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  • Networks of BeliefAn Introduction
  • William Morgan (bio) and Kyra Sutton (bio)

Who says everything is a network? Everyone, it seems. In philosophy, Bruno Latour: ontology is a network. In literary studies, Franco Moretti: Hamlet is a network. In the military, Donald Rumsfeld: the battlefield is a network. . . . Thus I characterize the first assumption—“everything is a network”—as a kind of network fundamentalism. It claims that whatever exists in the world appears naturally in the form of a system, an ecology, an assemblage, in short, as a network.

Alexander R. Galloway, “Network Pessimism”

It no longer registers as a shock to hear proclamations of an emerging age of networks, of algorithms, of artificial intelligence, of machine learning, robotics, ubiquitous digital devices, or the cloud. From economics to genetics, computation is heralded as the skeleton key to a treasure trove of the world’s best-hidden secrets.

Alexander R. Galloway’s above-cited “network fundamentalism,” we contend, reveals the extent to which these notions of networks are bound up with questions of belief. But is the belief that [End Page 1] “everything is a network” something that emerges in response to the emergence of ubiquitous digital connectivity? Or, rather, does the figure of the network have a deeper history that the digital simply brings into sharper relief? What is the relation between (belief in) the ubiquity of networks and late capitalism, that is, capitalism with cybernetic characteristics? Just as Galloway has pointed toward contemporary beliefs in networks, we ask whether belief is inherently networked and to what degree political-theological questions are themselves machinic, as Roberto Esposito would have it: the fabric of an exclusionary dispositif that has long held sway over our conceptions of politics, law, and theology.1

Apprehending networks and belief as intertwined phenomena requires understanding that the contemporary declarations of “network fundamentalism,” announcements of what we might call “the good news of computing,” are not merely the consequence of technological successes brought about by a newly empowered datadriven paradigm—the newfangled digital epistemology, which divorced computation from the model-and physics-based approaches dominant in the 1970s and 1980s, in favor of the more “real-world groundings” of ubiquitous data collection.

Instead, the present data-driven approach to network connectivity must be understood in terms of its aim at a profound transformation not only in how subjects experience the world but also of that world’s very nature. Analog activities are ontologically refigured by tech companies like Google and Facebook as information-rich behavior. The world at large is redisclosed as having been information all along, in a perpetual state of waiting to be harvested.

If an ontic explanation of increasing technical capacity and the revelations of data science are insufficient to apprehend what we have termed networks of belief, how then should we make sense of it? In response to that question, this special issue, “Networks of Belief,” presents an interdisciplinary conversation between and across new media studies, political theology, anthropology, religion and secularism studies, philosophy, and critical race theory via the figure of the network and the ever-contentious frame of belief in order to ask not only what it means to live in a networked world but also, perhaps more important, what it means to believe that we live in one. [End Page 2]

Belief in the Network

Why is the technical redisclosure of reality something we are willing to believe in? What does it mean for data-driven conclusions to be popularly persuasive explanations of everyday phenomena? These questions are essential to understanding the power of computation. Moore’s Law, personal computing, multicore processing, the Zettabyte, ubiquitous computing—none are sufficient to explain computational belief on their own. Overlooked in these explanations is that when we measure the technical capacities of present machines against their past incarnations, judgments made about their capacities to act are inherently arbitrary, for, paraphrasing Spinoza, we know not yet what a machine can do. Computational excitement is generated not only by retroactive comparison, but also by forecasting what machines will eventually become capable of. Time spent in the heady technoculture of Silicon Valley underlines the truth of this hypothesis: the future is where the money is.



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pp. 1-17
Launched on MUSE
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