standing ovation: an attempt to simulate human personalities

SystemsResearch andBehavioral ScienceSyst. Res.23, 825^838 (2006)Published online inWiley InterScience (www.interscience.wiley.com)
DOI:10.1002/sres.766
& ResearchPaper

Standing Ovation: An Attemptto Simulate Human Personalities

Miklos N. Szilagyi1 and Matthew D. Jallo2*1Department of Electrical and Computer Engineering, University of Arizona, Tucson, Arizona, USA2Department of Systems and Industrial Engineering, University of Arizona, Tucson, Arizona, USA

* CorIndusUSA.

Cop

Computer simulation of human personalities is presented using standing ovation as anexample of human social behaviour. Five personality components are used to influencebehaviour. The experiment has proven the efficacy of basing a nonlinear dynamic systemsimulation on precepts derived from personality traits. Copyright # 2006 John Wiley &Sons, Ltd.

Keywords standing ovation; personality; emergence of social norms; behaviour; simulation

INTRODUCTION

At the end of a concert, a crowd stands inapplause with the exception of one man who sitssilently. At the end of a speech, a woman standsin applause while the rest of the audienceremains seated. At the end of a musical, astanding ovation spreads across the audience likefire. We have all observed the standing ovation,but can it be simulated?

There is considerable interest in computersimulation of human social behaviour. Escapepanic (Helbing et al., 2000), Mexican waves(Farkas et al., 2002) and even rhythmic applause(Neda et al., 2000a) have been simulated orstudied. In this paper, we will investigate thephenomenon of standing ovation. There are tworeasons we are interested in it:

respondence to: Matthew D. Jallo, Department of Systems andtrial Engineering, University of Arizona, Tucson, AZ 85721,E-mail: [email protected]

yright # 2006 John Wiley & Sons, Ltd.

(1) I
t is a simple but characteristic example of thegeneral problem of emergence of norms.
(2) I
t is likely to depend on the psychologicaldistribution of the audience.
Simulation of human personalities is usuallyneglected in the literature. One of us has attemptedto take personalities into account in his simulationof social dilemmas (Szilagyi, 2003). In this workwewill try to do it more systematically.

Motivation is what drives the decision makersto their conclusions, and ultimately to theexecution of their decisions. Thus, the entireexperiment hinges on the simulation of motiv-ation. The relationship between personality andmotivation is intricate and well documented(Cattell and Kline, 1977), but for the purposes ofthis experiment motivation with regards to thedecision making process itself and the executionof this decision are combined.

Existing literature regarding applausedynamics, although sparse, suggests the follow-ing. Ideally, we clap to show approval or praise

Received March 2004Revised January 2006

Accepted February 2006

RESEARCHPAPER Syst. Res.

for something (Lupyan and Rifkin, 2003). How-ever, there are other social factors that influenceour decision of whether or not to applaud.Generally speaking, clapping at the correct timeprovides an emotional reward, whereas clappingat the wrong time results in the opposite effect,embarrassment. We clap when the expectedreward outweighs the estimated cost. Obser-vation indicates that if the applauder is not joinedby others, he/she will usually stop applauding.

Standing ovations are a special subset of theapplause spectrum. The standing ovation is aresult of standard applause not being sufficientlyexpressive for the individual, and similar beha-viours exhibited including whistling or yelling.However, we hypothesize that there are othersocial factors that affect whether or not onechooses to stand, such as the presence anddistances of other standing members of theaudience. This is logical because it follows thebasic paradigm: standing provides a greateremotional reward, but is potentially moreembarrassing if done at an inopportune moment.

Standing ovations are furthermore ideal forstudying because the dynamics are more definedand distinct, and geographic considerations alsoplay an important role which is appropriate forthe cellular automata approach chosen tosimulate the dynamics.

Some studies have indicated that applause takeson a rhythmic oscillatory pattern (Neda et al.,2000b). Rhythmic oscillatory patterns are a con-sistent phenomenon with many other socialfunctions (Glass and Mackey, 1988). Furthermore,the audience forms a social grid that can be studiedthrough the disciplines of discrete mathematicsand graph theory (Newman and Watts, 1999).

What makes us stand up at a performance? Is itsincere appreciation, peer pressure, respect,impulse, simply following protocol, or is it essen-tially random? It is all of these things and more.

The simplest psychological simulation of astanding ovation is dependent upon geography.If a person is behind many applauders, or thepeople to the side are applauding, the person ismore likely to applaud. This form of simulation isa very simple example of cellular automata, andcan be seen in other simple simulations such asthe ‘game of life.’

Copyright � 2006 JohnWiley & Sons,Ltd.

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Previous work similar to this experimentincluded principles, concepts, and phenomenaof ensembles with variable structure (EVS) (Sulisand Trofimova, 2001), and self-organization andresource exchange in EVS (Trofimova and Mitin,2002). The former included a broad array ofdriving forces, including personality. However,our experiment attempts to simulate the inter-action of personality factors, whereas theprevious work only took into account whatfactors agents shared.

Instead of simulating the results of purelyrational interactions our work simulates theresults of personality interactions. It should alsobe noted that our simulation implements a basiccellular automata approach, whereas Trofimo-va’s and Mitin’s (2002) work implementedadaptation algorithms and resource-exchangemodels. An excerpt from this previous studyshows the contrast (emphasis added):

Individual differences of agents were not abstracttraits, but three characteristics of outputresource. (a) fixed necessary expenses perstep (life expenses), which an agent cannotavoid; (b) maximum number of expenses perstep (including the cost to have a connection);(c) maximal allowed percentage of expensesderived from the residual of an agent.(Trofimova and Mitin, 2002: 355)

Our study is based on the individual differ-ences between profiles of abstract traits present inthe agents.

Another experiment simulated the approach-avoidance conflict, where three different kinds ofagents (hardcore, hangers-on and bystanders)were simulated in a fashion similar to our study.Although personality traits themselves were notconsidered, these three kinds of agents certainlyillustrated some results of psychologically influ-enced behaviour (Jager et al., 2001).

IMPLEMENTATION

Our initial experiments only considered simplematters of geography, and the results werenonetheless quite interesting. But in order tobetter approach a simulation based on personality

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M.N.SzilagyiandM.D. Jallo

Syst. Res. RESEARCHPAPER

factors, we incorporated two different sets ofpersonality profiles.

The first was based on the bookNew PersonalitySelf Portrait by Oldham and Morris (1995). Thebook is partly based on the DSM-IV, which is thestandard diagnostic manual used in psychiatryand psychology. Oldham and Morris haveidentified 14 personality components that com-prise every personality to varying degrees. Theuse of these 14 personality components enablesus to simulate any person by assigning appro-priate values to each component.

The second set of profiles we used is known asthe ‘Big 5’ set of personality traits (De Raad andPerugini, 2002). These five personality traitsindividually encompass a wider range of beha-viour than the 14 identified by Oldham andMorris, but are generally considered to be verydistinct and accurate representations of thetypical personality traits present in every person.

In both cases, each agent in the simulation wasrepresented as a profile of the distinct personalitytraits. The experiment is based on the premisethat there is a distribution of varying psycho-logical traits present in every person. A psycho-logical trait is a facet of personality thatcategorizes people based on the magnitude ofparticular characteristics they hold.

The primary difference between the two sets ofpersonality traits is that the set of 14 are surfacetraits while the set of 5 are source traits. Sourcetraits consist of the smallest number and simplestdiscrete classifications possible. Surface traits aresub-characteristics of source traits, thus there aremore of them. Surface traits are probably bettersuited for this type of simulation, because theyare more easily observable (and presumablyeasier to prepare for observation). Because sourcetraits are broader and less observable factors,they increase the likelihood of introducingarbitrariness into the system.

However, after thoroughly testing both sets ofprofiles via simulation, the Big 5 set was chosen.The behaviour exhibited by the two sets of profileswas reasonably similar, such that the set of fivewas chosen because of its greater simplicity. Inmost of the literature concerning the Big 5, thetraits are sub-divided into othermore specific sub-traits (surface traits). To gain a better under-


Standing Ovation

standing of the behaviours of the Big 5 traits on awhole, careful consideration was given to each ofthe sub-traits (Cattell et al., 1970).

There are several key concepts and termscritical to the simulation. The first is called theovation level. The ovation level is a value between[0,1] that indicates the enthusiasmwithwhich theagent is applauding. A value of 0 indicates thatthe agent is perfectly still and nonchalant. Avalue of 0.5 is assumed to be the point at which anagent stands in applause, and a value of 1 is themaximum enthusiasm with which an agent canapplaud (i.e. whistling, yelling, etc.).

K is a dampening coefficient that governs howquickly the simulation progresses. A higher Kvalue will result in a faster simulation. T is thenormalized elapsed time and P is the perform-ance quality (0�T� 1, 0�P� 1).

Each agent has a personality trait profile, whichis a set of five values in the range of [0,1] thatrepresent the magnitude of the various Big 5traits that the agent possesses. An agent equation isthe set of mathematical operations performed onthe ovation level of the agent based on itspersonality trait profile, that is, how it reacts tovarious external factors. The values from theagent equations are multiplied by the level of thecorresponding trait for each agent.

The following is a list of the Big 5 personalitytraits, and their corresponding agent equations:

Extraversion

Extraversion and introversion are perhaps themost widely studied of all the traits. Anextraverted agent is confident, outgoing andinterested in its surroundings. Introverted agentsare interested more in themselves, and prefer tospend time alone. We conducted simulationexperiments with the following agent equationsrepresenting extraversion, with the responsesbeing a function of extraversion level:

(1) R
esponse¼ (Linear function graphed inFigure 1)
This simple function results in extraversionbeing a direct and linear factor in how much theovation level is increased. The deficiency of this

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Figure 1. Extraversion response graph—Linear extraversion


approach is that lower levels of extraversion arenot represented as introversion.

(2) R

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esponse¼ (Polynomial function graphed inFigure 2)

In this function, a duality is integrated into theresponse. Extraversion levels of less than 0.5 areactually simulated as introversion. However, wefound that for the middle range of the function’s

Figure 2. Extraversion response grap

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domain the rate of change was too great. The nextfunction was ultimately chosen to represent theresponse to extraversion:

(3) R

h—

esponse¼ (Polynomial function graphed inFigure 3)

Here, the same duality is seen as in the functionabove. However, the response function becomessteeper at the extremes of domain. As the graph

Extraversion and introversion

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Figure 3. Extraversion response graph—Optimized extraversion and introversion


above illustrates, agents who are highly extra-verted will contribute to a higher ovation level,whereas introversion will actually reduce theovation level.

Agreeableness

Agreeable people will tend to resolve conflictbetter, communicate more effectively, be lessaggressive, etc. In contrast, a person who is notagreeable may be argumentative, prone toconflict and asocial. Experimentswere conductedwith the following agent equations representingagreeableness:

ð1Þ Response

¼ ððFrontðovation levelÞ

þ Sidesðovation levelÞÞ=2Þ �Agreeableness

The front function returns the average ovationlevel of the three agents directly in front of theagent of operation. For example, if two of thethree agents in front are smiling enthusiasticallyand the other is not reactant, the front functionwill return a value of three quarters. The sidefunction returns the average ovation level of


Standing Ovation

the two agents on either side of the current agent.The combination of these two functions is thendivided by two to provide an average. This valueis then multiplied by the agreeableness level ofthe agent. So, if the agent is highly agreeable andthere is a lot of applause observable then theagent will have a high ovation response. But if theagent is disagreeable, or there is not muchobservable applause, there will be little or noincrease in the agent’s ovation level. Whenimplemented into the cellular automata simu-lation, this equation lends a very geographicnature to the simulation that is characteristic ofsimpler cellular automata simulations. However,it lacks a duality because a decidedly disagree-able agent does not ‘do the opposite’ of what itobserves in its company (Figure 4)

ð2Þ Response

¼ ððFrontðovation levelÞ

þ Sidesðovation levelÞÞ=2Þ

� Response coefficient ðagreeablenessÞ

This equation is a modification of the previousone to take into account the behaviour of an agentwith a very low agreeableness level (or a high

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Figure 4. Agreeableness coefficient graph


level of disagreeability). The front and sidesfunction operate the same as they did in theprevious equation, but the agent’s level ofagreeableness is processed through anotherfunction as illustrated above. If the agent hasan agreeableness level of less than about 0.15, theagent is simulated as doing the opposite of whatit observes (otherwise it behaves the same as itdid previously). So, if an agent is highlydisagreeable and a lot of the agents around itare applauding, this will actually discourage anincrease in the ovation level of the agent.

Conscientiousness

A conscientious agent is result driven, practical,steady and efficient. Experiments were con-ducted with the following agent equationrepresenting conscientiousness:

ð1Þ � 2� T þ 1

This equation is a linear function representingtime multiplied by the agent’s conscientiousnesslevel. A time value of less than one half yields apositive value because it is the proper time to


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applaud (the first half of the general time span). Itwill yield a negative result as time goes on,because the conscientious agent will realize thatthe applause is becoming prolonged.

Neuroticism (Emotional Stability)

Neuroticism is emotional instability such asanxiety. Neuroticism results in unpredictablebehaviours within the context of a standingovation simulation so it was ultimately deter-mined that a random value would best representneuroticism as an agent equation. Two equationswere tested:

ð1Þ Response

¼ EachTime Random½�1; 1� �Neuroticism

The highly neurotic agent will sporadicallydecide to either applaud more enthusiastically orless enthusiastically. The function EachTime_R-andom provides a different random numberwithin the designated range [�1,1] on everyiteration. This equation was not chosen, becausethe rapid changes in the random values created

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an averaging effect that had little observableimpact on the simulation.

ð2Þ Response

¼ OneTime Random½�1; 1� �Neuroticism

The OneTime_Random function returns thesame random value within the designated range[�1,1] for the agents on each iteration. This bettersimulated the influence a neurotic personalitytrait would have on the agent, because it isunlikely that the agent’s mood would changemultiple times per second. Further study couldincorporate some type of slower moving sto-chastic.

Figure 5. Main simulation window

Openness (Intellect)

An open agent will tend to seek out newexperiences, and respond accurately to how itis feeling. The following equation was used torepresent Openness:

ð1Þ Response ¼ P�Openness

The level of openness that the agent exhibits isproportional to its response to the level of qualityin the performance (P).

Standing ovation is based on a dynamicsystem, so time is of crucial importance. Thecomputer must separate time into distinct spansand simulate what would have happened duringthat time span. This time span is called a ‘timeslice’ and it is also what allows multi-taskingoperating systems to run multiple applicationssimultaneously. .

In each time slice, all the rules of interaction areapplied to the agents. In our simulation, a timeslice is 100 ms. Each agent has a profile ofstrengths of its five personality components. Thesoftware allows the user to set up an audiencethat is entirely random in its trait distribution,null in its distribution or any user-defineddistribution.

The model consists of an audience of a pre-specified size and a performer in the front. Theaudience is in the shape of a rectangle, such asone might expect to find in a typical auditorium.


Standing Ovation

This is a usual configuration for cellular auto-mata modelling. Each agent is modelled as anautomaton whose behaviour can be described bysimple rules of interaction with its neighboursand its environment. These simple rules maydetermine quite complex behaviour (Wolfram,2002).

As the simulation progresses, the ovationlevels of the various agents will change witheach iteration. An iteration is one time slice.During an iteration, the software performs anumber of operations on each agent, one by one.It runs through the set of five equations for eachpersonality trait. The equations implement thecontribution of each trait as it was describedearlier. Once values are obtained from eachequation, they are added up, normalized, and theovation level of the agent is modified and storedin a buffer.

The purpose of the buffer is the following: Alloperations during one iteration are theoreticallyoccurring in the same period of time, so if the firstagent in the audience is modified directly,without a buffer, then other agents such as theone behind it will incorrectly use this newmodified value to determine their own newovation level. Using a buffer and applying allchanges at the end of the iteration eliminates thisprogressive inconsistency.

Figure 5 depicts the main simulation window.At the top of the window, there is a ‘slider’

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Figure 6. Component screen


control that allows the user to adjust the levelof the performance, which specifies how wellthe performer is doing. Below this is theaudience which consists of graphics of achronograph representing a sitting agent, anda standing person representing a standingagent. The audience size is variable, anddetermined in a previous introductory screen.This introductory screen also allows the user toselect the default distribution (null or random).Below the audience is the component profilethat shows the varying levels of personalitycomponents of the agent over which the cursorcurrently points.

‘Iterate’ advances the dynamic system by 100ms. ‘Reset’ resets the all the ovation levels to zerofor a new run. The user can select an agent byclicking on it, and multiple agents by dragging arectangle. Clicking on the ‘Components’ buttonbrings up a screen shown in Figure 6 that allowsthe user to adjust the component distribution ofthe currently selected agent(s).

Referring back to Figure 5, choosing the ‘SetComp.’ button (short for Set Component) brings upa screen where the user can set the values forcomponents for the selected agent(s). Finally, choos-ing the ‘Play’ button will automatically advance theiterations in real time, so that the user can watch thesimulation unfold as if it were a movie.

Figure 7. Extraver


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The following simulations illustrate the beha-viour of audiences that consist primarily of asingle personality trait (Figure 7).

Extraversion

The ovation level for each agent gradually roseuntil they all stood at the same time. This is anexpected and consistent result of the extraversionequation (Figure 8).

Agreeableness

Here the grouping effect typical of simple cellularautomata simulations is clearly seen (Figure 9).

Conscientiousness

In this run the conscientious agents stand up, andthen gradually sit back down (Figure 10).

Neuroticism

In this simulation, none of the agents stood(Figure 11).

Openness

This run exhibited similar results as conscien-tiousness, but a bit more gradual.

The following simulations show audiences ofvarying sizes with completely randomized pro-files (Figures 12–14).

sion simulation

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Figure 8. Agreeableness simulation


The above three simulations demonstrate acombination of all the trait behaviours. Groupingcan be seen from agreeableness, extraversionsparks the whole simulation, openness corre-


Standing Ovation

sponds to the performance quality, neuroticismcauses a certain level of unpredictability in thesimulation and conscientiousness causes theagents to respond according to the time frame.

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Figure 9. Conscientiousness simulation


SUMMARY

Any number of agent equations could have beenchosen to simulate the impacts of various trait

Figure 10. Neuroti


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distributions. Despite the apparent arbitrarinessof the equations used, the results are interestingand lay a framework for further experimentation.The equations given are merely examples; we

cism simulation

Syst. Res.23, 825^838 (2006)


Figure 11. Openness simulation

Figure 12. Random distribution simulation, 16� 8

Copyright � 2006 JohnWiley & Sons,Ltd. Syst. Res.23, 825^838 (2006)

Standing Ovation 835




encourage further experimentation with differ-ent equations.

Standing ovations are a complex and interest-ing phenomenon that a computer simulationcould only ever approximate. However, thesimulation has demonstrated the importance ofcertain key trait aspects in the dynamics of this


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phenomenon. The methods used in this simu-lation would also be useful in simulating a widevariety of other social phenomena, such as escapepanic, herding instincts, etc.

We believe that it is crucially important toinclude agent personalities in the simulationof any social behaviour. Evidently, the

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equations that represent the five personalitycomponents must reflect the particular situation;therefore they will be different for each type ofsimulation.

REFERENCES

Cattell RB, Kline P. 1977. The Scientific Analysis ofPersonality and Motivation. Academic Press: NewYork.

Cattell RB, Eber HW, Tatsuoka MM. 1970.Handbook forthe Sixteen Personality Factor Questionnaire (16 PF).Institute for Personality and Ability Testing, Inc:Champaign, IL.


Standing Ovation

De Raad B, Perugini M. 2002. Big Five Assessment.Hogrefe & Huber Publishers: Kirkland, WA.

Farkas I, Helbing D, Vicsek T. 2002. Mexican waves inan excitable medium. Nature 419: 131–132.

Glass L, Mackey M. 1988. From Clocks to Chaos:The Rhythms of Life. Princeton University Press:Princeton, NJ.

Helbing D, Farkas I, Vicsek T. 2000. Simulating dynami-cal features of escape panic. Nature 407: 487–490.

Jager W, Popping R, Van de Sande H. 2001. Clusteringand fighting in two-party crowds: simulating theapproach-avoidance conflict. Journal of ArtificialSocieties and Social Simulation 4: 3.

Lupyan G, Rifkin I. 2003. Dynamics of applause: mod-eling group phenomena through agent interaction.Poster presented at the 25th Annual Conference of theCognitive Science Society.

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Neda Z, Ravasz E, Brechet Y, Vicsek T, Barabasi AL.2000a. The sound of many hands clapping. Nature403: 849–850.

Neda Z, Ravasz E, Vicsek T, Brechet Y, Barabasi AL.2000b. Physics of the rhythmic applause. PhysicalReview E 61: 6987–6992.

Newman MEJ, Watts DJ. 1999. Scaling and percolationin the small-world network model. Physical Review E60: 7332–7342.

Oldham JM, Morris LB. 1995. The New Personality Self-Portrait. Bantam Books: New York.


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Sulis W, Trofimova I. 2001. Nonlinear Dynamics in theLife and Social Sciences. IOS Press: Amsterdam, TheNetherlands.

Szilagyi MN. 2003. An Investigation of N-PersonPrisoners’ Dilemmas. Complex Systems 14: 155–174.

Trofimova I, Mitin N. 2002. Self organization andresource exchange in EVS modeling. NonlinearDynamics, Psychology, and Life Sciences 4: 351–362.

Wolfram S. 2002. A New Kind of Science. WolframMedia: Champaign, IL.

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standing ovation: an attempt to simulate human personalities

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