Malleable, Digital, and Posthuman

A Permanently Beta Life

Ignas Kalpokas

• Publisher: Emerald Publishing Limited
• Year: 2021
• ISBN:
  9781801176224
• Topic: Sozialwissenschaften
• Subtopic: Soziologie

The world we live in is increasingly malleable and fluid, especially in regards to being human - rendering the self into a permanent beta version, co-constituted within agglomerations of platforms, devices, physical infrastructures, entities pertaining to physical and biological nature. This book proposes a posthumanist research methodology for future research in this area, providing a novel explanatory and methodological framework for studying today's world.

Malleable, Digital, and Posthuman studies four areas: the economy, the human self, politics, and research ethics and methodology. In the economic domain, Kalpokas focuses on the emergence of the attention economy and the ensuing shift towards personalisation and experience, shaping the (digital) environment for optimised user interaction. Consequently, the datafication and algorithmisation of the social world necessitates an art and craft of the self, establishing a co-constitutive interaction between the self and digital infrastructures. These changes also strongly affect politics, primarily through datafied management of the political and employment of predictive analytics in preparing ground for political action, thereby rendering collective identities and political leadership malleable and open to relentless beta testing.

With unique insights and an innovative framework, this book is essential reading for researchers in the areas of media and communication studies, politics and social theory.

Chapter 1

The Malleable Environment: Attention, Sensing and Wrapping

The first domain of betafication to be discussed in this book pertains to the environmental conditions (physical, technological and digital) of human existence and human interactions therewith. Such malleability should be seen in the context of ever-intensifying competition over attention within the digital domain. The sheer volume of digital products and providers thereof, combined with limited audience attention spans, means that it is becoming ever more complicated to get a message, service or any other kind of content unit across. This complexity brings about the necessity to maximise the experience and the satisfaction of the consumer through offering them more of what they seemingly want, whence not only the attention ultimately attracted but also the data used for and generated by such attraction become tradable commodities to be employed in further environmental modulation of needs and wants. And the more data are extracted, the more can be sold (and the more can be used to further retain the consumer in the same bounded space of extraction).

In the above sense, it is tempting to see consumers as themselves participating in their own subjection. And while such an interpretation might not be entirely incorrect, it nevertheless obfuscates a more profound development – the agglomeration of human users, data collection infrastructures, the data themselves and the algorithms used to crunch them, the coded architectures of platforms, inter-human and human–environment interactions, environmental conditions and all sorts of other conceivable elements into interactive wholes, wherein every element is, actually or potentially, affecting and being affected by any other element. This interactive malleability is referred to as permanently beta life.

Production of an integrated commodity that encompasses both the individual and the entirety of their environment is key to the above. The ever-expanding breadth of datafication, resulting from the pervasiveness of platforms and sensor-equipped devices, means that there is little – if any – room available to avoid the data gaze, turning the mundane into the paramount source of data and, even more prominently, metadata. Nevertheless, this production, if left by itself, would be simply wasteful as both data and metadata, once produced, have to be refined – i.e. made sense of. It is here that the algorithm – as the tool for making sense of and structuring everyday life – comes to the fore. The algorithm, thus, has clear descriptive and prescriptive capacities as well as a governance function in determining what is and what is not doable. The two – sensemaking and structuring – functions are seen to coalesce in the algorithmic management of affective proclivities. Hence, as consumers engage with content, they produce data that are algorithmically analysed and fed into the digital architecture of code in order to maximise engagement and data generation by wrapping consumers in personalised experience cocoons. In this way, the loop that ties the diverse elements of agglomerations together is closed, setting up the conditions for betafication.

1.1 The Struggle over Attention

While in the early days of the Internet, some would have questioned our capacity to fill it with content, the situation today is exactly the opposite – as our ability to filter through and make sense of the overabundance of content is overstretched, a new economy based on competition over attention is acquiring shape (Ibrahim, 2019, p. 1116). Indeed, it is hardly controversial that today's media environment is characterised by abundance, interactivity and mobility (Mazzoleni, 2017, pp. 140–141). In fact, today's entire screen-centric world is perhaps best described by a certain messiness or, at best, fuzziness. As Chambers (2019, p. 3) asserts,

…[t]oday's digital screen uses are associated with chaos and disorder: a blurring of work and home zones, spurred by notions of temporal excess, absorption, immersion and a squandering of time.

As suggested by cognitive bottleneck theory, when several cognitive tasks must be performed at the same time, performance will suffer, because ‘if attention goes in one direction it cannot be in another’; therefore, it is crucial that ‘[t]he choices we make and how we allocate our attention are crucial factors that determine how a currency is made out of attention’ (Tanner, 2020, p. 66). Under such circumstances, one must agree with Couldry and Hepp (2017, pp. 112–113) that the key challenge becomes a communication and information overload, whereby even if a message is heard, it is likely not to be attended to due to the audience's lack of time for doing so and the ensuing need to ‘drastically select from the environment’. This problem pertains equally to individual messaging (e.g. posting on social media), corporate communication, media products (including news and current affairs), marketing and new product offerings across industries, etc. And while for some competitive environment appears to be rather straightforward, suggesting that, for example, ‘to get our attention hooked to the screen, algorithms simply need to get us at our worst: our narcissism, vanity, gullibility, our non-negotiable righteousness, our anger, envy, lust, and greed’ (Hilbert, 2020a), the argument developed below is that the actual picture is much more complicated and interactive than such simplistic accounts of one-way exploitation would suggest.

Certainly, the necessity to select is not unique to the contemporary media environment – for example, Andrejevic (2020, p. 34) maintains that selectivity is the key premise of subjectivity, meaning that we always ‘construct our experience around gaps and omissions’. Nevertheless, the exponentially increasing quantity of stimuli to be selected from brings this capacity into overdrive, leaving us susceptible to – or, quite likely, even willing to rely on – priming through an algorithmically tailored choice architecture. The latter is, of course, not left to chance – it is part and parcel of audience data collection and analysis to aim towards predicting ‘future activity so it can be anticipated and shaped (or pre-empted) through environmental modulation’ (Andrejevic, 2020, p. 40). In that sense, the agency of humans and code (and of sensors and data repositories) becomes intertwined, paving way for the main theme of this book – malleability or betafication. In other words, the choice architecture in which users find themselves is tailored so as to nudge them towards particular choices (hence, ‘debugging’ them with the view of more efficient consumption of informational or material goods), but then our reactions as well as seemingly unconnected behaviours are construed as feedback (thereby augmenting – betafying in return – the pre-existing sets of data and, through them, algorithmic models that predict our behaviours) and those, again, end up reframing our experienced environment through a continuous loop that involves mutual testing and augmentation of users and their environment, all in a characteristic beta fashion.

Moreover, further contributing to the mismatch between supply and the capacity to at least develop awareness (let alone demand), there are, quite simply, ‘only so many people and so many hours in the day’ – hence the acuteness of the zero-sum competition over attention (Webster, 2014, p. 1). This scarcity of attention is only increased by ‘an overriding push by corporations and institutions to capture, mobilize, and profit’ from it, ultimately rendering the characteristics of attention very similar to those of money: ‘most of us do not have enough of it, we seek more of it, but it is unequally distributed’ (Doyle & Roda, 2019, pp. 1–2). And to narrow the window of available attention even further, despite the substantial increase in the freedom of consumption brought about by digital media, there are still larger social structures at play – most notably, patterns of how we live our lives, such as ‘when we work, where we live, what languages we speak, our socioeconomic status’, continue constituting ‘a remarkably stable, if mundane, force that shapes when and how audiences are likely to form’ (Webster, 2014, p. 14). As each of the aforementioned factors puts an ever-narrower limit to the amount of attention to be competed for, the stakes are simultaneously increased (as per the basic model of supply and demand). As a result, then, the arena of consumption becomes a scene for an open struggle over the control of attention, in which intercepting (or even hijacking) the loop of malleability becomes key to success.

Crucially, audience attention must be treated as a fixed resource while the ability to pay it is biologically hard-wired and, therefore, constrained, provoking a race for ever-more efficient capture in order to turn it into revenue. The latter is particularly important because attention allocation is a zero-sum game: ‘we allocate our attention to particular stimuli in the environment and ignore everything else’ (Stroud, 2017, pp. 479–480; see also Citton, 2017). Simultaneously, this implies that even if attention is caught by one actor, it can be ‘stolen’ at any given moment, leading to a hyper-competitive fracturing of the media environment in which each player ‘experiments with new designs, targeting strategies, and stimuli to steal attention’ and to subsequently retain it just ‘long enough to convince the potential customer to take some action’ (Vaidhyanathan, 2018, p. 80). The net result of this hyper-competitive fragmentation is a digital ‘ecosystem’, i.e. an infrastructure of resonances conditioning our attention to what circulates around, through and within us (Citton, 2017, p. 29). Under these conditions, it is clear that the current environment is strongly consumer-centric, but that consumer centricity is about attracting attention as effectively as possible. What suffices is that the user thinks they are served well, and that this perception is congruent with environmental feedback (which, in turn, underscores the importance of environmental beta testing in light of user expectations both pre- and post-allocation of attention).

Attention is also a significant resource in another important sense – crucially, ‘the simple fact of looking at an object represents a labour which increases the value of that object’ because attention is what attracts even more attention; it is because people are looking at something, discussing something and sharing something that other people are looking at, discussing and sharing that thing (Citton, 2017, pp. 47–48). That also reveals one more way in which attention has money-like characteristics: ‘[w]e increasingly pay with our attention’, exchanging it for enthralling experiences (Doyle, 2019, p. 56). After all, the algorithmic environment, acting as an ‘attention machine’, ‘directs your attention depending on the way in which other net users have directed their attention’ (Citton, 2017, p. 71). Hence, it is by no means an exaggeration to claim, as Sunstein (2018, p. 229) does, that audiences themselves ‘are commodities at least as much as they are consumers’, their attention (and data on attracting attention derived thereof) acting as both a currency to acquire more attention and as a consumable incentive to those whose attention is to be attracted. Notably, then, the digital self, represented in a data form, ‘is being sliced and diced into decontextualized parts, and bought and sold’ (Neff & Nafus, 2016, p. 62). Here, it becomes clear that the human person is both a consumer and a raw resource from which value can be extracted, thus also opening up a dual role in the betafication of the environment: as both the goal of environmental modification and an impulse for further modifications to be tested in turn. In this way, beta testing and ensuing adjustment happens in two ways: through direct human interactions and through anticipatory computation based on existing data and, therefore, behavioural models of the same users.

Of course, users themselves contribute by accepting profiles and accounts, and the data surrendered through them, as the default and the only way of interacting through the digital world, thus naturalising the dynamics of profiling in expectation of the world being seamlessly tailored to their own preferences and behaviours (Siles, Segura-Castillo, Sancho, & Solís-Quesada, 2019, p. 18), i.e. pre-beta tested for them in the second (data- and model-based) way stressed above. Nevertheless, in order to achieve such tailored comfort, one needs not merely to confess the private but to continuously do so (Szulc, 2019, p. 266), thereby permanently working to produce data in order to get the content for which one has to additionally pay anyway, doing so either directly (to the platform, e.g. as a subscription fee) or indirectly (to those who serve ads via the platform). And if predictions of data overtaking money in importance in the markets of very near future (see, notably, Mayer-Schönberger & Ramge, 2019) are to come to fruition, the drive towards extraction of data is only going to intensify, but without direct remuneration to the actual data-generating users, except perhaps for tailored satisfaction. Still, for the latter to work, the entire experienced environment must be appropriated in order to render it from a stable given to permanently beta. And that involves not only the coded architecture of platforms and other online spaces (that are adaptive by definition) but also imbuing technological/mechanical artefacts and the physical environment with ‘smart’ capacities.

Under the above conditions, consumer experience has become ‘a key competitive differentiator’, necessitating the feeling of being ‘uniquely understood and important’ (Wladawsky-Berger, 2018), thus underscoring the importance of ‘optimizing and personalizing the customer experience you provide’ (Stephenson, 2018, p. 57). In this sense, impressions become more reliable information transmitters and argument setters than facts – particularly because user perceptions and, therefore, likely development of and reaction to impressions are now easily knowable through data (Krasmann, 2020, p. 2101). In fact, ‘user experience design’ (Hemann & Burbary, 2018, p. 82) could be seen to have become one of the core activities of any business or other attention-seeking actor, making use that the offering is always moulded in the shape of the user to fit them like a glove.

Likewise, there simply must be a ‘click’, something that grabs the target audience's attention straight away, often through maximising pleasurable emotions (Léveillé Gauvin, 2018, pp. 293–294) as well as through the employment of tools for maximising personal relevance, such as real-time geodemographic targeting, coupled with the capacity to measure observable behaviour (Smith, 2019, pp. 1044–1045). However, this process is also always about more than merely the user themselves. Kotras (2020, p. 2), for example, uses a rather paradoxically sounding term ‘mass personalization’ to describe the above phenomenon. Despite its oddness, this term is particularly apt; it is a process of personalisation, but one carried out on a mass scale and with recourse to multiple others. At the heart of the matter are ‘algorithmic processes in which the precise adjustment of prediction to unique individuals involves the computation of massive datasets, compiling the behaviors of very large populations’ (Kotras, 2020, p. 2) – hence, the one and the many are inseparable, locked in a mutual recursive relationship sustained by algorithms. Similarly, for Lury and Day (2019, p. 18), ‘[p]ersonalization is not only personal: it is never about only one person, just be or just you, but always involves generalization’, underscoring that the one and the many are always two sides of the same coin and are irrevocably interrelated even in processes of user-centric personalisation, whereby this one-and-many hybrid has always already beta tested the environment for you.

Nevertheless, personalisation is going further than just real-time adjustment of the choice environment. Instead, the very notion of time is changing. Here, Bucher (2020a, p. 1711) contributes an extremely important insight by stressing the emergence of ‘a new temporal regime’ focused around right-time that is produced through and by algorithmic media (Bucher, 2020a, p. 1711). Instead of providing the experience of a shared nowness, right-time constitutes ‘a common personalized time regime’, the ‘algorithmic fabrication’ of which focuses on ‘delivering the promise of relevance rather than relevance itself’ (Bucher, 2020a, p. 1711). Hence, the premise of personalisation becomes not relevance as real-time provision of what we need, but the promise of relevance as in a ‘click’ between the user's self-perceived needs and the algorithmic offering. Seen in this way, right-time is the product of ‘the networked and relational nature of algorithms’ as a synthesis of mere computational processing and a much broader network of socio-technical relationships through which ‘[u]sers, advertisers, business goals, and other economic and ideological motivations ultimately condition the fabrication of right-time’ (Bucher, 2020a, p. 1711). Overall, then, complex agglomerations of diverse entities constitute the nature and fabric of time within and through a constant state of interaction (and potential tension) whereby right-time is produced out of what ‘right-time’ is for each one of them. Consequently, then, we do not necessarily live in a real-time world, but we increasingly live in an interactively co-constituted right-time world, each of those right-times pre-tested by datafied models, effectively rendering even time into a beta version.

The multiple personalisation measures described above must be seen to serve a triple purpose. First of all, the goal is to extract as much data as possible (the longer we are hooked and perform while hooked, the more data can be obtained). Second, the aim is to put that data into use, i.e. to articulate, sell and verify predictions of our behaviour or to expose us (and, again, measure and verify our responsiveness to) behavioural nudges, be it through advertising, exposure to certain information or other means. The latter also pinpoints the poignancy in Zuboff's (2019, p. 194) appeal to a right to the future tense, a right to independent future behaviour, free from such an intensive and aggressive future crafting. Indeed, as Berardi (2021, p. 43) concurs, under such conditions of intensive datafication and structuration, ‘[t]he future is no longer a possibility, but the implementation of a logical necessity inscribed in the present’ – something that not only is knowable but also can be brought forward through manipulation of the choice architecture (its pre-testing). Finally, the third purpose is distribution of rewards: as mentioned above, environmental tailoring and the wrapping of users in personalised experience cocoons is the only real way in which their data-generated labour is rewarded. Nevertheless, this reward is itself neither altruistic nor finite: as per the second purpose above, any interactions with the tailored environment are primarily sources of new data that is fed back into ever new experiences and predictions of reactions to those experiences (their pre-testing). Hereby, a loop is tied together: the individuals are, through their data, the sources of advance beta testing of the environment, but are simultaneously shaped, moulded and nudged through the same modifications, all enabled by thick layers of digital and physical infrastructure. However, as will be shown below, the story becomes even more complicated as environment itself becomes the infrastructure (e.g. through ‘smart’ sensory and interactive capacities) – in this case, environmental and infrastructural modifications become coextensive.

Rather tellingly, Nadler, Crain, and Donovan (2018) call the toolkit for attracting attention a ‘Digital Influence Machine’. For them, such a toolkit ‘incorporates a set of overlapping technologies for surveillance, targeting, testing, and automated decision-making designed to make advertising – from the commercial to the political – more powerful and efficient’, geared towards achieving the proverbial capacity ‘to reach the right person with the right message at the right time’ (Nadler et al., 2018, pp. 4–5). As this statement clearly is applicable to much more than just advertising but also to the provision of any experience product (and, as per above, there is now hardly any product that would not simultaneously also be an experience product, i.e. a product that is pre-testable by definition), one could easily see this condition as an arms race among providers, where every new and successful pre-testing technique ups the ante for everybody else, spurring the search for even more advanced tools for attention attraction. In fact, not only the accuracy but also the speed of action has become so important that actors are resorting to tools like programmatic ad buying, which automatically targets ‘the right person, at the right time, in the right place’, performing content placement and ad buying in real time (Hemann & Burbary, 2018, p. 32). Moreover, automatisation goes even further, reaching into automatic execution of purchase and post-purchase care, e.g. automatic purchase and delivery of related supplies (Siggelkow & Terwiesch, 2019, p. 77), which feeds into the emergent logic of pre-emption discussed later in this chapter. Such developments, therefore, help to further wrap users in personalised (pre-tested) and interactive (responsive to further live beta testing through use and encounter) experience cocoons that stay with them wherever they go.

Naturally, then, speed and personalisation are key (Colvin & Kingston, 2017), necessitating the employment of big data and algorithms, as discussed below. And as the capacity for data collection (including both more efficient extraction from exist...

Table of contents

• Cover
• Title
• Copyright
• Dedication
• Table of Contents
• Acknowledgements
• Introduction
• Chapter 1 The Malleable Environment: Attention, Sensing and Wrapping
• Chapter 2 The Malleable Self: Immersion, Self-optimisation and Gamification
• Chapter 3 The Malleable Political: Ascription, Shareability and Ventriloquism
• Chapter 4 The Posthuman: False Centrisms, Flat Ontology and Immersive Methodology
• Conclusions and Future Directions
• References
• Index