It’s the dawn of the posthuman century and so perhaps the irony of phrases such as “virtual help” and “simulated peace” contain the echoes of nostalgia redolent in an ever-accelerated technological era. I’m excited to attend a presentation on humanitarian aid & development sims by Ryan Kelsey and colleagues from Columbia @ the CNMTL

I’m interested in this primarily because of two dimensions in which I work: teaching How They Got Game at Duke, and participating as a project collaborator for the Virtual Peace project. Both gaming and pedagogy are in some ways new subjects for me, new in the sense of analyzing and building both games and courses (and courses about games and building games, with the case of How They Got Game).

The talk is focusing on two sim projects from the CNMTL, one a project begun last year, and another first started back in 2001. Tucker Harding of Columbia spoke about ReliefSim, a health-related turn-based learning simulation used in the classroom to help students develop a deeper understanding of dealing with and working under conditions of a humanitarian crisis. ReliefSim’s development began in 2001.

The crisis in ReliefSim is a forced migration. Students enter ReliefSim first by viewing a text-heavy html interface with long series of interactive selections. The initial interface reflects the overall idea that the sim is not really training as much as it is educational augmentation. Display categories include assessments, interventions, information gauges, team, and age breakdown. With this display a player does not get a picture of the greater context of the crisis (e.g., caused by warring factions along national borders), it immediately gives a sense of features and depth of impact

With the panel the player chooses actions and assigns those actions to members of the team. In turn one, for example, we assign a water supply assesment to Eric, a food supply assessment to Marilyn, and a population assessment to Ryan. When we click “end turn,” the interface gives us back data generated by the assessments. Good information for our crisis: 10,000 people involved, 1600 under 5, 3000 betweeen 5 and 14, and 5400 15 and up (no assessment for elderly and/or inform at this point). We also see we have a 15,000 kcal food supply where each individual needs a minimum of 600 calories. We also have 100k liters of water, with a 5 liter average per person water demand. Our food supply seems good as we can feed everyone an average of 1500 calories per day. We also have 10 liters per day per person. However, will our population grow? We can support up to 20,000 people on our minimum water supply and 25,000 based on food.

The second game, presented by Rob Garfield of Columbia, is the Millenium Village Simulation, developed by Jeffery Sachs. The game’s conceit is that you the player are a sub-Saharan farmer trying to support your family as you move from subsistence farming to generating income. The Millenum Village Simulation reflects Sach’s full-spectrum approach to treating poverty. You can’t just build schools, for example, if your village suffers from occasional malaria epidemics that wipe out entire groups of children.

The sim interface for the turn-based game is similar albeit sexier than the interface for ReliefSim. Not limited to tabular/textual representaton and selection, the player is shown a simple visual representation of the farmer in the context of a village, the village in the context of greater environmental factors.

The player for each turn is to allocate the farmer’s time (including his wife’s) across a set of development tasks, such as collecting water, farming, or organizing a small business. If we choose to assign hours to farming, we are given choices as to whether we want to perform subsistence farming (grow maize) or income-generating farming (grow cotton). At this point we don’t have any idea how much effort translates into a result. We selected four hours of water collection, but we have no idea how many hours are needed to meet basic needs.

As we took a turn I noticed that the daily allocation was being set in the interface for an entire season; each turn is a season. (Which season?) The game takes a general approach to location (sub-Saharan Africa is widely varied in terms of seasonal conditions, for example) and a rationalist-optimization-oriented approach to helpng a student learn to support a farmer in such a location.

As with the previous presentation, the presenters display stunning tools built in a general knowledge/time management orientation. The SN-LMS presenters evaluated server logs within a site to understand character of students; the game tests a student’s ability to delegate time in order to reach optimally managed conditions for the economic development of a farmer. Both suffer somewhat from a level of specificity that can only be gained by the detail of greater context. It’s not clear why cases are more strongly relied upon, even as frameworks for developing evolving and dynamic game scenarios.

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