Well, there were a total of four kittens in the attic, in addition to our Icarus, who fell from the sky last Wednesday. We got them all now, and are in the process of giving them away to friends and possibly to the Humane Society.

However, I did want to demonstrate a little bit more clearly the height from which the creature fell. I’m pretty impressed, honestly.

We’ll be back to our regular rambling later on.

Suppose we look closely at a system and build a model from it. That model will assuredly be useful in describing the system, and possibly predicting that system, but it is not necessarily the case that the model will allow us to build new variants or instances of the system. Examples of this are structuralist formulations of film or narratives. Valdimir Propp’s morphology of Russian folktales is a great example of this, the morphology may be used to analyze or describe folktales, but when reversed and used as a generational tool, far too much is missing. Other models may be used to generate artifacts. Examples of these can range from the work of Karl Sims, to CS paper generators, to any other abstract “generator” algorithm or program. These tend to be much more particular, applied to special cases and mini-domains rather than large scale categories.

There are other differences between generative and descriptive models. Descriptive models are generally much more useful for humans. Even if a descriptive model cannot be used to generate an artifact, it may still be used to check whether an artifact matches a given model. This is very helpful for human authors or creators, who might want to make something that mostly fits a model, but then perverts it, complicates it, or changes it in some interesting way. For instance, in the domain of superhero comics, Alan Moore’s Watchmen follows the overt tropes of the genre, but exposes a psychological depth that is otherwise not traditionally present. Other works might attempt to synthesize genre models, or for the sake of parody, will stress elements of the model to extremes. Descriptive models are enormously powerful as tools for creativity.

Generative models tend to be useful, not for human creators, but for electronic ones. A generative model has the structural elements of a system specified down to an algorithmic level, such that any algorithm processing machine may use the model to generate an artifact. Generative models must also be narrow enough for the algorithm to cohesively express a domain. With this level of precision, the model must take positions on how to express its domain. Value and content judgments are necessary, as decisions must be made regarding what to include or exclude, what to prioritize or emphasize. As a result generative models tend to much more resemble artifacts themselves, representing a certain creative take on a domain, rather than a broad model of the domain at large. The CS paper generatorhas a certain embedded model of what CS papers are like, and its approach serves to illustrate the absurdity of this model. Genetic Image has a similar understanding of how an image should be composed, and it is fundamentally and intrinsically limited by this viewpoint in terms of what it can express as a tool. As tools, constructive models are limited, but their value becomes apparent when they are seen as artifacts. Instead of critiquing a model through a carefully constructed parody, generative models critique or expose their own perspectives through their very execution.

From far enough away, both descriptive and generative models are similarly constrictive. The constraints of a structuralist descriptive model of a genre is as limiting as one that reduces the model to an algorithm. Furthermore, a descriptive model may be made into a generative model if it is focused enough, if one takes sufficient positions in their interpretation to resolve the inherent ambiguity of the descriptive model. Ambiguity is probably the key difference between the two. A descriptive model allows for a variety of interpretations, as such it is resistant to critique because of its open nature. A generative model has removed all ambiguity to the point where it may be executed algorithmically, but as a result may be critiqued and used as an expressive tool.

Meet Icarus. She’s quite cute, isn’t she? Like her namesake, she fell from the sky. Or, more precisely, from our roof. The attic seems to have developed a small cat infestation. Audrey and I found her while leaving for the gym. Two people who were nearby were watching over her and gave us a more complete account. Evidently she was on the top of our roof, calling for her mother, then jumped down, and miraculously, only broke her tail. We took her to the animal hospital and got her patched up, and now she seems to be energetic and happy.

We’re going to call the Humane Society to see if they can take care of the ones still up in the attic. This little one, though, I don’t think we can part with.

I just published the first set of real, actual qualifying exams. I have heard on some authority that I have somehow passed, a miracle which surprises me as much as anybody. The results of this labor are posted here for your pleasure and as a warning for future generations to come.

So, I’ve been a LaTeX user for a while. I tend to write papers with it, but lately it’s been giving me trouble. It might be because the standard editors are terrible and haven’t undergone noticeable revision in years. It might also be because getting a distribution is a confounding and upsetting experience. It might also be because the latex then bibtex then latex sequence just drives me crazy. Anyway. I’m currently investigating other options. My only hope is to be able to find a nice and useful way of generating and formatting citations with it.

In other news, I am planning on revising some aspects of this webpage design over the summer. The layout and navigation are a little confusing, and despite how much trouble it is to make nice icons for everything, they generally help people navigate my strange and various sections. Alongside this, I’d like to make the research section searchable and database formatted. The current section is great, but it would be even better if it could be broken apart and stored separately in a nice database. Tagging is also something that seems to be popular and useful now, so I may also get around to revising to accommodate that.

Who knows when these changes will come (it’s not like I have a lot of leisure time) but hopefully changes will begin to show up gradually.

Recent thoughts have led me to start thinking about how one might define “model” in a more precise and accurate sense. This is a first jab, so the definition may change a bit, but I think that the following set of qualities will probably remain important. I am also trying to construct a definition that will be useful for a scientific perspective, as well as perhaps an abstract literary one. This may be unusual, but I think ultimately more valuable:

Components of a model:

1. A set of instantiated entities that are the objects of the model. These entities may have properties or qualities that define them.
2. A set of rules or concepts that govern how these entities interact and function.
3. An interpretation function which maps from the real world (or from another model) onto the set of entities.
4. A set of operations or procedures that can take place, changing the model’s state. One operation might be a time-step, others might be triggered externally, making the model interactive.

At a sufficient layer of abstraction, there may be no difference between entities and rules, but I distinguish them here to draw attention to the fact that entities describe the “what” of the model, while the rules will describe “how”. The separation of these seems to be important, but is also a significant difference between the traditions of object oriented programming. An object oriented approach to models probably could work, but would require some effort to resolve ambiguities.

Models and information: The information content of a model is stored in its entities and properties. It makes sense that, in observation, not all of the data content of the model is necessarily visible. Some of the procedures that may be enacted on the model might make bits of information visible to the interactor. Frequently, in terms of interacting with it, the interaction might also change the model’s state. The result of this is a construction of a black box, which may not be fully known or understood unless the model is open. A consequence is that some models may not be interpreted directly, but rather require interactors to make their own models of the model’s operation and structure.

A further consequence of the interpretation of models is that individuals build their own unique models of other systems, and the individual construction of a model is an intrinsically creative act. Developing a model is equivalent to the interpretation of something. In communication of models, there is a process of interpretation, re-presentation, and re-interpretation. As a result, communication turns into a giant game of “telephone”, where a model will ultimately change throughout its communication. Additionally, when someone begins to form a model of something, concepts might be blended with other knowledge and ideas, forming a hybrid model that is influenced not only by the presented model, but also by other internal and associated knowledge.

Models and metaphors: Linguistically, metaphors are often used to describe systems analogically. When metaphor is used to describe something, it invokes references and associations that connect the antecedent to the metaphorical term. Similarly, while models may be used to describe and represent something abstractly (which is not metaphor but representation), models may also employ structures (in the formation of their properties or rules) that are metaphorical of some other system. The resulting effect of this is that systems will reference each other through endless regression of metaphors. The use of metaphor is also a tool that an observer or constructor may use to interpret or develop a model.

Models and adaptation: When applied to adaptation (specifically of some other work or media artifact), there is a double-use of models. An adapted work will have a simulated model, and also a representative model. The first of these reflects the mechanics of the adapted material, while the second uses visual or context association to connect the adaptation to the original work. The two of these are separate constructions, but for an adaptation to be successful, both need to be addressed.

In games, there is a phenomenon known as “skinning” which takes the representational model of a game and replaces it with representational model from some other system (for instance, replacing chess pieces with characters from a popular cartoon). Jesper Juul writes about this in Half-Real, however, while the fiction or representational layer may be replaced, it also must be consistent. If the chess pieces are being replaced with characters from a cartoon about a family, it would not make any sense for the king piece to be represented by one of the minor characters, as opposed to, say, the head of the family. Even though representational layers are considered to be “arbitrary” they require a consistency in their analogy without which the adaptation falls apart.

Models and art: Contemporary art is interesting in how it relates to model establishment and interpretation. Art takes place at a highly symbolic level, and makes use of multiple layers, as is the case with adaptation. An art work or installation might make use of some internal model or system, but that is expected to be connected externally to other networks and layers of meaning. Analogy and metaphor tend to be used extensively in the interpretation of the work. Generally, the meaning only becomes clear when it is connected to other systems of meaning located in the history and traditions of art, philosophy, politics, and popular culture. This is notable because the connections and meaning are not established within the work, but rather outside of it. This places the work as one model, which is self contained, but makes metaphorical connections and relations to other external models. Bringing this in mind makes clear that isolated models are also tools for discovering meaning (as well as consequences, information, and relationships) in other, broader models and systems.

The time has come to ask an important question in regards to my research endeavour. Generally, I am not one to turn problems down or leave questions unpursued and unanswered. However, at this time, I need to actually pin down and begin thinking about what, specifically, I am going to write my dissertation on.

This issue is critical because there are seemingly two diverging paths that can be taken. One of these paths goes down a route of educational technology, looking at how games or software (simulation, more particularly) can be used to convey information about a domain, exposing methodology and its underlying ideas. This idea is arguably more fundable, and is grounded in the fine tradition of educational technology. I think that this approach would reveal important things about how knowledge might be viewed and communicated. Extended further, it might provide a method for exploring how to generally communicate models through software and simulation.

The second idea follows a more “games and narrative” direction. The idea underlying this is adaptation, specifically applied to fictional worlds. While there are many narrative to game adaptations in certain genres (notably action or fantasy), not very many games are adapted out of other kinds of narratives, for instance social dramas, romances, comedies, etcetera. We know that these narratives are systematic in their own right, so the idea behind this research would be to develop a method for adapting these kinds of narratives. This approach would require identifying, specifically, a domain to examine for adaptation, and identifying a few works to make adaptations. While this idea would be risky, (admittedly dangerous in that I don’t want to follow Chris Crawford’s path and be faced with an impossible task). At the same time, were it succcessful, the idea also has the potential to make a significant impact on game industry.

For a variety of reasons, it is difficult to distance myself from either idea, and ideally I’d like to do a bit of both. A common thread may be find in the idea that both are tied to representing and communicating models. The real question is whether any hybrid of these two ideas is possible, useful, or worthwhile. And that is what remains to be seen.