Zealous Zombies, Panic Prevention, Crowd Simulation
PDF, 18 pages
If George A. Romero had visited the BBC website on July 9, 2013, he might have smiled whimsically at a short article in the science section. Even on first glance, the respective headline that read “Essex University uses ‘zombies’ in evacuation study,”1 hardly seemed to refer to an empirical behavioral study using probands from some prevailing generation of allegedly shallow-brained and sheepish B.A. students. On the contrary, it alluded to a project that presumably for the first time designated academic honors to zombies: A team around mathematician Nikolai Bode and biologist Edward Codling modeled the exit route choices in emergency scenarios by using data generated by a zombie-themed computer game. In this interactive virtual environment the players – as opposed to the protagonists of Romero’s classical zombie movie Dawn of the Dead (USA 1978) who seek shelter in a deserted shopping mall – had to escape from a building.2 This simulated environment was filled with computer-controlled agents – the zombies – who also tried to escape from the scenery, competing with the players for viable exits. Would they avoid crowded areas and try to find individual routes, or would they go with the herd? Would the model show rational choices, or would it show patterns rather associated with an egoistic behavior uncontrolled by social or cultural constraints, which is commonly simply called panic?
On any account, the study contributes to a debate about the collective behavior of human crowds in critical situations and of the affects involved in these interaction processes that has now lasted for more than a century. The discourse spans from early theories of mass psychology around 1900 to recent approaches in fields such as complex systems studies. Given this historical index it is certainly not a coincidence that the paper had been published in the journal Animal Behaviour. From the very beginning, human mass behavior had been compared to the behavior of animal collectives, and accordingly had been subsumed under a cloud of being irrational, unconscious, or purely instinctive – and therefore devoid of everything that would characterize a self-determined subject. And whilst the authors associated with mass psychology3 included insights from 19th-century natural scientists into their writings, today’s approaches intertwine biological, sociological and psychological findings in computer-technological models of collective dynamics.4 Still, an ongoing mutual query concerns the role of the affects and affections that are distributed within these collectives, and how they contribute to the overall formation and dynamization of collective movements and decision making. Around 1900 this questionnaire involved hypotheses about the spreading of psychic qualities like fear, anger and other emotions throughout human crowds. Eventuated by contagious affective forces (Gustave Le Bon) of transportation between individuals (Gabriel Tarde), this led to the emergence of the mass and its animalistic and explicitly non-humanistic side effects in the first place. Hence, (mental) emotions and (bodily) affects are firmly bound together in the writings of mass psychology.
Interestingly, in recent years the perception of affects and affection in human collectives substantially changed. In an article on new forms of techno-collectives with the title Networks, Swarms, Multitudes, the media scientist Eugene Thacker states that it is the very separation of emotions and affects which is essential for adequately describing their novel modes of collective organization.5 In addition, Thacker’s article shows how the metaphorical portraiture of human crowds first transformed from attributions like the mass to decentralized and technizised concepts like networks and further to more ephemeral notions like swarms under the impact of (mobile) media and networking technology. From this stems a first aspect regarding a Timing of Affect. Recurring to Spinoza’s and Deleuze’s understanding, Thacker defines affect as a mode of collective organization induced by local communications, by the locally organized circulation of signs, and by the self-organized movements of swarming bodies. According to this and in contrast to the traditional notions of mass psychology, these affects exist outside of the individual body and lie in the relations between them: “Affect is networked, becomes distributed, and is detached from its anthropomorphic locus in the individual.”6 These affect-relations become the constitutive force of the specific relationality in collective bodies: As biological studies in large groups of animals showed, affects are distributed through the constantly changing and moving collective by individual bodily actions and reactions. This for instance leads to the interesting effect that a bird flock or a fish school as a whole is capable of reacting substantially faster to external stimuli (like an attacking predator) than a single bird or fish. Signals – visual, acoustic or through air pressure – are detected via the eyes, ears and body receptors. Every animal only processes the incoming movement information from a certain relatively small number of neighboring individuals. By this distributed interaction structure, an affective stimulus like a predator inducing fear to some members spreads by and as a bodily movement information through the collective and results in a global behavior that is an adequate reaction to the stimulus. Some authors thus refer to such swarms, flocks, herds and schools as sensory integration systems.7 The Timing of Affect here enfolds as an evolutionary advantage of the collective.
Usually, writes Thacker, these network affects – the intensification of dynamic processes and the emergence of unpredictable events in network structures – ought to be distinguished from the network effects – the technical infrastructure, the rationality, formality and to numerically computable knowledge about networks. However, in swarms these network affects intriguingly mix up with the network effects. The given – in traditional forms like, for example, the telephone – wired network structure with nodes and edges on which the network affects run and are initiated, in swarms is replaced by a topology where the nodes – that is, each swarming individual – function also as edges of the network. The network as such only emerges on the basis of the spreading of network affects.8
This novel mode of dynamic collective organization has gained substantial impact in the humanities and culture discourses over the last several years. Driven by the rapid development of mobile network technologies, social swarming in humans became a buzzword for a (widely appreciated) subversive potential against less dynamic and more hierarchical forms of collective organization, of opening up novel modes of group movement, and even of (metaphorically) re-conceptualizing mass panic as a mode of dynamic resistance against control societies.9 In this regard, a second aspect of a Timing of Affect concerns its alleged potential to open up novel ways of engaging in political action, either by more flexibly and more spontaneously organizing manifestations on the street with the help of mobile devices and communication applications, or by novel forms of synchronized online protest in social networks.
Nonetheless, this social swarming discourse with its focus on biological and social ideas of affective orderings widely neglects or underestimates the adherent media genealogy. More profoundly than on a mere metaphorical level, since the 1990s the related media-technological developments are based on the recursive intertwinement of a biologization of computer science on the one hand, and of the computerization of biological research on the other. Profiting from principles and findings of this so-called computational swarm intelligence, research projects in areas such as crowd control, evacuation planning, or crowd sensing, for instance, seek to formalize a variety of mass dynamics. Thereby, they transform all sorts of affective behaviors over time into calculable movement vectors. Fostered by the capacities of sophisticated multi-agent computer models, simulation tools, and automated observation and tracking techniques, these studies – and this will be my guiding thesis – initialized a thorough de-psychologization of approaches which had formerly been dominated by (mass) psychological concepts and socio-psychological experimental settings. Thereby, the current dynamic models not only question the conventional ties of human crowd behavior to poorly defined affective attributes such as fear, panic, excitement, or herd instincts,10 but also doubt the recent and oftentimes euphoric notion and the hitherto proclaimed freedom and unpredictability of socio-political network affects. Both lines of thought nowadays are countered by media-technical, time-sensitive control infrastructures. From this derives a third and theoretically prominent aspect of a Timing of Affect, located in the attempts to model affective behavior as bio-physically describable events in space and time by means of computer simulation and computerized tracking systems.
In the following juxtaposition of older approaches to human crowd behavior (part II and III) and actual studies with multi-agent systems (part IV) the article will shed light on this physicalization and de-psychologization of affects. This will be exemplified in the contexts of mass panic as an instance of affective, critical collective behavior. Thus, the text explores how the uncanny body politics of the mass and its eerie affects have been transformed into the computable logistics of mathematically defined agent systems.
In this regard, a second zombie-related event of the summer of 2013 comprises more than just coincidence and illustration: The blockbuster movie World War Z (Marc Forster, USA 2013) confronts the audience with numerous impressive crowd sequences depicting masses of zombies invading a cityscape. Whilst aesthetically appealing to the conventional notions of mindless rioting masses, the underlying software uses refined multi-agent animation models for choreographing the animated zombies. Generated by Moving Picture Company’s (MPC) crowd rendering software ALICE, their inherent computational swarm intelligence methods are quite similar to those used in scientific multi-agent simulations of dynamic collectives. Or, more bluntly put: Softwares like ALICE, Weta Digital’s Massive, or Adobe’s After Effects provide simulated mass dynamics which come after affects. The Living Dead of Romero’s times today are revived by agent-based forms of artificial life – just like concepts of human crowds consisting of imbecile individuals are challenged by much more differentiated models of collective dynamics, fostered by crowd simulation and crowd tracking techniques.
This article will not review the abovementioned and well-known treatises of mass psychology – there is little or rather no controversy over the fact that in the writings of LeBon, Tarde or Sighele, human crowds trigger a depravation of the human individual to animalistic behaviors. The crowd is therefore described as far less intelligent, but far more emotionally tangible than individuals acting alone. “In this account, ‘instincts’ will overwhelm socialized responses, and collective bonds or social norms will then break down as personal survival becomes the overriding concern.”11 Masses are, as German media theorist Joseph Vogl once put it, events “where the social is always accompanied by the anti-social.”12 More interesting are those animal studies which actually contributed to the mass psychologists’ theories of corporeal affections and contagions. These approaches already attempted to identify some basic biologically feasible modes of affective distribution shared by animal and human crowds. And they resulted – a fact that is not at all self-evident in the late 19th century – from observations in something one may dare to call early animal field studies.13
For example, Francis Galton portrayed the specific herd behavior of ungulates which he observed over several weeks during a trip through South Africa. Galton was most fascinated by the “blind gregarious instincts” of wild oxes and unable to identify signs of a normal social behavior:
[The oxes] are not amiable to one another, but show on the whole more expressions of spite and disgust than of forbearance and fondness. […] Yet although the ox has so little affection for, or individual interest in, his fellows, he cannot endure even a momentary severance from his herd. If he be separated from it by strategem or force, he exhibits every sign of mental agony; he strives with all his might to get back again, and when he succeeds, he plunges into its middle to bathe his whole body with the comfort of closest companionship.14
Galton realistically recognized this asocial togetherness as induced by fear of standing alone. This serves as a functional protection against predators. The aggregation increases the chances for survival of each individual. As a group, it is far more difficult to ambush the oxes in surprise. Every ox, writes Galton, transforms into a fiber in a widespread detector-network: “[A]t almost every moment some eyes, ears, and noses will command all approaches, [every single beast] is to become the possessor of faculties always awake, of eyes that see in all directions, of ears and nostrils that explore a broad belt of air.”15
One encounters similar notions in the comparative-psychological studies of French zoologist Alfred Espinas. In his account of wasps, though, not fear is identified as a constitutive affective factor of collective action, but enragement and agitation. The respective insects, writes the author, do not rely on any kind of spoken language in order to communicate with each other, neither do they make use of direct bodily contact – as observed in ants by Espinas and the swiss natural scientist Auguste Forel, insects which use their antennae for information exchange.16 Espinas gives a simple explanation: If an individual would sense a certain level of agitation in other individuals of the same species, it would be immediately imprinted by it and be “taken away” by the movements of the others, thus instantly imitating their inner and outer state by a mere and automatic imitation. “In the whole field of intelligent life it is a common law that the imagination of an agitated state evokes the same state in the observer.”17 In wasps, writes Espinas, the “energy level” of the agitation is intra-individually transferred by the intensity of the humming sound which the individuals produce, resulting in the same state of excitement (also known as: state of mind).18 Corresponding to Galton’s ideas, the wasps interconnect to a network of multiple coupled sense organs – and here, as well, a surrounding media-technological zeitgeist of electricity surfaces in the conceptual accounts. Interestingly, Espinas even describes the exponential scaling effects of these affections in a fictional mathematical model of quantified emotional feedback loops. This would work like in a parliamentary speech situation, where a speaker tries to arouse his audience. The auditorium would reflect his engagement within the crowd and back to the orator, and in a rapid cascade of positive emotional feedback, the crowd would quickly turn into “something entirely different” – that is, not simply into a conglomerat of individuals, but into a single, somehow connected multitude.19
With this line of thought, Espinas follows the path of an organismic logic which conceptualizes the transmission of mutually escalated stimuli to “nervous bodies”20 or “collective organisms”21 which ensure their integrity by the intra-individual interchange of affects.22 Espinas and Galton both confront human and animal collectives on a shared behavioral level unimpressed by rationality and consciousness, which nevertheless enables and guarantees an aggregate behavior that corresponds to changing external factors without an underlying centralist control structure.
While Galton uses this common ground to criticize the slavish instincts of the ordinary people in mass societies and calls for “outstanding individuals,” Espinas puts forward the ubiquity of sociality on all complexity levels of biological life. Both their naturalists’ views on animal groups thus is imprinted by and mixed up with mere proto-sociological (and, to a certain extent, ideologically biased) hypotheses about the structure of human societies. And yet – or rather because of this – in both authors, the notions of affect, of emotion and of instinct and their discrimination remain rather indifferent and unclear. Affect and emotion seemingly intermingle and overlap and only serve to distinguish a certain psycho-corporeal behavior from conscious individual reactions and actions. However, such observations from early ethological studies served as illustrative examples and empirical foundations for the mass psychologists’ hypotheses of pre-conscious, affective contagion in human crowds, resulting in a somewhat blind and overagitated group mind – and thus in their disavowing characterization.23
Humans thus are depicted as rather deficient swarm members. While birds, fish and other herd animals often develop adequate collective dynamics even in case of great danger and are also beheld at all times as miraculous and astonishing phenomena, human masses tend to behave less acceptably in such cases and are far more critically perceived. In his fundamental tome Crowds and Power, Elias Canetti called such affective behaviors the “disintegration of the crowd within the crowd,”24 eventually resulting in panic and thereby emanating a paradox: A shared fear would beset the mass, but at the same time would lead to extreme individual reactions. Anybody would kick and push and trample, thus emphazising his singularity with all force, resulting in a highly uncoordinated mass movement. In accordance with Canetti’s text, panic has for a long time been assumed “to be the natural response to physical danger and perceived entrapment.”25 But despite this common belief and regardless of the numerous articles from fields like social psychology and disaster studies that until the 1980s fostered the characterization of panic as an infectious, egoistic, asocial and even irrational behavior in large crowds,26 panic has always remained a vague term. As early as 1963, a scholar from Hudson Institute complained: “The literature on panic research is strewn with wrecked hulks of attempts to define ‘panic.’ When these definitions are placed side by side, one is confronted by chaos.”27 “[They] range from ‘uncontrolled flight’ to cognitive states or inappropriate perceptions leading to irrational behaviors.”28 And in a recent overview, Enrico Quarantelli delivered the punch line concerning the diversity and heterogeneity of the notions by stating that: “the only common dimension is that whatever it is, panic is something that is bad.”29 As an effect, a recent encyclopedia article defines mass panic only very broadly as “a breakdown of ordered, cooperative behavior due to anxious reactions to a certain event” often accompanied by the “attempted escape of many individuals from a real or perceived threat in situations of a perceived struggle for survival.”30
These definitional difficulties arose in a scientific environment which for decades mainly concentrated on the dangerous potential of masses as a whole, rather than on the security of individuals within a crowd.31 Or, as sociologist Clark McPhail noted in 1991: “Students of the crowd, with certain exceptions, have devoted far more time and effort in criticizing, debating and offering alternative explanations [for mass actions, SV] than they have to specifying and describing the phenomena to be explained.”32 Only some authors in disaster sociology and safety science from the late 1950s onwards began to turn aside such perspectives on processes of a collective consciousness (or, for that matter, a collective unconsciousness) of crowds. They instead started studying the individual behavior and psychology involved, challenging the former notions of irrationality and asociality.33 Thus,
when people, attempting to escape from a burning building pile up at a single exit their behaviour appears highly irrational to someone who learns after the panic that other exits were available. To the actor in the situation who does not recognise the existence of these alternatives, attempting to fight his way to the only exit available may seem a very logical choice as opposed to burning to death.34
Such an individual-based perspective on mass dynamics offered an alternative way for representing, evaluating and addressing crowd disasters. Research emancipated from former accounts which sought to bind together individual with mass psychology and continued with the quest for a group mind, a somehow identical state of mind of people in a crowd.35 However, the study of individual behavior in cases of panic proved difficult. When scientists attempted to identify the effects of cooperative or competing behavior in cases of restricted escape routes by simulated room evacuations and psychological laboratory- and group experiments, thereby trying to evaluate the rationality of individual behavior in cases of panic, these endeavours resulted in rather insufficent data.36
The experiments have failed to explore the social dynamics of crowd movement directly, why and where flight behaviour and/or crushing occurs and how it can be prevented. The single group in the psychological experiments has been assumed to possess the essential properties of the far larger crowd. Ways in which a crowd’s composition will vary […] in different types of settings and situations […] are not represented in the laboratory based psychology experiments.37
Socio-psychological approaches from the 1960s to 1990s thus inevitably neglected the effects of specific spatial environments on crowd dynamics. Moreover, an empirical account of mass panic seemed little feasible in terms of realism. Neither would it be easy to evoke a human mass panic in an experimental setting as such, nor would the conjoint threat to the sample individuals be without problems from an ethical standpoint.38 Add to this a complementary strain of animal experiments that had to deal with the questionable correspondence of observations in mice or ants to human panic behavior. And if one takes into account some models developed in engineering during the same time which tried to describe human mass movements in analogy to physical phemomena like hydraulic flows or granular particles in pipe systems and tanks, they introduced their particular set of flaws: For example, they reduce the individual potentials of deviating behavior to identical elements, and, according to Jonathan Sime, put forward a
notion that people can be equated with nonthinking objects encourages an emphasis on crowd control through centralized (autocratic) building control systems, rather than crowd management through distributed (democratic) building intelligence.39
As an outcome, quite a few studies began to look at case studies of real-life disasters, taking them as empirical evidence for studying panic behavior. And somewhat surprisingly, “systematic studies of a variety of different emergencies and disasters have each emphazised the sheer lack of crowd panic.”40 Qualitative studies, interviews with disaster victims, or fatality demographics most often revealed that the individual behavior was far from anti-social. Panic behavior in the classical understanding seemed indeed to be a myth.41 On these foundations, emerging approaches like the affiliation model42 and the normative approach43 stated that even in disaster situations people were unwilling to leave companions behind and that behavior was to a great extent “structured by the same social rules and roles that operated in everyday life.”44 And while these models accounted for behaviors based on pre-existing relationships or elements, the social identity model tried to explain the oftentimes observed sociality even in groups of complete strangers, calling for a “model of mass emergent sociality,”45 turning the older notions upside down.
But even if this turnaround somehow rehabilitated the image of the psychology involved in human crowd dynamics and assigned a decisive role to cognitive decision-making and not merely to affective behaviors, these models were only able to look backwards in history. Undeniably, they insinuated consequences for the design of disaster management strategies which started to include more direct and distributed communication of officials with a panicking crowd instead of just trying to regulate it by centralized brute force.46 But also without a doubt, crowd disasters still occurred, and with sometimes high fatality rates47 – with or without an assumed mass emergent sociality, and in most cases due to scarce spatial resources. Thus, the planning of preventive measures of undesired crowd dynamics in environments like stadiums and other highly populated buildings or jammed plazas called for complementary strategies.
The insufficiency of socio-psychological approaches owes to the fact that “despite of the frequent reports in the media and many published investigations of crowd disasters, a quantitative understanding of the observed phenomena […] was lacking for a long time.”48 However, since the middle of the 1990s the collective dynamics of large crowds and agglomerates are studied with novel techniques such as computer simulations. These approaches aimed at complementing the socio-psychological findings with computer models that would provide the means for defining and predicting specific parameters of crowd dynamics and disasters. The formerly criticized simplifications into non-thinking objects in mechanistic model analogies are also complicated and elevated to another level: In so-called Agent-based Computer Simulations (henceforth: ABM), agents can act as individual or group decision-makers. Autonomy replaces the former (and easier) modeling of homogeneous objects. Individual agents can be described by a variety of different and differing agent attributes and agent methods. The former define the internal dispositions of an agent, the latter determine the capabilities of an agent to interact with others and the environment.49 Instead of the criticized centralistic approach of the former mechanistic models, ABM operate in a highly distributed fashion, and thus epistemically generate collective behavior in crowds as an accumulation of intrinsic, individualized influence factors such as agent velocities, collision probabilities, acceleration or pressure forces, or simulated perceptual constraints. These studies continue – under the conditions of advanced object-oriented software models – in the movement away from vague concepts and notions such as asocial or irrational. They convey a regulatory approach that deals much more neutrally with something which now is called “non-adaptive behavior”50 and results in statements like the following: “Here, however, we will not be interested in the question whether ‘panic’ actually occurs or not. We will rather focus on the issue of crowd dynamics at high densities and under psychological stress.”51 ABM coalesce the formerly separated areas of psychological behavioral studies and of the mechanistic modeling approaches in virtual programming environments. In this process, the models couple the earlier mechanistic references with bio-physical groundings of collective behavior. The latter are based on the mathematical definition and the computer-generation of a variety of autonomous virtual agents and their simulated inter-individual information exchange. And as an effect, they clarify the relations between certain spatial environments and a realistic human crowd behavior, insofar as the environments can also now be conceptualized as “an information system through which people move.”52 Henceforth, they enable a quantitative account of mass panic which shows novel qualities, for instance emerging pressure waves in the crowd which precede crowd disasters as typical patterns.
Some groundbreaking work in ABM derives from the simulation of biological systems such as swarms, flocks, and herds, which show how complex behavior on a collective scale can emerge even from a set of very few and simple decision and behavior rules in each individual. Two of the seminal computer simulations – which have also been quickly adopted to and modified for biological studies in animal collectives53 – have been William Reeves’ particle systems and Craig Reynolds’ boids model. Since their design in the mid-1980s, models of these kinds have been advanced to far more complicated agent systems.54 Terzepoulos, Thalmann, Helbing and others for instance started to model human crowds and equipped their agents with ever-more detailed artificial senses and biophysical control. This led to a more realistic behavior in relation to other agents and the simulated environment compared with the mechanistic models, for example, when it comes to cohesion or avoidance or to the coordination with neighboring individuals.55 Furthermore, in some models the agents get the ability to learn from already experienced situations and memorize by way of evolutionary or genetic algorithms. Or they are pre-programmed with certain preferred cultural determinants or social forces,56 for example with conventions on how to avoid other pedestrians or to choose a certain side when walking in a corridor. And they take into consideration scaling effects: “In some sense, the uncertainty of the individual behaviors is averaged out at the macroscopic level of description.”57 Instead of assigning instances like a group mind or collective consciousness to human crowds, these computer-based simulation studies look for the development of certain typical global patterns as an effect of various local and individual movements and movement decisions. These dynamics only emerge synthetically in the runtime of their simulation models and are not observable by real-life experimentation or by pure mathematical-analytical approaches.
As an outcome, a large enough number of such lifelike autonomous agents, put together in a virtual spatial environment, would show a collective behavior similar to real life in specific situations. And this holds true especially for evacuation scenarios with high densities, where human behavior is much easier to model and to predict due to the entailed environmental and perceptional constraints. By the modulation of the parameters involved one then can identify and tune the relevant factors involved by experimenting with the simulation model. However, these ABM do not attempt to implement a sort of artificial psychology, since internal processes in the agent are only relevant insofar as they result in certain motions in time and space, and thus in the emergence of certain global patterns. The models do not attempt to describe the emotions or the bodily affects involved in crowd dynamics, but only calculate (with) the motions defined by individual agent movement capabilities and environmental constraints. As an outcome, human crowd behavior can no longer be described as a degeneration of humans into animals. Rather, the computational abstraction of biological movement rules enables an operative and quantitative description of crowd dynamics in humans. And furthermore, the network affects, as defined by Thacker, cannot be separated from the inherent network effects, since the models realistically calculate cases of panic only with the help of effective simulated motion data. Or, to put it shortly: There is little point in pursuing strategies of affective computing58 when it comes to realistically modeling the dynamics of affective behavior in human collectives. Physically described and quantified effects depict what had been assigned to affects, and the more advanced models realistically produce crowd phenomena like the freezing-by-heating-effect, the faster-is-slower-paradox, or the emergence of phantom panics.59
For the last ten years – and implying the only recent development of algorithms that can simultaneously handle thousands, ten thousands or more lifelike agents – researchers have attempted to literally calculate disasters with the help of such ABM models, or rather: to calculate survival and prevent disasters in real life by running disastrous crowd scenarios in their computer simulations. In this context, one simulates for example the behavior of pedestrians in various spatial environments, with differing velocities and grades of density. As a consequence, one can for instance identify feasible architectural interventions to improve the speed of evacuation of a certain building. It seems interesting in this context that the computer simulation tools are not exclusively developed in scientific laboratories, but that SFX companies like the abovementioned Massive Software also provide sophisticated engineering simulations.60 This owes to the fact that their know-how in depicting collective dynamics of Orcs, zombies and other mindless movie characters can be employed to simulate and study more realistic scenarios as well. Those simulations can guide the modelers to counter-intuitive solutions, (e.g., to place a column directly in front of an exit, which substantially increases evacuation speed.) The situations can be tested under different environmental conditions, for example by adding smoke or fires to the scenarios which further constrain the orientation of the agents. And if combined with advanced methods of crowd capturing – that is, the live feedback of data generated by the automated analysis of digital video images of mass phenomena into the ABM models – the simulation can help event organizers and emergency response personnel to detect emerging, potentially critical crowd situations at an early stage. Once typical patterns (e.g., of so-called movement waves) are identified which indicate catastrophic outcomes at a later stage, various counter-measures can be tested in the computer model and the optimal reaction strategy can be identified.
Even more refined systems are underway: A reseach project of the German Research Centre for Artificial Intelligence in Kaiserslautern generates pedestrian-behavior models by inferring and visualizing crowd conditions from pedestrians’ GPS location traces. Coined crowd sensing, it was tested in 2011 and then applied during the 2012 London Olympics. The system is able to infer and visualize crowd density, crowd turbulence, crowd velocity and crowd pressure in real time. This works by the collected location updates from festival visitors. The researchers distributed a mobile phone app that on the one hand supplied the users with event-related information, and on the other hand periodically logged the device’s location, orientation and movement speed by GPS and the built-in gyroscope. Then, it sent the data back to the running model. The system allegedly helped to assess occurring crowd conditions and to spot critical situations faster compared to traditional video-based methods.61
Calculating disasters today means to coalesce empirical data of past catastrophies, observational data of mass events, and the computer-based experimentation and scenario-building with virtual ABM models of realistic agents and spatial environments. It thus combines analytical and synthetic approaches, supported by advanced visualization techniques, in the areas of crowd simulation, capturing, and sensing. With the latter, the crowd itself becomes kind of an operational medium – not only for its internal organization, but as a medium that helps regulating the multiple sensations and possible affections in a crowd in a real-time feedback loop to a computer model – a model, that in turn itself feeds back to the real-life crowd, sending information or warnings to the handheld devices of the app’s users. However, one would still rather question the applicability of the proposed feedback loop, as most people with the crowd sensing app most likely would not read the (individualized) directives appearing on their smartphones in the case of panic.
The employment of ABM in crowd control and evacuation studies signifies a turn from socio-psychological approaches and studies of group behaviors to physically describable parameters. Despite the fact that ABM incorporate findings from the biological study of animal collectives, they do not seek to directly determine a certain nature of affects like fear or panic, but facilitate virtual computer experiments that indirectly account for the spreading of affects by making observable collective movement patterns. What has often been an inquiry of the missing half-second,62 now turns into the minute description of individual movement vectors and capabilities of group individuals under certain critical conditions, and of the emergence of typical global movement patterns. Regardless of the nature of the involved affects, the (pre-) calculation of their effects in most cases suffices to deter undesired outcomes and feasible reactions to vaguely described pre-conscious psychological states. The preoccupation with these effects operationalizes the involved affects and situates them as bio-physical movement parameters. Such operational, effective softwares – sometimes even developed in the special effects business – successfully come after affects. Nonetheless, they not only might calculate disasters and provide for life-saving strategies, but they could also be utilized to counterattack the proposed potential of the socio-political network affects of social swarming. But anyway: The latest thing one should do in the face of these technologies is to behave like a zombie from the onset.
1 “Essex University uses ‘zombies’ in evacuation study,” BBC News, July 09, 2013, http://www.bbc.co.uk/news/uk-england-essex-23239221 (retrieved August 14, 2013).
2 Nikolai W. F. Bode and Edward A. Codling, “Human Exit Route Choice in Virtual Crowd Evacuations,” Animal Behaviour 86 (2013): p. 347–358.
3 See: Gustave Le Bon, Psychologie der Massen (Stuttgart: Kröner, 1982); Gabriel Tarde, L’Opinion et la foule (Paris: Alcan, 1901); Scipio Sighele, La Foule Criminelle. Essai de Psychologie Criminelle (Paris: Alcan, 1901).
4 For an overview, see: Dirk Helbing and Anders Johansson, “Pedestrian, Crowd and Evacuation Dynamics,” in: Robert A. Meyers, ed., Encyclopedia of Complexity and Systems Science (New York: Springer, 2009), p. 6476–6495.
5 Note that the term “collective” in this article is used in a mere operational and technical understanding and that its political dimensions and meanings are not taken into account. Collective thus simply alludes to a crowd or group consisting of multiple interacting individuals.
6 Eugene Thacker, “Networks, Swarms, Multitudes,” CTheory, May 18, 2004, http://www.ctheory.net/articles.aspx?id=423 (retrieved August 31, 2013).
7 Carl R. Schilt and Kenneth S. Norris, “Perspectives on Sensory Integration Systems: Problems, Opportunities, and Predictions,” in: Julia K. Parrish and William H. Hamner, eds., Animal Groups in Three Dimensions (Cambridge: Cambridge University Press, 1997), p. 225–244.
8 See: Thacker, “Networks, Swarms, Multitudes.”
9 See: Howard Rheingold, Smart Mobs. The Next Social Revolution (Cambridge: Basic Books, 2002); Kai van Eikels, “Schwärme, Smart Mobs, verteilte Öffentlichkeiten. Bewegungsmuster als soziale und politische Organisation?,” in: Gabriele Brandstetter, Bettina Brandl-Risi and Kai van Eikels, eds., Schwarm(E)motion. Bewegung zwischen Affekt und Masse (Freiburg: Rombach, 2007); Tiqqun, Kybernetik und Revolte (Berlin/Zurich: Diaphanes, 2007).
10 For a more detailed discussion, see: Sebastian Vehlken, “Angsthasen. Schwärme als Transformationsungestalten zwischen Tierpsychologie und Bewegungsphysik,” Zeitschrift für Kultur- und Medienforschung 0 (2009): p. 133–147.
11 John Drury and Chris Cocking, “The Mass Psychology of Disasters and Emergency Evacuations: A Research Report and Implications for Practice,” Research Paper (University of Sussex, 2007), http://www.sussex.ac.uk/affiliates/panic/Disasters and emergency evacuations (2007).pdf (retrieved August 31, 2013).
12 See: Joseph Vogl, “Über soziale Fassungslosigkeit,” in: Michael Gamper and Peter Schnyder, eds., Kollektive Gespenster. Die Masse, der Zeitgeist und andere unfaßbare Körper (Freiburg: Rombach, 2006), p. 171–189, here p. 178 (trans. Sebastian Vehlken).
13 For a more detailed account, see: Sebastian Vehlken, Zootechnologien. Eine Mediengeschichte der Schwarmforschung (Berlin/Zürich: Diaphanes, 2012).
14 Francis Galton, Inquiries into Human Faculty and its Development (New York: MacMillan, 1883), p. 49.
15 Ibid., p. 75–76.
16 Auguste Forel, Les fourmis de la Suisse (Zurich: Schweizerische Gesellschaft, 1873).
17 Alfred Espinas, Die thierischen Gesellschaften. Eine vergleichend-psychologische Untersuchung (Braunschweig: Vieweg, 1879), p. 343–344 (trans. Sebastian Vehlken).
18 Ibid., p. 344.
19 Espinas, Die thierischen Gesellschaften, p. 343–347.
20 Compare: Eva Johach, “Schwarm-Logiken. Genealogien sozialer Organisation in Insektengesellschaften,” in: Eva Horn und Lucas Mario Gisi, eds., Schwärme – Kollektive ohne Zentrum (Bielefeld: Transcript, 2009), p. 203–224.
21 See: Espinas, Die thierischen Gesellschaften, p. 349.
22 Ibid., p. 183–187.
23 See: Edward A. Ross, Social Psychology. An Outline and Source Book (New York: MacMillan, 1908); William McDougall, The Group Mind (New York: G.P. Putnam’s Sons, 1920).
24 Elias Canetti, Crowds and Power (New York: Continuum, 1960), p. 26–27.
25 Anthony R. Mawson, “Understanding Mass Panic and Other Collective Responses to Threat and Disaster,” Psychiatry 68.2 (2005): p. 95–113, here p. 95.
26 See: John P. Keating, “The Myth of Panic,” Fire Journal 76.3 (1982): p. 57–61.
27 Nehemian Jordan, “What is Panic?,” Discussion Paper HI-189-DP (Washington, DC: Hudson Institute, 1963), cit. Enrico L. Quarantelli, “Conventional Beliefs and Counterintuitive Realities,” Social Research 75.3 (2008): p. 873–904, here p. 876.
28 Lee Clarke and Caron Chess, “Elites and Panic: More to Fear than Fear Itself,” Social Forces 82.2 (2008): p. 993–1014, cit. Paul Gantt and Ron Gantt, “Disaster Psychology. Dispelling the Myths of Panic,” Professional Safety 57.8 (2012): p. 42–49, here p. 43.
29 Enrico L. Quarantelli, “Conventional Beliefs and Counterintuitive Realities,” Social Research 75.3 (2008): p. 873–904, here p. 876.
30 See: Helbing and Johansson, “Pedestrian, Crowd and Evacuation Dynamics,” in: Robert A. Meyers, ed., Encyclopedia of Complexity and Systems Science (New York: Springer, 2009), p. 6476–6495.
31 See: Serge Moscovici, The Age of the Crowd (Cambridge: Cambridge University Press, 1985).
32 Clark McPhail, The Myth of the Madding Crowd (New York: de Gruyter, 1991), p. XXIII, cit. Jonathan D. Sime, “Crowd Psychology and Engineering,” Safety Science 21 (1995): p. 1–14, here p. 4; see also: Hadley Cantril, “The Invasion from Mars,” in: Eleanor E. Maccoby, T. M. Newcomb and Eugene L. Hartley, eds., Readings in Social Psychology, (New York: Henry and Holt, 1958), p. 291–300; Enrico L. Quarantelli, “The Nature and Conditions of Panic,” American Journal of Sociology 60 (1954): p. 267–275; Anselm L. Strauss, “The Literature on Panic,” Journal of Abnormal and Social Psychology 39 (1944): p. 317–328.
33 Jonathan D. Sime, “Crowd Psychology and Engineering,” p. 10, cit. Enrico L. Quarantelli, “The Behaviour of Panic Participants,” Sociology and Social Research 41 (1957): p. 187–194; see also: Alexander Mintz, “Non-Adaptive Group Behaviour,” Journal of Abnormal Social Psychology 46 (1951): p. 150–159.
34 Ralph H. Turner and Lewis M. Killian, Collective Behaviour (Englewood Cliffs: Prentice Hall, 1975), p. 10, cit. Sime, “Crowd Psychology,” p. 5.
35 See: Helbing and Johansson, “Pedestrian, Crowd and Evacuation Dynamics,” p. 6483; Miles Hewstone, Wolfgang Stroebe and Klaus Jonas, eds., Introduction to Social Psychology (Oxford: Blackwell, 1988).
36 See: John C. Condry, Arnold E. Dahlke, Arthur H. Hill and Harold H. Kelley, “Collective Behaviour in a Simulated Panic Situation,” Journal of Experimental Social Psychology 1 (1965): p. 20–54; Sharon Guten and Vernon L. Allen, “Likelihood of Escape, Likelihood of Danger and Panic Behaviour,” Journal of Social Psychology 87 (1972): p. 29–36.
37 Sime, “Crowd Psychology,” p. 7.
38 Drury and Cocking, “Mass Psychology,” p. 13.
39 Sime, “Crowd Psychology,” p. 11.
40 Drury and Cocking, “Mass Psychology,” p. 9.
41 See: Keating, “The Myth of Panic,” p. 56–61.
42 Anthony R. Mawson, “Panic Behavior: A Review and New Hypothesis,” paper presented at the 9th World Congress of Sociology, Uppsala 1978.
43 Norris R. Johnson, William E. Feinberg and Drue M. Johnson, “Microstructure and Panic: The Impact of Social Bonds on Individual Action in Collective Flight from The Beverly Hills Supper Club Fire,” in: Russel R. Dynes and Kathleen J. Tierney, eds., Disaster, Collective Behaviour and Social Organization (Newark: University of Delaware Press, 1994), p. 168–189.
44 Drury and Cocking, “Mass Psychology,” p. 11.
46 See: Gantt and Gantt, “Disaster Psychology,” p. 47–49.
47 Even in combination with advanced computer modeling techniques, crowd disasters still occur. Take for example the Duisburg Love Parade Disaster in 2010.
48 Helbing and Johansson, “Pedestrian, Crowd and Evacuation Dynamics,” p. 6484.
49 Charles M. Macal and Michael J. North, “Tutorial on Agent-Based Modeling and Simulation, Part 2: How to Model with Agents,” L. Felipe Perrone, Barry G. Lawson, Jason Liu, Frederick P. Wieland, eds., Proceedings of the 2006 IEEE Winter Simulation Conference (Monterey, December 3–6, 2006), http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4117582, p. 73–83 (retrieved March 4, 2013).
50 Dirk Helbing, Illés Farkas and Tamás Vicsek, “Simulation Dynamical Features of Escape Panic,” Nature 407 (September 2000): p. 487–490.
51 Helbing and Johansson, “Pedestrian, Crowd and Evacuation Dynamics,” p. 6483.
52 Sime, “Crowd Psychology,” p. 10.
53 See: Vehlken, Zootechnologien.
54 William T. Reeves, “Particle Systems – A Technique for Modeling a Class of Fuzzy Objects,” ACM Transactions on Graphics 2.2 (1983): p. 91–108; Craig W. Reynolds, “Flocks, Herds, and Schools: A Distributed Behavioral Model,” Computer Graphics 21.4 (1987): p. 25–34.
55 Helbing, et al., “Simulating Dynamical Features of Escape Panic,” p. 487–490; Soraia Raupp Mousse, Branislav Ulicny and Amaury Aubel, “Groups and Crowd Simulation,” in: Nadia Magnenat-Thalmann and Daniel Thalmann, eds., Handbook of Virtual Humans (New York: John Wiley, 2004), p. 323–352; Wei Shao and Demetri Terzopoulos, “Autonomous Pedestrians,” in: Ken Anjyo and Petros Faloutsos, eds., Proceedings of the 2005 ACM SIGGRAPH/Eurographics Symposium on Computer Animation (Los Angeles, July 29–31, 2005).
56 See: Kurt Lewin, Field Theory in Social Science (New York: Harper, 1951); Dirk Helbing, “A Mathematical Model for the Behavior of Individuals in a Social Field,” Journal of Mathematical Sociology 19.3 (1994): p. 189–219.
57 Helbing and Johansson, “Pedestrian, Crowd and Evacuation Dynamics,” p. 6478.
58 See: Rosalind W. Picard, “Affective Computing,” M.I.T Media Laboratory Perceptual Computing Section Technical Report No. 321 (Cambridge, MA, 1995); Marvin Minsky, The Emotion Machine (New York: Simon and Schuster, 2006).
59 Helbing and Johansson, “Pedestrian, Crowd and Evacuation Dynamics,” p. 6487–6489.
60 See: http://www.massivesoftware.com/engineering.html (retrieved August 31, 2013).
61 Compare: Martin Wirz et al., “Inferring Crowd Conditions from Pedestrians’ Location Traces for Real-Time Crowd Monitoring during City-Scale Mass Gatherings,” Proceedings of the 2012 IEEE 21st International Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises (Toulouse: WETICE, 2012), p. 367–372; Werner Pluta, “Crowd Management Smartphone soll Massenpanik verhindern,” http://www.golem.de/ news/crowd-management-smartphone-soll-massenpanik-verhindern-1209-94331.html (retrieved June 28, 2013).
62 See for example: Marie-Luise Angerer, Vom Begehren nach dem Affekt (Zurich/Berlin: Diaphanes, 2007), English translation Desire After Affect (London: Rowman & Littlefield International, 2014); Brian Massumi, “The Autonomy of Affect,” Cultural Critique 31 (1995): p. 83–105; Hertha Sturm, “Wahrnehmung und Fernsehen – Die fehlende Halbsekunde,” Media Perspektiven 1 (1984): p. 58–64.
studied Media Studies and Economics at Ruhr-University Bochum and at Edith Cowan University, Perth. From 2005-2007, he was a DFG scholarship holder in the Graduate School »Media of History – History of Media« at Bauhaus-Universität Weimar, and from 2007-2010 research assistant (PreDoc) in Media Philosophy, University of Vienna. From 2010-2013 he was a research assistant (PostDoc) at Leuphana University Lüneburg. Since 2013, he is Junior Director of the Institute for Advanced Study on Media Cultures of Computer Simulation, Lüneburg.
Marie-Luise Angerer (ed.), Bernd Bösel (ed.), Michaela Ott (ed.)
Timing of Affect
Epistemologies, Aesthetics, Politics
Softcover, 344 pages
PDF, 344 pages
Affect, or the process by which emotions come to be embodied, is a burgeoning area of interest in both the humanities and the sciences. For »Timing of Affect«, Marie-Luise Angerer, Bernd Bösel, and Michaela Ott have assembled leading scholars to explore the temporal aspects of affect through the perspectives of philosophy, music, film, media, and art, as well as technology and neurology. The contributions address possibilities for affect as a capacity of the body; as an anthropological inscription and a primary, ontological conjunctive and disjunctive process as an interruption of chains of stimulus and response; and as an arena within cultural history for political, media, and psychopharmacological interventions. Showing how these and other temporal aspects of affect are articulated both throughout history and in contemporary society, the editors then explore the implications for the current knowledge structures surrounding affect today.