Play-Persona: Modeling Player Behaviour in Computer Games

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3.1 From personas to Play-personas It is here suggested that game designers could benefit from procedural, data-backed preemptive models of play behavior. Additionally, game rules and spaces can be used to carve channels in the minds of players for helping them organizing experiences and guiding the emergence of sense- making patterns. Play-personas are offered as such devices, helping the emergence of narratives to make sense of what happens in game worlds. Play-personas can influence and control the ways that players categorize what they experience in game worlds; persona constructs can be triggers that inspire, incite and compel players into certain actions. These mind patterns can be expressed as behaviours undertaken in game worlds using mechanics and rules that the game affords [33]. Play-persona hypotheses emerge as relations between parameters derived from the set of interaction and navigation possibilities offered by the game in terms of rules and spaces. Play-personas are defined as clusters of preferential interaction (what) and navigation (where) attitudes, temporally expressed (when), that coalesce around different kinds of inscribed affordances in the artefacts provided by game designers [6]. Moving beyond narrative descriptions of motivations, needs and desires distilled in ethnographic interviews, play-personas are expressed also as procedural description of preferential behaviours in terms of game mechanics used. This procedural description augments and strengthens the idea behind personas as formulated by Cooper because, due to the intrinsic numeric nature of procedural descriptions, it is immediately possible to compare different play-personas, provided that they are scored according to compatible parameters. At the same time it becomes possible to compare and evaluate play-personas postulated a-priori as hypotheses by the designers during the production of a game with actual behavior expressed by players engaged with the game, if directly coupled with instrumentation data in the form of gameplay metrics 6 gathered from game engine software during play sessions. It is in this respect that play-personas are both theoretical models of ideal users (metaphors) and data-driven representations of player behaviours (lenses). 6 The term “gameplay metrics” refers to data about players’ behaviour in a game (location, use of skills, powers, abilities, interaction with other players, deaths, etc.), automatically recorded during a play session. 256
4 Defining play-personas using gameplay metrics Initially, during the concept and early design phases [29], persona modeling serves as a planning tool for implying the player behaviors in the design of the game. In this phase of game production, play-personas are metaphors for the actual players, in a manner similar to the way that personas can form metaphors of customers when developing a website [10, 19]. As soon as a playable version of a game is available, play-personas become data-driven rather than theoretical (Figure 1). In other words, actual player behaviors are mapped and any patterns in their behavior located. These form the basis for defining a new set of concrete play-personas which can be compared with the theoretical, initial set. The data utilized can potentially be both qualitative and quantitative, stemming from e.g. surveys, interviews or analysis of gameplay footage. However, an effective approach towards gathering data on the in-game behavior of players in large quantities and with high precision and detail is gameplay metrics, an approach that takes inspiration from the data- driven personas used in general HCI [see e.g. 35-37]. Gameplay metrics form objective data on the player-game interaction; these are usually registered by the game engine or a dedicated logging program. In general, gameplay metrics can be recorded for any type of user-initiated behavior where interaction takes place in or with the virtual environment. Additionally, the behaviors initiated by agents or systems operating in the virtual environment outside of the control of the player, e.g. autonomous agents [32], can form the basis for metrics logging. Metrics also include information about the user hardware configuration, game install language, etc. which, in an industry context, is useful for e.g. marketing, as well as in a user-oriented research [38]. Figure 3: Two sides of the same coin: Play-personas as metaphor and lens. The black dots on the “possibility-field” plane represent game mechanics of the specific game. Thanks to the a-priori description of the persona-as-metaphor a certain subset is individuated. This persona hypothesis can be checked against 257
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Play-Persona: Modeling Player Behav
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4.2.3 PLAYER EXPRESSION 53 4.3 PEIR
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ACKNOWLEDGEMENTS I would like to th
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ABSTRACT This thesis proposes a fra
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Analysis seeks to break down comple
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Considering games as acts of commun
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ensemble - the syntagm. Selection r
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described in terms of likelihood of
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ook: FLUX, published in 2008. This
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2. A THEORETICAL FRAMEWORK FOR MODE
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“positivism” and “anti-positi
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with the game world based solely on
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on that text. Later, Barthes introd
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according to their mental state. Wh
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Following the humanistic tradition
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In most games it is not realistic t
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ehaviour. Assessing assumptions on
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Independently on the identity of ea
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main focus of the brand has always
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3.4 Play-persona as a lens (a poste
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Figure 10: personality and subperso
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4. CREATING EXPERIENCES The MDA fra
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Play-styles are sets of isotopic ga
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4.2.3 Player expression Player expr
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Players will eventually understand
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Combining both of these two approac
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REFERENCES 1. Aarseth, E. (1997): C
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52. Hunicke, R. LeBlanc, M. & Zubek
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96. Wiggins, A. (2006): Data Driven
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ARTICLE 1 - TOWARDS A THEORY OF THE
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flow between the player, the avatar
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6. The player constructs knowledge
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are sets of rational, bodily, and b
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ehaviorism asserting that a compreh
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4.3 Origin of subpersonalities Acco
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environment and the emanating subpe
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flow line), one must increase the c
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7.1 Explorer’s path FIGURE 5 Expl
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the roof section, where he/she will
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14. DE SOUSA, R. 2003. Emotions. In
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ARTICLE 2 - DESIGNING LEVELS FOR EN
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2 STRUCTURE OF A GAME LEVEL The fir
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3.2 Symbolical Reading The movement
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The hangar is open for workers and
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gather info, spying through keyhole
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6.2 Play-Personas I am going to def
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The Butcher persona would have quit
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ARTICLE 3 - WAVING EXPERIENCES: PLA
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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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Page 207 and 208: ARTICLE 9 - PLAYER MODELING USING S
Page 209 and 210: unsupervised learning in capturing
Page 211 and 212: 3. TOMB RAIDER: UNDERWORLD The popu
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Page 221 and 222: TABLE I THE FOUR PLAYING BEHAVIOR C
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Page 227 and 228: 13. G. N. Yannakakis and M. Maragou
Page 229 and 230: ARTICLE 10 - PSYCHOLOGY OF PERSONAL
Page 231 and 232: Personality is described as recogni
Page 233 and 234: Inputs: Outputs: Primary attack (ra
Page 235 and 236: Due to the fact that each game metr
Page 237 and 238: 12. Jennett, C., Cox, A.L., Cairns,
Page 239 and 240: ARTICLE 11 - GAME METRICS AND BIOME
Page 241 and 242: His research interests are biometri
Page 243 and 244: 5. R. L. Hazlett. Measuring emotion
Page 245 and 246: ARTICLE 12 - ANALYZING USER BEHAVIO
Page 247 and 248: elated to player-game interaction a
Page 249 and 250: 10. Kuniavsky, M.: Observing the Us
Page 251 and 252: ARTICLE 13 - PATTERNS OF PLAY: PLAY
Page 253 and 254: Play-personas are here introduced a
Page 255: It is only natural that game design
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Page 261 and 262: gameplay, descriptiveness of player
Page 263 and 264: In the case of TRU, the three param
Page 265 and 266: into which sections of the levels t
Page 267 and 268: Runners: This group completes the g
Page 269 and 270: 17. Iser, W.: The Implied Reader. J
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Play-Persona: Modeling Player Behaviour in Computer Games

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