Postscript on the network society: The social inflationary era (Or why we live in hyperconnected yet disaggregated societies).
Jaseff Raziel Yauri-Miranda and Israel Arcos Fuentes
Si bien vivimos en un mundo hiperconectado y la tecnología es central para la cultura y el poder, este proceso rescinde de los sujetos y del conocimiento a expensas de la performatividad y la datificación. Para argumentar esto, nos basamos en el legado de Castells y complementamos la teoría de la sociedad en red con un enfoque multidimensional desde las dimensiones psicológica, económica y política. En primer lugar, abordamos el nivel individual o proceso de individuación en el que la condición y la capacidad de autorrepresentación de los sujetos dependen de ensamblajes o redes tecnológicas mayores. Esto nos lleva a cuestionar la regresión en la inmanencia ontológica o capacidad de agencia de los individuos. En segundo lugar, abordamos el nivel meso entre agencia y estructura, es decir, el nivel de los actores sociales para explorar nuevos cambios en la economía digital. Esto nos lleva a cuestionar cómo los actores nodales en la economía de la red gestionan los datos de los individuos, pero también se convierten en « víctimas » de su propio proceso de datificaión. En tercer lugar, llegamos al nivel estructural, expresando que la sociedad red está siendo sustituida por un sistema de actores-redes-datos denominado ‘inflación social’. Mientras que la sociedad red prioriza las conexiones y la distribución descentralizada, la inflación social enfatiza la diferenciación exponencial y la autoperpetuación de los sistemas de datos a cualquier costo. La teoría cosmológica inflacionaria llevada a las ciencias sociales, constituye un sistema que crea nuevas redes, actores y epistemologías, acelerando la expansión de una lectura técnica de la realidad en la que incluso ésta se engloba para cumplir con la demanda de datificación en curso.
Palabras clave: sociedad red, inflación social, hiperconectividad, datificación.
Although we live in a hyperconnected world and technology is central to culture and power, this process rescinds from subjects and knowledge at the expense of performativity and datafication. To argue this, we draw from Castells’ legacy and complement the network society theory with a multi-dimensional approach from psychological, economic, and political dimensions. Firstly, we address the individual level or individuation process in which subjects’ condition and capacity for self-representation depend on larger technological assemblages or networks. This leads us to question the regression in the ontological immanence or capacity of agency from individuals. Secondly, we address the meso level between agency and structure, that is, the level of social actors to explore new shifts in the digital economy. This takes us to question how nodal actors in the network economy manage individuals’ data but also become a ‘victim’ of their own datafication process. Thirdly, we reach the structural level, expressing that the network society is being replaced by a system of actors-networks-data called ‘social inflation’. Whereas the network society prioritizes connections and decentralized distribution, social inflation emphasizes exponential differentiation and self-perpetuation of data systems at any cost. Likewise, the cosmological inflationary theory taken to social sciences, this system creates new networks, actors, and epistemologies, accelerating the expansion of a technical reading of reality in which even this is encompassed to fulfill the demand of ongoing datafication.
Keywords: network society, social inflation, hyper-connectivity, datafication
Bien que nous vivions dans un monde hyper-connecté et que la technologie soit au cœur de la culture et du pouvoir, ce processus annule les sujets et les connaissances au détriment de la performativité et de la datafication. Pour argumenter cela, nous nous appuyons sur l’héritage de Castells et complétons la théorie de la société en réseau par une approche multidimensionnelle des dimensions psychologique, économique et politique. En premier lieu, nous abordons le niveau individuel ou processus
d’individuation dans lequel la condition et la capacité d’autoreprésentation des sujets dépendent d’assemblages ou de réseaux technologiques plus larges. Cela nous amène à nous interroger sur la régression de l’immanence ontologique ou de la capacité d’agence des individus. Dans un second temps, nous abordons le niveau méso entre agence et structure, c’est-à-dire le niveau des acteurs sociaux pour explorer les nouvelles mutations de l’économie numérique. Cela nous amène à nous interroger sur la manière dont les acteurs nodaux de l’économie des réseaux gèrent les données des individus, mais deviennent aussi « victimes » de leur propre processus de datafication. Troisièmement, nous arrivons au niveau structurel, exprimant que la société en réseau est en train d’être remplacée par un système d’acteurs-réseaux-données appelé « inflation sociale ». Alors que la société en réseau donne la priorité aux connexions et à la distribution décentralisée, l’inflation sociale met l’accent sur la différenciation exponentielle et l’auto-perpétuation des systèmes de données à tout prix. La théorie cosmologique inflationniste, en sciences sociales, constitue un système qui crée de nouveaux réseaux, acteurs et épistémologies, accélérant l’expansion d’une lecture technique de la réalité dans laquelle même celle-ci est incluse pour répondre à la demande permanente de dataification.
Mots clés : société en réseau, inflation sociale, hyperconnectivité, dataification
Networks assumed a significant role in Castells’ opus magnum, ‘The Information Age’ trilogy, in the latter half of the 1990s. He became possibly the most prominent figure globally in adopting network terminology in social theory (Castells 1996, 1997a, 1997b, 1998). At the turn of the last century, his work, alongside other theories such as Foucault’s relations of power (Foucault, 1991), Deleuze’s social control (Deleuze, 1992), and Hall’s codes of communication (Hall, 2011), can be considered a cornerstone to interpreting the shifts toward flexible social relations, the decentralization of communication, and the globalization of the world in political, economic, and technological domains.
It is known that Castells’ network concept is derived from the increased relevance of networks as the emerging form of social organization, epitomized by the idea of global networks of instrumental exchanges. Probably, he did not shed light on the internal dynamics of networks but was nevertheless able to use the network as a powerful metaphor that aptly portrayed his idea of the new social morphology of informational societies based on digital technologies.
Baring this in mind, this text aims to amend Castell’s work with new insights from psychology, economy, and political philosophy to propose the ‘social inflationary era’ as a further step in the evolution of the network society. This is because, since the writing of Castells’ work, we have seen the development of digital technologies that have changed the form in which actors interact and communicate to construct networks. Also, in the analysis of the network society, there is still room to explore the internal dynamics and the distribution of power at different scales. Rather than mere power relation between contingent social actors that establish connections based on utility, current economic shifts challenge the way networks expand and survive. Even economic monopolies (a figure that disrupts the metaphor of a horizontal network) depend on exponential growth and the assetization of intangible goods and digital data.
Lastly, beyond new technological and economic shifts, we argue that, ultimately, the network society is being replaced by a system. The main difference between networks and systems is that the former prioritizes connections and decentralized distribution, whereas the latter emphasizes exponential differentiation and self-perpetuation at any cost. This system is comprised of actors-networks-data and highlights data as the means and goal to create new networks, social fields, actors, and even epistemologies to interpret the world. Thus, the network society has taken us to a system differentiation, in which the ontological condition of actors and networks are defied by a continuous cycle of datafication. We called this cycle social inflation because, as physical objects are accelerating to distant parts of the universe, this system creates new sub-systems that turn difficult, in cognitive and epistemological terms, to connect the previous actors and networks. Ultimately, this process of differentiation is accelerated by the search for data correlations, analysis, and combinations that rescind from subjects, knowledge, and power (as even economic oligopolies are subsumed in this differentiation process).
The text is structured as follows: The second part reconsiders Castells’ network society and legacy. It mentions that this framework can be complemented by an agency-structure scale, as well as by recent developments related to technology (datafication). The third part covers the individual agency. It argues that the digital persona, the residual self-projection of an individual, overrides previous ontological grounds in which power and resistance were constructed. Despite resistance can be always exercised, it means the replacement of immanence by imminence, from an active subject towards a fragmented digital double, as the preeminent characteristic of individuals in the network society.
The fourth part expresses the meso-level and covers social actors that are relevant to the network society: data processors. In that sense, we express that tech giants are the main examples that alter the distribution of sources, information, power, and management of networks. Also, these promote asymmetric networks based on the assetization of intangible values and services. This means that digital data fuels the network society but also turns against tech giants as these become fragile monopolies in a hyper-competitive world. Even powerful organizations are encompassed by a systemic transformation explained in the fifth part. In this part, we cover the structural level and the amalgam between actors-networks-data that characterize the ‘social inflationary era’. At this level, differentiation and self-perpetuation anchored in datafication replace actors and networks based on utility and interdependence. As the social inflation system expands, the ontological condition of subjects is replaced by the acceleration of a technical hermeneutic reading of reality in which even this is not sufficient to fulfill the demand of ongoing performativity and datafication. Finally, we close this essay by making a brief analogy between the cosmological inflation and the social inflationary system we have elaborated. Overall, this text turns its attention to many dimensions indispensable to understanding Castells’ idea of the network society, which creates a thematic connection between his theory and the discussion of micropolitics and micropolitics, from individuals to systemic levels.
Manuel Castells created one of the most ambitious macro theories of our time, which endeavored to interpret the transformation of contemporary society as a reflection of the transition from industrial to the informational mode of development. His theory is a political economy-oriented macro-analysis of the tensional relationship between the instrumental networks of the informational economy and historically-rooted identities and the worldwide developments conditioned by this. The concept of network entered Castells’ thinking in the late 1980s and became a key explanatory category in The Information Age trilogy (Castells, 1996; 1997a; 1998)
The concept of the network served not only as a recurring theme but essentially as an interpretative framework for practically all his subsequent works (Castells, 2001; 2009; 2011; 2012). To define his theory, Castells (1989: 32) writes:
Networks, on the basis of new information technologies, provide the organizational base for the transformation of socially and spatially based relationships of production into flows of information and power that articulate the new flexible system of production and management.
One can see a shift from management institutionalization and rational economic thinking into a fluid scheme of material connections to allocate productivity and sources. It is the solid class mode of production from Marxist roots turned into a flexible assemblage of economy and power. In “The Information Age” and many later works Castells defined ‘network’ rather formally as a set of interconnected nodes:
I shall first define the concept of network, since it plays such a central role in my characterization of society in the information age. A network is a set of interconnected nodes. A node is the point at which a curve intersects itself. What a node is, concretely speaking, depends on the kind of concrete networks of which we speak. (Castells, 1996:470). The inclusion/exclusion in networks, and the architecture of relationships between networks enacted by light-speed operating information technologies, configurate dominant processes and functions in the network society. (Castells, 1996: 470)
At the core of his description is the observation that networks are characterized by binary logic (inclusion/exclusion) and decentralized structures is fundamental. The existence of networks is determined by the utility of the nodes of the network. If some node ceases to serve the network, it will be phased out or replaced, and the network rearranges itself analogously to cells in biological processes. The importance of each node is determined by its ability to gain credence within the network by sharing information and to program and connect networks by mastering protocols that connect them with other networks (Castells, 1996; 2009; Stalder, 2006).
According to Castells, the network logic affects the economy and also shaped the spatial organization of society. He devotes a lot of effort to show how the new techno-economic paradigm affects macro-regions, nations, regions, and cities. This explains why his approach has been associated with economic geography (Goyal, 2007). One of the most insightful concepts introduced by Castells (1996) was the space of flows, which implies that network logic replaces place-centricity, i.e., “what counts in an economy is not being in the right place but rather being part of the right network” (Anttiroiko, 2015: 9). In this view, time is prominent and dictates network functioning and rhythm, whereas space becomes subsumed by a temporal dimension of accelerated rearrangement.
Similarly, when analyzing new forms of resistance, activism, and political engagement, the network society appears to be relevant (Castells, 1997a). He scrutinized civic movements and protests that erupted in various parts of the world in one of his later works, ‘Networks of Outrage and Hope’. Here, he presented the cases from the Arab uprisings to the ‘indignados’ movement in Spain and the Occupy Wall Street movement in the United States as networks supported by new communication tools (Castells, 2012).
However, Castells perceived political resistance essentially as autonomous communication networks irrespective of the actual penetration, use, and significance of information and communication technologies (ICTs) in the given real-life settings. Likewise, if everything from Facebook to protests in the streets of Seattle is explained in terms of a preordained network logic, the ability to accurately account for the emergence, forms, and operations of political actors dilute. In such analyses, the metaphorical use of the concept of the network appears to be a social counterpart to technological determinism (Anttiroiko, 2015).
In that sense, the network of interpersonal relations as a paradigmatic instance of analysis reflects methodological transactionalism, which may be extended by integrating social relations with non-human aspects of social reality as well as with the macrostructures that condition human behavior. This extension has been expressed in the actor-network theory (ANT), for example, in its attempt to overcome the agency-structure dichotomy and advance relational materiality (Crawford, 2005).
When one considers ANT and the agency-structure dimension, the network society is permeated by power and so by the allocation of asymmetric sources and information. Thus, not only connections and fluid interdependence are important in a macro analysis of networks, but also the different scales in which individuals, actors, information, and power are distributed. In that sense, it is necessary to complement network society with actors, but also to reintroduce an agency-structure perspective to oversee new shifts in the very functioning of the network society theory. Also, this analysis should cover, as mentioned, both human behavior and non-human features that redefine the network as a heuristic concept that allows the interpretation of society and even reality.
In that sense, this text aims to extend the network society theory with two sociological approaches. Firstly, we incorporate non-human and technological development that was not present at the time of Castell’s writing: datafication. This consists of the recent developments in big data analysis and automated management that have changed the very understanding of networks and the distribution of power that is typical from surveillance studies. This approach can be called dataveillance as it highlights the process of data gathering, combination, and distribution within an assemblage of monitoring and supervision in the hands of data processors. It brings up the proliferation of rhizomatic networks based on surveillance and data collection. Secondly, we aim to reintroduce a scale (rather than a dichotomy) between agency-structure to produce a holistic social analysis. This scale allows us to elaborate a microsocial and macrosocial picture of network societies. In the extremes of this scale, the agency is attained to individual actions/behavior and the structure is related to big economic and political actors in the dataveillance network.
Yet, both agency and structure are not reduced to actors and connections. A network is also comprised of autonomous communication, symbols, and functions that the two mentioned approaches can sometimes leave behind. Thus, these approaches are finally complemented by systems theory rooted in philosophical reading (hermeneutics) to elaborate a more comprehensive dialogue with Castell’s work. System theory focuses on communication, symbols, and functions beyond actors themselves as those also have some level of autonomy and redefine the latter ones (Luhman, 1986, 1995, 2006). In that sense, we argue that, ultimately, dataveillance network societies are enhancing a new paradigm called the social inflation system. In this shift, the ontological condition of individuals, the process of creation of symbols and communication rooted in material functions (economic networks), and the very interpretation of reality are dictated by a system or amalgam between actors-networks-data. As this system grows and expands, it emphasizes data collection, transformation, correlation, and distribution; spreading across many social networks and actors, posing new challenges to Castells’ macro-theory.
Thus, the postscript on the network society presents open questions that are worthy of consideration in the coming future by addressing three levels: the process of individuation (in order to cover human factors and the agency level), the shifts in economy (covering human-technological factors between social groups through a meso-level analysis between agency and structure), and the inflationary system expansion (covering the non-human factors such as communication and functions, and the sociological structural level). The latter step is an attempt to analyze not only society but also the current expansion of social fields, communicative tools, and networks based on digital data. At this level, the system recedes from individuals and knowledge, as data is always incomplete or provisional (Kaplan, 2018); needing to be matched with ulterior data in an autopoietic logic. Hence, we argue that we have entered an era of technological hyperconnection that relies on differentiation (accelerated bifurcation and expansion of new data niches) and disaggregation of actors and networks (where immanent ontology becomes indistinguishable from performativity in the hermeneutic reading of reality). In other words, reality itself is encompassed by a system of accelerated datafication that dislocates actors’ utility and connection, some of the core characteristics of the network society as stated by Castells. Let us develop this by discussing the three mentioned levels.
On individuals’ condition: immanence vs. Imminence
Amidst the array of connections, interactions, and actors in the network society, many scholars affirm that the ways we use digital technology (from labor to entertainment, education, etc.) entail visibility, representation, meaning, and material opportunities to people (Caluya, 2010; Lyon, 1994, 2007; Wilson and Norris, 2017). At the individual level, this insertion can be represented by a continuous creation of a provisional identity that is sorted, rearranged, and managed by data processors. This creation of ‘data doubles’ has been defined as the digital persona.
In psychology, the self is the inner personality, turned toward the unconscious, and the persona is the public personality that is presented to the world. For example, the persona that Jung knew was based on physical appearance and behavior (in Walters, 1994). With the increased datafication in the last decades, Jung’s persona has been supplemented, and to some extent even replaced, by the summation of the data available about an individual. The digital persona has become a provisional construct, “a rich cluster of interrelated concepts and implications. […] The digital persona is a model of an individual’s public personality based on data and maintained by transactions, and intended for use as a proxy for the individual” (Clarke, 1994: 78).
In a society where everything can be measured, compared, and rendered in a culture of performativity (Peters and Besley, 2019), it becomes natural that the digital personal constitutes itself as the main fuel to supply what Castells called the network society. As mentioned above, Castells expressed that power, culture, information, and society are marked by a sort of decentralized assemblage of relations based on transaction and utility. Yet, it is important to highlight that power is not equally distributed as the network itself becomes an apparatus of capture, measurement, and performativity.
In that sense, network societies match perfectly with dataveillance societies. The encounter between network theory and digital surveillance differs from physical and old electronic networks and involves classification and monitoring of the digital persona. As dataveillance is virtual and almost omnipresent, it requires indirect contact from a fixed individual to deploy ubiquitous electronic devices and construct the persona through multiple online interactions. Data performance is immediate and contingent, rather than fixed or attached only to one purpose. Hence, two classes need to be distinguished: Personal dataveillance, in which a previously identified person is measured, generally for a specific reason; and mass dataveillance, in which groups of people are watched for different purposes according to the data organizations (Van Dijck, 2014).
At the individual level, this can be exemplified by the commodification of the digital persona as a consequence of the dataveillance network society. Commodification consists of the acceleration of commercial architecture on the Web stressing “exploitation and enclosure, transforming users into commodities that can be sold on the market” (Petersen, 2008: 7). To Andrejevic (2011) data users are commodities with market value who have little choice over how and when this data is generated, and little say in how it is used. In this sense, he describes the generation and use of individuals’ data as the alienated or estranged dimension of their activity. To the extent that “this information can be used to predict and influence user behavior, [thus] it is an activity that returns to users in an unrecognizable form” (Andrejevic, 2011: 286).
Fuchs (2011) has also expressed that contemporary digital networks are based on the exploitation of ‘prosumers’ (producers and consumers) that create data. This argument could be summarized as the realization of digital techniques through which prosumers are electronically sorted and exploited. Users create content and information that return in the form of personalized commodities paradoxically extracted from them. On the Web, individuals do the semiotic work in the frontend while algorithms and engineers develop the backend production of informational services to users. In the case of giant platforms, algorithms seek to predict what kind of information will be of interest to each individual giving personalized experiences (which includes advertising but not only) back to users (Striphas, 2015).
In other words, this resembles the constant production and consumption of users’ data in a retro-alimentary circuit between user-platform-user to produce add-value experiences. Meanwhile, this circuit monetizes those informational experiences to third players and owners of the platforms. It means the total fusion between production and the means of production in a perfect flow that keeps capital, information, and goods being distributed to a few shareholders and owners of the backend platform. Thus, at the agency level, individuals interact under asymmetric power-informational conditions in which many are commodified.
This kind of interaction also alters the very individuality and behavior of the watched. For instance, McGrath (2004) and Whitson (2013) express that the power of dataveillance could produce gamification. Gamification means that subjectivities or users voluntarily expose their personal information, which is then used to drive behavioral change. It serves as an emulation of self-supervision, as it provides real-time feedback about users’ actions through large amounts of data. In other words, individuals become monetized by their own individuality, by their digital persona, and become regulated through rewards and achievements in the gamification performativity logic.
Both trends are increased insofar as algorithms regulate and classify large amounts of individual data. Despite humans’ influence over algorithms (from the design to retro-alimentation, and evaluation), the decision-making of automated codes gets more autonomy to establish the criteria, functions, semantics, and results to interpret data. In a market economy where “prioritizing is something we do on daily basis to cope with the information onslaught” (Diakopoulos, 2015: 3), algorithms prioritize information in a way that focuses on certain things at the expense of others. By definition, prioritization is about discrimination. As a result, there are profound consequences to individuals that should be considered in this economy since algorithms increase the divisionary logic of selection/exclusion when it comes to analyzing and interpreting society.
For example, where knowledge serves to establish authority and underpins judgment, dataveillance claims neither; its value is intrinsically speculative, derived from a stochastic range of possible transformations across multiple contexts. In fact, “the performative capacity of algorithmic dataveillance is proportionate to the indeterminacy and deferral of its value as information” (Kaplan, 2018: 180). As when a person enters a house of mirrors and sees his/her image distorted due to the movement and the position of the mirrors, the digital persona is sorted, bounced, and rendered by socio-technical tools in many ways and with different purposes (financing, insurance, search engines, social networks, online advertising and so on). Thus, what matters is not the digital persona per se, but the ways and capacity to extract and render information from this source. Thus, the person who is in front of the mirror dilutes with the mirrors themselves as the projections, images, and the array of angles become ‘infinite’ and transcendent.
In that sense, if one considers Castell’s network society, current automated tools emphasize the word network at the expanse of society as these increase the separation between a person and his/her digital persona; or between producer and product, and sender and receiver of messages (encoding and decoding procedures as expressed by Hall, 2001). Because of the commodification and gamification of data, there are more flexible schemes and relations to encode and decode individuals. The exploitation of users’ data and the redefinition of their behavior, ultimately, accelerate the abstraction of individuals from their individualities; highlighting the recombination of data fragments to recreate and represent subjects. In that sense, the Deleuzian ‘dividual’ (Colwell, 1996), or the abstraction of the self, is too short to catch up with the current management of individuals as they become contingent flows of subjectivation. Individual subjects are no longer fixed subjects, neither producers and consumers of content. Their personality is blurred in a set of profiles, each one representing a digital persona. At the same time, the individual is turned into a provisional prosumer commodity monetized and detached from the original ontology that is typical of a subject that is able to exercise creation and interpretation.
The ontological condition of a political subject was traditionally based on initial immanence, the active capacity of self-representation to create knowledge in a complex reality; in other words, on the capacity of the persona (the outer layer of self-representation) to constitute the inner self and vice-versa (Guo and Ma, 2018). It was about the external world being the ground to construct personality. Today’s dataveillance network society erases a fixed ground to grow an immanent ontology at the expense of provisional meaning, continuous emulation, and multiple representations. That is, imminence prevails over immanence. The continuous zapping or the chameleonic transitions of the digital persona are scattered and cannot be joined to create immanence. These transitions are like broken fragments that prevail over the image of a political subject and citizen. Thus, one can see how the network dataveillance society starts to become a hermeneutic system (a mode of reading) to digest scattered data from subjects to resemble a diffuse individuality. And, following the gamification logic, the more we are distorted and scattered, the more we like to play and project ourselves through the fragments and broken pieces of the mirror.
On economic shifts: Powerful yet fragile monopolies
All of this leads, in one way or another, to one of the central questions of the network society and the Fourth Industrial Revolution. How should data be governed, by whom, and towards which goals? Control and ownership of data are central to the data-driven economy and a form of knowledge governance, as well as an important public policy issue.
The dataveillance network society poses particular challenges as it functions by gathering, analyzing, and disseminating data, including individuals’ (sometimes sensitive) personal information. In many cases, such as smart cities, individuals essentially lose their right to withdraw, so governance of personal data takes on even more gravity with regard to consent and responsible use. In this scenario, big techs have acquired a central role and redefine the way networks are constructed and distributed (in terms of power but not only). Thus, it is important to understand how the digital economy affects and redefines the social ground on which individuals and groups interact.
So far, economic networks operated through vertical growing (companies’ expansion from within) and lateral growing (external acquisition of services and products). That is, economic connections and actors in the network society depended on the market development and the exchange of services and products. Yet, as expressed before, the blurred line between consumer and producers have also changed the forms in which companies operate in the network society.
Today’s technological platforms benefit from direct network effects among users on one side of a platform and indirect network effects stemming from cross-platform complementarity (Evans and Schmalensee, 2016). Digital platforms also have distinct supply-side economies of scale, with high fixed costs for initial investment, but low marginal costs. This can create a positive ‘feedback loop’: more sales means lower unit costs and a greater value proposition for new customers (Varian et al., 2004). Therefore, users tend to converge on a particular platform in a ‘winner takes all’ phenomenon (Galbraith, 1995) where network effects combined with increasing returns can orient the market in favor of a dominant firm.
A traditional firm can only collect data on its own customers, but a digital platform can access a vast amount of data related to all sellers and buyers on multiple sides of its platform. Digital platforms are able to capture large volumes of information about users from many different sources. The volume, velocity, and variety of the harvested data—the so-called ‘3 Vs’—enable ‘data network effects’. These function similarly to traditional network effects: more users contribute to more data generation, which helps to improve services and products, in turn leading to better user targeting and services. As a result, market checks on producer surplus increase competition to retain monopolies. In turn, in a ‘too big to fail’ allegory, ignoring established platforms becomes increasingly unlikely in the current network society (Kira et al., 2021: 1340).
Thus, as mentioned, there are concerns that the growing market power of technology companies that control the nature and volume of data collected and processed across digital ecosystems could translate into a systematic commodification and gamification of individuals based on monopolies. At the meso-social level, data collection and processing have upended revenue models and market functioning, making access to data an important source of power. To respond that power, data protection and competition law have originated from different social concerns and specific legal tenets (Jacobides et al., 2020).
The emergence of digital markets and the role played by giant data business actors have redefined the previous idea of decentralized networks with many actors that interdepend on each other by the exchange of services and products. Big techs have reestablished the idea of nodal actors with enough power to redefine the whole network society. Beyond being nodal actors, these firms not only have redefined the structure but also the very functioning and orientation of the network society by the emergence of digital monopolies.
And if the expansion of monopolies is a historical phenomenon (as old technologies like automation and telecommunication have also been promoted by a few giant firms), what is new is that current digital companies monopolize something that typically belongs to the sphere of intimate experience: forms of self-expression, social ties, or users’ memories and habits gathered from an infinite source or the digital persona. Their presence in our lives is often presented as intrusive, but this intrusiveness reflects, in fact, the ephemeral nature of their assets (Balzam and Yuran, 2022).
Yet, big techs not only grow because of data sources rooted on intangible values (emotions) but also because these are constrained and fall prey to the economic network they have redefined. This is explained by the nature of the economic growth and the kind of assets they can manage.
In terms of economic growth, it is known that big techs were initially funded by venture capital funds (Lerner and Nanda, 2020). Most of these companies – especially those created in this century – have subsisted on venture money for long periods before going public. Additionally, big techs have subsequently acquired many other digital technology companies themselves backed by venture capital (Amor and Kooli, 2020). As venture capitals have spread their investments over a wide spectrum of businesses with the hope that a few successful ones will generate enough profits to compensate for the overall investments they make; this financial logic forces start-ups to intentionally pursue a monopoly as nothing more than survival strategy.
In that sense, the dataveillance network society does not rely on a free market or a common agreement between nodal actors to make governance happen. Rather, the economic functioning of the current network society relies on a few monopolies/conglomerates that depend on small initiatives to keep running and being competitive. At the same time, small projects and start-ups, rather than being mere appendices or peripheric actors, support the promise of continuous expansion of revenues, feeding big actors and the network as a whole with the promise of big expansion as the main strategy to survive in this environment.
Therefore, big techs can be named fragile monopolies because they are victims of their own success. As individuals in the case of the digital persona, big techs have also become allegories of their initial economic condition. Their economic “ontology” relies on imminent revenues and continuous innovation extracted from small start-ups and lateral growth, rather than on immanent organizational management and vertical growth. In the incessant and relentless scheme of oligopolistic expansion, keeping the large size and the lead of the market takes these firms to promote a fragile assetization paradigm in the network society.
A monopoly needs to have “proprietary technology, network effect, economies of scale and branding” (Thiel and Masters, 2014: 48). The combination of these features reflects the typical form of big tech monopolies today: They do not monopolize an existing market but create their own market, monopolized from the outset. They create new economic niches, and so, new data realms and services. These companies monopolize markets that they have created and designed for that specific purpose. In such cases, the product is the vehicle for applying the business model, rather than the business model being the vehicle to distribute the product.
When it comes to profits, firms like Google, Meta, and Amazon generate profits when the data they collect exceeds the need for service improvement and serves targeted advertising. The logic of assetization, then, is one of the forces that motivate what Marc Andrejevic (2019: 33) calls “the imperative of total data collection”, which informs the world of automated media. In this model, what prevails is the capacity to make connections (inferences) with the data at disposal. This explains the success of intermediate platforms and the horizontal expansion of data business. This imperative resembles the workings of intelligence agencies, as Andrejevic demonstrates with a quotation from a CIA officer: “The value of any piece of information is only known when you can connect it with something else that arrives at a future point in time” (ibid.: 33). The analogy is telling. The data that big techs collect can be trivial and worthless at a first glance. Yet, it is the emergent quality of the totality which confers a promise of value on it and entails power to these firms in the so-called network society.
This is the context of the fragile nature of the new monopolies: these firms cannot stop data collection and use of even the most trivial pieces of the digital persona because a monopoly position in the digital economy is inherently under threat. This overflow of services may appear to reflect a limitless drive for profit and control. It seems as if some of these companies aim to invade every aspect of our lives. That may well be the case. But paradoxically it can nonetheless be understood as a survival strategy in the ongoing imminence of their economic position. That is, big techs are also inserted into a system that incites data inference (intermediating services), fast growth, and the assetization of intangible goods as keys to rule power in the current dataveillance network society.
On systemic changes: The social inflationary era
At this level, we argue that the network dataveillance society has created a sphere (a systemic dimension) that goes beyond the ontology of individual and social actors, as well as material and economic shifts. In that sense, we reconsider social systems theory expressing that systems are not restricted to communication as defined in the traditional sense (Luhmann, 1995) or to a continuous cycle of power and resistance in dataveillance (Mann et al., 2020). Traditionally, systems were also understood as a set of interactions that allow the proliferation of networks (Luhmann, 2006; Peters and Besley, 2019). Rather, we argue that the network society not only allows the constitution of social systems that differentiate one system from another. Ultimately, it also reshapes and alters the functioning in which this and other systems are connected. In other words, the dataveillance network society constitutes a system that alters reality in holistic manners by re-configuring the hermeneutic comprehension of individuals/things. Let us explain this.
The dataveillance network society goes beyond individuals and social actors to constitute a system. This system is defined as the amalgam of actors-networks-data. As this system encompasses everything and becomes rampant, the idea of a network recedes at the expense of systems with relative autonomy from individuals and actors to distribute information and power. It is the network becoming a social system. At this level, the dataveillance network society would be more connected with watching and measuring data for the sake of performance and productivity to create more data (from labor to entertainment). Rather than disciplining souls (governmentality) and biological bodies (biopolitics) to constitute a social order or social control (Deleuze, 1992; Foucault, 1991); this system renders data in a tautological logic: everything is data and so, everything can produce even more data.
Proof of this is the aforementioned assetization of goods and services by big techs in the digital economy. In this sense, information that would be banal or ‘normal’ like many posts and messages on social networks has reached, for the first time in human history, the status of a systemic value per se. This system has obtained a retro alimentary logic insofar as datafication has reached a status that gradually substitutes previous hermeneutics to interpret reality. To use the science fiction metaphor from Philip Dick, the sleeping electronic sheep also dreams about another electronic sheep dreaming.
This system is the continuation of a historical process in which social systems have reached a stage of auto-poietic reproduction or, in rough terms, of self-perpetuation (Luhmann, 1986). In the same logic, informational activities have reached a self-evident value, feeding internal demands to collect, analyze, and produce even more information. Data generates data despite the observers, gazes, and actors. In that sense, data demands the production of more data to analyze the previous one, which in turn needs to be matched and recombined with more information to be assimilated. But the self-referential process is not limited to data in the technological realm. It is being complemented with a process that we call ‘social inflation’.
Since the informational revolution in the last decades, ‘social inflation’ is a process in which every system and social domain gives birth to a subfield that in turn constitutes a new epistemological system (Luhmann, 2006). Like a branch that stems from a previous one, the social systems are increasing in number and volume. It combines the Deleuzian ‘difference’ or becoming (Ansell-Pearson and Pearson, 2012) with epistemological expansion and knowledge entropy caused by data. In physics, the universe expanded and the celestial bodies accelerated to distant points of the universe in a process called inflation. The current expansion and configuration of the universe is a product of that inflationary expansion. In the same allegory, social objects and informational systems are increasing and accelerating to produce new ones. In this vision, the network society is part of a system or universe in which new differentiated objects and data become cognitively separated and distant from previous ones.
Despite the technological hyper-connectivity, one challenge consists of giving an overall sense to the production of enormous data volume. The idea of the network itself is too short to catch up with the bulky information produced every day. A network still denotes a sense of interdependence and ephemeral connection. Yet, social inflation tears apart different subjects and things in an irreversible connection. Only provisional matching and inferences can be created to give a sense of reality. That is, mathematician allegories and metonymic algorithms are the main languages to ‘interpret’ social inflation.
To do this job, machines and automated procedures deploy provisional tools to interpret targets and data sets, seeking to simplify and reduce the complexity of information. Yet, those tools create new domains of specialization, social knowledge, and technical expertise. Those characteristics, in turn, contribute to increasing the inflationary expansion of entropic data that demands new procedures to interpret it. As mentioned before, this explains why intermediate platforms, which offer data services and connect different datasets, are as important as data monopolization to rule politics and economy.
Yet, the ‘social inflation’ goes beyond the theoretical ideas of the network society, governmentality, social control, and monopolistic expansion. The social inflation emphasizes entropic differentiation and data inference at any cost. And this capacity is exponentially growing. One smartphone can process the same amount of data produced by analogic-bureaucratic intelligence agencies during the Cold War. Today, bigger amounts of data are produced in shorter periods. And to the problem of data volume, the quality and integrity of data are inversely proportional to the capacity and velocity to process it. Thus, the more data we produce, the more we need to create tools to clean information and listen amidst the noise; especially through the labor of giant data companies who constitute the biggest market field in the global economy (Smyrnaios, 2018), as expressed in the previous section.
In that sense, the very idea of active citizenship and individual autonomy is disaggregated by the ‘social inflationary’ expansion of entropic data. Iaconesi (2017) describes how spectacularized information visualizations (also called ‘data smog’) disaggregates people from their abilities and responsibilities to understand relationships between the multiple ecologies in which they live, and the possibilities they have. This social distance is amplified as individuals are exposed to content they potentially like. The more people are clustered and categorized in proxy categories in the governance of the digital persona, the more they are enclosed in informational bubbles. Ultimately, these categorization tools try to reduce complexity but as a side effect they also reduce otherness and alterity from our reach. This brings on a series of controversial effects, such as the diminished sensibility to and acceptance of diversity (Bozdag and Van den Hoven, 2015), rising levels of cognitive biases (Bozdag, 2013), diminished tolerance, and social separation (Boyd & Crawford, 2012).
Moreover, informational bubbles and the disaggregation of social diversity in individuals’ interactions may bear the possibility that individuals progressively inhabit a controlled infosphere, in which a limited number of subjects can determine what is accessible, usable and, most important of all, knowable. This power asymmetry also implies the fact that users can systematically be unknowingly exposed to experiments intended to influence their sphere of perception to drive them to adopt certain behaviors instead of other ones (Zuboff, 2019).
One might still be skeptical about the apocalyptical visions that utter a sort of alienation and people manipulation at the advent of new technologies. However, what is different today is the mentioned capacity to encapsulate users on information bubbles that go beyond material devices and personal choices. Despite the ability to choose alternative sources of information, or to turn off smartphones, there is less room to escape from tautological systems in the social inflation. Individuals became allegories of themselves (digital persona) but also sources of information and continuous fragments of a society. As they become data, data itself constitutes a system of performativity for the sake of more data. Even markets and states strive to give a provisional orientation to the production and monitoring of data, but, at the same time, these also fall in the system of social inflation.
As a strategy to survive, powerful organization expand in monopolistic forms to access different databases and handle the tools to analyze data. Since total control over data is not possible, as reality itself is an infinite source for data processors, those organizations enhanced their ability to gather and monitor information from key sources: individuals. Beyond being a source of production and labor, a population has become, at the same time, a reserve of information that can be better exploited for the benefit of dataveillance (Castelluccia, 2020). This exploitation also goes beyond the commodity form and gamification, as expressed above. Nowadays, populations are also a potential source to feed the pipelines of information. Data subjects are valuable because of their constitution: they are subjects of data. Every database now could be as valuable as material sources to determine productivity and wealth in the coming future. The expansion and the capacity to manage populations through data is perhaps the new frontier of power to decide the future of humanity, at least during the current century (Zuboff, 2019).
Informational flows are as vital as water and food to live in our current societies. Following the evolution of data governance, the traditional economy of scarcity (of material goods) has been supplemented by a new economy of abundance (of immaterial goods). Sharing and distributing material artifacts usually decreases their value but sharing and distributing immaterial artifacts almost always increases their value (Martínez Cabezudo, 2014). This context transcends the labor horizon, affecting mutual interactions, the sense of own reality, and the interactions with reality itself (Jandrić et al., 2019). The digital fusion of material and immaterial production goes beyond the economic sphere to directly address the cultural, the social, the political, and the ontological.
In that sense, this type of production redefines biopolitics because it does not only affect life, producing docile bodies and material goods but also the inner conditions for social relations in a system simultaneously rooted and detached from life (Aradau and Tazzioli, 2020). Thus, it is not necessary to consolidate breakthroughs such as quantum computing, cybernetics complexity theory, and deep machine learning to realize that we have arrived at the age of algorithmic biopolitics that characterizes the social inflation theory. Its current phase, the ‘biologization of digital reason’ (Peters and Besley, 2019) is a distinct phenomenon that is emergent from the application of mechanical reason to biology and the biologization of digital procedures. Indeed, the promise of those technologies works like utopia dreams to justify a technological Manifest Destiny from big techs to connect and ‘save’ humanity.
Moreover, there is no need to move to science fiction scenarios to realize that the social inflation extends and redefines our understanding of reality. The network dataveillance is a system in which data flows is like the oxygen that keeps individuality alive but distorted. We have mentioned that this distortion is a continuous hermeneutics based on the imminence of data analysis instead of the construction of knowledge based on the immanence of a subject. Also, there is accurate potential from automated data processors to reach each person as the techno-social interaction between individuals and machines allows or closes different opportunities to understand the world, and so to live. Thus, giant data processors always strive to deliver or maximize performance and personal experience for each user, as well as discover new data universes and virtual realms.
The network dataveillance system works thanks to the differences among the bulky data. In anonymous data, national security realms, and market domains, the recombination, correlation, and matching of data fragments is even more important than the own individuals to create and allocate normality within a population (Van Dijck, 2014). Considering probabilistic and statistical judgments, personal data systems, as mechanisms for the sake of identification and assessment, are part of the social system that constitutes the architecture of a continuous performance society that even alters previous ideas of simulation and reality.
Nowadays, simulation not only refers to divisions and criteria to distribute symbols that emulate realness back to individuals in the sense of hyperreality (Baudrillard, 1981). If we consider systems theory and differentiation as driving forces of the social inflation, then it means to assuming the insufficiency and indeterminacy of hyperreality or simulation. In other words, the more data is collected from individuals, the more the dataveillance system uncovers that there is to know, which makes people recede even further into their massive mystery and unknowingness (Kaplan, 2018). Far from marking the limit of the ‘Church of Data’, this apparent paradox is simply its functional principle. The aim of politics and economy through data nowadays is not modeling or understanding an external object (to simulate reality) but the endless reproduction of this object’s statistical indeterminacy and opacity as the protocol of the system continuing operation.
Considering that, the myth of our era consists of the illusion that data can speak for itself. The ‘social inflation’, in the new hermeneutic cycle of things and subjects, proclaims that data needs more data. However, this assumption never grasps or fosters a quest for more knowledge, because there is nothing to know (data is always provisional and incomplete, it is a fragment of something that is yet to come) and no sense in knowing (to give cohesion and coherence to incomplete data and reality). There is no necessity to know and give sense to a reality comprised of data fragments as the dataveillance system becomes tautological. In that myth, the only thing data processors can do is to declare they can interpret the world and people without their mediation and resistance, and by finding ‘relevant’ correlations.
The current dataveillance system is an autopoietic cycle of tokenistic efficiency. Its cynical meaning, as expressed by Kaplan (2018), disallows a ground for reality representation and undermines subjectivity and agency (even if individuals can exercise some degree of resistance in the first and second levels). This system is inserted in a major process called ‘social inflation’ that brings up a symbolic efficiency that enables a self-referential expansion marked by differentiation and informational bubbles. In that expansion, systems override the classical society network that continually marks the unity of cognition and expands the disaggregation of actors in systems that concentrate power as long as they expand data deficits that must be overcome. As the deficits will ever persist, because data is never enough, the expansion of this phenomenon constitutes an efficient system indistinguishable from endlessly recurring failures. As a universe that spreads and differentiates continuously, such is the exponential growth that characterizes the systemic expansion of the network-actors-data cosmos.
Traditionally, even within Castell’s network theory, the expansion of data and information was also the expansion of knowledge. Aggregated data produced accumulated knowledge throughout history. However, nowadays the problem is not only catching up with the bulky volume of data produced every day. The problem is also that datafication privileges the collection of information over the process of interpretation. When the two should complement each other, the former dictates the rhythm and form to understand the latter. Datafication entails a form of analysis that can only be conducted with the arrival of more data at some point in the future. It looks forward and sees the world as a huge data source to be managed.
The dataveillance network system does not look to the past as previous data is always incomplete and can always be improved with the development of new data sets, new inference tools, new codes for matching, and new outputs that still are provisional and fragmented. Rather than a corpus of knowledge that depends on active subjects to be produced and transformed; data speaks for itself but paradoxically is never complete. The dataveillance network system rescinds from the immanent ontology of subjects as these are also encompassed by a techno performativity reading of reality for the sake of data harvest and use. The recombination, correlation, and matching of data fragments is even more important than the own individuals to create and allocate power within a population.
And if controlling data sets and establishing intermediate services to recombine and match data fragments is essential in this era, even these activities are encompassed by a process of exponential differentiation and social disaggregation called the “social inflationary era” or “social inflation”. Even the giant data processors that rule the economy in the network society are fragile monopolies that have been encompassed by a system of expansion of new networks, actors, fields, and epistemologies that accelerates a sort of tautological ramification and autopoietic data perpetuation.
The network society becomes an inflationary system that links actors through continuous and provisional data inferences; a proliferation of networks based on potential matching that is never complete; thus, it is never wrong. Rather than an interdependent network comprised by specific actors and nodes, it is a fluid field that flows in every direction, from the micro-level of individuation to the structural level of economy and interpretation of reality itself.
In many physicist models, the inflationary phase of the universe’s expansion was very short at the beginning of the universe but it could last forever in at least some regions of the universe. This occurs because inflating regions expand very rapidly, reproducing themselves. Unless the rate of decay to the non-inflating phase is sufficiently fast, new inflating regions are produced more rapidly than non-inflating regions (Steinhardt, 1983; 2011). In such models, most of the volume of the universe is continuously inflating at any given time.
Likewise, the expansion of the social inflationary system gives rise to new fields, and new systems of ‘networks-actors-data’ in which the emphasis on data reading represents a new hermeneutic paradigm to interpret everything. This reading becomes indistinguishable from ineffectiveness, as a failure can be overcome by incessant datafication. This process does not work as a representation of the world (or simulation) but as a substitution and creation of new worlds that cannot be unified, just provisionally correlated. New inflating systems are produced more rapidly than non-data-driven systems. In such cases, most of reality is continuously data inflated towards unforeseen directions.
In early inflationary models in physics, for any one observer, the distance to the cosmological horizon or observable universe is constant. With exponentially expanding space, two nearby observers are separated very quickly; so much so, that the distance between them quickly exceeds the limits of communications. The spatial slices are expanding very fast to cover huge volumes. Things are constantly moving beyond the cosmological horizon, which is a fixed distance away, and everything becomes homogeneous.
Likewise, in the social inflationary expansion; despite technological connection, social objects and subjects become disaggregated and the distance between them exceeds political agency and knowledge communication. As everything becomes dataficated, everything becomes homogeneously important and so, irrelevant, until a new ‘universe’ is born to repeat that expansion.
Jaseff Raziel Yauri-Miranda. University of Deusto
Israel Arcos Fuentes. University of the Basque Country
Amor, S. B. and Kooli, M. (2020). Do M&A exits have the same effect on venture capital reputation than IPO exits?, Journal of Banking & Finance, 111, 105704.
Andrejevic, M. (2019). Automated media. London: Routledge.
Andrejevic, M. B. (2011). Surveillance and alienation in the online economy. Surveillance and Society, 8(3), 278-287. https://doi.org/10.24908/ss.v8i3.4164
Ansell-Pearson, K., & Pearson, K. A. (2012). Germinal life: The difference and repetition of Deleuze. London: Routledge.
Anttiroiko, A. V. (2015). Castells’ network concept and its connections to social, economic and political network analyses. Journal of Social Structure, 16(1), 1-18.
Aradau, C., and Tazzioli, M. (2020). Biopolitics multiple: migration, extraction, subtraction. Millennium, 48(2), 198-220. https://doi.org/10.1177%2F0305829819889139
Baudrillard, J. ((1981)2019). Simulacra and simulations. In Crime and Media, Greer, C. (Ed.). 69-85. London: Routledge
Balzam, G., & Yuran, N. (2022). Assetization and the logic of venture capital, or why Facebook does not ‘feel’ like a monopoly to Zuckerberg. Science as Culture, 31(1), 107-120.
Boyd, D., & Crawford, K. (2012). Critical questions for big data : Provocations for a cultural, technological, and scholarly phenomenon. Information, communication & society, 15(5), 662-679.
Bozdag, E. (2013). Bias in algorithmic filtering and personalization. Ethics and information technology, 15(3), 209-227.
Bozdag, E., and van den Hoven, J. (2015). Breaking the filter bubble: democracy and design. Ethics and Information Technology, 17(4), 249-265.
Caluya, G. (2010). The post-panoptic society? Reassessing Foucault in surveillance studies. Social Identities, 16(5), 621-633. https://doi.org/10.1080/13504630.2010.509565
Castells, M. (1989). The Informational City: Information Technology, Economic Restructuring, and the Urban-Regional Process. Oxford: Blackwell.
Castells, M. (1996). The Rise of the Network Society. The Information Age. Economy, Society and Culture, Vol. I. Oxford: Blackwell.
Castells, M. (1997a). The Power of Identity. The Information Age. Economy, Society and Culture, Vol. II. Oxford: Blackwell.
Castells, M. (1997b). An Introduction to the Information Age. City 2 (7): 6-16.
Castells, M. (1998). End of Millennium. The Information Age. Economy, Society and Culture, Vol. III. Oxford: Blackwell.
Castells, M. (2001). The Internet Galaxy: Reflections on the Internet, Business and Society. Oxford: Oxford University Press.
Castells, M. (2009). Communication Power. New York: Oxford University Press.
Castells, M. (2011). A Network Theory of Power. International Journal of Communication, (5), 773-787.
Castells, M. (2012). Networks of Outrage and Hope: Social Movements in the Internet Age. Cambridge, UK: Polity Press.
Castelluccia, C. (2020). From dataveillance to datapulation: the dark side of targeted persuasive technologies. HAL Science Ouverte. Retrieved from https://hal.archives-ouvertes.fr/hal-02904926 in 10/17/2020
Colwell, C. (1996). Discipline and control: Butler and Deleuze on individuality and dividuality. Philosophy Today, 40(1), 211-216. https://doi.org/10.5840/philtoday199640148
Crawford, C. (2005). Actor network theory. In: Encyclopedia of Social Theory, Ritzer G (ed)., 1-4. Thousand Oaks, CA: Sage.
Deleuze, G. (1992). Postscript on the Societies of Control. October, 59, 3-7.
Diakopoulos, N. (2015). Accountability in algorithmic decision-making. Queue, 13(9), 1-24. https://doi.org/10.1145/2857274.2886105
Evans, D. S. and Schmalensee, R. (2016). Matchmakers: The New Economics of Multisided Platforms. Boston: Harvard Business Review Press.
Foucault, M. (1991). The Foucault effect: Studies in governmentality. University of Chicago Press.
Fuchs, C. (2011). Web 2.0, prosumption, and surveillance. Surveillance and Society, 8(3), 288-309. https://doi.org/10.24908/ss.v8i3.4165
Galbraith, J. K. (1995). The winner takes all… sometimes. Harvard Business Review, 73(6), 44–45.
Goyal S (2007). Connections: An introduction to the economics of networks. Princeton: Princeton University Press.
Guo, A., & Ma, J. (2018). Archetype-based modeling of persona for comprehensive personality computing from personal big data. Sensors, 18(3), 684-697.
Hall, S. (2001). Encoding/decoding. Media and cultural studies. New York: Blackwell Publishers.
Iaconesi, S. (2017). Interface and data biopolitics in the age of hyperconnectivity. Implications for design. The Design Journal, 20(sup1), S3935-S3944.
Jacobides, M., M. Bruncko and R. Langen. (2020). Regulating Big Tech in Europe: why, so what, and how understanding their business models and ecosystems can make a difference. London: London Business School. https://ssrn.com/abstract=3765324.
Jandrić, P., Ryberg, T., Knox, J., Lacković, N., Hayes, S., Suoranta, J., and Ford, D. R. (2019). Postdigital dialogue. Postdigital Science and Education, 1(1), 163-189. https://doi.org/10.2307/3685230
Kaplan, M. (2018). Spying for the People: surveillance, democracy and the impasse of cynical reason. JOMEC Journal, 12, 166-190. http://doi.org/10.18573/jomec.165
Kira, B., Sinha, V., & Srinivasan, S. (2021). Regulating digital ecosystems: bridging the gap between competition policy and data protection. Industrial and Corporate Change, 30(5), 1337-1360.
Lerner, J. and Nanda, R. (2020). Venture capital’s role in financing innovation: what We know and How much We still need to learn. The Journal of Economic Perspectives: A Journal of the American Economic Association, 34(3), 237–261.
Luhmann, N. (1986). The autopoiesis of social systems. Sociocybernetic paradoxes, 6(2), 172-192.
Luhmann, N. (1995). Why systems theory. Cybernetics and Human Knowing, 3(2), 3-10.
Luhmann, N. (2006). System as difference. Organization, 13(1), 37-57. https://doi.org/10.1177/1350508406059638
Lyon, D. (1994). The electronic eye: The rise of surveillance society. University of Minnesota Press.
Lyon, D. (2007). Surveillance studies: An overview. Polity.
Mann, S., Nolan, J., and Wellman, B. (2020). Wearables and Sur (over)-Veillance, Sous (under)-Veillance, Co (So)-Veillance, and MetaVeillance (Veillance of Veillance) for Health and Well-Being. Surveillance and Society, 18(2), 262-271. https://doi.org/10.24908/ss.v18i2.13937
Martínez Cabezudo, F. (2014). Copyright y copylef. Modelos para la ecología de los saberes. Sevilla: Aconcagua Libros.
McGrath, J. E. (2004). Loving Big Brother: performance, privacy and surveillance space. Psychology Press.
Peters, M. A., and Besley, T. (2019). Critical philosophy of the postdigital. Postdigital Science and Education, 1(1), 29-42. https://doi.org/10.1007/s42438-018-0004-9
Petersen, S. M. (2008). Loser generated content: From participation to exploitation. First Monday, 1-11.
Smyrnaios, N. (2018). Internet oligopoly: the corporate takeover of our digital world. London: Emerald Group Publishing.
Stalder F (2006). Manuel Castells and the Theory of the Network Society. Cambridge, UK: Polity Press.
Steinhardt, P. J (1983). Natural inflation. Very early universe, 251-266.
Steinhardt, P. J. (2011). The inflation debate. Scientific American, 304(4), 36-45.
Striphas, T. (2015). Algorithmic culture. European Journal of Cultural Studies, 18(4-5), 395-412. https://doi.org/10.1177%2F1367549415577392
Thiel, P. A. and Masters, B. (2014). Zero to one: Notes on Startups, or how to Build the Future. New York: Crown Business.
Van Dijck, J. (2014). Datafication, dataism and dataveillance: Big Data between scientific paradigm and ideology. Surveillance and society, 12(2), 197-208. https://doi.org/10.24908/ss.v12i2.4776
Varian, H., J. Farrell and C. Shapiro (2004). The Economics of Information Technology. Cambridge: Cambridge University Press. https://EconPapers.repec.org/RePEc:cup:cbooks:9780521605212.
Walters, S. (1994). Algorithms and archetypes: evolutionary psychology and Carl Jung’s theory of the collective unconscious. Journal of social and evolutionary systems, 17(3), 287-306.
Wilson, D. and Norris, C. (Eds.). (2017). Surveillance, crime and social control. New York: Routledge.
Whitson, J. R. (2013). Gaming the quantified self. Surveillance and Society, 11(1/2), 163-176. https://doi.org/10.24908/ss.v11i1/2.4454
Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. London: Public Affairs.