This time right after the spring break (and with no class material additionally covered therefore), I would like to try bringing a very casual topic to the post – a board game about disaster management, more specifically, Pandemic.
It is one of the most popular cooperative board game, published by Z-Man Games in 2007. (I guess many of you might already have played this before.) Four diseases have broken out in the world, and players acting as CDC agents work cooperatively to stop the spread and discover all cures before diseases wipe out the world.
I am bringing this as a topic because the framework of this game is really well-suited to the optimization models. The game board is a world map, which is exactly a network with 48 big cities as nodes and arcs connecting nodes with equal distance, as you can see in the picture. Arrivals of the disease to the city as well as contagion outbreaks are probablistic events, implemented as the frequency in the card deck. Moreover, since this is a co-op game, we can say that there is only one stakeholder, which is a setting welcomed by operation researchers.
There exist many kinds of strategic decisions players can make, and one of the main decision is locating the research centers so that players can access all cities easily from the research centers. This can be considered as either a maximal covering or a p-median facility location problem. Actually, some people already studied this problem using basic network analysis. Quick spoiler: when we locate 3 research centers, Atlanta/Hong Kong/Cairo minimizes average distance. To read more about the list of ‘optimal placement’ decision they figured out, you can view Optimal Placement of Research Centers in Pandemic and Overanalyzing Board Games: Network Analysis and Pandemic.
Some say that there’s no fun if we attempt to break everything down into efficient mathematical equations. But I’d rather claim that it is always interesting to see how our intuitive decisions works compared to a computed ‘optimal’ one, or study why the discrepancy between the model and reality is happening. And we know that we can’t do so without doing the math.