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Original Research Article

In-Formed Spaces

Conceptualising the “Smart City” as Assemblage of Anticipation

Fernando N. van der Vlist

(Graduate School of Humanities,) University of Amsterdam, the Netherlands

Working paper

Published online: 1 February 2014

Abstract

In this paper I develop, one the one hand, a critical and philosophical understanding of the anticipation of the smart city, and on the other hand, an understanding as well as problematisation of the role of computer simulation in the imagination and enactment of technology-focused futures and the new (topological) spaces they render actionable. Even though the smart city is a highly interdisciplinary concept and technology-oriented future orientation, the ontological status and epistemological a priori of its methods, techniques and practices, and the implications or repercussions these may have, remain unclear. To investigate this, I first trace a genealogy, or rather pre-history, of the city as machine. This will situate the subsequent analyses in which I consider the dominant conception of the city as a complex system, which, I argue, constitutes the ontological basis upon which the smart city is conceived. The concept of “assemblages of anticipation” is deployed as a methodology to explore the case of computer simulation within the imagination as well as enactment of particular possible futures that are either encouraged or discouraged. Furthermore, I problematise the ontological status as well as epistemological a priori presumed by simulation. Finally, I discuss some recent developments in the emerging field of interactive architecture to argue that the meaning of spatial design has fundamentally shifted to the design of topological space, as a result of which new problems and social and economic inequalities may arise.

Keywords

smart city, logics of anticipation, simulation modeling, topological space, anticipatory knowledges, assemblage

smart city
logics of anticipation
simulation modeling
topological space
anticipatory knowledges
assemblage

Through its monuments, written records, and orderly habits of association, the city enlarged the scope of all human activities, extending them backward and forward in time. By means of its storage facilities (buildings, vaults, archives, monuments, tablets, books), the city became capable of transmitting a complex culture from generation to generation, for it marshalled together not only the physical means but the human agents needed to pass on and enlarge this heritage. That remains the greatest of the city's gifts. As compared with the complex human order of the city, our present ingenious electronic mechanisms for storing and transmitting information are crude and limited.
— Lewis Mumford ([1961] 1989, 569)

[T]he rise of continuously computed environments has made logistics perhaps the central discipline of the contemporary world – though one curiously unsung – as it has pursued the goal of ‘intelligent logistics processes’.
— Nigel J. Thrift (2004, 589)

1. Introduction

The concept of the smart city has gained a lot of traction especially in the last decade. Debates in Western countries about urban and spatial planning are increasingly influenced by the concept of the smart city (Hollands 2008). Consequently, the term lacks a clear definition but has instead been described in many different ways. Intelligent cities, virtual cities, digital cities, and information cities are all emphasising the notion that information and computer technology are central to the operations or performance of the city. (Batty et al. 2012, 483; Sassen 2011). According to an often-cited report by the Centre of Regional Science (SRF) in Vienna, European medium-sized smart cities can be identified, as well as ranked (e.g. Vanolo 2013), along six dimensions: smart economy, smart mobility, smart people, smart living, and smart governance (Giffinger et al. 2007, 11). This immediately tells us that the concept is imagined “on top of” the existing infrastructures and technologies. Moreover, it emphasises growth and inovation, and offers a universal framework for ranking cities in Europe but also in other regions around the world according to the same set of evaluation criteria. In this regard, the adjective “smart” should be understood as an attractor for growth and economic advantage: the capacity of the city to attract capital, and by extension growth. “Smartness”, then, is not merely technical, but the degree of intelligence, depending on the person, the system of cooperation, as well as the digital infrastructure and tools that a community offers its residents (Komninos 2002). Most importantly, however, smart cities constitute an anticipated future, an imagination of what the future city would be. As an idea, the concept is positioned at the intersection of academic, corporate as well as governmental and private interests and concerns.1 As a result, its significance lies in the fact that it invigorates and shapes research agendas, urban plans, policies, standards, and so on.

Arguably, some of its most important source of inspiration has been the science fiction subgenre of cyberpunk developed in the 1980s and 1990s (Abbott 2007, 122). This is the case, for instance, for early notions of global cities (Sassen 1991), and cities as communication systems. Later, these visions became part of public imagination, to a large degree as a result of Mark Weiser's highly influential and far-travelling concept of ubiquitous computing (1991). Sam Kinsley, a British sociologist and cultural geographer attributed its popularity to the fictional short story of Sal (2010, 2011), whose everyday life is narrated by Weiser as a way to take part in his envisioning of the future. Stories, animated future visions, and (paper or digital) prototypes have a way of making present futures as if they were already here, and this can rhetorically be very powerful. In his book Imaginary Futures, Richard Barbrook for example conducted an analysis of how technology, and the Internet in particular, has continuously been promising social equality, but never delivered on that promise (see also Winner 1986). Even though the smart city is such a highly interdisciplinary concept and technology-oriented future vision, the ontological status and epistemological a priori of its methods, techniques and practices, and the implications or repercussions these may have, remain understudied. Moreover, as an idea and promise, at this point we are still in the position to criticise the development of the smart city.

One of the main goals of this study is to draw attention to the powerful and complex mechanisms of anticipating futures, which ultimately politicises many of our daily activities and rhythms, however mundane these may be (e.g. driving a car). Specifically, in this paper I investigate the following two research questions: How can we develop a philosophical and critical understanding of the anticipation of smart cities? And how can we understand as well as problematise the role that computer simulation plays in the imagination and enactment of such technology-focused futures and the new (topological) spaces they render actionable? In order to explore these questions, I will develop a theoretical and critical account of what I will call the “assemblage of anticipation” of the smart city. As I will argue, the enactment of technology-focused futures greatly depends on our present capacity to imagine these futures. The future spaces of possibility with the smart city are negotiated in the present, and computer simulation plays a crucial role in the (sometimes automatic) production of these spaces. To this end, I first trace a genealogy or rather pre-history of the city as machine. This will situate the subsequent analysis in which I consider the dominant conception of the city as a complex system, which, I will argue, is the ontological basis upon which the smart city is conceived. The concept of “assemblages of anticipation” is introduced as a methodology to explore the case of computer simulation within the imagination as well as enactment of particular futures. Furthermore, I problematise the ontological status as well as epistemological presumptions of simulation. Finally, in the last section I explore recent developments in the emerging field of interactive architecture to argue that the meaning of spatial design has fundamentally shifted to the design of topological space, as a result of which new problems and social and economic inequalities arise.

2. The City as Machine: A Pre-History of the “Smart City”

The understanding we have of smart cities is not something completely new that breaks with earlier conceptions of the city, nor is it something timeless that has always already been there. Rather, I argue that the specificities as well as the current popularity of the concept in the public imagination can be understood from a number of perspectives. In this first section, such a perspective on the pre-historical2 or conceptual line of descent will succinctly be introduced so as to “set the stage” for the subsequent analysis. As I will argue in the next section, it is crucial that we understand some of these philosophical origins if we are to develop an understanding of the particular “logics of anticipation” (Kinsley 2011) at work here. The perspective I will explore concerns the strong resonance of twentieth century modernist ideas about architecture and urban planning in the contemporary imagination of smart cities. In particular, I focus on the relationship between technologies and environments for living that is negotiated through the theorisation of the modern city.

2.1. Machines as Milieus

Early twentieth century discourse of rationalisation represented the move towards a new architecture in which the “machine” would become the dominant metaphor (Wright 1910; Oud 1918). This was an architecture of efficiency, economic use of (natural) resources and functionality (Heynen et al. 747, 771). This tradition in architecture relies on technologies of standardisation (Berlage 1911) and the adherence to logical principles (Rossi 1966; Grassi 1967) for the organisation of space fundamentally grounded in knowledge, and the Truths about human living that this tradition of building supposedly could capture and materialise. The “machine” paradigm did away with ornaments (i.e. all that is non-functional) to emphasise the natural or organic strength of building materials. For example, the strength of wood lies in the specific properties of wood itself (cf. Heynen et al. 2004, 747). The machine represented pure function. Interestingly, Le Corbusier tried to incorporate his understanding of “organicism” based on Darwin's theory of evolution in his “machinistic” vision of architecture (1923), essentially equalising nature and technology (yet technology is superior) and shifting the meaning of architecture towards ergonomics (cf. Heynen et al. 2004, 751). Similarly, in his impressive book The City in History (1961), Lewis Mumford presents a history of the city (sic) arguing that its transformations are the result of an “organic” balancing act of culture, technology and nature (see also Mumford 1934). After the Second World War, Peter Cook (1964) argued that architects should look towards science-fiction comics for new inspiration leading to the emergence of “cyborg architecture” with Kisho Kurokawa's “Capsule Declaration” (1969). It refers to the conception that human, machine and space become a unified organic body without internal conflicts,3 and fits with structures such as the “geodesic domes” originally invented by R. Buckminster Fuller a decade before that. Important to note here is that these modern period domes (as well as the other aforementioned conceptions for that matter) really were a product of their time in terms of the structural possibilities imaginable because of new and cheaper production techniques. Gradually, the human-centred “machinic” understanding of architectural and urban space thus turned into an ecological understanding of space in which the dynamics of a particular environment shape the very space they constitute. As such, architectural spaces or “machines” are not simply tools of some kind anymore but rather milieus to exist alongside or with.4 Besides the promise of control over the environment, conceiving of space in this manner also raised new issue concerning the environmental effects of building as well as living in cities. In his Operating Manual for Spaceship Earth (1969), Fuller addresses these concerns in a peculiar manner: using a metaphor (the spaceship), Fuller conjures up a vision of humanity and the world as a mechanical vehicle, the resources of which can be managed and sustained if only there is enough information on how to do so (the manual). Furthermore, there is also the idea of being able to “steer”, “navigate” or “govern” the spaceship,5 to which I will return in the next section.

2.2. Information and Behavioural Patterns

The influence of systems theory in architecture and urban planning can also be located in Christopher Alexander's seminal book A Pattern Language (1977). In it, Alexander describes and categorises an extensive collection of patterns as a way to provide a manual for designing timeless structures and spatial arangements. This particular publication has been very influential in forwarding the idea that patterns can capture reality in a way that precedes spatial or temporal specificities, the resonances of which can arguably be located by tracing the gradual integration of information and computer technologies with space. In Soft Architecture Machines (1975), Nicholas Negroponte proposed “computer-aided participatory design”, where the resident would “participate in or control the design of his [sic] own house.” ([1975] 2003, 355). Here, the general idea is that “each individual can be his own architect” (356). Consequently, if everyone is his or her own architect, then the city is in fact without architects, and human needs are continuously, “organically”, incorporated in their environments. Tracing this lineage further, Myron Krueger introduced the concept of a “responsive environment” (1977), which “perceives human behavior and responds with intelligent auditory and visual feedback.” (423). While Krueger's concept is more about the monitoring and processing of behaviour, Mark Weiser's extremely influential concept of “ubiquitous computing” (1991) envisions the disappearance of technologies and devices into our daily rhythms until they are indistinguishable from them. Yet, it seems that Krueger's concept is more relevant to us here than Weiser's, especially considering the notion of “ambient intelligence”. Somewhat related to this, Rob Kitchin and Martin Dodge, in their recent publication code/space (2011), explore how computer code is increasingly interwoven into the fabric of our everyday life. Their central argument is that we should focus on the role of software and computer code in maintaining as well as producing space. Finally, smart cities are also “generic cities” (Koolhaas and Mau [1995] 1998): every city can be a smart city. Generic cities represent sameness and repetition (i.e. pattern), uniformity and universality (i.e. non-specificity), and do not have identities, centres or histories.

3. Imagining Futures: Conceptualising Urban Problems

3.1. The City as a Complex System

If we are to conceptualise as well as realise urban spaces as milieus of machines over which particular modes of control may be exerted, then we also need to rework our understanding of what that milieu actually is or how it works. Drawing from a research agenda by Michael Batty et al. (2012), cities are increasingly understood as complex systems. As they argue, this is because cities are “more than the sum of their parts and developed through a multitude of individual and collective decisions from the bottom up to the top down.” (483). Positioning complexity as the unifying system to understand the dynamics of the urban environment raises the question of what complexity actually is. As explained by Stephen Wolfram in his seminal A New Kind of Science (2002):

Most often complexity seems to have been thought of as associated with the presence of large numbers of components with different types or behavior, and typically also with the presence of extensive interconnections or interdependencies. But ocassionally—especially in some areas of social science—complexity was instead thought of as being characterised by somehow going beyond what human minds can handle. (1068)

In an investigation of the properties of complexity, Ladyman, Lambert and Wiesner (2012) observe the broad range of ideas present in complex systems research. Features they identified are nonlinearity, feedback, spontaneous order, robustness and lack of central control, emergence, hierarchical organisation, and numerosity (4–10). This list of features makes it clear that this no simple definition can be given, and thus that the concept of complexity can be of philosophical interest. Moreover, the fundamental idea that living systems are complex because of their interactions and components should be seen as an indication of the strong links with (mathematical) theories of communication and information (Wolfram 1985, 2002; Bar-Yam 2000; Ladyman, Lambert and Wiesner 2012; see also Shannon 1948). Emphasising that complex systems must be computable, Ladyman, Lambert and Wiesner ultimately propose to adopt a “realist” position that builds on Crutchfield's Statistical Complexity (Crutchfield and Young 1989) and takes patterns as features of the world, rather than simply tools for prediction behaviour in a system (cf. 33–34; see also Mirowski 2002). Conceptualising the city as complex system means accepting these features of complexity as forces underlying change in the “organic” urban environment. This brings us to the next question of how such a conceptualisation could then be realised (i.e. engineered).

3.2. Emergence and Embodiment

Not coincidentally, complex systems theory originates in general systems theory (von Bertalanffy 1968) as much as in cybernetics (Wiener [1948] 1961). The theoretical conception of complexity required a material model for the study of that theory. Similarly, the theoretical conceptions of the city introduced above require techniques that embody these conceptions. Drawing from Bruce Clarke and Mark Hansen's Emergence and Embodiment (2009), the adoption of second-order cybernetics – or, as they prefer to call it, neocybernetics, “the cybernetics of cybernetics” (4) – is of particular interest to us here (cf. Hayles 1999, 2004, 2005; Heylighen and Joslyn 2001). Clarke and Hansen observe a distinction between emergence as the movement from the simple to the complex (cf. Wolfram 2002), and the neocybernetics view of it as the movement from chaotic complexity to manageable complexity (Clarke and Hansen 11; see also Crutchfield and Young 1989). This is at the same time why emergence, understood as the capacity of complex systems to result in recursive patterns of the interactions between the agents of that system (Wolfram 1982, 2002; Kauffmann 1995; Holland 1999; Hui 2011; DeLanda 2011), is such an important property for the (techno)science and management of smart cities. In their synopsis, Clarke and Hansen summarise that “The crucial shift [Heinz von Foerster] inspired was from first-order cybernetics' attention to homeostasis as a mode of autonomous self-regulation in mechanical and informatic systems, to second-order concepts of self-organization and autopoiesis in embodied and metabiotic systems.” These two concepts incorporate the workings of living, biological organisms and refer to the capacity of a system to maintain as well as reproduce without interference. In other words, such systems are closed and self-referential: “to maintain their autopoiesis, (self-referential) systems must remain operationally (or organizationally) closed to information from the environment.” (9). Consequently, a specific version of metabiosis emerges from this blend of concepts. Metabiosis refers to the (complex) idea that each individual organism potentially depends on all other organisms to favourably “prepare” the environment in which it may flourish. But if such a complex system is self-referential (that is closed), then the outcomes of the interactions are always just another generation of same fundamental expressions (cf. Wolfram 2002). In other words, the future state of the system becomes calculable and predictable. There are thus important limitations to this concept as a result of the reduction inherent to any conceptual framework.

3.3. Assemblage Theory and Anticipatory Knowledges

Besides investigating smart city conceptions of urban space itself, we also need a theoretical framework to make grasp the particular material and semiotic arrangements of people, institutions, devices, methods, techniques, and so on that are assembled to make possible the simulation of these concepts. To this end, I deploy the concept of assemblage, which offers a way to think about society in terms of the dynamics of contingency and structure, organisation and change (Deleuze and Guattari [1980] 2001; DeLanda 2006). J. Macgregor Wise (2005) summarises the assemblage described by Deleuze and Guattari as having two axes:

One axis is the creation of territory, on strata, thus moving between making (territorialization) and unmaking (de territorialization) on the Body without Organs. The other axis is the enunciation of signifiers, collectively, moving between technology (content, material) and language (expression, non-corporeal effects). Assemblages are made and unmade along each of these dimensions. (80)

Furthermore, Wise as well as Manuel DeLanda (2006) emphasise that it is important to realise that assemblages are never stable or in a static form, rather, “it is not the arrangement or organization but the process of arranging, organizing, fitting together.” (Wise 77, emphasis in original). As such, pinning down assemblages for carefully observation is like nailing jelly to the wall. Assemblages can only be observed in their continuous state of becoming something else. Relating this to the smart city, one of its key features is the integration of information and computer technologies with existing infrastructures. At the same time, I argue that the smart city constitutes first and foremost an idea that we constantly “put ahead of us” as imaginary future (Barbrook 2007). The smart city thus retains a particular kind of connection between past, present and future. While I will identify some specific material devices, methods, institutions, and so on that help establish and maintain this connection, I will first discuss the more abstract notions of emergent associations and the anticipation of futures.

3.4. Assemblages of Anticipation

The notion of being able to locate technology-oriented expected futures in the present has been explored, among others, by Sam Kinsley (2010, 2011, 2012) and Anne Galloway (2004, 2012), and Ben Anderson (2007, 2010). Especially Kinsley's concept of “anticipation” as a set of logics, techniques, objects, practices and knowledges (sic), is of relevance to the operationalisation of this research. As he writes, “anticipatory techniques are a means of establishing the presence of what has not happened and may never happen, an “indeterminate potentiality” (Massumi 2007).” (2012, 1557). Moreover, they exist as parts in assemblages: “Futures are apparently made present as objects, such as reports on trends, stories, or models, and are felt as anxieties or hopes, but those futures do not cease to be absent insofar as they have not and may never happen.” (1557). The type of object that probably best exemplifies such an “anticipatory technique” in the context of this study is the simulation model, which could be considered a particular kind of digital prototype. Anticipatory techniques thus contain particular kinds of knowledge. Kinsley calls these “anticipatory knowledges”, referring to “the basis for stable means of future orientation, which both generate and derive from the techniques . . . for anticipating futures.” (1562; see also Anderson 2007). Crucially, this “assemblage of anticipation” – if I may call it that – is enacted and acted upon in accordance with a set of underlying logics. These “logics of anticipation” (2011, 6–7) are the particular rationales guiding “anticipatory action”, ultimately resulting not in “anticipatory experiences” (2012, 155): the anxieties and hopes people may have about these futures that may not happen. Assemblages of anticipation exist in the present, and as such they can be located and mapped. Galloway (2010) attempts such an effort to “locate present associations and expectations that serve to encourage particular futures and discourage others.” (27). Using the performative theoretical framework of the “sociology of translation” (or “association”, cf. Latour [2005] 2007), she argues that “locative media and mediated locations involve persistent tensions between pasts, presents, and futures that make certain identities and objectives possible or probable, and other impossible or improbable.” (32). Moreover, the interesting notion of “stability” or “obduracy” (Pinch, Ashmore, and Mulkay 1992) introduces the dimension of normalisation or acceptance of future visions as they materialise in the form of objects, policies, standards and protocols, models, and through such methods and practices as simulation, (citizen) participation, open data, social media and online communications, sensors, and so on.

4. Enacting Futures: The Case of Computer Simulation

The previous sections were a means of introducing the epistemological as well as ontological a priori present in the use of computer simulation to imagine futures of the smart city. I have introduced a genealogy or pre-history of the smart city concept, and argued that it conceptualises the city as a complex system. At the same time, I have deployed assemblage theory to provide a way to map and study “assemblages of anticipation”, and the particular futures it may encourage or discourage. In the following I will show how both lines of argument converge in the case of computer simulation, a move by which I intend to isolate some particular groups of actors or agents of the assemblage that are crucial in the imagination as well as the enactment of smart city futures.

4.1. Computer Simulation: Articulating and Exploring Scenarios of Possibility

Modeling and computer simulation is and will be a fundamental component of the practice of complex systems engineering as a consequence of the inherent features of complexity (cf. Burkhart 2007; Fougères 2011). Furthermore, simulations and prototypes in general are rhetorically persuasive. If it can be prototyped, then it can be built, visualised and interacted with. Returning to Kinsley, “Enacting a form of future technology use, for example through paper prototypes, allows the technology being used as if it were actually functional to be questioned and reimagined as if it functioned otherwise. The result can be subsequently fed into the generation of prototypes for product development.” (2012, 1560). Externalising concepts in the form of objects, representations or practices helps to generate “anticipatory knowledges”, and render futures actionable (cf. Anderson 2010) as well as imaginable. In a basic sense, simulation refers to “the imitation of the operation of a real-world process or system over time” (Banks 1998, 3, emphasis added). In order to simulate, however, a simulation model that represents the real system (i.e. set of characteristics, behaviours or functions) has to be developed first. Furthermore, Jerry Banks notes that simulation “involves the generation of an artificial history of the system, and the observation of that artificial history to draw inferences concerning the operating characteristics of the real system that is represented.” (3). This not only confirms the earlier point that smart cities do not have a real memory, but also raises the question of how simulations are assembled. That is, what kind of artificial history has been arranged for a particular simulation, and what kinds of models are deployed.

However, not all simulations intend to imitate reality accurately. As exemplified by Seymour Papert's MicroWorlds (1980), computer simulation can also be a powerful tool for the kinds of cognitive exploration that would be impossible without the support of technology. A city simulator, for instance, may be a city-building game or may be used by urban planners as a tool to understand how cities are likely to evolve in response to a specified event such as a policy decision, an environmental catastrophe, human migration, employment locations (see e.g. Paglaria et al. 2012), travel, or perhaps a war scenario. Furthermore, there are many different software applications supporting the simulation of different dimensions of the city. To give a number of examples: first, the M-Atlas system was developed to “master the complexity of the mobility knowledge discovery process. [It] is an integrated querying and mining system, centred onto the concept of a trajectory, i.e., a sequence of time-stamped locations, sampled from the itinerary of a moving object.” (Batty et al. 2012, 502). Second, MATSim and Simulacra, are both frameworks for the study of movement and location. MATSim is an open-source framework, and supports large-scale multi-agent-based transport simulations supporting the simulation as well as analysis of private cars and transit traffic flows (“Agent-Based Transport Simulations”), and Simulacra focuses on land use transport modeling, urban complexity and sustainability research, and explicates that it should be used to explore reality rather than trying to imitate it (“Simulacra”). Third, AnyLogic is an example of a multi-method large-scale urban simulator designed for use by urban planners and supports three paradigms: discrete event, agent-based and system dynamics (“Multimethod Simulation Software and Solutions”). Fourth and finally, UrbanSim and LEAM (Land Use Evolution and Impact Assessment Model) are both used by metropolitan planning agencies and military bases (but like the other examples, developed by research institutions) for land use and transportation planning (“Land Use Evolution and Impact Assessment Model” “UrbanSim”).

4.2. The “Science of Cities”: Coordinating Urban Technologies

The notion of “smart” city growth as the efforts to achieve greater efficiencies through the effective and sustainable coordination of the functions of growth (e.g. transportation, land speculation or conservation) probably captures the current goals of what has been called “the science of cities” (e.g. Batty et al. 2012; Mattern 2013). As Michael Batty et al. observed:

Smart cities are often pictured as constellations of instruments across many scales that are connected through multiple networks which provide continuous data regarding the movements of people and materials in terms of the flow of decisions about the physical and social form of the city. Cities however can only be smart if there are intelligence functions that are able to integrate and synthesise this data to some purpose, ways of improving the efficiency, equity, sustainability and quality of life in cities. (2012, 482)

So conceived, smart cities are made of data, and this data can be captured, processed, analysed and ultimately fed back into the development of the city (expressing ideas about the city-as-machine; cf. Mattern 2013). In practice, this means the pursuit of algorithm-driven urban efficiency using Big Data. The interesting thing, however, is to look at the various methods, techniques and devices associated with this practice. In a recent essay, Shannon Mattern (2013) has written about a number of these methods of measurement and what has been called “methodolatry”: the idolisation and aestheticisation of method (Janesick 1994; Mattern 2013) resulting in, for example, “data fetishism” or what Evgeny Morozov calls “solutionism” (2013). Currently, most efforts go into the sensing of mobility and transport data (e.g. locational data, the importance of which has been observed to increase as a proxy for all kinds of sociological information, e.g. by Savage and Burrows 2007, 892), and their integration with other forms of spatial data that have been acquired at earlier points in time (cf. Batty et al. 2012, 487). This is again an expression of the way in which the smart city is built and imagined “on top of” existing infrastructures and other technologies. Three particular sets of “city science” practices to collect and integrate these data stand out: urban sentience, participatory or cooperation frameworks, and open data initiatives. In the first case, the use of sensors in urban environments leads to “ambient intelligence” (see also Crang and Graham 2007; Shepard 2011) and with it new modes of governance enabled by the monitoring and surveillance of public spaces (e.g. Filipponi et al. 2010). The second case introduces the peculiar notions of the city as a laboratory for innovation (e.g. Batty et al. 2012, 485) or “living labs” (e.g. Bell and Dourish 2006; Schaffers et al. 2011). Both are examples of participatory “ecosystems” where living – understood in an inclusive sense – is rendered economically competitive (see also Shapiro 2003, 2005; Dodge and Kitchin 2004; Yigitcanlar and Velibeyoglu 2008; Hollands 2008; Chourabi et al. 2012) as a result of “public-private-people partnerships” (e.g. we have “citizen scientists”, “urban explorers”, or smartphone-equiped agents tracking everyday activities and mobilities). The third and final case, open data initiatives, is at the same time an expression of the second case (everyone is allowed to help make the city smarter or the possibility to crowd source data sets), but should also be seen in a larger effort to integrate databases across spatial and temporal scales (e.g. integrations with social and locative media and online communications, see Barreneche 2012, Badger 2013).

4.3. Simulation Modeling and Digital Prototyping

Each method or technique of data acquisition, processing or analysis has epistemological repercussions. Similarly, the developed simulation models embody a set of ontological presumptions (e.g. Buckhart 2007; Fougères 2011), which I have tried to situate in the previous sections. With regards to the question of environmental effects of architecture, Ronald Wall wrote:

Let us be honest, we live in a world where we cannot measure the effect of our actions. . . . What [devices like the Berlage Mixer] are trying to do is to simulate the actions of our world and to try to put them within an integral context so that if we affect something in the world we can see what its effects are elsewhere. (MVRDV 2005, 1298)

Wall's view represents the more nuanced understanding that simulation software is not just used to prototype futures of reality itself, but rather to test scenarios (e.g. “tested urbanism”), that is the playing out of a set of interactions between agents over time. As became clear from the four examples of computer simulation software above, city simulations are generally (multi-)agent-based and deployed for discrete event simulation (DES) and testing dynamic complex systems. Complexity, as chaos, is thus reduced (i.e. made computable) to the interacting of a large number of interacting agents, the diagramatisation of time as a linear sequence of events, and the understanding of change in the system as inseperable from the passing of time. In each case, movement or change is preferred over stagnation. Of course, these representations are useful especially for the simulation of mobility such as human migration or transportation and spatial arrangements such as land use. Thus, simulation models build “on top of” existing categories of activity in the city, and as such they gain value to those interested in the future state of that category of the system.

One important challenge, then, lies in the fact that new models need to be conceived that deal with the new categories of flows of information that we may anticipate in the near or distant future (cf. Batty et al. 2012, 507). Because simulations run closed systems, the outcomes of the interactions will always just be another generation of same fundamental expressions: they are calculable and persuasive as such (cf. Boschetti, Grigg, and Enting 2011). Furthermore, it is these kinds of trajectories or development paths that form the basis for all kinds of risk assessments or analyses (e.g. engineering safety analysis or locating crime by probability). As Louise Amoore argued, “It is precisely the emergence of novel forms of correlation that are distinctive to the data derivative form in the domain of security, though of course these are familiar qualities in the financial derivative.” (2011, 28). The key point to take from this, then, is the emergence of practices and modes of governance based on “anticipatory knowledges” (cf. Anderson 2007) of futures that may never even happen.

4.4. The Ontological Status of Simulation

The functions of simulation, which are crucial components to “assemblages of anticipation”, are increasingly integrated into everyday life in the form of visualised knowledge (Hui 2012). Yuk Hui poses the following question in a discussion of Manuel DeLanda's book Philosophy and Simulation (2011):

What accounts for the reality brought forth by the simulations and what are the relations between the principles of simulations and the transformations imposed? That is to consider a plane on which the simulations themselves become attractors and create a new topography which is not reflected on the screen. (Hui 2012)

For DeLanda, simulation creates its own spaces ordered by mathematical simulation models. These spaces of topological figures and continuously rearranged data sets open up the possibility of philosophy, which can be visualised and imagined. DeLanda develops the concept of “synthetic reason” to refer to the kind of reasoning that is neither purely theoretical, nor actual. Rather, simulation with its epistemological a priori conjures up new “spaces of possibility” for synthetic modes of reason with the ontological status of emergence: “it still refers to something that is objectively irreducible.” (3). If this is indeed the case, then perhaps the broader question of an epistemology of emergence, conceptualised as recursive patterns, can also be raised (see also Dixon 2012). Rather than for positing patterns as unifying principles to simulate reality (using simulation models), simulation is useful to prototype the balances and relations between functions in abstract mathematical models. This opens up a particular “potential design space” (Kinsley 2012, 1561): we imagine, design and prototype the articulation of possible versions of the smart city based on imagined futures in the present, and the integrative levels of imagination that have preceded it. Still, as mentioned earlier, simulation has a performative dimension as it materialises through “anticipatory action” and in the form of objects, policies, standards, research agendas and so on, leading to the “stabilisation” and “anticipatory experience” not only of these possible future spaces, but also of the systems, models and the assumptions they represent.

5. The Making-Actionable of Anticipation: Emergent Associations

To reiterate, simulation and other “anticipatory techniques” do not predetermine the future, but rather “set the stage” to encourage some futures and discourage others by articulating particular spaces of possibility or the outlines of emergent associations to which concurrent developments can then be directed (cf. Galloway 2010, 27; Kinsley 2012, 1559). In this final section, I will briefly explore recent developments in interactive architecture and urban planning that follow some of the techniques, methods or concepts discussed previously to see how these actually feed back and materialise in practice. Finally, I add a brief discussion of the implications for what Kinsley calls the “politics of anticipation”.

5.1. Designing Topological Spaces

The emerging field of interactive architecture and urban planning (e.g. Oosterhuis et al. 2012; Fox 2009) conceptualises architecture not simply as environments that are responsive (e.g. Krueger 1977) or permeated by networked devices anymore (e.g. Weiser 1991). Rather, it fits with the concepts and ideas discussed in this paper in that it takes concepts such as complexity, emergence or adaptivity, which the architect then tries to materialise with information and computer technologies, sensors, and so on. Simulation and modeling are of central importance to this kind of architecture. As Tomasz Jaskiewicz writes, “Instead of designing spatial, singular forms, architects are now expected to design processes that produce architectural spaces.” (2012, 189). The design of architectural space has become the materialisation of simulated topologies, which are modelled based on the monitoring or “sensing” of human behaviour. These topologies are then described by imposing the real world on these topologies, where sets or aggregates of data points are compared to physical structures:

[S]patial environments can be seen as nested ecologies. Regions contain cities and villages. Cities contain urban blocks. Urban blocks contain individual houses. Those houses contain flats, which contain rooms, containing furniture made up of elementary building materials (that in turn are made up of particles and can be dissected several scales of magnitude further down the scale to the boundary of the known universe). (192)

Note that Jaskiewicz only makes distinctions of scale here, which makes arrangements of subatomic particles just as much part of the domain of architecture as flats or urban blocks. This understanding of what it means to design space – which I argued is conceptualised as complex system – is completely different from traditional architecture. An architect now designs spaces without atmosphere. It is someone who designs performative models based on principles that simulate and derive these topological spaces from human behaviour.

5.2. Politics and Competing Futures

The topological space itself consists only of data points and interrelations, subjected to strict mathematical functions (cf. Friedrich 2012, 248). “Swarm architecture”, for instance, offers one possible approach to the design of topological space (e.g. Oosterhuis 2012). It refers to a time-based architecture consisting of “point clouds” (Friedrich 2012), the modeling of which can be though of as the continuous re-positioning of points in space to form sets or “clouds” of different shapes and distribution (241). The purpose of the approach is spatial layout, to which topological modeling is then added in another mode of operation. For Friedrich this is what constitutes “deep space”: consisting not just of points and relations, but also volumes of these points and connections. This means that topologies can be traversed, queried and sorted with software, and this has real consequences for political, economic, social geographies (e.g. Graham 2005; Murakami Wood and Graham 2006). A qualitatively and quantitatively new kind of political space of anticipation thus becomes discernable, which Kinsley refers to with the concept of a “politics of anticipation”. In line with Kinsley, I understand politics as a “space of dissensus and contestation which is not reducible to politics” (Barry 2001, 207, qtd. in Kinsley 2011, 7). The central problem here is how competing (imagined) possible futures are resolved or “stabilise”, a question that I have tried to give one possible answer to.

6. Conclusions

In this paper I set out to develop, one the one hand, a critical and philosophical understanding of the anticipation of the smart city, and on the other hand, an understanding as well as problematisation of the role of computer simulation in the imagination and enactment of technology-focused futures and the new (topological) spaces they render actionable. The enactment of technology-focused futures depends on our present capacity to imagine these futures. The future spaces of possibility with regard to the smart city are negotiated – contested as well as accepted – in the present, and computer simulation plays a crucial role in the (sometimes automatic) production of these spaces.

The first and second sections were both used to introduce the ontological and epistemological a priori present in the use of computer simulation to imagine futures of the smart city. In the first section I explored the resonace of twentieth century modernist ideas about architecture and urban planning. In particular, the discourse of rationalisation represents the move towards an architecture of functionality of which machines would become the dominant metaphor. After the emergence of “cyborg architecture”, the human-centred “machinic” understanding of architectural and urban space gradually turned into an ecological understanding of space in which the dynamics of a particular environment shape the very space they constitute. These influences were then related to influences of system theory and cybernetics on urban planning. This included Alexander's architecture of patterns and Krueger's responsive environments, which paved the way for what is now sometimes called “ambient intelligence”. In the second section, I argued that the city is increasingly conceptualised by architects and urban planners as a complex system that can be computed or engineered. Consequently, I turned to the frameworks of general systems theory (von Bertalanffy 1968) and cybernetics (Wiener [1948] 1961)6 to show how this particular understanding of the city as a complex system could be engineered. The reduction of emergence – one of the features of complex systems – to recursive patterns of the interactions between agents of a system was introduced as well. Through a discussion of second-order cybernetics, I then return to an earlier point of the relationship between the “organic” and the “machine” to argue that the outcomes of the interactions in a complex system are always just another generation of same fundamental expressions (cf. Wolfram 2002). As such, a system – understood as a set of coherent functions or behaviours – is predictable by definition.

At this point, I introduced the concept and methodology of “assemblages of anticipation” which I deployed later in the third section to explore the case of computer simulation within the imagination as well as enactment of particular futures. For this I drew from Sam Kinsley's work on anticipation to assemble a framework to understand the case of computer simulation, a move by which I intended to isolate some particular groups of actors or agents of the assemblage that are crucial in the imagination as well as the enactment of smart city futures. Using the framework of assemblage theory allowed me to situate simulation within the “logics of anticipation” (Kinsley 2011) as well as within arrangements of data acquisition, integration, and simulation modeling. For the discussion of these I referred back to show how the ideas introduced in the previous sections were operationalised in these particular objects and methods that encourage some possible futures and discourage others (Galloway 2010). In the last section, I explored recent developments in the field of interactive architecture to argue that the meanings of architecure and spatial design have fundamentally shifted to the design of topological space. Ultimately, these shifts result in a “politics of anticipation”, from which new problems and social and economic inequalities arise. Our task is not so much to engineer better simulation models, methods of data acquisition, regulations or policies, but rather to anticipate these new geographies, problems and new social orders, which are always already possible, actionable and coming into existence.

7. Endnotes

1. This includes projects such as Amsterdam Smart City, SmartSander; research and media labs such as the MIT SENSEable City Lab and Smart Cities group, Centre for Advanced Spatial Analysis; conferences such as The Programmable City; software applications, city dashboards or management centres such as Centro de Operaçõoes Prefeitura do Rio, CASA's London City Dashboard; augmented reality (AR) software such as SmartSantanderRA, Layar; and research and development (R&D) proposals such as futurICT. Furthermore, a list of smart cities by continent and country can for instance be found in Nam and Pardo (2011, 282).

2. It is pre-historical because I conceptualise the smart city as an imaginary future (Barbrook 2007) that has continuously been “put ahead of us”, and thus never actually “is” nor “has been”.

3. This refers to the collapse of distinctions, or rather that there were no distinctions in the first place.

4. Such an understanding of the human environment also provokes a reconceptualisation of the human as a cyborg, the implications of which include the collapse of clear bounderies between identities (e.g. Haraway 1991), and the concept of originary technicity or “originary prostheticity” as Bernard Stiegler refers to it (1998, 98–100), referring to the notion that the human is fundamentally inseperable from technics.

5. Norbert Wiener's term “cybernetics”, which is the science of control and communication in the animal and the machine, stems from the Greek word κυβερνώ (kyvernó), meaning to “steer”, “navigate” or “govern”.

6. Which are the very functions or behaviours that constitute or define that system in the first place.

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Info
Title: In-Formed Spaces
Subtitle: Conceptualising the “Smart City” as Assemblage of Anticipation
Type: Research article; Assignment
Author.name: F. N. (Fernando) van der Vlist
Author.affiliation: Graduate School of Humanities, Faculty of Humanities, University of Amsterdam
Instructor.name: Dr. N. A. J. M. (Niels) van Doorn; Dr. M. J. (Michael) Dieter
Instructor.affiliation: Dept. of Media Studies, Faculty of Humanities, University of Amsterdam
Abstract: In this paper I develop, one the one hand, a critical and philosophical understanding of the anticipation of the smart city, and on the other hand, an understanding as well as problematisation of the role of computer simulation in the imagination and enactment of technology-focused futures and the new (topological) spaces they render actionable. Even though the smart city is a highly interdisciplinary concept and technology-oriented future orientation, the ontological status and epistemological a priori of its methods, techniques and practices, and the implications or repercussions these may have, remain unclear. To investigate this, I first trace a genealogy, or rather pre-history, of the city as machine. This will situate the subsequent analyses in which I consider the dominant conception of the city as a complex system, which, I argue, constitutes the ontological basis upon which the smart city is conceived. The concept of “assemblages of anticipation” is deployed as a methodology to explore the case of computer simulation within the imagination as well as enactment of particular possible futures that are either encouraged or discouraged. Furthermore, I problematise the ontological status as well as epistemological a priori presumed by simulation. Finally, I discuss some recent developments in the emerging field of interactive architecture to argue that the meaning of spatial design has fundamentally shifted to the design of topological space, as a result of which new problems and social and economic inequalities may arise.
Keywords: smart city, logics of anticipation, simulation modeling, topological space, anticipatory knowledges, assemblage
Length.words: 6,930
Length.reading: 39 mins
Date.submitted: 10 Jan. 2014
Date.evaluated: 27 Jan. 2014
Date.publishedonline: 1 Feb. 2014
Language: English (United Kingdom)
Documentation.style: Modern Language Association (7th ed.)
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