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SEBASTIAN VEHLKEN AFTER AFFECTS ZEALOUS ZOMBIES, PANIC PREVENTION, CROWD SIMULATION

I. ZOMBIELAND

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,

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.

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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

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.

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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

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.”

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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

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.

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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.

II. ARITHMETICS OF AGITATION

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

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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

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.

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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

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),

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“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

III. (MIS-)UNDERSTANDING MASS PANIC

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 dan-

ger and are also beheld at all times as miraculous and astonishing phenomena, man-

masses#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

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.

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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 reac-

tions to a certain event” often accompanied by the “attempted escape of many individu-

als 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]

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. 6476–649531 See: Serge Moscovici, The Age of the Crowd (Cambridge: Cambridge University Press, 1985).

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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 individ-

ual 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

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: Had-ley Cantril, “The Invasion from Mars,” in: Eleanor E. Maccoby, T. M. Newcomb and E.##1two pages? .#please check# Maccoby, Theadore M. Newcomb and Eugene L. Hartley, eds., Readings in Social Psy-chology, (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).

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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

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.

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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 surpris-

ingly, “systematic studies of a variety of different emergencies and disasters have each

emphazised the sheer lack of crowd panic.”40 Qualitative studies, interviews with disas-

ter 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 unwill-

ing 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 iden-

tity 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, they#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 soci-

ality, and in most cases due to scarce spatial resources. Thus, the planning of preventive

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. 11.45 Ibid.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.

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measures of undesired crowd dynamics in environments like stadiums and other highly

populated buildings or jammed plazas called for complementary strategies.

IV. THE COMPUTER-SIMULATED CROWD:

FROM AFFECT AND EMOTION TO MOTION

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 disas-

ters, 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 simula-

tions. 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 fash-

ion, and thus epistemically generate collective behavior in crowds as an accumulation

of intrinsic#ally?# 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 (

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).

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#baptized#called# “non-adaptive behavior”50 and results in statements like the fol-

lowing: “Here, however, we will not be interested in the question whether ‘panic’ actu-

ally occurs or not. We will rather focus on the issue of crowd dynamics at high densi-

ties 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 quantita-

tive 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 sys-

tems such as swarms, flocks, and herds, which show how complex behavior on a collec-

tive 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 indi-

viduals.55 Furthermore, in some models the agents get the ability to learn from already

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

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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 about#please decide# the individual behaviors is aver-

aged 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 sheer#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 den-

sities, 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 #men#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

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.

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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 realis-

tically 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 – research-

ers 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 inter-

esting in this context that the computer simulation tools are not exclusively developed

in scientific laboratories, but that SFX #companies# like the abovementioned Massive

software #Massive ist keine Firma sondern eine Software# also provide sophisticated

engineering simulations.60 This owes to the fact that their know-how in depicting col-

lective 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.

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).

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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.

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).

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V. AFTER EFFECTS

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.

62 See for example: Marie-Luise Angerer, Vom Begehren nach dem Affekt (Zurich/Berlin: Diaphanes, 2007), English translation Desire and 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.