Who and What Connects the Dots? Emma Kunz’s Method as Infographics and the Politics of Probability

Introduction

Since ancient times, and increasingly in the last two centuries, societies have relied on various forms of data visualisation and network analysis to ‘resolve problems’ by elaborating reliable information. Illustrating developments in science, statistics, cybernetics, system theory, and information technology, data visualisations have facilitated informed decisions in many aspects of life, training people (and machines) to sense and analyse everything by sequencing data into ‘patterned’ forms of visualisation.¹ Today, infographics, diagrams, curves, maps, stochastic methods and algorithms have become indispensable tools in corporate management, business investment, security, health, and even ‘future modelling’, where they visualise quantitative calculations of potential futures according to the laws of probability. However, techniques such as predictive analytics, horizon scanning and stochastic methods are as problematic as they have become potent: they are the effects of an ideological investment in ‘governing’ the future. Invasive, biased, inaccurate, and exploitative predictive analytics are today determining choices and opportunities; for instance, car insurance premiums calculated on the basis of postcodes and gender, predictive policing, pre-emptive monitoring of people’s ‘clicks’ for targeted advertising and political campaigns. Besides the operational flaws, glitches, and injustices that many scholars have analysed, probabilistic modelling confronts us with fundamental ethical questions regarding the agency it exercises through its deterministic ‘pre-emptive power’.² Supposedly safer in the hands of unbiased machine learning devices that feed on the constant loop of data, ‘human agency’ today negotiates different levels of uncertainty and risk, where numerical calculations, graphs and charts make visible what is only probable. This is not so much a problem of representation but one of inference: it is not how the model reflects reality but how the model designs reality.

This article explores probabilistic methods of visualisation (and the futures they generate) through the lens of Emma Kunz’s oracular 1938-63 drawings made with the aid of a pendulum. By assessing Kunz’s practice as experiments in the visualisation of probability using both chance procedures in art and contemporary ‘future modelling’ techniques, the article invites reflection on the proximity and cross-fertilisation of artistic, oracular, and scientific approaches to the representation of probability and ‘decision making’. Kunz’s oracular techniques are here explained in the contexts of radiesthesia and stochastic methods (from Greek στόχος: ‘aim, guess’), as well as data visualisations where probability distributions are used to model random processes in fields as diverse as social sciences, information technology, media, and the market.

Through a close reading of Kunz’s modus operandi, her methods are placed in a broader artistic field (of such contemporaries as Marcel Duchamp, John Cage, Iannis Xenakis and musique concrète) that devised ‘games of probability’ by calibrating the artist’s intentions (and aesthetic control) with the agency of chance factors. These artistic techniques are framed as practices of distributed agency that mirror contemporary modelling techniques’ ability to create as well as to analyse. In assessing how future modelling techniques teleologically infer (or determine) propensities in decision making, I suggest that the relevance of Kunz’s method lies in her calibration of cognition and agency. This resonates with posthumanist discourses around distributed (human and machine) cognition as well as with the more recent developments in what Jennifer Gabrys has called ‘sensing technologies’: a combination of technics, behaviours and social factors that effectively sense (but also bear the burden and the costs of) what might happen.³ I conclude that from data collection and modelling to response-ability, Kunz’s practice of distributed agency indicates that there may be antidotes to what Franco Berardi Bifo and Mark Fisher have called ‘the slow cancellation of the future’, and that this is located in the expanded horizon of who and what may sense the future.⁴

Kunz’s Method Beyond Esoterism and Abstract Art

Emma Kunz (1892–1963) was a Swiss healer and researcher whose striking oracular drawings have been exhibited as art only after her death. As a herbalist, she cultivated medicinal plants, discovered healing properties of stones, and produced remedies. For those who knew her, she was a mystic with telepathic abilities; for art historians and curators, an outsider, an exponent of early twentieth century Spiritual Abstraction, and a precursor of artists working with grids, such as Agnes Martin.⁵ Diagrams and statistical visualisations have also been discussed in relation to her work, as well as mandalas and other esoteric symbolist forms and even Marshallese Islands Stick Charts.⁶ The rediscovery of her work in recent years, including major exhibitions in museums and at public galleries, coincides with a widespread interest in sensing technologies as aids (or antidotes) to the current reliance on predictive and pre-emptive technologies based on probabilistic methods to ‘design’ and govern the future. The interest in divinatory and oracular practices such as Kunz’s may not just be a reaction to this mathematical overdetermination but the effect of ever-more voracious probabilistic reasoning in search of new forms of ‘sensing’ the future. Either way, her creative process provides clues to the relationship between divining and the use of probabilistic methods to model and shape the future.

Kunz’s intricate schematic drawings on graph paper were instruments for her practice as a healer; this involved the use of the oracular method radiesthesia (literally: the art of sensing radiation), a healing practice that gained momentum in Europe at the beginning of the twentieth century. As a curative method, radiesthesia emerged from the much older practice of dowsing, the art of detecting water wells or minerals with the help of a divining rod or a pendulum. Even though dowsing has never been accepted as a science (serious experiments have been made to demonstrate its fallacy as pseudoscience), the practice had an extraordinary influence in antiquity. Dowsers were consulted to recommend where to build homes, churches, or castles, due to their proximity to water and to detect instability, geopathic stress, and negative energies attached to particular places.⁷ Ancient Egypt, the Pythagoreans, the Medieval Arab tradition, and the Jesuit missionaries’ pursuit of herbal remedies in foreign lands are all precursors by modern practitioners.⁸

A Jesuit priest and a contemporary of Kunz, the Abbe Mermet (1866–1937) popularised the practice of ‘medical dowsing’ and ‘pendulum diagnostics’ in the early twentieth century France and Switzerland through the 1935 manual Comment J’ Opere. Around the same time, Abbe Alexis Bouly (1865–1958) became famous across Europe for his dowsing abilities, and in 1929 he founded the Society of Friends of Radiesthesia.⁹ By the end of 1933, similar societies of practitioners and supporters of radiesthesia existed in England and France, particularly in Alsace, where potassium deposits were discovered in 1906 using this method. Replacing the traditional divining rod with a pendulum, practitioners of medical radiesthesia began to use it to diagnose physical conditions and suggest remedies. As Dr Ibrahim Karim, a contemporary practitioner, explains, radiesthesia ‘is the science of using the vibrational fields of the human body to access information about other objects of animate or inanimate nature by establishing resonance with their energy fields, using specially calibrated instruments and a scale of qualitative measurement to decode this information’.¹⁰ Practitioners of radiesthesia rely on geometrical shapes to manipulate energy, too.

Although much of radiesthesia involves using charts like many other spiritualistic practices, there are no known examples of handmade drawings of the complexity and originality of Kunz’s. Most importantly, as diviners habitually use pre-existing charts to interpret the pendulum’s movement, Kunz used the pendulum to make her charts. She made each of her drawings with a specific question in mind and then set a pendulum in motion on top of large square sheets of graph paper placed horizontally on a table. Tracking the movement and pauses of the pendulum by marking the ‘energetic’ points where the pendulum changed position, Kunz connected the dots with the help of a ruler. Anton C. Meyer, her only biographer and one of her clients, reported that making her drawings took several days since she continually returned to the images.¹¹ When a patient visited her for advice and natural remedies, she stared at the images to detect ‘energy disruptions.’ Little is known, however, about her thought process, which she kept deliberately secret; we can only speculate about what sort of questions she asked.¹² It is nonetheless worth considering how Kunz drew on empirical processes of recording physical phenomena and visualising them.

Her self-published 1953 book Design and Form as Measure, Rhythm, Symbol and Transformation of Number and Principle reveals that her drawings attempted to translate nature’s laws.¹³ To do so, she developed a precise methodology based on systematic recording movement and its translation into a geometrical form that has a striking similarity to data visualisations. This similarity is not only visual but also methodological. In the 1930s, research in mathematics and probability theory led to the development of new stochastic methods, where the collection of random variables could be indexed (and visualised) through a line or data set (or charts) to model random probability distributions. In the modelling of stochastic processes, each ‘variable’ in the index corresponds to a unique element in a data set, producing visualisations and probability curves that map random phenomena (and not just numbers) through the space-temporal axis and vectors.¹⁴ Linked with the developments of systems thinking and game theory, the probabilistic patterns emerging from such stochastic processes could also be used to model (and forecast) all sorts of phenomena by introducing new methods for a simultaneous visualisation of time and space (the Cartesian axis is utilised in many random fields). For instance, stochastic methods were used in Los Almos in modelling experiments that led to the production of the nuclear bomb and the appraisal of its destructive power.

With their affinity to stochastic methods, Kunz’s oracular drawings sit precisely at the intersection between divining and forecasting, illuminating their proximity and differences, and questioning our reliance on probability as an agentic instrument for designing the future. Numerical patterns have been widely employed throughout history as aids for decision making, but it is only in the last two centuries, when forecasting emerged as a ‘science’ of prediction, that data visualisations have become planning tools. The trust we have in visualisations is due to three critical factors. First, visualisations are data-driven – they construct information (data) as evidence. Second, they are structural – that is, they are concerned with the relation and interpretation of this evidence (with numbers, small and large). And third, they are predictive because they enable us to compare data from different periods and their possible reoccurrences. These factors define the parameters of the aesthetic of the probable where images mathematically elaborate data to visualise ‘probable’ futures. The ‘aesthetic of the probable’ enables time and space to be reorganised and reconceptualised through probability ‘curves’ in order to be incorporated into the ‘visions’ of today’s organisational systems and decision-making processes.¹⁵ This implies a redistribution of sensible experience according to logic of data accumulation and the pre-emptive goals it serves.

Every line and every dot in Kunz’s drawings charted a ‘random’ movement of the pendulum as she marked the paper at each ‘peak’ and weaved the dots together to visualise the working of invisible forces. Her pendulum might have moved erratically and independently from any measurable cause, but so do many other random phenomena, and yet, they are mapped and used in forecasting. Identifying Kunz’s work as inner intuition or clairvoyance excludes a crucial aspect of this method: the fact that the process is data-driven. Moreover, her commitment to organising space (the gird as the energetic field) through time steps that tracked the reoccurring movement of the pendulum was a way of visualising physical phenomena by creating patterns of frequencies and, therefore, probability. She may not have used counting or measuring, but her art’s schematic and mathematical appearance is not involuntary. Dealing with the physical as much as with the spiritual world, she used probabilistic methods (such as time steps and pattern recognition) to index the pendulum’s trajectory (or behaviour) and prescribed action plans. Kunz’s diagnostic images were, in a sense, data visualisations of random phenomena performing oracular duties: schematic and numerical patterns obtained through data collection and organisations to answer questions and produce evidence to inform decisions about the future.

From Chance Procedures to Patterns of Probability

What if, instead of using the pendulum, she had recorded how much her clients ate, slept and/or performed other daily actions? Would her diagnoses appear more plausible to the contemporary eye? One can elaborate the relevance of her method in relation to probability theory and visualisation, as well as the use of chance procedures in art. Let us consider both. In his seminal 2006 text The Emergence of Probability, Ian Hacking explains that probability theories developed on the back of fatalistic worldviews and in response to a world that had become uncertain, chaotic and unstable, in other words, a world ‘dominated by chance’.¹⁶ Greater reliance on probability emerged from positivistic thinking in the nineteenth century, where the belief in science prompted developments in statistics and the use of timelines, propelling methods of data visualisation that were able to monitor and visualise the frequencies of events in time. These methods helped to diagnose or predict natural and social phenomena by determining their causes and preventing their reoccurrence in the future: social phenomena, patterns in economics, odds at the game table and causes of accidents.¹⁷ Where regular patterns were found, new universal laws followed, allowing for many aspects of life to be governed or pre-empted.

Hygiene, illness, violence, safety: new methods for governing social life and work began to be introduced. This rationalisation process also introduced new standard measures of time and space. The scientific euphoria of positivism did not last long, however, as intellectuals, mathematicians and artists in the twentieth century began to criticise its determinism. For instance, mathematician Henry Poincaré (1852–1912) believed that universal laws were just human conventions and wrote in 1912 that ‘chance is only the measure of our ignorance’.¹⁸ In the troublesome years that followed, the notion of uncertainty gained prominence, as did the idea of controlling it through risk. Theorists such as Poincaré and John Maynard Keynes changed how probability was conceived by introducing notions such as uncertainty and volatility, significantly impacting economic theory. WWI and WWII provoked political, philosophical, and moral crises everywhere, and in those times of even greater uncertainty, different approaches emerged in the UK and the US to suggest how turbulent times should be managed. Keynes proposed an economic theory that calculated probabilities according to future propositions rather than past events.¹⁹ John Von Neumann likewise devised complex computational methods that monitored and calculated (but also influenced) the behaviour of complex systems. These new theories of probability were instrumental in responding to the scientific, technological, political, and economic transformation of the post-war period, which presented unprecedented problems for decision makers in every field, especially where there was no prior knowledge to tap into. In this context, game theory (or decision theory) and cybernetics’ understanding of uncertainty in terms of ‘complexity’ introduced system analysis and new stochastic methods to study the implementation of auto-regulatory systems in nature, machines, and societies. At the same time, schematic ‘patterns’ began to emerge that mapped factories, computers, experiments, traffic and social relations: through them, the ‘invisible’ workings of human, animal, biological, social and cultural relations became apparent.

Examples of the above probabilistic ideology are countless: from the 1930s, hand-drawn visualisations of social networks in Jacob Levi Moreno’s and Hellen Jennings’s pioneering work on ‘sociometry’ in Who shall Survive (1934) to Edward Tufte’s information designs theorised in Visual Display of Quantitative Information (2001) to the stock market charts in the Bloomberg Terminal. Patterned visual displays of data made ‘probability’ apparent to the eye, becoming visual aids for the diagnosis of many phenomena, and risk in particular. Viral diseases, international drug dealings, space debris, or the distance between the eyes in face recognition technologies are all forms of probabilistic pattern visualisations obtained through data analysis.²⁰ At the same time, artists committed to ‘information’ as a tool to investigate the world and the interaction of political, social, and economic forces. Using systems to go against the system became common practice for the Information Art movement, whilst aleatoric musicians devised ways to translate stochastic curves into musical composition, such as Iannis Xenakis. Placing Kunz in a linage of artists that produced data driven and structural works to visualise chance helps us understand how creativity might be interrogated and mobilised to redistribute, calibrate or re-balance the different power forces that are at play in the making of the future.

Marcel Duchamp and John Cage, among many other of Kunz’s contemporaries, adopted computational methods of data to play with chance, probability and determinism. In 1914, influenced by Poincaré’s ideas about chance, Duchamp made his seminal work Three Standard Stoppages, an experiment made ‘to imprison and preserve forms obtained through chance, through my chance’.²¹ He drew on a black canvas the random shapes obtained by dropping three one-meter-long strings from the height of one meter and used the resulting shapes as a template for cutting three pieces of wood. Encasing the whole experiment in a box, he labelled it ‘canned chance’ and added a statement called The Idea of Fabrication: ‘[i]f a straight horizontal thread 1 meter long falls from a height of one meter straight onto a horizontal plane, twisting as it pleases creates a new image of the unit of length’.²² The work, considered by Duchamp as a ready-made, was, from a methodological point of view, a metrics exercise where the artist monitored, visualised and standardised the random shapes of a piece of string falling on a surface. Since Duchamp’s metre changed every time it was dropped, he demonstrated that there is no such thing as a universal standard metre. His rebellion against determinism was not just conceptual but aesthetic, for it formally activated a game of chance as a method, redistributing creative agency between the artist and other external factors beyond his control.²³

How do such chance procedures engage with probabilistic reasoning, their visualisation techniques and the agency they exercise over the future?²⁴ Duchamp’s predilection for the use of chance in human, natural, and artistic affairs manifested in a deliberate calibration of agency of external forces, which he systematically monitored by (?) mapping their manifestation over time. Similarly to Emma Kunz, he entered into a relation to external forces by recording different time steps on a canvas, a process of monitoring that justifies Kunz’s drawings’ affinity to probabilistic data visualisations.²⁵ If probability, in broad terms, is the study of the chances of an event occurring or reoccurring, then Kunz’s and Duchamp’s experiments are games of probability as much as ‘chance procedures’. The artist sets the parameters for an experiment to be undertaken, then monitors their results over time and registers them in a way similar to musical notation.

Likewise, Cage used counting, sequencing and computational systems as an integral part of chance procedures. For instance, in Changes and Disappearances (1979-82), different prints were made through a modular repetition of basic forms dictated by a complex system of questions and answers that Cage methodically recorded.²⁶ Even if Cage intended to mirror the infinite possibilities of chance-dependent patterns in nature, his work is far from disordered. It seems as if the systematic nature of his methods had produced an underlining structure that resembles a mathematical order. Was this a ‘natural’ order, or was Cage’s working method what determined its regularity?²⁷ Cage used counting and sequencing as a basis for mark making and producing modular systems for aleatoric, accidental phenomena: ‘Chance brings us closer to nature in her manner of operations.’²⁸ Thinking along the same lines later in life, Duchamp wrote: ‘The whole world is based on chance […] or at least is a definition of what happens in the world we live in’.²⁹ These artists have studied the ‘frequencies of events’ not to find regularities and possible predictions but as a homage to the irregularities that mark the patterns of existence. For them, recording and producing ‘new data’, counting, and mapping were not a means to forecast future trends but to celebrate the marvels of indeterminacy, unpredictability and randomness. Cage could not have made this intent clearer by choosing Diary: How to improve the world (you will only make matters worse) as the title of one of his published diaries:

Each day provides an example of chaos […]. We never know what is going to happen. The kind of trouble that people have with the weather, we now have with every aspect of our lives. (laughing) I think the thing that I like about the butterfly is that it’s like a grain of sand or that little bit of dust. It doesn’t seem important. And yet for a scientist to say that it is important and that it’s part of the network of cause and effect is pleasing. In other words, it takes our minds away from a hierarchical attitude towards nature.³⁰

Just like Duchamp’s and Cage’s attempts to ‘imprison’ chance, Kunz’s artistic process has the characteristics of the current methods of network analysis and visualisation: it identifies patterns out of indeterminacy. The data-driven approach shaping her images is as much, if not more important than the symbolism many have read in her drawings. Kunz’s work thus concerns relations where agency is distributed between the artist (who records) and the pendulum’s chance movements. Thus seen, her images appear as ‘pattern recognition’ technologies, data-driven images that map movement in time and space coordinates. We do not know how Kunz interpreted the individual works. However, together they form an inventory of patterns where a pendulum, and apparently random behaviour of concentric and exocentric pulls, becomes apparent through the ordering principle of lines, colours, and rays. Each movement produces data, and this data, monitored over time, generates ‘patterns of probability’. Kunz may not have, strictly speaking, invented a forecasting technology, but she did create a method to visualise probability for oracular purposes. Data-driven, structural and predictive, drawing was, for her, an empirical process of recording physical phenomena and making incalculable futures probable juggling in a dialectic interplay between human agency and the action of the pendulum.

Diagnostic Images, but Do They Predict?

As art historian Annelise Zwez wrote in 1998: ‘We might speculate whether Emma Kunz, in aiming her celebrated statement that “my pictures are for the 21^st century” foresaw that […] the twenty first century will develop the technology to measure subtle energies by scientific means’.³¹ In the present moment, when scientists and politicians are concerned with governing the future through probabilistic methods of forecasting, the question of how to sense the future (with what technologies) and how far this sensing can be developed remains unanswered. Complicating the matter is also the fact that contemporary forecasting is not necessarily based on events that occurred in the past but on simulating future behaviour to achieve goals. It is a form of ‘teleological reasoning’ we inherited from game theory, which suggested that if one wants to win in a game but there are no past data available to inform decisions, they can at least pre-emptively monitor (and predict) the other players’ actions. This logic has, over the years, trained humans and machines to acquire an ‘expanded vision’ through the accumulation of data and new modes of visualising complexity based on the ‘virtuality’ (or modelling) of the future, of which the weather is an example. The resulting future modelling techniques have become the symbol of a changed understanding and the use of probabilistic thinking that no longer simply evaluates things from the past but also projects options into the future.³²

The paradox is that diagrams and infographics fulfil the operational needs for decision-making protocols regardless of their mathematical accuracy or adherence to reality. As Hermann Mitterhofer and Silvia Jordan wrote in Imagining Risk, ‘diagrams are characterised by normativity […] they invite and to some extend “prescribe” particular ways of perception and action. That is to say that diagrammatic reasoning is always rule based’.³³ If probability and its visualisation have historically performed the function of ‘problem-solving’ (in physical space or in the future), it has also introduced some form of normativity into reality. As Aurora Wallace wrote about crime maps, for instance: ‘The map is the phenomenon objectified, and once objectified, it is its own proof’.³⁴ The problem, therefore, is not only the accurate use of mathematics to calculate probability but ‘the modeller’s choice of objectives’.³⁵ This is even more complicated in the age of artificial intelligence, where data as evidence automatically becomes a ‘prescription’, purely by being in the feedback loop’s compressed dimension where stimulus and response are instant, almost simultaneous, real-time operations. By finding pathways through millions of possibilities, algorithms also design possibilities through processes of automation that collate past, present and future events and scenarios. However, because they work according to a target-orientated logic (they are given specific objectives), they are also ‘inductively generating potential attributes from the patterns within a corpus of data’.³⁶ In other words, they reorient and legitimise contemporary thinking via computation and redistribute our perception of the world according to a future-orientated logic. Future modelling entails that present decisions are made in the name of a future that might never be; in a way, this forecloses the possibility for many other incalculable futures to happen.

On a societal level, predictive analytics can generate inequality and perpetuate biases. Such processes of modelling through probability have been interrogated by many contemporary artists engaging with machine learning and AI, including Hito Steyerl, who has shown future modelling techniques to be incomplete and uncannily amorphous.³⁷ As Louise Amoore points out, the problem with over-reliance on probabilistic methods is also one of more significant uncertainty because, for example, algorithms influence each other through ‘iterative and co-evolving interactions’ or feedback loops that escape the very notion of control by conflating cognition and agency.³⁸ Describing the immanence of randomness in programming, Luciana Parisi understands algorithms not as measuring or ‘recipe’ tools but as performing entities acting through automated prehension of data; this process is not so much about choice but about irreversible contagion where algorithms and other entities form ‘occasions of experience’ and ‘expose us not only to new modes of living, but also to new modes of thinking’.³⁹ They are not only tools for decision making but agents that act through processes of automation.

This inductive logic, which infers meanings in the future through probability, only describes the world in terms of its purposes, forgetting that humans (as well as animals and plants) sometimes simply exist, and, unlike algorithms, make choices without future goals but rather based on different forms of sensing or simply enjoying their present condition. When at stake is not just profit, efficiencies and targets of specific institutions but the very survival of interrelated, interconnected forms of existence that are affected by them, asking who and what connects the dots to scan ‘future horizons’ is imperative. It is not a question of human versus machine or sensing versus knowing, but a question of to what purposes probabilistic calculations are deployed. A solution to this impasse might come precisely by recalibrating the agency the different forms of sensing have in designing (or modelling) the future. It points to collaborative sensing technologies whereby, in Katherine Hayles’s words:

distributed cognition replaces autonomous will; embodiment replaces a body seen as a support system for the mind, and a dynamic partnership between humans and intelligent machines replaces the liberal humanist subject’s manifest destiny to dominate and control nature.⁴⁰

This is particularly relevant if we think of the sensing apparatus designed to monitor and detect factors of change in the environment, such as pollution, where human and more-than-human factors not only contribute to the shared process of sensing but are also co-actors in the increase, reduction or distribution of pollution across societies. As Gabrys writes, these sensing technologies effectively need to describe ‘experiences and relations that are re-worked across entities, environments, and technologies […] away from a classificatory and exclusively human project.’⁴¹

Can we think of Emma Kunz’s method, too, as a sensing apparatus that detects and transforms relations between humans and other forms of agency? Returning to Kunz’s method, Hayles’s insights about ‘distributed cognition’ cited above encapsulate her prescient ability to consider probabilistic tracking as an opportunity for extending human knowledge of the world as well as for decentring human agency. Redistributing her creative process (or research) across computational methods on the one hand and across embodiment on the other, Kunz translated her diagnostic images into a coherent practice of everyday life, thus producing (or materialising) the futures she (fore)saw. Her work resonates with post -anthropocentric approaches in theory and philosophy, such as multispecies and more-than-human ontologies, speaking to the contemporary necessity for more holistic or transdisciplinary knowledge as a ‘move of survival’.⁴²

Concluding Remarks

The rediscovery of Kunz’s work over recent years speaks to the modern sensibility of the contemporary technological world and its acquaintance with circuit boards, graphs and diagrams and ‘divinatory forms of address’.⁴³ Modern sensibility is perhaps to look to different diagrams for different kinds of answers. Oracular technologies are today the expression of a ‘sentience’ that is as unexplainable as it is fascinating, one which forms symptomatic escapism from the present explosion of predictive analytics with their statistically based technologies and probabilistic thinking. With its combination of computation and spirituality, Kunz’s work satisfies both the need for structure and the aspiration to transcendence, speaking to a world that has become increasingly chaotic and yet increasingly governed by systems of ‘rational’ control.

The political, emancipatory value of her work, therefore, does not point to mediumistic and new-age sensibility but shifts our ‘economies of attention’ towards the interdependence of different forms of agency and the importance of balance. Registering the oppositional forces pulling the pendulum in different directions, she connects the dots between inward and outward, left and right, and up and down to diagnose energy flows, liberation, compression, and the imbalance between the two. As disharmony between opposite forces produces illness, her healing method attempted to restore equilibrium between what exists within the body and its surroundings. In this regard, Kunz’s sensing apparatus invites us to deepen our relations toward that which is beyond the human sensorium. The point is not so much whether the ‘invisible’ forces may or may not become visible through her drawings but how they invite relations that are not based exclusively on human comprehension, understanding and logic. What Kunz chose to sense, whatever that might be, had ethical implications for how she operated in the world, for her daily praxis. Her ‘data-driven’ methods suggest different ways of embodied knowing and the body’s relations with other forms of existence, including technologies and energy. Kunz’s understanding of the concept of ‘energy’ resonates with Indigenous and Shamanic cosmologies worldwide in that it recognises agency as a series of co-poietic acts, where every human gesture resonates, and responds or conflicts with other forms of agency in the world.⁴⁴ And what is this understanding of co-agency, if not a scaled-down version of broader discourses around energy paths and the role of human, animal, vegetal and geological factors in producing, conserving and preserving energetic resources for our survival? How can these decisions be ‘balanced out’ or, to use Kunz terminology, re-polarised through more ethical understandings of their side effects?

Kunz’s message is thus both moral and methodological: she asks us to recalibrate decision making (and agency), not just as data-driven and evidence-based, and points to practices of knowing that are ‘less about adding information and more about a process of deepening relationships’.⁴⁵ In this context, the development of collaborative modes of sensing (or sensing technologies) that expand beyond the spectrum of human experience ‘across entities, environments and technologies’ is crucial to track and predict transformations in the environment (such as pollution) as well as make sense of the complex interactions between these factors and the political implications of such interactions: their agency. At a time when the urge to consider more-than-human ontologies has become a mantra in humanities debates that critically interrogate the current state of the world and its future, Kunz’s diagnostic pictures point to strategies where agency ceases to be a power struggle between humans and between humans and other forms of existence and becomes a relation of interdependence and coexistence. On a practical level, this means that in dealing with the determinism–uncertainty impasse, sensing and paying attention refers less to knowing about the future and more to the kind of ‘present’ (or presents) one decides to sense.⁴⁶

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Additional information

Notes on contributors

Francesca Laura Cavallo

Francesca Laura Cavallo is a curator, art historian, and interdisciplinary researcher whose work focuses on the intersection between art and the activation, management and perception of risk. Francesca is currently researching Sirens and Warnings as a part of the AHRC project Preemptive Listening at the Royal College of Art. This project continues her long-term curatorial and academic work on Pre-emption, Preparedness, Pre-enactment, including her PhD ‘Sensing it Coming: the Aesthetics of Risk’ (Kent University 2020). Francesca is also an Honorary Research Fellow at the Centre for Indigenous and Settler Colonial Studies at the University of Kent and the founder of Brazil Footprint 00, a programming platform and research network for ecologically minded, de-colonial and intersectional art practices across Brazil and the UK. She has curated exhibitions, festivals and public programmes at the Science and Industry Museum; the Barbican Centre; Turner Contemporary; Manifesta 11; Cabinet (NY); the ICA; 98 weeks in Beirut; and Andersen Museum in Rome. Email: [email protected]

Notes

1 See Halpern, Beautiful Data.

2 For those who have problematised infographics see D’Ignazio and Klein,“Feminist data visualization.”; and Spiegelhalter, Pearson, and Short. “Visualizing Uncertainty About the Future,” 1393–1400. For ‘pre-emptive power’ see Goodman, Sonic Warfare and Massumi “The political ontology of threat,” 52–70.

3 See Hayles, How We Became Posthuman; Gabrys, “Sensors and Sensing Practices,” 723–736; Amoore, “Introduction: Thinking with Algorithms,” 3–16.

4 Berardi, After the Future, 17; Fisher, Ghosts of My Life.

5 For the spiritual abstraction reading of Kunz’s work see Szeemann, “More Than Art,” 55-90; Althaus and Schneider, World Receivers; for geometric abstraction: De Zegher, Catherine, 3 X Abstraction.

6 Zwez, “… Provided They Know How to Handle Numbers,” 91–118. Teicher, “Kaleidoscopic Visions,” 134.

7 Foulkes, R. A. “Dowsing Experiments,” 163-168; Marks, "Investigating the Paranormal," 10. See also the 1991 Kassel study that filmed and controlled various dowsers in actions but lead to no positive result organised by the Society for the Scientific Investigation of the Parasciences (GWUP).

8 See Karim, "Biogeometry: Physical Radiesthesia," Accessed August 20, 2022.

9 The term radiesthésie was coined in 1930 by the Abbé Bouly, in France. The French Association des Amis de la Radiesthésie was founded in 1930 and the British Society of Dowsers in 1933.

10 Karim, see note 8.

11 Meier, “Emma Kunz. Life and Work,” 19–54.

12 Ibid. Meier recounts that she was very concerned about contemporary affairs and wrote letters about the political turmoil in Europe and the unfolding of a global conflict, but none of these letters remain.

13 See https://www.emma-kunz.com/en/emma-kunz/

14 Meaning that each random variable of the stochastic process is uniquely associated with an element in the set. See Gardiner, Stochastic Methods, 3.

15 For more about the ‘aesthetic of the probable’ see Cavallo, “Sensing it Coming.”, 304.

16 Although the transition between these world views is hardly definable, and they may have coexisted for very long times and in different geographical places.

17 For example, Florence Nightingale’s ‘Diagram of the Causes of Mortality’ between 1854 and 1855 used colour coding to demonstrate that the principle causes of death in the Crimean War were not wounds but their related illnesses.

18 Henri Poincaré, “Chance,” 64.

19 See Keynes, Treatise on Probability.

20 For Halpern (note 1) cybernetics trained people to sense and analyse the world according to a communicative objectivity that manifested itself through the repetition and sequencing of data into ‘patterned’ forms of visualisation. This aesthetic process was the formal embodiment of an ideological shift towards ‘controlling’ and ‘governing’ the processes of knowledge formation through models designed for the analysis of complex systems.

21 These notes come from Duchamp’s Box from 1914. His italics. Quoted in Duchamp and Schwarz, Marcel Duchamp, 595. See also Poincaré, ‘The Measure of Time’, 222–234.

22 Ibid.

23 See Molderings, Duchamp and the Aesthetics of Chance; Iversen ed., Chance: Documents of Contemporary Art.

24 Chance procedures in art have hardly been linked with the historical development of probability and its role in decision making. And yet many refer to probability as an art rather than a science. These include Jeffrey, Probability and the Art of Judgment, and Hamming, The Art of Probability.

25 Note the difference with ‘automatic’ drawings such as, for example, Max Ernst’s experiments with Lissajous figures in 1945.

26 In an earlier work Cage had consulted I Ching to choose the notes for the composition of his Music of Changes. The ancient oracular system has become a ‘prescriptive’ agent in his compositions.

27 Significant to our discourse is how these artists’ homage to chance and unpredictability does not come from traditional methods of representation and irrationality (see Romanticism or Expressionism) but by systematically monitoring and mapping the recurrence of events; that is, by using the very computational methods and aesthetics of ‘objectivity’ of the calculus of probability. It shows how the visual vocabulary of probability has permeated not just science and decision making, but the formal lexicon of artistic practice and vice-versa.

28 Quoted in Lippard, “Introduction to 557,087.”

29 Duchamp, et al., 80.

30 Lohner, “The Making of Cage’s One,” 272.

31 Emma Kunz Artist, Researcher, Healer, 1998.

32 This philosophy of decision making, was effectively tested in Project RAND’s early post-war research into system theory in aid of military decision makers, which, according to David Jardini, was intended to ‘sharpen their judgment and provide the basis for more informed choices.’ As RAND's scope evolved, writes Jardini, ‘Systems analysis served as the methodological basis for social policy planning and analysis across such disparate areas as urban decay, poverty, healthcare, education, and the efficient operation of municipal services such as police protection and fire-fighting. Quote from Jardini, “Out of the blue yonder,”13. See also Bradfield, et al. “The origins and evolution of scenario techniques,” 795–812.

33 Mitterhofer and Jordan, “Imagining Risk – The visual dimension in risk analysis,” 327.

34 Wallace, “Mapping City Crime and the New Aesthetic of Danger,” 19.

35 O'Neil, Weapons of Math Destruction, 129.

36 Amoore, ibid., 4.

37 See for example Steyerl’s video work This is the Future, 2019.

38 Amoore, ibid., 6.

39 Parisi, Contagious Architecture, XIII. Full cit.: ‘infinite amounts of data irreversibly enter and determine the function of algorithmic procedures. It follows that contagion describes the immanence of randomness in programming.’ See also Whitehead, Science and the Modern.

40 Hayles How We Became Posthuman, 288 cited in Amoore, ibid., 12.

41 Gabrys, ibid., 725.

42 “Our interdisciplinary moment is a move of survival” wrote Homi Bhabha in an interview with JT Mitchell; see Mitchell, “Translator translated.”, 80–84.

43 See Rosamond, “The Future-Oracular,” 141–158.

44 See for example Whyte, “Indigenous Climate Change Studies.”

45 Hoxley “Unsettling the Apocalypse,” 169.

46 “What counts as a sensing entity or site of sensation.” Gabrys, ibid., 727