In the Sociology of Emotion and Affect Studies, affects are usually regarded as an aspect of human beings alone, or of impersonal or collective atmospheres. However, feelings and emotions are only specific cases of affectivity that require subjective inner selves, while the concept of ‘atmospheres’ fails to explain the singularity of each individual case. This article develops a theory of social affect that does not reduce affect to either personal feelings or collective emotions. First, I use a Spinozist understanding of the ‘body’ to conceptualize the receptivity and mutual constitution of bodies, to show how affects do not ‘belong’ to anybody; they are not solely attributable either to the human or to any kind of body alone, but emerge in situations of the encounter and interaction (between bodies). Next I build upon Jean-Marie Guyau’s concept of transmissions to show how we can theorize affect as an emerging transmission between and among bodies. Finally, I demonstrate how we now have a complete conceptual frame for theorizing affect in relation to all bodies in any given social scene, the grand composition of which I call affectif.

In this article, we make sense of financial algorithms as new objects of concern for organizational ethnography. We conceive of algorithms as ‘objects of ignorance’ jeopardizing traditional ethnography from the perspective of its categories and methods. We investigate the organizational politics taking place within high-frequency trading – a sub-field of algorithmic trading where automated decision-making without human direction has reached a peak, and show that financial algorithms raise particular epistemic and methodological challenges for practitioners and ethnographers alike. Consequently, we develop a typology for various interpretations of algorithms as ethnographic objects, accounting for their structural ignorance and shedding light on a continuum of the changing human-machine/trader-algorithm relation. To this end, we use the concepts of ‘quasi-object’ and ‘quasi-subject’ as developed by Michel Serres, and make the point that in order to study financial algorithms ethnographically, we need to think anew the dynamic relationship they embody, and acknowledge their constitutive heterogeneity.

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Developing an agenda for social studies of High-Frequency trading (HFT), this paper introduces the culture(s) of HFT as a sociological problem of dealing with knowledge and practice. High-Frequency trading is often discussed as a purely technological development where all that matters is the speed of allocating, processing and transmitting data. Indeed, the speed of executing a trade and data transmission is accelerating and it is fair to say that algorithms are now the interacting agents preprogramed to operate in the financial markets. However, we make the point that HFT is first and foremost a cultural phenomenon. More specifically, both individuals and collective agentssuch as algorithmsmight be considered cultural entities charged with very different ways of processing information, making sense of it and turning it into knowledge and practice. This puts forward issues relating to situated knowledge, distributed cognition and action, the assignment of responsibility when regulating high-speed algorithms, their history, organizational structure and perhaps more fundamentally, their representation.

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