The word “model” can mean a lot of things. From critical theory, we generally inherit the word as a schema of interrelated concepts. From the digital humanities, we can see models as sets of parameters (proxy or directly measurable) that are interrelated and form the principle of definition for an idea, character, or process. A groundbreaking, (though not exhaustive) progression traced from recent pieces by Graham Sack, Franco Moretti, and Ted Underwood (here, here, and here) all take generous and exploratory positions toward modeling from the perspective of the humanities. What’s ultimately at stake is what can be operationalized, measured, and bound together as part of an interdependent schema of variables that may have explanatory value in a literary text. For making critical, cultural, or historical arguments, these approaches to models in the humanities bring new evidence to interpretative practices of literature.
I am relentlessly positive about this direction in scholarship. This kind of modeling is different from say, climate modeling or some forms of network modeling or agent-based modeling, where the goal is to produce simulations based on changes to specific parameters in the model or models at hand. This practice of modeling tends to emphasize prediction. For now, let’s just call these two groups explanatory and predictive models. Yes, we can certainly think of models in different groupings. I don’t really think these two categories are the best, but my purpose is to provoke us to think about what the digital humanities could gain by participating in predictive models in addition to explanatory ones. Sack’s work actually employs some agent-based modeling, but the ultimate end is a simulation of narrative and characterization.
The predictive modeling I’m trying to temporarily cordon off for the sake of discussion makes predictions about non-literary systems with physical, time-based contingencies. Climate, traffic, flooding, fires, emergency response, religious conflict, etc. In addition to models that were aimed at or derived from literary texts, what if we took humanities knowledge-making practices (for both modeling and other kinds of interpretive practices) into the realm of predictive modeling? What if, in addition to using computational methods on literary texts, we used models as ways to take theories and methods derived from the evolving study of literature and have them inform computational models? Here we wouldn’t be interested in a machine reading of Hamlet; we’d be interested in taking the epistemology of humanities research methods and using them to inflect computational models that aim to solve problems in domains like medicine and civic infrastructure. The current work around modeling is crucial to make the latter possible, but what I am describing now is different in that the outcome of the scholarship is not an interpretation of a text(s), but a collaboratively produced solution space posited by a model.
What if a model aimed at simulating economic development around a rail line could consider critical race theory? What if that notion of development were expanded to consider other variables and evidence? What if the evaluation of the overall modeling and decision process included experts from the humanities?
Key to this would be a distinction between the methodologies of humanities and social science. This kind of modeling is not new to social science, but social science has limitations about what it typically models and considers to be objects of study. Prospect Theory might be helpful for modeling some specific kinds of human decisions, but history and cultural theory may help situate that behavior in a broader imaginary that may change a prediction or change the significance of a set of outcomes. It’s the possibilities offered by this shift in emphasis that make decision support systems and intriguing area for digital humanities.
Decision support systems (DSS) are information and computing tools that aid in high-level decisions that must consider multiple and complex datasets and variables. How to treat a tumor, where to build a dam, how to promote economic growth in a given neighborhood, how to mitigate storm damage; how to make an organization more effective; these are all use cases for DSS. Enter the Complex Systems Framework (CSF). Developed by Robert Pahle’s group at Arizona State University, CSF is designed to ingest and arbitrate among multiple models and datasets, as well as the real-time manipulation of the system by collaborating experts.
CSF schematizes decision support as a web of data inputs, computational models, expert adjustments, and visualizations that culminate in a solution space of possible courses of action. Key to this DSS is the ability to include any module in a given decision support environment, making it possible to add new models without necessarily redesigning the entire system. While this is not unique, it is inviting. Developing humanities-informed models and datasets for existing problems or designing a DSS around humanities modules alone is not only possible, but also stageable in a multiscreen decision theater where experts interact with one another en route to recommending a solution.
CSF’s take on decision support is to facilitate a conversation, not spit out the most expedient solution. The collaborative aspect of decision making in this DSS makes it a viable place for humanities and decision science to explore cooperation.
And this cooperation is, to me at least, one of the most exciting possible payoffs of the engagement of the humanities in modeling; a seat at the (sometimes figurative, this time literal) table when it comes to making decisions and recommending solutions for problems that intersect with the social and environmental grand challenges that face us all.
Bethany Nowitzkie’s keynote for DH 2014 is slated to foster discussion around about the role of DH in the Anthropocene. This area is another one of those areas where I’m relentlessly positive, and the conncetions between DH and environmental humanities still have a lot of growing to do. Let this relatively recent engagement with modeling, while changing how we can read literary texts, also extend in the direction of environmental humanities. Within this broader movement, DSS is one area where there is promise to change how we orient the insights of the humanities and digital humanities.
NB: In the upcoming year I’ll be heading a team at ASU that will include humanities scholars alongside users and designers of CSF. As we begin to develop new modules and reframe problems we’ll be sending out updates to generate conversation specifically around DSS and DH.