In The Measure of Reality: Quantification and Western Society 1250-1600 (Cambridge University Press, 1997), Alfred W. Crosby summarized the arc of 350 years of development this way:
Beginning in the miraculous decades around the turn of the fourteenth century (decades unmatched in their radical changes in perception until the era of Einstein and Picasso), and continuing on for generations, * * * Western Europeans evolved a new way, more purely visual and quantitative than the old, of perceiving time, space and material environment. * * * In practical terms, the new approach was simply this: reduce what you are trying to think about to the minimum required by its definition; visualize it on paper, or at least in your mind, be it the fluctuation of wool prices at the Champagne fairs or the course of Mars through the heavens, and divide it, either in fact or in imagination, into equal quanta. Then you can measure it. * * * Then you posses a quantitative representation of your subject that is, however simplified, even in its errors and omissions, precise. You can think about it rigorously. You can manipulate it and experiment with it, as we do today with computer models. (Ibid., 227, 228-229)
COVID-19 has now exposed the extent to which those inversions have come fully to government at last. The pandemic has now exposed as well the way in the perception of politics and human organization, its reality is shaped, understood, and controlled through quantitative representation. CVID-19 has now also exposed the extent to which, whatever the lingering elements of the post 1945 era ancien regime intelligentsia suggest otherwise, the reality of politics is being manifested as the product of a perception of data that produces a managed simulation of the world in which it seeks to control. Modelling is the way we create these simulacra and through that creation creates for itself a more prominent role in the political management of human affairs. The character of that role is not as a tool of politics but as politics itself. That, in essence (and we deal almost entirely now in a world that operates by reducing objects to its essence and then layering those reduced essences into models of reality), is what COVID-19 has revealed in 2020.
What follows are short reflections of the implications revealed by an ascendancy of data driven Pandemic; likely the most important legacy of COVID-19. Its effects have touched virtually every aspect of collective life.
One of the most interesting aspect of the COVID-19 pandemic has been the way in which it is exposing growing disjunctions--growing gaps--between the principles, customs and traditions on which a political order is organized and the way it has been operated after January 2020. Those who have made it a point of protecting the conceptual basis of the old order have been especially sensitive to this disjunction and have, even early in the emerging era of pandemic, sought to alert and correct (or better put, fill) the gaps that have existed for a while but which are now inescapably apparent. These efforts are particularly notable in the context of the developing conceptual regimes of human rights (e.g., Europe at a Crossroads; Respecting democracy, rule of law and human rights in the framework of the COVID-19 sanitary crisis).
The major social, political and legal challenge facing our member states will be their ability to respond to this crisis effectively, whilst ensuring that the measures they take do not undermine our genuine long-term interest in safeguarding Europe’s founding values of democracy, rule of law and human rights. It is precisely here that the Council of Europe must carry out its core mandate by providing, through its statutory organs and all its competent bodies and mechanisms,the forum for collectively ensuring that these measures remain proportional to the threat posed by the spread of the virus and be limited in time. The virus is destroying many lives and much else of what is very dear to us. We should not let it destroy our core values and free societies (Respecting democracy, supra).
The magnitude of these disjunctions has acquired a somewhat different character in the context of pandemic. COVID-19 responses has centered the use of metrics and data-based analytics in managing responses to infection in a more comprehensive way. These metrics take two forms. The first is data based surveillance. The second and more indicative of the future, is modelling.
The first of these, monitoring, surveillance, reporting and data harvesting is well worn territory--at least as measured by the span of a human life (e.g., Digital tools against COVID-19: Framing the ethical challenges and how to address them; Data Driven Management of COVID-19: The Case of Taiwan). These are meant to serve both as tools and as the substance of regulation. As tools, these are the means by which accountability can be quantified and reduced to a value against which other values can be compared (or compared against triggers). They also serve (as a sort of performance of accountability) of the proof of the value or "correctness" of political and policy choices made. At the same time they also substitute for the regulations they are meant ot make visible where the construction of the systems or premises of data gathering, surveillance, and the like, are left to those constructing or implementing them, and where their choices effectively have normative effect.
The way in which COVID-19 is counted is an example of where both functions merge. The counting is meant to be used to account for the presence of the disease (Why Belgium's Death Rate Is So High: It Counts Lots Of Suspected COVID-19 Cases). It is also meant to provide the measure against which political decisions might be made, for example, eliminating "stay at home" rules" or economic closures (Wolf administration clarifies metrics used for reopening Pennsylvania). Yet the numbers, which appear so solid because of the unassailable solidity of a number as an object whose meaning cannot be varied (three always means three), exist only in themselves. When one tries to giove them meaning in context, that becomes far harder (The Misleading Arithmetic of COVID-19 Death Rates). Where the counting rules vary, then a comparative accounting becomes impossible--though politically valuable. The value comes in when public entities are tempted to use numbers strategically to support political decisions and then seek to suppress numbers that might undermine their choices in favor fo data that supports it (Florida medical examiners were releasing coronavirus death data. The state made them stop ("The moves to withhold information comes at a sensitive moment for state leaders. Florida’s coronavirus death toll is continuing to rise and state officials have begun talks about when and how to start reopening.")). It is a temptation to use the unassialability fo numbers to hide what or misdirect, or better put, to manage numbers the way that infections of COVID-19 are managed through policies of stay in place rules.
Most affected countries have inadvertently under-reported deaths. Studying mortality data in 12 countries, The New York Times found that in March at least 36,000 more people died during the coronavirus pandemic than the official death counts. These include deaths from the contagion as well as those from other likely causes. And a Financial Times analysis of overall fatalities during the pandemic in 14 countries found that the death toll from coronavirus may be almost 60% higher than reported in official counts. (India coronavirus: The 'mystery' of low Covid-19 death rates)Numbers do not lie, but numbers do not speak truth either. They are objects and symbol of something that has meaning (three means three), but the significance of that meaning cannot be determined except within a specific context (three apples, three oranges in Spain and thirty oranges in Italy). The understanding of a number (as (a) sense or signification, (b) meaning or intention and (c) significance or ideal worth; Lady Welby, "Significs," in Encyclopedia Britannica (1911)) is also dependent on the way in which the number is given meaning. In the case of COVID-19, that has proven hard to do.
If only there were more understanding to be had. The more I look at the numbers, the more I see their flaws. Here are my top 10. 1. The number of infected is close to meaningless. Only people who get tested can be counted * * * 2. The tests aren’t accurate and the inaccuracies aren't symmetric. In particular, they produce many more false negatives than false positives * * * 3. The number of tests doesn’t equal the number of people tested. Because the tests are so inaccurate, some people get tested twice to be more sure of the results.* * * 4. The numbers aren’t in sync. People sometimes die weeks after being hospitalized, and they get hospitalized a week or more after testing positive for the virus.* * * 5. The meaning of hospitalization is changing. Officials have recently presented flattening hospital admissions as a positive sign. But it takes a lot more to get somebody to the hospital these days. * * * 6. Deaths aren’t reported immediately or consistently.* * * 7. Deaths outside hospitals aren’t being reported.* * * 8. The policy for attributing deaths isn’t consistent. Once somebody is gone, why waste a valuable test?* * * 9. Officials may have incentives to hide coronavirus cases. China, Indonesia and Iran have all come under scrutiny for their statistics. “Juking the stats” is not unknown in other contexts in the U.S., either. * * * 10. What happens in one place, or on average, might not be applicable everywhere. (10 Reasons to Doubt the Covid-19 Data)It in this context that numbers serve a critical role in triggering policy decisions, in providing the conditions necessary to justify governmental (or private) action or in justifying political positions, especially in the context of the competition among states eager to prove that their systems were better suited to meeting the crisis (by reference to triumph measured by numbers). The current contest between the United States and China provides an ostentatious case in point (China questions US handling of coronavirus amid global backlash).
COVID-19, of course, is not just about counting; it is also about keeping track. Some of it appears to be less problematic when measured against the old constraints on the state: ("Public health surveillance is the ongoing, systematic collection, analysis, and interpretation of health-related data essential to planning, implementation, and evaluation of public health practice. For surveillance of COVID-19, and the virus that causes it, SARS-COV-2, CDC is using multiple surveillance systems run in collaboration with state, local, territorial, and academic partners to monitor COVID-19 disease in the United States." CDC, Coronavirus Disease 2019 (COVID-19.; for Europe, see Strategies for the surveillance of COVID-19 (April 2020)). But surveillance has a large, and for some troublemsome element as well. First tracking people's movements poses a challenge to old notions of privacy, whether the tracking is undertaken by the state (Utah’s new Covid-19 contact tracing app will track user locations) int eh and elsewhere (Automated Law and COVID-19: Data Driven Measures With National Characteristics In China and Israel and the Future of the Law-Governance Complex). It is also undertaken by private enterprises. With respect to these, it has become clear now the ease with which large providers of mobile devices can turn those into tracking tools (Will lockdown end by default? Data shows Americans in 44 states are venturing out more often and rejecting social distancing measures).
It is here, and for the most part only here, that there is a point of convergence between COVID-19 driven data based governance and the traditional discourse and human rights/constitutional principles. The European Union efforts under its regulatory/rights framework provides a nice example of the engagement (Guidelines 04/2020 on the use of location data and contact tracing tools in the context of the COVID-19 outbreak Adopted on21 April 2020). These focus on the relation of data to the individual; it does not focus as well on data and the transformation of governance and democratic ordering. That is to be expected under a framework in which the individual and individual rights are centered but in which the use of data by collective entities is understood as a species of techniques with little effect on key principles that support the character pf the political model adopted in liberal democratic states (but see, Council of Europe, Respecting democracy, rule of law and human rights in the framework of the COVID-19 sanitary crisis: A toolkit for member states ("The major social, political and legal challenge facing our member states will be their ability to respond to this crisis effectively, whilst ensuring that the measures they take do not undermine our genuine long-term interest in safeguarding Europe’s founding values of democracy, rule of law and human rights." Ibid., p.2)). And, indeed, that partial convergence of the framework for protection of individual liberties under constitutional and international frameworks suggests limited utility of old conceptions of such rights systems in the face of a technology of government the measures of which tend to fall between the cracks of that framework.
|Pix From "Don't Believe the COVID-19 Models"|
Newer, “agent-based models” are like the video game SimCity, but with a rampaging pathogen: using computing power unimagined even a decade ago, they simulate the interactions of millions of individuals as they work, play, travel, and otherwise go about their lives. Both of these approaches have often nailed projections of, for instance, U.S. cases of seasonal flu. ((Influential Covid-19 model uses flawed methods and shouldn’t guide U.S. policies, critics say).Societies have moved from using big data based games for entertainment, to seeing in them the potential to re-create the entirety of a human society, and on that basis extending the ability of those with the power to do so to intervene in the affairs of (simulated) humanity. In effect, data driven pandemics now constitutes a form of time travel in which models carry us forward to alternative futures that can be embraced or avoided by traveling back in time from the outer temporal space of the model and correcting the factors that produced a specific temporal outcome (Mass. General Hospital Builds COVID-19 Simulator To Help Predict Impact of Policy Decisions).
What was once the realm of politics (choices) is now the stiff of statistics. Indeed, modeling turns politics from the subject --the means by which decisions are made and authenticated (in accordance to current theories of popular participation, however attenuated) to its object. In a political context dominated by models, politics and political decision making become the thing that must be managed and directed in accordance with the genius of the model. Politics moves, therefore, from the act of decision, to the act of model making and application.
One gets the sense, and one would not be wrong in sensing, that the politics of COVID-19 responses, and its structuring is now driven not by judgment based on data, but on the judgment of the models that are used to construct the alternative world scenarios whose reality is assumed and on that basis is used to make a determination of what ought to be imposed.
The epidemiological models of COVID-19’s initial outbreak and spread have been useful. The Imperial College model, which predicted a terrifying 2.2 million deaths in the United States, agitated drowsy policymakers into action. The University of Washington’s Institute of Health Metrics and Evaluation (IHME) model has provided a sense of the scale and timeline for peak hospitalization. Other models have estimated the effects of quarantine and of travel restrictions, or sought to find the pandemic’s turning point. Despite some notable flaws, the epidemiological models have cumulatively had a beneficial effect on the national conversation. Their ability to incorporate some epidemiological knowledge and the limited available data led to better—and harder to dismiss or deny—predictions of the near future than mere guesswork would have allowed (A call for a new generation of COVID-19 models; see also New Model Forecasts When States Likely to See Peak in COVID-19 Deaths).Where bad policy choices once called for better decision making within the normative structures of politics (liberal democratic or Marxist Leninist); now bad choices call for better models that is better simulacra. "Thousands of policymakers across the country, mostly at the state and local level, will need to decide where and when to re-open schools, ease business and social distancing restrictions, allow sports to resume, and make a myriad of other choices. * * * Existing models have been valuable, but they were not designed to support these types of critical decisions." (A call for a new generation of COVID-19 models). Political decisions are calculated and driven by the model; politics is undertaken in the choice of the model, decisions about the way it is populated with data, and decisions about the relationships deemed critical in the process of reduction and essentialization that flattens lived reality to the simulation within which policy choices can be tested (Influential Covid-19 model uses flawed methods and shouldn’t guide U.S. policies, critics say; Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy). The political classes--in this model (here used with the irony it has come to deserve)--are reduced to the administrators of the systems whose decisions they are charged with implementing.
There is a certain irony here. For years before COVID-129 there has been a chorus of people (me included) who warned about the dangers of simulation, of data driven governance, and of the Artificial Intelligence (AI) and big data models necessary to rationalize these into something "useful" even where they saw the utility in the project. The object, at its heart was to more closely align the new mechanics of governance and the new government it would birth, more closely with the normative values that appeared to still matter (or at least on the basis of which political collectives still officially adhered). But in the rush to "conquer" or "overcome" COVID-19, all of this has been thrown to the winds. Or at least those who were doing the warning have been been sidelined in the rush to embrace, without the slightest hesitation, the suzerainty of the model as the chief vehicle through which a politics of pandemic could be rationalized, and thus understood, implemented (Criticized in application in Why No COVID-19 Models Have Been Accurate, And How To Fix That). In its place, one finds a politics of modelling--one that does not dispute the animating premise of the new governmentality; that the model must be obeyed--but one that merely invests political power within contests for championing first the "right model" and then the "correct application" of that model.
As Nietzsche might suggest, our priestly class has broken the idols of our old gods and raised a new shiny, scientific and data driven god for the priests for serve as intermediary and on whose alter human society will be offered (The Antichrist; § 42). This is not done with evil intent; the opposite may well be true. But the intention is constant (Modeling the Spread and Prevention of COVID-19) and its priests are no longer political figures with direct connection to the people who they represent. Instead they are the researchers and technicians who now practice politics in their construction and tending of the models from which they derive their power and whose oracles are offered up to those with formal political power, who ignore than at their peril.
And the greatest irony--simulacra are as imperfect as the world it seeks to model. It reflects not just the flaws of the world simulated, but it may also amplify and distort those flaws in ways. There is already an awareness of the ways in which models may reflect. "The essence of these arguments lies in an important, and perhaps counter intuitive observation: Using data and technology in a decision-making process doesn’t make a decision automatically free of problematic (and possibly illegal) social discrimination." (e.g., Avoiding prejudice in data-based decisions). But that produces a contradiction. A simulation is useful only to the extent that it reflects the world it models. But to model that world also reveals the disjunctions between its idealized view of itself, and the reality of the way that those ideals may have little or different connection with the actual behaviors of human actors. Here one begins to see the way in which politics may insinuate itself in the production and use of models. It is at the stage where one determines what world the model will simulate--the world as it exists or the world as the models would like to exist. To create a simulation that reproduces an idealized reality is to further distort the simulation and to reduce its utility. It also suggests that simulation cannot avoid the embedding of politics--only that the politics is hidden within the premises and modalities of simulation construction. That is, that a simulation is used not merely to see how a specific set of factors have effects on communities or individuals; but at the same time how human and communal behavior relates to the ideal version of itself. As such, a simulation does not necessarily reflect reality but instead the reality of perfectibility.
But the politics of simulation is not evidenced merely by the way in which it relates to the tensions between the real and idealized version of the society it simulates. It also embeds politics in the way a simulation chooses to emphasize (amplify) certain human or individual characteristics and reduce others to a marginal space. This is not just a matter of data harvesting (the information that is used to build simulations and that which is ignored--a well known issue), rather it is the way that this data is weighted and embedded within the mathematical relationships that together create a functioning simulations. Thus, politics, and prejudice, filter into the construction of the mathematical relationships through which a simulation is constructed and then manipulated to aid in the construction of conclusions. This is unavoidable--the essence of simulation is essentialization that is in turn the expression of the need to reduce the complexity of reality to a manageable level. Reductionism, then, produces an important site for politics. The way in which one decides how and why reality is reduces; the choice of who makes those decisions; the way that reduction is expressed; the expression of the essential; these are the spaces in which politics is practiced in a world in which human society is managed through models. But note the consequence--where politics is now embedded in the construction of models, it no longer is centered on those who are formally vested with political authority--whether that authority is exercised through a Marxist-Leninist or a liberal democratic model.
The same applied to the use of the model. Part of the value of simulation is predictive. But prediction assumes that nothing changes. The real value of simulation is to test policies and other interventions before they are actually put into effect. How one chooses those interventions, how those interventions may be constrained by higher order values; how those solutions may be guided by the political character of a society, and how those intervention reflect the customs and expectations of a society will make a great deal of difference. The way the range of choices are constructed, and the importance of these meta-principles serve to mold the spectrum of choices deemed plausible points to yet another point of politics in simulated governance (AI can help with the COVID-19 crisis - but the right human input is key). Where the power to develop choice spectrum are delegated to technicians and modellers, those external constraints either disappear in the service of a "value free" choice spectrum is constructed, or more likely, the choice spectrum will reflect the personal views of the modeler. One has yet to consider the rules for testing those choice processes--except at the level of the ideal. There is much talk that aspects of modeling (AI; surveillance, data use, etc.) must conform with the highest ideals of society, but there is very little in embedding those ideals into the practices of simulation as a substitute for engagement in the real world which it is meant to serve (e.g., here, here, and here).
For all that, there is a certain comfort to this data driven turn, one that might have raised hackles in almost any other context in which political decisions are being made. This is a culture well trained in the certainty of science and the power of quantification as inherently less ambiguous and deceptive than words. They provide our contemporary wizards with the power to "read the past, present, and future in accordance with numbers and computations" (Crosby, supra, p. 124) based on the faith that reality is mathematical (Ibid., p. 123). That is simulations can be used to create our world in a form in which it is possible to travel in time and in so doing to change predicted outcomes by changing present responses now makes those of us with power feel like we (they) might wield the power of the gods. That the power is flawed and imperfect is of little moment in terms of its utility for exercising power.
So if epidemiological models don’t give us certainty—and asking them to do so would be a big mistake—what good are they? Epidemiology gives us something more important: agency to identify and calibrate our actions with the goal of shaping our future. We can do this by pruning catastrophic branches of a tree of possibilities that lies before us. (Don't Believe the COVID-19 Models)The model becomes the "Holy Spirit" of a magisterium of experts in whose hands decisions are now vested (for the analogy in contemporary religion, see, e.g., The Holy Spirit's Assistance to the Magisterium in Teaching: Theological and Philosophical Issues). And these are useful gods indeed--even better than the gods of law and theory; for these gods are less intelligible; they are immanent within the algebraic formulations of mathematically driven reductionist relations, and they may be only interpreted, constructed and applied by a priestly class whose power is now assured. These are the matters that ought to give us all pause; but not panic. That pause need not be directed toward a reactionary effort (and on pursued ultimately in vain) for the return of the old order. It is far too late for that (and in a sense that effort also constitutes a sort of simulation of nostalgia). It ought to be directed toward the principles on which our new high priests of modelling, and their control of the political collective can be constrained and made responsive to whatever higher order set of principles we believe, as a collective, express our (and the ultimate irony here) essence.