Using Twine for Games Research (Part I)

Recently, someone sent a request over the DiGRA list for experiences and information related to academics learning to make games. I replied with a bit of handwavy proselytizing for Twine, copied (and slightly edited) below:

For a surprisingly diverse range of game design logics, Twine makes an excellent prototyping tool for research. Carolyn Vaneseltine writes about using Twine for prototyping here:
http://www.sibylmoon.com/twine-as-a-prototyping-tool/ 

I'd really like to see Twine in use especially for games *research*, for a few reasons:

• Deployability. It's absolutely the "most time-effective path" from game idea to "an arbitrary person with the internet can play it."
• Resilience to bitrot. As long as people care about creating backwards-compatible web standards and supporting JavaScript, Twine games will be playable. If we consider our research to be describing "permanent ideas," then people need to be able to play with those ideas decades later!
• Most people find it quite easy to learn, and it supports quite a range of mechanics, as long as you can conceptualize them in some turn-based way. It's really one of the better settings for thinking about the *systems* in games first without having to muck about with event handling and graphics rendering.
• Reusability/reproducibility. If the point is to communicate some system of mechanics, then I think Twine works pretty well because the source code is usually readable, easy for the author to release, and easy for outsiders to manipulate. 

Of course, I realize a lot of research goals in games involve aesthetic experiences that Twine won't help at all with creating. But I think it's overlooked as something that would often suffice for the "proof of concept" work we do in research, where instead people sink lots of time into clunky 3D simulations that distract more from the research than they contribute to it.

Afterwards, though, I began to question my own recommendation. I've tried using Twine for research-like ideas (see: numerous attempts at replicating the behavior of Prom Week as a text game) with very limited success, and certainly nothing leading to publication. Nonetheless, it has still been my favorite tool for dabbling in digital game design due to the lack of assumptions it imposes about what kind of game you are making, and I still feel in my soul that it should be possible to communicate this generality in a compelling way to the research community.

So, since the contributors to Twine recently released a new, web-based editor along with a tantalizing new macro and expression language, I decided to launch a period of focused study using Twine to implement some "classic" prototypes and document my roadblocks. I'll use my blog as a "lab notebook" for these experiments, which will incidentally also contain a review/critique of Twine 2.

Before I get into that, though, I want to be more explicit about what I mean by "games research." To be quite honest, the range of research activities discussed on the DiGRA list is quite mystifying to me, and I would love it if someone could write an overview of "things people at universities are doing that they call games research." I have a sense that there are at least the following categories:

1. Humanities-based scholarship on the cultural context and contents of games, patterned off of "media studies." They ask questions such as how gender and race are represented in the assets and stories of games, as well as how game mechanics reflect cultural values.
2. Human-Computer Interaction-based study of how humans play digital games, asking questions about what happens to them psychologically under different conditions and whether games can be designed to induce specific experiences (such as education).
3. AI-based study of how games can simulate more realistic or engaging experiences. A lot of the questions seem to surround programming the behavior of non-player characters, including everything from pathfinding in geometric space to dialogue and storytelling.

My personal studies align most closely with the third category, although because my background is programming languages and not AI, I think of the interesting questions as less about characters and more about representation of game states and behaviors, i.e. "how do we model arbitrary fictional worlds that react to input?" and "how do we reason about the semantics of how these worlds evolve?"

Note that I would also count something like, say, an operating system as a "fictional world," but on the other hand, I'm not as interested in games where the primary and necessary challenges are things like "very quickly calculate the outcome of lots of geometric and physical constraints changing and render them at a certain frames-per-second requirement." So in that sense, what I'm calling "games research" is really a particular intersection of formal system modeling and games, and that's the focus of my current hypothesis under examination (that Twine is a useful tool for stating and answering questions about these systems.)

This post has already exploded in length, so I'm going to save the actual results of experiments so far for the next post. Meanwhile, I'll describe some examples I intend to describe as case studies:

• Hack-and-slash: player character has a record of attributes like health and inventory and can strategically alternate rest, adventuring, and spending coins (won through adventuring) on more powerful adventuring tools.
• Dining philosophers: classic problem in synchronizing concurrent systems made interactive by allowing a player to control (all, one, or several?) philosophers: they can pick up a fork on the table, set down a fork they are holding, or eat if they are holding two forks.
• Social simulation a la Prom Week: multiple characters with sentiments toward one another that can change via player-selected pairwise interactions.

The central questions I'll be asking about these examples are (a) is it easy to encode in Twine, and if so, how is it done? and (b) what do we learn from the encoding? I'll try to pose some example-specific questions before diving into the implementation, as well.