In the Jorge Luis Borges fable “On Exactitude in Science,” an empire’s cartographers construct a map so detailed that it covers the entire territory. Yet the map ends up decayed and tattered when the subjects find it unwieldy and useless. Jean Baudrillard (1994, 1) claims that this fable “possesses nothing but the discrete charm of second-order simulacra . . . and, if one must return to the fable, today it is the territory whose shreds slowly rot across the extent of the map.” Now it seems we live in a world of models: complex computer simulations of the atmosphere, hydrosphere, and biosphere. Much of what we know about the Anthropocene and the many ways that humans have dramatically altered the Earth comes to us through models, from the global General Circulation Models that track climate change to agent-based models of local decision-making practices. With all of the names that have been proposed to identify our current era, maybe there’s no harm in offering one more: the Simulocene, a world made by modeling.

There’s a story I like to tell when asked about my research. About fifty years ago, in the mid-1960s, the U.S. Army Corps of Engineers began building an enormous physical model of the Chesapeake Bay. The model covered nine acres and was housed within a fourteen-acre warehouse on Kent Island, just across the Chesapeake Bay Bridge from Annapolis. Water could be run through the concrete estuary in order to understand the effects of various engineering projects on the quality and quantity of water in the actual estuary. With over $25 million in funding from the federal government, the model was constructed over the course of twenty years and became a tourist site in its own right. Unfortunately, by the time the physical model was completed, another model had already taken its place. In 1983, the first computer model of the Chesapeake Bay watershed was unveiled with the promise of more complex versions to follow as computing power and knowledge developed over time (Keiner 2004). The physical model was largely abandoned—although it had one last moment of glory when it was used to locate the heroic victim of a plane crash in the icy Potomac River, who had helped save many of his fellow passengers but fell through the ice at the last moment. The model was brought up to full flow, and the few remaining researchers were able to successfully pinpoint the location of the passenger’s body. Somewhat ironically, perhaps, the researchers used cut-up IBM punch cards as stand-ins for the body, throwing them into the model estuary to see where they ended up. The body was found right where the model predicted it would be (Center for Land Use Interpretation 1998).

I tell this story because it reminds us that models are not simply virtual objects that live in the so-called cloud. They are real things that exist and interact with the world. They not only shape the way we think about the systems they are built to represent, they also have material effects on those systems. Richard White (1996, 116) makes this point about the Columbia River in the Pacific Northwest:

In the virtual Columbia electronic fish swim past electronic dams on video terminals. Change the electronic river and the fate of the electronic fish is graphically displayed . . . That the various virtual Columbias depend on the actual Columbia for some of their own electrical power only compounds the ironies and connections.

Paul Edwards’ (2010) concept of computational friction helps us make sense of not only the materiality of models and the labor that is needed to make and manage them, but also the way that models— through their construction, maintenance, and use—mediate and generate social relationships. Models have needs in terms of data, code, and computational power. In addition, the scientific knowledge encapsulated within the models requires the interaction of researchers who are often working on disparate concepts and tools. More complex models have more intensive and complex demands, and, as a result, more complex social organizations form around them. The General Circulation Models that Edwards describes are exemplary: global-scale models both enable and require global networks of relationships and organizations, like the International Panel on Climate Change. There is an almost symbiotic relationship between models and the organizational systems that produce them. In the Simulocene, the model and the territory are inextricably linked.

This is also true in the Chesapeake Bay’s watershed. The first computer model of the watershed was completed in 1983, the same year that the Chesapeake Bay Program was created. In 1987, the watershed model was coupled to an estuary model that simulated the effects of these nutrients in the bay itself, spurring the development of the Chesapeake Bay Modeling System: a combination of four models growing increasingly complex with every iteration. At the same time, the Chesapeake Bay Program has grown in size and scope by including more of the watershed states and additional research institutions, federal and state agencies, and advocacy groups to form a massive partnership that extends throughout the watershed and beyond. The model has allowed the partnership to grow by providing evidence to justify the inclusion of all of the states whose waters flow toward the Chesapeake. In turn, the various relationships embodied in the partnership have made it possible to build a more complex and detailed model. The institution and the simulation exist in a kind of symbiosis, and together they play a significant role in shaping the material watershed.

In the Simulocene, perhaps both Borges and Baudrillard are correct: models are not only tools for knowing the world around us, they also performatively intervene to make a world around themselves. The two models I have discussed here—the physical and the computational—represent both different ways of knowing the Chesapeake and its watershed and dramatically different regimes of managing ecological relationships. As material, institutional, and conceptual components assemble around our physical and computational models, the question we must ask is: what kind of world do our models create, and what other worlds might we manifest through simulation?