Play-Persona: Modeling Player Behaviour in Computer Games

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Combining both of these two approaches allows designers to sharpen game experiences without falling into neo-positivist, data-driven fallacies, nor solipsistic decisions taken by designers too abstracted from players. The play-persona framework belongs to the same group of research that attempts to find player types, such as Bartle’s types [13], Yee’s motivations [100] and Bateman’s categories [14]. While each of these approaches is structured either top-down (deductively) or bottom-up (inductively), play-personas, with their two-headed nature of metaphors and lenses, succeed in making use of both approaches. 5.1 Future work This research took place at a particular moment where EIDOS was investing resources into the development of a metric suite designed to collect data about player behaviour, therefore it is clearly noticeable how most of the articles possess a bias towards quantitative analyses and interpretations. This happened not because of a belief in the superiority of quantitative methods, but because of the opportunity to use and experiment with a tool that in game development was still rather new. Future work should focus on how to integrate the methods explored during this research with qualitative approaches to observing the human side of players: interviews, self reports, video footage, participant observation, ethnographic and anthropological enquiries, etc. Another venue worthy of in-depth exploration is investigating how to incorporate biometric data 4 into the definition and use of play-persona profiles. In this research, Peircian semiotics has been merely scratched, attempting to describe the relation between aesthetic codes, ludic codes and play experiences; but future work could explore the possibility to adapt Peirce’s theoretical complexity to account for the emergence of meaning from game worlds. Furthermore a handful of semioticians following the footsteps of Varela and Maturana are working on cybersemiotics: “a combination of a Peircian-based biosemiotics with system science, cybernetics, autopoiesis theory, second order cybernetics and information science” [108]. This angle views the interplay of mind and body as a sign and attempts to understand how the prerequisites for human consciousness are laid down in the evolution of the human’s bodily system. This could help reading the acts of communication of the avatar as a digital body. 4 Among all possible biometric variables the most important are: electroencephalograms, heartbeat rate, blood pressure, skin conductance and eye gaze tracking. 58
5.1.2 Applications of play-personas to other fields The play-personas framework could successfully be employed during design and evaluation of any product as long as remote collection of metric and/or biometric data is possible. Beta or evaluation versions could be distributed with metric clients that not only log bugs and failures, but also record patterns of use; this data can be used to evaluate goals set in the hypothesised models of expected behaviours. If traditional HCI personas are used in software development as early tools to focus on features and specifications, expanding them as both metaphors and lenses allows early releases of products to become at the same time test beds for the verification of hypotheses of use set forth initially. Traditional HCI personas stop being useful before the end of the production cycle, expanded personas could accompany a product well after its release; the cast of personas itself can actually grow after the release of a product. Even architecture and urban planning could employ similar approach to design and evaluate spaces by utilising GPS locators to monitor the use of spaces. Both advertisement and marketing of anything from phones to buildings would find a treasure trove in the persona descriptions as metaphors and lenses, enabling truly personalized communications and yet respecting the need for privacy of users, since it is not necessary to link identities to patterns of use. 59
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Play-Persona: Modeling Player Behav
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4.2.3 PLAYER EXPRESSION 53 4.3 PEIR
Page 7 and 8: ACKNOWLEDGEMENTS I would like to th
Page 9: ABSTRACT This thesis proposes a fra
Page 12 and 13: Analysis seeks to break down comple
Page 14 and 15: Considering games as acts of commun
Page 16 and 17: ensemble - the syntagm. Selection r
Page 18 and 19: described in terms of likelihood of
Page 20 and 21: ook: FLUX, published in 2008. This
Page 23 and 24: 2. A THEORETICAL FRAMEWORK FOR MODE
Page 25 and 26: “positivism” and “anti-positi
Page 27 and 28: with the game world based solely on
Page 29 and 30: on that text. Later, Barthes introd
Page 31 and 32: according to their mental state. Wh
Page 33: Following the humanistic tradition
Page 36 and 37: In most games it is not realistic t
Page 38 and 39: ehaviour. Assessing assumptions on
Page 40 and 41: Independently on the identity of ea
Page 42 and 43: main focus of the brand has always
Page 44 and 45: 3.4 Play-persona as a lens (a poste
Page 46 and 47: Figure 10: personality and subperso
Page 49 and 50: 4. CREATING EXPERIENCES The MDA fra
Page 51 and 52: Play-styles are sets of isotopic ga
Page 53 and 54: 4.2.3 Player expression Player expr
Page 55: Players will eventually understand
Page 61 and 62: REFERENCES 1. Aarseth, E. (1997): C
Page 63 and 64: 52. Hunicke, R. LeBlanc, M. & Zubek
Page 65: 96. Wiggins, A. (2006): Data Driven
Page 69 and 70: ARTICLE 1 - TOWARDS A THEORY OF THE
Page 71 and 72: flow between the player, the avatar
Page 73 and 74: 6. The player constructs knowledge
Page 75 and 76: are sets of rational, bodily, and b
Page 77 and 78: ehaviorism asserting that a compreh
Page 79 and 80: 4.3 Origin of subpersonalities Acco
Page 81 and 82: environment and the emanating subpe
Page 83 and 84: flow line), one must increase the c
Page 85 and 86: 7.1 Explorer’s path FIGURE 5 Expl
Page 87 and 88: the roof section, where he/she will
Page 89 and 90: 14. DE SOUSA, R. 2003. Emotions. In
Page 91 and 92: ARTICLE 2 - DESIGNING LEVELS FOR EN
Page 93 and 94: 2 STRUCTURE OF A GAME LEVEL The fir
Page 95 and 96: 3.2 Symbolical Reading The movement
Page 97 and 98: The hangar is open for workers and
Page 99 and 100: gather info, spying through keyhole
Page 101 and 102: 6.2 Play-Personas I am going to def
Page 103 and 104: The Butcher persona would have quit
Page 105 and 106: ARTICLE 3 - WAVING EXPERIENCES: PLA
Page 107 and 108: types" [12]. Personas are construct
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esults in the relatively easy task
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Crew actions schemas are: • Squad
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choice given until the mark “poli
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REFERENCES 1. Bozec, P. "My Everyth
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ARTICLE 4 - WEAVING EXPERIENCES IN
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In order to be able to take acti
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persona could be narratively define
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of non lethal, silent, and clean de
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ARTICLE 5 - DEFINING PERSONAS IN GA
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software is however limited in that
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event sets, i.e. capturing metrical
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objects and entities within it. Eve
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metrics include tracking camera vie
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Figure 4: Example of interaction me
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looting is a play-style observed in
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players can choose between differen
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Play modes: Running, climbing, fall
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according to their positioning on a
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14. Medlock, M.C., Wixon, D.; Terra
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ARTICLE 6 - PLAY-PERSONAS: BEHAVIOU
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experiences are evaluated and remem
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B) It is possible to infer user nee
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personas are hypotheses that emerge
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Expert Rookie Grunt Athlete Chess-
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Procedural description: Shooting: -
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In the case example for Tomb Raider
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2. Cooper, A.: The Inmates Are Runn
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ARTICLE 7 - TOWARDS GAMEPLAY ANALYS
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The requirement for improving user-
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The literature on game metrics is m
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ecause they all stem from in-house
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If a player kills a traitor they re
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Figure 3 (Top): diagram showing the
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titles where there is a trend towar
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a secondary variable together with
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player spent at the location and wh
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3. Börner, K., Penumarthy, S.: Soc
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34. Thompson, C.: Halo 3: How Micro
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ARTICLE 8 - ANALYZING SPATIAL USER
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on basic statistical analysis metho
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visualization or for creating overv
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egarding actions undertaken in-game
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ased methods within the games area
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2.4 The EIDOS Metrics Suite The dat
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is played in Third-Person perspecti
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one cause of death, and performing
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ArcGIS permits different layers to
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4. Cadez, I., Heckerman, D., Meek,
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35. Whiteside, J., Archer, N. P., W
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ARTICLE 9 - PLAYER MODELING USING S
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unsupervised learning in capturing
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3. TOMB RAIDER: UNDERWORLD The popu
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2 - Environment; the percent of tot
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(ESOM) [5] to emphasize the distinc
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populated middle-left cluster (Fig.
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weight values we repeat the trainin
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TABLE I THE FOUR PLAYING BEHAVIOR C
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(d) Number of Deaths, D (e) Complet
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equests for specific in-game puzzle
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13. G. N. Yannakakis and M. Maragou
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ARTICLE 10 - PSYCHOLOGY OF PERSONAL
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Personality is described as recogni
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Inputs: Outputs: Primary attack (ra
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Due to the fact that each game metr
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12. Jennett, C., Cox, A.L., Cairns,
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ARTICLE 11 - GAME METRICS AND BIOME
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His research interests are biometri
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5. R. L. Hazlett. Measuring emotion
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ARTICLE 12 - ANALYZING USER BEHAVIO
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elated to player-game interaction a
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10. Kuniavsky, M.: Observing the Us
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ARTICLE 13 - PATTERNS OF PLAY: PLAY
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Play-personas are here introduced a
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It is only natural that game design
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4 Defining play-personas using game
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TRU is the eighth installment in th
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gameplay, descriptiveness of player
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In the case of TRU, the three param
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into which sections of the levels t
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Runners: This group completes the g
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17. Iser, W.: The Implied Reader. J
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