Games are made up of different kinds of experience; game designers are experience designers for players. We are specifically interested in the mechanical experiences of players interacting with challenges. Impossible challenges for any human's abilities are trivial to create. One class of “mechanically interesting challenges” is one calibrated so that the capabilities of the player are noticeably stressed, but not to their breaking point. This view has been a focus of flow (e.g. (Csikszentmihalyi 1997; Sweetser and Wyeth 2005)), and dynamic difficulty adjustment (DDA) (e.g (Denisova, Guckelsberger, and Zendle 2017; Zohaib and Nakanishi 2018)). We want to better understand player capabilities to understand how to create more tailored experiences. A standard Human-Computer Interaction (HCI) view sees the running trace of a game as a sequence of communications between two 'programs', the player and the game (Fig. 1). We would like to see both sides as typed, and their interaction as typed too.
But does it even make sense to assign ‘types’ to a
player? Work on “player types” is abundant; the focus of
player typologies have been behavioural (e.g.
We have a clash of terminology: we mean type as used in programming languages, not in game studies! Let us say instead that a type for X is a description of a static property of X. More specifically, the type of a player is the set of constraints expressed by that player’s capabilities – physical and cognitive. Constraints like how fast can the player react (correctly) to seeing a particular event happen on screen; or how fast can a choice of appropriate reaction be made.
Types do not exist in a vacuum, they need to assemble into a coherent, compositional type system to be useful. The predicates that interest us describe ability constraints that are measurable and correlate with being able to conquer game challenges.
Which gives us a starting set of questions:
3. are the capabilities and measures adequate?
The Model Human Processor
Or Looking for Love in Super Mario Party (Nd Cube 2018), where the player must be first to look at the heart when it appears. This game is a a choice reaction task, relying on recognition, planning (to pick the right direction) and motor execution. The task is made harder by the game presenting more options (the other suits, Fig. 4b), or trying to trick the player’s senses by changing the colour of the heart or of the other wrong choices — messing with the player’s instinct to go after “red” (Fig. 4a). Thus the game actually tests for inhibition, object recognition, and selective attention/reaction time.
Both examples also involve motor constraints: using the
controller correctly and quickly. At this level of
psychomotor, events happen in a matter of milliseconds. Bailey’s
work on the time frame of reaction tasks
So what would type rules look like? For generic humans, we could have sense : [1; 38]
process : [70; 300] rate of information decay in memory stores (Fig. 2). But we’d like more precise abilities than just generic perceptual, cognitive and/or motor processing limitations.
We can critically examine some game examples as a
means of synthesizing applicable capabilities. Consider
Messy Memory from Mario Party 3
Sensory reception Neural transmission to brain
Cognitive procession Neural transmission to muscle Muscle latency and activation
Total
a; b : [t1 + t2 t5; t2 + t4]
where [ ; ] denotes a time interval in milliseconds, ajjb t
that processes a and b may be done in parallel “up to”
t milliseconds, and we use ; for sequential composition.
There is a rich literature around reactive systems (Wan
and Hudak 2000; Jeffrey 2012) with novel ideas that could
likely be adapted for such type systems
More interesting would be to measure detailed, specific operations on a per-player basis, which would significantly narrow the given intervals. Players would have to consent to such measures, and games would have to be taught to adapt accordingly. Note that games currently do this without consent (via DDA), albeit on fairly crude inputs.
Player type systems open up the possibility of static type checking for experiences. If the designer knows what kind of experience they are trying to create, ability models can let one verify that a challenge is achievable. More precisely, if a player’s process for beating a challenge c is p, then if that process can take less time that is allotted for the challenge, that challenge is achievable.
p : [t1; t2] c : [0; t3]
t1 < t3 c achievable
If t1 and t3 are extremely close, then the player might be extremely frustrated, as “peak performance” is stressful.
In other words, rather than a seeing these time intervals as purely what is feasible, a more refined model would use a probability distribution and a convolution would be needed to perform sequencing. A still more refined model could introduce stress, fatigue and other similar factors.
It also opens up the possibility of recontextualizing DDA as dynamic type checking on player types; since DDA aims to align the game with the player’s abilities, in essence it could be seen as type checking.
There are a whole host of open questions: • is this really compositional? (jj is iffy) • does it scale to longer time frames? • does it scale to other parts of the player experience? • how many pieces of the model of Fig. 1 should be seen as independent components? • is such data collection ethical? • would this reinforce ableism or highlight unconscious assumptions at design time, when they are easier to fix?
Jeffrey, A. 2012. Ltl types frp: linear-time temporal logic propositions as types, proofs as functional reactive programs. In Proceedings of the sixth workshop on Programming languages meets program verification, 49–60.
Sweetser, P., and Wyeth, P. 2005. Gameflow: A model for evaluating player enjoyment in games. Comput. Entertain. 3(3):3–3. Wan, Z., and Hudak, P. 2000. Functional reactive programming from first principles. In Proceedings of the ACM SIGPLAN 2000 conference on Programming language design and implementation, 242–252.
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Zackariasson, P.; Wa˚hlin, N.; and Wilson, T. L. 2010. Virtual identities and market segmentation in marketing in and through massively multiplayer online games (mmogs). Services Marketing Quarterly 31(3):275–295.
Zohaib, M., and Nakanishi, H. 2018. Dynamic difficulty adjustment (dda) in computer games: A review. Adv. in Hum.Comp. Int. 2018.