reasoning is capable of overcoming System 1’s biases. At the level of institutional knowledge creation, the statistical sciences are comprised mainly of formalized, shared results of System 2 reasoning.
Tetlock’s work, from Expert Political Judgment and on, is remarkable for showing that deference to one or the other cognitive system is to some extent a robust personality trait. Famously, those of the “hedgehog” cognitive style, who apply System 1 and a simplistic theory of the world to interpret everything they experience, are especially bad at predicting the outcomes of political events (what are certainly the results of ‘complex systems’), whereas the “fox” cognitive style, which is more cautious about considering evidence and coming to judgments, outperforms them. It seems that Tetlock’s analysis weighs in favor of System 2 as a way of navigating complex systems.
I would argue that there are academic disciplines, especially those grounded in Heideggerian phenomenology, that see the “dominance” of institutions (such as academic disciplines) that are based around accumulations of System 2 knowledge as a problem or threat.
This reaction has several different guises:
A simple rejection of cognitive psychology, which has exposed the System 1/System 2 distinction, as “behaviorism”. (This obscures the way cognitive psychology was a major break away from behaviorism in the 50’s.)
A call for more “authentic experience”, couched in language suggesting ownership or the true subject of one’s experience, contrasting this with the more alienated forms of knowing that rely on scientific consensus.
An appeal to originality: System 2 tends to converge; my System 1 methods can come up with an exciting new idea!
The interpretivist methodological mandate for anthropological sensitivity to “emic”, or directly “lived experience”, of research subjects. This mandate sometimes blurs several individually valid motivations, such as: when emic experience is the subject matter in its own right, but (crucially) with the caveat that the results are not generalizable; when emic sensitivity is identified via the researcher’s reflexivity as a condition for research access; or when the purpose of the work is to surface or represent otherwise underrepresented views.
There are ways to qualify or limit these kinds of methodologies or commitments that makes them entirely above reproach. However, under these limits, their conclusions are always fragile. According to the hegemonic logic of System 2 institutions, a consensus of those thoroughly considering the statistical evidence can always supercede the “lived experience” of some group or individual. This is, at the methodological level, simply the idea that while we may make theory-laden observations, when those theories are disproved, those observations are invalidated as being influenced by erronenous theory. Indeed, mainstream scientific institutions take as their duty this kind of procedural objectivity. There is no such thing as science unless a lot of people are often being proven wrong.
This provokes a great deal of grievance. “Who made scientists, an unrepresentative class of people and machines disconnected from authentic experience, the arbiter of the real? Who are they to tell me I am wrong, or my experiences invalid?” And this is where we start to find trouble.
Perhaps most troubling is how this plays out at the level of psychodynamic politics. To have one’s lived experiences rejected, especially those lived experiences of trauma, and especially when those experiences are rejected wrongly, is deeply disturbing. One of the more mighty political tendencies of recent years has been the idea that whole classes of people are systematically subject to this treatment. This is one reason, among others, for influential calls for recalibrating the weight given to the experiences of otherwise marginalized people. This is what Furedi calls the therapeutic ethos of the Left. This is slightly different from, though often conflated with, the idea that recalibration is necessary to allow in more relevant data that was being otherwise excluded from consideration. This latter consideration comes up in a more managerialist discussion of creating technology that satisfies diverse stakeholders (…customers) through “participatory” design methods. The ambiguity of the term “bias”–does it mean a statistical error, or does it mean any tendency of an inferential system at all?–is sometimes leveraged to accomplish this conflation.
It is in practice very difficult to disentangle the different psychological motivations here. This is partly because they are deeply personal and mixed even at the level of the individual. (Highlighting this is why I have framed this in terms of the cognitive science literature). It is also partly because these issues are highly political as well. Being proven right, or wrong, has material consequences–sometimes. I’d argue: perhaps not as often as it should. But sometimes. And so there’s always a political interest, especially among those disinclined towards System 2 thinking, in maintaining a right to be wrong.
So it is hypothesized (perhaps going back to Lyotard) that at an institutional level there’s a persistent heterodox movement that rejects the ideal of communal intellectual integrity. Rather, it maintains that the field of authoritative knowledge must contain contradictions and disturbances of statistical scientific consensus. In Lyotard’s formulation, this heterodoxy seeks “legitimation by paralogy”, which suggests that its telos is at best a kind of creative intellectual emancipation from restrictive logics, generative of new ideas, but perhaps at worst a heterodoxy for its own sake.
This tendency has an uneasy relationship with the sociopolitical motive of a more integrated and representative society, which is often associated with the goal of social justice. If I understand these arguments directly, the idea is that, in practice, legitimized paralogy is a way of giving the underrepresented a platform. This has the benefits of increasing, visibly, representation. Here, paralogy is legitimized as a means of affirmative action, but not as a means improving system performance objectively.
This is a source of persistent difficulty and unease, as the paralogical tendency is never capable of truly emancipating itself, but rather, in its recuperated form, is always-already embedded in a hierarchy that it must deny to its initiates. Authenticity is subsumed, via agonism, to a procedural objectivity that proves it wrong.
A number of lines of inquiry have all been pointing in the same direction for me. I now have a question and I’m on the lookout for scholarly references on it. I haven’t been able to find anything useful through my ordinary means.
I’m looking for a phenomenology of probability.
Hopefully the following paragraphs will make it clearer what I mean.
By phenomenology, I mean a systematic account (-ology) of lived experience (phenomen-). I’m looking for references especially in the “cone” of influences on Merleau-Ponty, and the “cone” of those influenced by Merleau-Ponty.
By probability, I mean the whole gestalt of uncertainty, expectation, and realization that is normally covered by the mathematical subject. The simplest example is the experience of tossing a coin. But there are countless others; this is a ubiquitous mode of phenomenon.
There is at least some indication that this phenomenon is difficult to provide a systematic account for. Probabilistic reasoning is not a very common skill. Perhaps the best account of this that I can think of is in Philip Tetlock’s Superforecasting, in which he reports that a large proportion of people are able to intuit only two kinds of uncertainty (“probably will happen” or “probably won’t happen”), another portion can reason in three (“probably will”, “probably won’t”, and “I don’t know”). For some people, asking for graded expectations (“I think there’s a 30% chance it will happen”) is more or less meaningless.
Nevertheless, all the major quantitative institutions–finance, telecom, digital services, insurance, the hard sciences, etc.–thrive on probabilistic calculations. Perhaps there’s a concentration here.
The other consideration leading towards the question of phenomenology of probability is the question of the interpretation of mathematical probability theory. As is well known, the same mathematics can be interpreted in multiple ways. There is an ‘objective’, frequentist interpretation, according to which probability is the frequency of events in the world. But with the rise of machine learning ‘subjectivist’ or Bayesian interpretations became much more popular. Bayesian probability is a calculus of rational subjective expectations, and transformation of those expectations, according to new evidence.
So far in my studies and research, I’ve never encountered a synthesis of Merleau-Pontean phenomenology with the subjectivist intepretation of probability. This is somewhat troubling.
Digifesto 