Mel has a very interesting work in progress paper up on her blog on “The territory of the post-professional“. We sometimes share very similar research interests. I’ve also looked at questions of territory and technological assemblages in my Communications Technologies & Change unit this semester.
In one week we looked at the relation between predictive algorithms and the individuation of subjectivity. Here is the entry for that week:
Buying Stuff Online and How Your Credit Card is You
Transformations of economy, emergence of global market. Globalisation. Function of credit cards as technology of communication/identity. eBay, Steam and online commerce. Amazon.com and the algorithmic production of surplus value.
|Required reading||Merskin, D. (1998). “The Show for Those Who Owe: Normalization of Credit on Lifetime’s Debt.” Journal of Communication Inquiry, 22(1), 10-26. [Particularly the section “A brief history of credit”.]Merskin offers a critical reading of the reality TV show called Debt and the ways credit card and personal debt have become ‘normalised’ in US society. Read the section “A brief history of credit” (pages 11-16) for a quasi-genealogical account of the development of the credit card. What is the ‘credit card’ assemblage?|
|Recommended reading||de Vries, K. (2010). “Identity, profiling algorithms and a world of ambient intelligence.” Ethics and Information Technology 12(1): 71-85.This is another tough reading, but useful for thinking about the way the everyday technological assemblages of communication contribute to or produce our identity. ‘Identity’ here is meant in a cultural sense. The classic example that de Vries explores to some length is the use of algorithms to predict consumer behaviour on shopping websites and suggest commodities we might be interested in purchasing through online shop fronts like Amazon.com. The relevant section is “Identity in a world of profiling algorithms and ambient intelligence” (pages 76-79), but it is worth exploring at length to gain a critical understanding of the ways complex internet-based commercial interactions can affect the production (and prediction) of identity.|
In the lecture I did a kind of archaeology of the credit card in terms of the shifting composition of socio-technological relations across the long histories of some of the elements that constitute the ‘credit card assemblage’. The required research for this, so as to do the lecture, was a bit crazy. I learnt a great deal! Then I shifted gears a bit to talk about the function of predictive algorithms that are part of online shopping platforms. The de Vries reading is very good on this (and also pretty tough for third year undergraduates). In the context of predictive algorithms and algorithmic-based platforms (that aren’t necessarily ‘predictive’) there are two points I want to make with regards to Mel’s paper, specifically the paragraph introducing ‘algorithmic living’.
Firstly, unlike previous forms of self-knowledge in familiar ‘quantifications of the self’ (Weight Watchers, etc.) determined by a medium/average (statistical sense) of rough (molar) demographic categories, algorithmic indicators are far more mobile and the level of quantification is determined by the ‘resolution’ of the algorithm. ‘Resolution’ in this sense pertains to the ‘machinic affects’ of the ‘counting assemblage’; what are the forms of machinic visbility afforded by the technological assemblage of which the algorithm is but one (protocol) level? What are the ‘actions’ or ‘gestures’ being indeed by the algorithm?
Secondly, the (algorithmic) map (of aggregate molecular ‘actions’ of user-mulitiplicities) has become the (existential) territory (for the individuating assemblage of an ‘app’ or ‘platform’ user). Yes, the map is the territory (I’m phrasing it like that just to fuck with the old school semioticians a little bit:). The classic examples of this are Amazon.com or Google. Amazon indexes various ‘actions’ by users and users this for the ‘suggestions’ section. The capacity to index such actions are one of the affordances (action possibilities) of the platform or what I would call the machinic affects of the algorithm. The machinic affects are determined by the resolution of the algorithm. What actual action does the algorithm index? Visits? Location of mouse pointer or scrolling behaviour? Maybe. Definitely (in the case of Amazon): purchases, wishlist contents, ‘Kindle’ sharing behaviour, and so on. The aggregate map is produced by a multiplicity of such actions, this map then serves as part of the ‘territory’ by which other users of the same platform are individuated (as ‘dividuals’, cf. Deleuze). ‘Territory’ in this context is derived from the later work of Guattari.
What is interesting about Mel’s focus on ‘time’ and its management as a mode of self-governance is that by taking into account the above process of individuating there are two versions of temporality are in play: intensive and extensive. Management of time is traditionally ‘time’ as extension; there is a range, which is divisible into ‘units’ of time. The individuation of a subject is an intensive process and operates at the level of ‘anticipation’ (relations of futurity) and ‘retention’ (relations of pastness). The ‘past’ in this context is literally and practically active; a multiplicity of ‘pasts’ from a multiplicity of users indexed according to their actions ‘feed’ (‘feed’ in the sense of both ‘appetite’ or ‘appetition’ (Whitehead) and ‘user feeds’ ie who you follow) into the pure present of algorithmic mapping and serve as a dynamic/selective virtual architecture that scaffolds the embodied process of the individuating subject who is actively anticipating his or her ‘next’ action. The ‘next’ action is the subject of such operations; this ‘next’ is an intensive temporal relation.
Management of time is only traditionally premised on the extensive dimension, as contemporary ‘social’ platform-based apps also include a valorising function which tempers time with a qualitiative experiential dimension. If you had a good time, then you’ll ‘like’ the shared photo. If you ‘like’ the book and ‘rate’ it on Amazon, then you bestow the assumed extensive time taken to read the book with a valorised experiential quality.