Find the first of our COVID-19 commentaries here. If you feel moved to critique or expand upon either essay, shoot your submissions to vonstillej@gmail.com, and we'll slap it up on the blog posthaste.
This
short series of texts will attempt to sketch the relation between human users
and coronavirus tracking apps and the hybrid that emerges therefrom. For the
sake of maximal clarity, the series will consist of a numbered and lettered
list, according to the content index below:
I. A
typology of coronavirus tracking apps according to territorial affinity.
II. A
typology of such apps according to the extent of state enforcement of their
findings.
III.
Understanding the function of tracking apps as “fuzzy logic”
a.
Tracking apps as a two-threshold operation of fuzzy logic
IV.
The temporal conflict between user and app
a.
Fuzzification of causality as a result of this conflict
V.
Fuzziness of causality as a result of interfacial time
a.
Fuzzy, interfacial time as viral time
b.
Brief clarification of the term “interface”
VI.
Recastings of governance and territory in the context of viral time at
different scales
a.
Governance as interfacial and doubly hybridized
VII.
Construction of viral space-time
VIII.
Final image built of the above
I.
Let
us assume that coronavirus tracking apps—including but not limited to those
recently rolled-out in South Korea, China, and
(underdevelopment in) the UK—, which alert
users in various ways when they have come into close contact with someone who
later tests positive for the virus, fall into one of two types with relation to
geography: (1) Bordered trackers, which use only data from within specific
territories, and (2) Generalized trackers, which either aggregate data from
various territorial datasets without exclusive partnership with any
territoriality or utilize data from a worldwide organization like WHO.
II.
Let
us assume that these apps do and will also fall into two types with relation to
governmental entities: (A) Compulsive trackers, which alert authorities
directly when someone is likely to be infected or has tested positive and
legally require quarantine thereafter, and (B) Voluntarist trackers, which gather
and provide similar information (contact with a positive-tested individual or a
positive test of the user) but leave subsequent action up to the app’s user.
III.
In
all these cases, we may view the app’s progression of colored states—e.g. green
for healthy, yellow for likely infected, and red for a positive test—as an
operation in what, in mathematics, is called “fuzzy logic.” As opposed to
classical Boolean logic, where each proposition is given a truth value either
of 0 (false) or 1 (true), fuzzy logic allows for intermediate states between false
and true, presenting a measurement of certainty.
The logician may then pick a number between 0 and 1 as a threshold value:
Anything above that number is considered true
enough to justify a certain action, and anything below that number is not true enough to justify that action.
Once a situation has yielded a certainty value between 0 and 1 and that value
has been compared to its threshold, that value can be “crispified,” reduced
either to a practical 0 or 1
depending on which side of the threshold it falls. Action then depends upon
this crispy result.
a.
We
can consider the functionality of coronavirus-tracking apps as a two-step (or two-threshold)
operation of fuzzy logic: A green status indicates that the certainty value of
infection has not passed either threshold, and the app user is likely healthy.
A yellow status indicates a passing of the first threshold: The user has likely
come into contact with someone who is sick, and so at least the action testing is now justified (or required,
depending on policy). A red status indicates a passing of the second threshold
and a crispification to 1, so that the action quarantine is justified.
IV.
Of
course, perception is not so simple, and neither is the actual process of
infection: The app may not register that a user has come into contact with an
infected person until that person has actually tested positive, which
introduces a fourfold time-lag dependent on the incubation period of the virus,
the time it takes after the emergence of symptoms for someone to get tested,
the time it takes for the test to register a positive result, and the time it
takes for that positive result to enter into the app’s dataset. And so, by the
time someone’s app proceeds to a yellow or red status (or, regardless of the
app’s construction, to a fuzzy value that can be crispified to 1 to justify
some action), they may have been infected for several days a few weeks—let us
say, for the sake of subsequent examples, that the average lag is 14 days.
a.
The
app then reaches backward through time-dependent data points to alert those who
have come into contact with the affected user, effectively rewriting the perceived causal series of those users’ last two
weeks. Once the app’s function has entered into users’ conscious experience,
they no longer experience linear time, but fuzzy
time, in which each action projects onto a series of fuzzy values between 0
(healthy) and 1 (infected).
V.
The
fuzzy time that emerges from the intersection of tracking apps’ time-lag with
users’ perception of linear causality is a form of interfacial time: The user experiences time as a more or less
linear series of causal successions. The app, on the other hand, perceives time
as a networked set of 14-day blocks, so that if something happens on day 13
that affects someone who joined the network on day 1, that event on day 13 now
redefines that user’s experience from day 1 onward, in addition to the
experiences of all those who have come into contact with that user within that
time-frame. On the other hand, the app assumes the causality perceived by the
user when it determines how the infection will spread. The fuzzy time produced
by tracking apps, then, is the result of the interaction of user-perceived time
with app-perceived time such that each determines aspects of the other, and
both the app and the user must take this interfacial time into account—rather
than either version of time on its own—when making decisions.
a.
The
fact that a user must join the network before living in this fuzzy, interfacial
time (the combination of which shall henceforth be called viral time) means that viral time is embedded: It is neither the time perceived independently by the
app, nor the time experienced independently by the user. Rather, it emerges
exclusively from the interaction between the two. Both the user and app can be
considered prosthetic to the other, in the sense that each interactively
defines the other’s method of being in the world. Viral time is embedded because
its effective range, in the frames of reference of both app and user, is a
subset of the total time experienced by each.
b.
Note
that my usage of “interface” and its variations is not short for “user
interface” but refers to a two-sided interaction in which neither side is
privileged.
VI.
This
section refers to the typology introduced in sections I and II, in addition to
the notion of viral time developed in III-V: In the case of 1A, state power has
begun to operate on viral time. State operation is fundamentally interfacial in
the sense that governance is now performed, not by a governmental bureaucracy,
but by the interface of that bureaucracy with the app. This is a step toward the
posthumanity—or, more accurately, the hybridization
of government as implied by Jorge Luis Borges in “A Weary Man’s Utopia” and made explicit
in Benjamin Bratton’s concept of the “stack.” Importantly,
government no longer operates on a purely human version of time, but on viral
time, which is not restricted within a range from beginning to end of the current
pandemic, but rather is viral because of its lack of single-species (re)productive
mechanism. In the case of 1B, state operation has become fundamentally
interfacial as in 1A, but it locates its control mechanism inside the user-app
interface (rather than the state-app interface), as described by Gilles Deleuze
in his “Postscript on the Societies of
Control.”
This can, to utilize Peter Sloterdijk’s terms from the Spheres trilogy, be described as a foamification of hybrid
governance. By extension, in case 2A, the mechanism of the state—or, more
usefully now, the territorial authority—becomes subject to a twofold
interfaciality: That between the territorial authority and the app, and that
between the various territorial authorities, or between the various territorial
authorities and a global operator like the WHO. This implies a third, secondary
intersection between different territories’ viral time, dependent upon the time
it takes for different medical systems to produce test results and introduce
them into the app’s dataset. (This third intersection is operant, of course, in
cases 1A and 1B, but it produces a more significant effect when different
territories’ medical systems must interact.) The mechanisms of control are also
located, in 2A, in this system of 2 (secondarily 3) interfaces. Finally, in 2B,
this combination of interfacialities applies at the level of territorial
authority (whose boundaries are blurred by that combination), but mechanisms of
control are located within the user-app interface, which is now expanded beyond
territorial/political borders. And so, once more to utilize Sloterdijk’s
terminology, the foamification of hybrid governance reaches toward or, in the
case of global data aggregation, achieves planetary diameter.
a.
In
all cases, governance (not to be synonymized with government) becomes hybridized as it begins to run on viral time. This
means that it is no longer meaningful to speak of human governance but of interfacial governance. After all, the
production and distribution of tracking apps is not a simply governmental
function: In some cases, territorial authorities (e.g. the UK) have provided
funds toward production of such apps or have mandated their use (e.g. China),
but private undertakings have determined their availability: Apple, for
instance, has mandated that only
professional medical organizations may place their coronavirus-related apps in
its app store. And so, in this case (and, crucially, in future analogous cases)
it makes no sense to speak of governance as state power or as a purely human
endeavor, nor to refer to the action of governance as primarily an exercise of
humanity. Governance is doubly hybridized as public/private and human/app, and
its primary operative temporality is not human but viral.
VII.
The
introduction of viral time is accompanied by the introduction of viral space,
which emerges from the user-app interface in a process analogous to that which
produces viral time. For instance, one app may cross the first threshold in its
fuzzy logic (from green to yellow) when a user has passed within 100 meters of
someone who later tests positive. As viral time interfacializes the experience
of causality, viral space interfacializes the experience of bodily boundaries, moving the user-world
border from the skin to a spherical radius of 100 meters. In a viral space so
constructed, we may specify the radius of Sloterdijk’s foamy units as 100
meters. Like viral time, this space is not reducible either to human
proprioception or the functions of any given app, but emerges from the
interface between the two. The interaction between viral time and viral space
produces an interfacial viral space-time
which forms the four observable dimensions for the app-user hybrid.
VIII.
In
the most expanded instance, we therefore have an image of a planetary,
spherical foam whose fixed-diameter hybrid bubbles writhe, pass each other, and
collide, popping on impact. Each bubble leaves a 14-day trail in its wake which
is no less material than the bubble as conceived in the present reference frame
of human chronology, producing a non-metaphorical and non-representational
hauntology in which the viral past, present, and future may affect each other
at precisely the speed of data transmission, without exclusive dependence upon
linear time; and in which the barrier of the skin is no longer the primary or
operant dividing line between a being and its world.
