Making everything ac-count-able: The digital twinning paradigm

Abstract

This editorial investigates the epistemic and media-theoretical significance of digital twinning as a decision-making practice. Digital twins purport to calculate futures based on sensor data in conjunction with generative AI, cloud computing, and Internet-of-things architectures; they shape institutional decisions and are used to make such decisions accountable. To illustrate this, examples from the logistics, transportation, and military sectors are contrasted to earlier simulations and described as “phenomenotechniques.” We argue that digital twins are recent expressions of a technocratic paradigm characterized by the imperative to make everything worldly “count,” datafying and modeling it within digital environments in real time for future predictions. Digital twins are thus performative agents in a network of feedback loops between humans, machines, environments, and algorithms. This article concludes with an overview of the special issue, placing digital twins in the phenomenological context of media that are seamlessly and simultaneously logistical, spatial, and transformative.

Managing futures: digital twins

Digital-twinning projects today seem both countless and omnipresent: in manufacturing, materials science, object and system performance, logistics, smart farming, medicine and healthcare, infrastructure and urban planning, retail and e-commerce, among many more areas. They are used to calculate and manage futures, to extrapolate current states into future environments. To name just a few examples: factory production is being optimized by digital-twinning technologies; there are digital twins of the cities of Boston, Namur, Munich, Singapore, Amersfoort, and Almere; there are elaborate digital-twinning projects for preserving cultural heritage and to digitally twin entire ecosystems such as the world’s oceans. Digital-twinning technologies are also becoming increasingly important in military operations and in space research. That digital twins can be used for heterogeneous purposes shows their fundamental operationality as data-driven digital technologies. They are part of datafied and computable environments. Moreover, digital twins allow overarching data exchange; they are media of cooperation.

Institutions have always relied on technologies to manage complexity. Technologies of digital twinning take this to an unprecedented level. Their ability to (allegedly) mirror analog real-time processes within digital environments allows institutions to not only visualize but also actively shape and intervene in possible futures. Digital twins continue and amplify technology’s long-standing drive toward supposedly perfect future simulation and control. Throughout the history of technology, institutions continually tried to eliminate uncertainty through precise data and computation: From bureaucratic paperwork to electronic databases, each technological advance has brought us closer to a world in which decisions can be made through the automatic processing of worldly information. As the following three recent implementations of digital-twinning technologies make clear, digital twins are the latest phenomenon in this evolution (Fathy et al., 2021; Granacher and et al., 2022; Mi and et al., 2021; Villalonga and et al., 2020).

British company Network Rail and Germany’s Deutsche Bahn frame digital twins as central to the future of rail-based infrastructures. With the Digitale Schiene Deutschland (“Digital Rail Germany”) initiative, for example, Deutsche Bahn collaborates with NVIDIA to detect weak points in Deutsche Bahn’s rail system, evaluate optimizations, and identity ways to improve the future performance of its infrastructure. For this purpose, Deutsche Bahn has installed a complex network of sensors that datafy almost everything considered important for monitoring its entire ecosystem in real-time: it maps and simulates in a digital twin not only the trains but also objects on or near the tracks, people, stations, and the time processes of network operations. This digital twin is based on a high-resolution digital map that creates a photorealistic simulation of the precisely measured rail network, allowing operational irregularities and risky scenarios to play out in virtual worlds. The digital twin is also key to realizing fully automated driving in the future, for it will indicate when a so-called “smart” train will be able to make reliable decisions. As of 2025, the development of sensors is well advanced; AI-based decision-making, however, is still in its infancy, due to the massive amount of data required to develop solid AI. Deutsche Bahn’s “Data Factory” will help determine when a train equipped with sensors and generative AI is ready for autonomous driving.

Digital twins also find considerable application in the logistics sector. The company DHL, for example, advertises its operational sophistication in numerous brochures by referring to its digital-twinning technology. Employing imagery of architectural drawing boards and early cars modeled in wood and clay, DHL suggests that global logistics networks have been revolutionized through computer technology that simulates behavior and operations in 3D models. Until recently, however, there was still an unbridgeable gap between model and reality: No two manufactured objects are ever truly identical, even if built from the same drawings, as machines undergo different adjustments or show different signs of fatigue over time. Digital twinning-technologies now promise that “the physical and digital worlds can be managed as one” (Dohrmann et al., 2019) through an interplay of ubiquitous sensors attached to things, people, and environments, the Internet of things, AI, pervasive datafication, and cloud computing: the “discourse network,” per Friedrich Kittler (1990), of digital twins. DHL’s marketing thus points to a new dimension of simulation technologies promised by digital twins: real-time data loops between a physical object, process, or system, and its digital “counterpart.” For DHL, digital twins do not simply optimize supply chains; they are primarily used for “decision-making” (Dohrmann et al., 2019) regarding scalable future planning and management, in order to design, operate, and optimize products and logistics, from the situated local to the networked globe.

As the United States Air Force’s “Model One” reveals, digital twins are becoming increasingly important for managing potential futures in military planning. The multi-million-dollar Model One digital twin, developed in collaboration with Istari Digital, is a cutting-edge initiative to digitally transform military operations through advanced simulations that produce predictive warfare strategies. Model One uses digital-twinning technology to simulate real-world conditions, helping the Air Force anticipate future military outcomes and improve operational strategies. The system integrates over 50 different military simulations into a unified platform, enabling more accurate predictions and faster decision-making in complex battlefield scenarios. By creating interconnected digital simulations, the US Air Force hopes to gain an edge in modern networked warfare, during which the speed of data processing and decision-making is crucial.

These three examples reveal a number of features that characterize digital twins more generally. All three digital-twinning applications are: first, real-time simulations; second, they implement data exchanges that are bidirectional, from analog to digital environments and vice versa; third, they are expressed in visual representations on electronic graphical user interfaces; fourth, they are realized through and embedded in complex interactions, data exchanges, and algorithmic computations with other technologies, such as generative AI, cloud computing, and Internet-of-things infrastructures; fifth, they are based on processes of extensive datafication via sensors embedded in environments and attached to things: their maxim is that “everything counts,” everything of possible relevance in the world should be datafied via sensors; sixth, the stated digital twins may be purely digital, but they affect the analog realm: they have real-world consequences in the transportation sector, in passenger shipping, in global logistics and supply chains, or in military situations; seventh, their guiding principle is that they should allow for a reaction to or even intervention in the future. Because of this, digital twins contribute essentially to decision-making in powerful institutions, or, to put it in the words of Forbes Magazine, digital twins are seen as the “Solution To Better Decision-Making” (Taylor, 2023).

Digital twins versus simulations

Digital twins have a number of characteristics that distinguish them from other forms of simulations, both earlier forms of digital simulations and analog ones, created with simple means like pen and paper or even in the sandpit. As Philipp von Hilgers (2012) has shown, the history of war is full of examples of how strategies and potential futures were played through with such simple media. A digital twin is a virtual model of a physical, technical thing (e.g. a complex engine), or a biological entity (e.g. a human or an animal organ), or a complex ecosystem (e.g. an entire ocean), or even a sociotechnical environment (e.g. a whole city). The digital twin represents not only the current state of that entity but also simulates its possible future ones. It is in a real-time data loop with the physical entity via datafying sensors which make things and processes sense-able, and environments and even planets computational (Gabrys, 2019) so that changes in the physical environment affect the digital twin.

To date, theoretical concepts of digital twinning construct digital twins as composed of three layers: first, a physical object, system, or process; second, a vivid data-based digital model of that object, system, or process; and third, two-way data streams between both entities, the physical and virtual twin, so that both directly influence the other. In doing so, the theoretical concepts of digital twinning implicitly reference Harold Garfinkel’s research on mock-ups as models for representing and assessing social phenomena (Garfinkel, 2019 [1943]). Cooperative data that run through production and operational chains as a digital thread form the real and virtual world of digital twins and are the focus of their discussion.

Related to product lifecycle management, Michael Grieves, who with John Vickers is one of the main proponents of digital twins in the 21st century, defines the procedure of digital twinning a little more restrictively. According to Grieves (2014), three dimensions must be present for a digital model to be considered a digital twin: “a) physical products in Real Space, b) virtual products in Virtual Space, and c) the connections of data and information that tie the virtual and real products together.” Grieves (2014) argues that digital twins can also represent extensions of those “twinned” products and should enable real-time interaction with the environments of which these twins are counterparts.

This brief definition makes clear that digital twinning, in contrast to earlier forms of digital simulation, is characterized by two-way and real-time dataflows between digital and analog environments and that it is not just about visualization and modeling. It is about complex technological operations and translation chains: It is, among other things, about testing modules or products, optimizing and (re)structuring lifecycles, factory production, performance, system behaviors, and, as mentioned above, logistical and transportation infrastructures, and military strategies. Simply put, digital twins are about futures as such: be it the micro-scale material fatigue of a production robot, the behavior of a biological organ such as the heart, the performance of an entire supply chain, the improvement of a production line in a factory, the risk of situated military operations, or the optimization of logistical goods shipments across the globe.

However, Grieves’ minimal definition is ambiguous. While it is true that, to be defined as a “twin,” a digital twin must have a real-world correspondent—be that an environment, ecosystem, object or a process—the temporal structure or sequence of these equivalents complicates this relation. There can be digital twins of things and entire ecosystems that do not yet exist in the analog world: these include, on the one hand, simulations of rather more micro-scale things like the behavior of a chemical substance, or on the other hand, simulations of entire metropolises, nationwide energy infrastructures, or transport networks made possible by sensor technology, AI, cloud computing and the Internet of things. Digital twins therefore do not merely virtually re-present and simulate actual physical processes and things, but, on the contrary, occasionally constitute them, resulting in scenarios where they do not rely on physical models or have precedent physical counterparts.

Digital twinning thus promises not only the potential to make futures predictable through recognition and correlation of the virtual and physical (Chun, 2021), but also the ability to do so without a preexisting real-world counterpart. As Grieves has emphasized, there is no “requirement that one type of twin, the physical twin, must exist before there is the other type of twin (. . .) This means the digital twin can exist prior to there being a physical counterpart and can also exist after the physical counterpart ceases existence” (Grieves, 2022). As long as a digital twin exists, whether or not its counterpart already exists or will exist in the analog world is almost insignificant, as it’s simply a matter of engineering and marketing decisions.

Crucially, digital twins imply that the real world is just one possible realization of the primarily virtual world. This insight might fundamentally change the way in which agency is distributed across physical and virtual actors. Data models are responsible for more and more decisions; and more-than-human environments like the “dataverse” and other “360°” immersive media (Stiegler, 2021) reveal that much as the physical and the virtual are inseparable or unstable categories, so too are the “physical” and the “digital twin.” Digital twins might represent the ultimate fusion of material and informational systems, bringing us closer to a world where machines no longer just record reality but actively constitute it. This also highlights the difference between what is commonly referred to as “data doubling” and digital twinning. ¹

Digital twins are therefore currently the most important drivers of the fourth industrial revolution (Attaran et al., 2023; Javaid et al., 2023). Ever more complex technical products and processes are now developed and tested in the virtual sphere before they emerge in the “real” world. Future artifacts and practices are first produced as software models and simulated as digital twins. The prevalence of digital twins in industry and research creates a fundamental paradigm shift in digital-media technologies. The digital is neither a real-time virtual representation of a real-world physical object nor an entirely separate object: it is much more, for it allows for the analysis of future performances of objects without the physical presence of these objects.

Digital twins as visual techniques

As the previous sections make clear, digital twins are not mere representations, data doubles, mirrors, or copies. They are active performative agents in a network of feedback loops between humans, machines, environments, and algorithms. This is not new; it is rather a culmination of a historical process in which visual-media technologies mold human experience, automate control, and reshape the relationship between humans, machines, and their environments. From the perspective of Media, Data and Cultural Studies, as well as the Social Sciences, digital twins exert a crucial influence on current media societies beyond the optimization of supply chains or production lines. They visualize complex processes and make visible otherwise mostly invisible phenomena or structures: for the actors involved, digital twinning is fundamentally about seeing (Korenhof et al., 2023) things such as infrastructures that are usually invisible when functioning routinely (Star, 1999). Digital twins allow complex processes, and thus also sociotechnical assemblages or whole ecosystems—things not directly visible or observable—and their possible future states to be visualized, supposedly, at a glance. Because of this, digital twins also influence the ways current media societies see themselves.

The affordances and designs of digital-twin user interfaces have largely driven their easy incorporation by large 21st century companies. Digital twins, as fundamentally visual screen-based techniques, fit into the widely practiced and global dispositive of control rooms (Deane, 2015), which in most cases contain seemingly endless rows of screens. With their dashboard logics, which deploy visualization techniques such as diagrams, graphs, and time curves, control rooms and urban operations centers display events in real time. They also add further data and information “so that users can divine the how and why and redirect future action” (Mattern, 2015). The usefulness of dashboards, Shannon Mattern notes, thus lies not in their monitoring function alone, but rather in their ability to shape the future.

Dashboard designs promise potentially seamless scalability, visualizing phenomena from the energy consumption of individual households and houses to the ecological footprint of entire cities and regions. In relation to dashboards, Mattern (2015) explains, “[t]he prevalence and accessibility of data are changing the way we see our cities.” In the same vein, Orit Halpern (2014) in her book Beautiful Data describes the changes in and historical conditioning of perception and knowledge in the middle and second half of the 20th century, which are essentially produced by data visualizations and a cybernetically influenced understanding of the world as a sum of informational processes. The design of “beautiful” graphics as a kind of data-driven “aesthetic infrastructure” (Halpern, 2014: 15)—relevant not only for calculation and measurement but also for administrative actions—characterizes dashboard logics. It also now typifies digital twinning as a quasi-post-cybernetic vision of complete regulation and control.

Digital twins now hold this promise of seamless scaling and action. It is therefore no surprise that Grieves and Vickers (2017) already recognized the question of scale as their crucial criterion. Digital twins go beyond dashboards because they do not purport to authentically map a current event and extrapolate it into the future, and thus always lag behind a real-time process, even if only by a micro amount. Instead, they are more of an action-oriented model in which everything counts and scales.

Digital twins as “phenomenotechniques” and imaginaries

If the maxim of digital twins is “making everything count,” what counts as “everything”? After all, it is difficult to capture all relevant parameters of a thing or process or infrastructure as an aggregate of data, let alone complex ecosystems such as entire oceans or complete sociotechnical systems. Indeed, digital twins can be seen as a “sociomaterial imaginary” (Lupton, 2021: 409) because they define what a system, a city or a thing is and what it is not, by the choice of parameters that are incorporated into the twin. Digital twins purport to visualize a process, a system or an infrastructure as it is. Grieves (2014) defines digital twins as “virtually indistinguishable” from their real-world equivalents (p. 1). Grieves and Vickers (2017) also define digital twins as “a set of virtual information constructs that fully describes a potential or actual physical manufactured product from the micro atomic level to the macro geometrical level” (p. 94). But digital twins actively produce what they claim to show.

Digital twins are what French science philosopher Gaston Bachelard (1934) called “phenomenotechniques”: they actively produce and shape the phenomena that they purportedly simply represent (Rheinberger, 2005). In the same vein as Bachelard, Danish physicist Niels Bohr, reflecting on his experimental work, pointed out that measurement and visualization devices are not passive artifacts but instead are interwoven with the images and data they allegedly objectively produce: thus, the concept of objectivity as such is historical and dependent on technologies (Daston and Galison, 2007). More recently, Karen Barad (1998) writes of phenomena that are not given but only take shape through technoscientific practices in the context of sonography. As phenomenotechniques, digital twins do not show images of biological bodies, infrastructures or industrial machines; they in the first place produce and mold these phenomena within our post-digital situation, in which computation is already “embedded within the environment, in the body, and in society” (Berry, 2014).

Crucially, digital twins do not show the entire complexity of a city, for example, but only what is considered relevant to be digitized by programmers and decision-makers. In digital twinning, everything that is not quantifiable is actively ignored. Only what can be digitized can be incorporated into digital twins, that is, what can be expressed numerically values: Everything that counts—that is, everything that can be made countable—becomes visible in the digital twin as a potential actor. At the same time, digital twinning renders invisible anything assumed not to contribute to the performance of a thing, process, and so on.

Digital twins are not mirror images—which would suggest that there is a physical original that they merely mirror—but rather normative world views that allow statements to be made about the engineers, programmers, and institutions involved in the construction of those twins. Digital twinning can be described as a mode of governance through design, as “steering representations” (Korenhof and et al., 2021). Scholars in Critical Data Studies and Science and Technology Studies have emphasized that data and computerized representations of the world are never neutral, raw, or self-evident; instead, they are sociotechnical constructs (Bowker, 2006: 184; Gitelman, 2013; Kitchin, 2014a) as they are based on preliminary decisions about what is to be datafied and are also dependent on what can be datafied at all. Because of this, digital twins are the result of institutional negotiations that determine how much data is sufficient to produce a digital twin that may not be perfect but is at least “good enough.”

Furthermore, a digital twin—contrary to Grieves’ assertion—is not a mere “virtual window” (Grieves, 2014: 5) for it cannot be reduced to its interface, the digital visibility of processes, objects, human organs, or built environments. We also have to take into account other materialities, such as: onsite sensors that generate (potentially biased) data; the geopolitics of networks over which these data are transmitted; the locations of the computations and extrapolations of those data; other informational dimensions, such as the politics of algorithms and software; and the people who handle those algorithms and interact with what digital twins show on the graphical user interface. All of this helps determine what a digital twin can reveal. Digital twins are therefore not panoptic technologies in Michel Foucault’s (1977) sense—forms of total and complete surveillance—but rather oligoptic technologies in Bruno Latour’s (2005) sense—sites that produce social structures and institutional knowledge.

Digital twins and institutional decision-making

Technologies of digital twinning pose the question: how do data practices affect and mold decision-making within institutions (Vertesi, 2020)? Digital twins are more and more involved in negotiating what counts as appropriate administrative action. As we explain, their function in decision-making and, in turn, making this decision-making accountable is central.

The modern idea of the digital twin is often traced back to the early 2000s, when the concept of the “Mirrored Spaces Model” was proposed as the integration of both virtual and physical entities for product lifecycle management (Grieves, 2005). But the history of digital twinning begins in a broader sense, avant la lettre, at NASA, where a simulation of the operations of a physical counterpart was tested early on: During the Apollo 13 moon mission in 1970 when the spacecraft suffered damage (“Houston, we’ve had a problem”), NASA engineers and scientists needed a modified plan to bring the crew back to Earth. They therefore tested potential solutions on a paired module on Earth before instructions were given to the team in space. From a genealogical perspective, digital twins thus originate from a technological practice of problem-solving on the one hand, and data-driven institutional decision-making on the other.

In these early days, the enormous financial outlay and technological requirements limited the application of digital twinning to areas such as space exploration. The reduction in cost and broader distribution of digital computers in the early 2000s made it possible to apply digital twins in less exclusive and restricted institutional environments. Against this backdrop, Grieves (2005), and later with Grieves and Vickers (2017), explored digital twinning in the context of factory production and product lifecycle management, coining the term “digital twin” and popularizing it through various white papers. In doing so, Grieves and Vickers outlined human decision-making as a central aspect of digital-twinning technologies (Grieves and Vickers, 2017: 88), thus referring implicitly to digital twins’ defining characteristic: Digital twins are not merely about visualizing potential futures, but about decision-making actions in the now.

Crucially, digital twins are not just about data-driven decision-making but rather about making decision-making accountable. Decision-making in institutions is always difficult because of the large number of actors involved and the need to justify and document what counts as a “good” decision across all dimensions—socially, ethically, economically, politically, etc. Digital twins in the institutions that use them have often become the benchmark for making decision-making accountable. In our earlier examples, what the digital twin of the German railroad systems presents as a valid intervention counts as a good institutional decision for Deutsche Bahn with regard to automated emergency braking; a good logistical decision for DHL is one that the digital twin models and presents to optimize logistics; a good military strategy will be one that the Model One digital twin evaluates as good. Digital twins are therefore important not only to model the most plausible possible futures or to make up-to-date institutional decisions but also to legitimize what may be considered an appropriate institutional decision. A digital twin can now easily document and justify to a third party why a decision was made.

The data-driven dashboards mentioned previously already sought to achieve accountability in governmental organizations (Mattern, 2015). It is therefore no coincidence that early reports on dashboard applications make the concept of accountability explicit, such as “The Baltimore CitiStat Program: Performance and Accountability” (Henderson, 2003) or “Using Performance Data for Accountability: The New York City Police Department’s CompStat Model of Police Management” (O’Connell, 2001). This accountability is now being carried over from dashboard to digital-twinning technologies: The latter are therefore not merely about simulating futures as precisely as possible and on the basis of large amounts of sensor data in conjunction with generative AI. As our opening three examples show, the institutional acceptance of decision-making based on visualizations of futures proposed by digital twins is much more important. Institutions react to the futures visualized by digital twins as if these scenarios would become real one day and were not just the result of computerized calculations of probabilities based on all-encompassing datafication. The effects of digital-twinning technologies and practices are therefore real on several levels: they not only manifest themselves in the optimization of supply chains or machines in factories but also affect what is considered socially acceptable institutional decision-making and planning.

Digital twins as techno-ideological paradigms

In 1962, Kuhn pointed out that a paradigm is a generally established and accepted practice and theory that is meaningful for both how one acts in the world and how one reflects on it scientifically in order to produce further knowledge; this practice unites researchers (and it could be added: practitioners) across disciplines in their ways of looking at and questioning the world (Kuhn, 1962). Paradigms are historical concepts since they are symptomatic of specific times. Kuhn’s theory, although widely criticized for well-founded and solid reasons, nevertheless describes well the current phenomenon of the effectiveness of digital twins. They can be understood as paradigmatic of how institutional actions can be made understandable and accountable. Digital twinning reorganizes the way in which institutional knowledge is produced, how institutional decision-making is carried out and authorized. The true power of digital twins lies in their action in the now: they serve as a benchmark for decision-making in heterogeneous fields and across multiple disciplines, such as medicine, urban planning, military, logistics, etc. As such, digital twins represent nothing less than a paradigm shift in the way decision-making based on digital technologies is made responsible, comprehensible, and justifiable in the technologized, post-digital, 21st-century Global North.

As Agamben (2002) notes, the term “paradigm” has two significant dimensions in Kuhn’s work. On the one hand, it refers to what the representatives of a scientific community have in common in their practice and their world view: the way in which they conduct science as well as the theoretical assumptions, techniques, and equipment involved. On the other hand, a paradigm is also a singular object or element that stands for a whole. That is, it stands for a whole class of other objects or techniques and at the same time constitutes them; or, as Agamben puts it, “[t]he paradigm is in this [second] sense just an example, a single phenomenon, a singularity, which can be repeated and thus acquires the capability of tacitly modeling the behavior and the practice of scientists” (Agamben, 2002). Digital-twinning technologies may be singular phenomena, but they are exemplary for an entire techno-ecology and technocratic thinking: They largely determine the decision-making practices of scientists, urban planners, biologists, etc. Together with big data as a paradigm shift described by Kitchin (2014b), digital-twinning technologies are a zeitgeist phenomenon that seemingly allows algorithmic control and also engenders “data-driven governance” (Knopf et al. in this special issue) or even a “space of intervention” (Amoore in this special issue).

Digital twins represent the techno-ideological paradigm of our time. They have their own ethos in the context of a technocratic view of the world, which presumes that everything observable or at least sense-able can also be made countable, accountable, and computable. While digital twinning originally only involved technical systems, it nowadays also predicts other parameters, such as human movement patterns and occasionally also social aspects. Digital twins are thus emblematic and paradigmatic of a technocratic view of the world defined by the belief that everything can be calculated and controlled, which means that digital twins reactualize cybernetic ideals. Digital twinning, however, goes beyond the visionary cybernetic management projects such as Cybersyn from the early 1970s (Medina, 2014). Digital twins are technopolitical artifacts, or rather they are inscribed with a techno-ecology, as they are more and more involved in institutional decision-making that can ultimately affect us all. It is in this context that digital twins unfold their true power.

Twinning is less about planning and more about dynamic management: evaluating and justifying certain (institutional, bureaucratic, logistical, etc.) decisions. Digital twins in large companies are not successful because they predict guaranteed futures, but because they predict futures as such, regardless of whether they ever materialize. This is the basis for accountability in post-digital environments under the conditions of digital twins. In this sense, digital twins are not neutral tools, they are media of power in the age of algorithmic governance.

Contributions to this special issue

The papers in this special issue address these topics from a diverse range of perspectives and disciplines, and they work with multiple methods and theoretical approaches. This collection of fourteen papers outlines practices of digital twinning, from the concrete to the abstract, from the present to the historical, and from the specific to the general.

The first set of articles concentrates on digital twinning as digital practices of decision-making, steering, and governing. These papers, situated at the intersection of Media Studies, Science and Technology Studies, Critical Data Studies and Critical Geography, discuss the problems of bureaucratic vision enabled by digital twinning. Sophia Knopf, Hadrien Macq and Alexander Wentland demonstrate how digital twinning reconfigures established governance practices. Through a comparative analysis of urban digital-twinning projects in Boston, Namur, and Munich, they investigate how digital representations of cities are co-produced in situ with ideas about their desirable governance that leverages digital methods, technology, and data as tools to enhance its capabilities for better decision-making. Oliver Dawkins and Rob Kitchin identify digital twins as a participatory steering representation, reassessing different concepts of urban digital twinning as powerful new means for mobilizing the direct involvement of a “virtual public sphere” in urban planning and governance. In contrast to this, Mirko Tobias Schäfer, Sofie de Wilde de Ligny, Sharon van Geldere and Albert Meijer deconstruct digital-twin technologies. According to their research, the notion that urban digital twinning inherently improves democratic processes is largely based on speculative beliefs. They demonstrate that the discrepancy between promise and practice becomes especially evident when digital twinning is promoted as an environment for direct interaction between the municipality and its residents. Based on a genealogical examination, Michael Richardson and Zoe Horn scrutinize Alibaba’s City Brain platform and Palantir’s Foundry platform to show how digital twins seek the preemptive capture of futures in the present. According to the first four papers, digital-twinning projects are best understood as logistical media: as commodified operating systems sustained by a material and discursive shift from real-time processing to prediction, from dependency on multiple data streams to their holistic amalgamation.

The second set of papers investigates how the discussion of digital infrastructures is transformed into an all-encompassing (national, global, elemental, planetary, and outer space) digital environment. Adam Wickberg and Susanna Lidström’s paper analyzes how the two-way exchange between marine ecosystems and their digital twin creates feedback loops between the digital and physical realms that are emblematic of “environing media.” As they show, the sociotechnical imaginary of the twinned ocean fosters the narrative that a balance between sustainable exploitation and protection can be ensured through the use of increasingly sophisticated digital-twinning technologies. Jussi Parikka argues that wind and other fluids are moved as software and data in the logistical circuit that underpins digital twinning. “Elemental media” become enclosed in software operations and in data units that circulate in and across different platforms. Leighton Evans’ critical analysis of Tuvalu’s Future Now Project, a “digital nation” in the metaverse, draws on the deep-ecology approach of environmental restoration. According to his paper, the danger of translating Tuvalu’s tangible reality into the digital realm harbors not just the loss of the physical land to climate change but also the loss of the depth and complexity of Tuvaluan existence when its cultural artifacts become mere commodities in the marketplace of digital experiences. Jihoon Kim understands digital twins as planetary media and a scientific and sociotechnical assemblage of human and nonhuman elements. Arising from planetary-scale computation, they serve to monitor, govern and forecast transformations in the Earth’s environmental, geophysical, biological, and human domains. From this perspective, digital twinning enforces the scientific and economic drive toward the growing integration of all earthly ecosystems and human activities in virtual environments and intermediaries, including digital platforms. In Edward King’s historiographical paper, the narrative, which makes Apollo 13 the first digital twin, is examined critically. According to King, NASA’s Twin Study embodies the ambiguity of space media by being both a tool of ecopolitical control and an epistemological pathway beyond myths of individuality toward human dependency and relationality. Digital twins as insignia of a deep ecology and as environing, elemental, planetary or space media therefore underline, as above all Evans but also Wickberg/Lidström, Parikka, Kim, and King show, that natural landscapes and ecosystems exist independently of any human intention to steer and regulate the environment: In contrast to the first set of papers, which emphasize human agency, in the second set, the focus lies on the agency of the digital twins.

A third set of papers puts digital twinning in a wider context and looks at the communicative, transformative, and transcendent potentials of the data streams between the mirrored entities of the physical and virtual worlds. Louise Amoore’s essay describes the figure of the twin as a historically important trope of experimentation in the human sciences. Against the background of the history of twinning studies, she proposes three distinctive aspects of the contemporary politics of digital twinning: digital twins understood, one, as a space of intervention; two, as a novel logic of prediction; and three, as a probable or optimal course of action. Each of these aspects is discussed through the situated digital twinning domains of factories (BMW Regensburg), clinics (King’s College London’s Smart Surgery) and battlefields as integrated digital models of weapons, soldiers, and landscapes. In their paper, Emma Fraser, Clancy Wilmott and Will Payne draw on the work of urban and new media theorists to argue that digital twins are part of a long history of urban media and computation. Expanding on Walter Benjamin’s notion of phantasmagoria and Antonio Negri’s interpretation of the city as a factory, they argue that the logics of the factory inherent in early digital twins are intensified and expanded in the more recent translation to urban digital twins. Orit Halpern also argues for historical consciousness in the discussion on digital twinning. Her paper traces the rise of resilience as a dominant epistemology and practice, which leads to new techniques such as digital twinning. She therefore describes smart city twinning as part of urban resilience. According to her paper, digital twinning is producing new data spaces that traverse borders and potentially open up new understandings of the relationships between subjects, social groups, territories, and life. Anne Ulrich investigates digital twins and data doubles as a guiding metaphor of cybernetics and contemporary digital culture. She understands digital twinning to be spectral because of its paradoxical disembodied corporeality, as the digital representation on the one hand and the physical body on the other are not only connected via an arbitrary relationship but rather physically. John Seberger and Geoffrey Bowker also update the concept of spectrality for the doppelganger motif. They are skeptical about the techno-solutionism of digital twinning. Through an analysis that spans analytical and discursive modes of “data doubles” and “data doppelgangers” in Media Studies, Critical Data Studies, and Infrastructure Studies, they have developed the concept of the “double-goer” that transcends its separation into the category of the Other and merges, shadow-like, with its primary.

A synoptic perspective on all contributions reveals that digital twinning is an entangled practice of a) decision-making, steering, and governing, b) infrastructuring, spacing, and environing, as well as c) data streaming between physical objects/processes and the vivid digital models of those objects/processes. While the first set of papers understands digital twins as logistical media, the second set focuses the role of digital twins as spatial media, and the third set puts digital twins into the context of transformative media. The special issue thus places digital twins in the phenomenological context of media that are in a seamless manner simultaneously logistical, spatial, and transformative.