ARC 605 – TBD: Uncertain Architectures

Instructor: Mark Shepard
ARC 605
Fall 2020

DESCRIPTION: 

That we are living in uncertain times is obvious. The combination of a global pandemic that continues unabated with a media environment delivering conflicting public health information has forced many to devise their own risk calculations based on ad-hoc rubrics that continually change. In the US, this deepening sense of uncertainty is compounded by election year politics driven by wedge issues such as racial justice, police reform, immigration, gun rights and climate change, to name just a few. The polarization of the electorate by divisive campaign tactics has spilled over into public health debates, complicating efforts to develop an effective response to control the spread of the virus at a national scale. Whether or not you wear a mask in public has become an indicator of political party affiliation, not a reflection of best public health practices. Within this context, where few things are certain and most are only probable, we weigh our individual and collective actions in terms borrowed from probability theory: degrees of confidence.

This graduate design research studio will investigate how to map a course of action in face of uncertain conditions. We will study how uncertainty has historically been addressed quantitatively in terms of probability theory, and review attempts to apply these theories to architecture. We will probe the qualitative aspects of doubt, and explore how cultivating skepticism within a critical design practice can lead to forms of agency that are as optimistic as they are effective. Taking the field of proxemics as a point of departure, we will ground our investigations in the spatial milieu with which these uncertain times confront us. Specifically, we will revisit techniques of underspecification, flexibility and adaptation in architecture in light of contemporary design challenges. We will problematize probabilistic models and methods involving statistical inference through open-ended skirmishes with machine learning environments, developing an eye for the latent biases and hidden agendas often in play with these technologies. Finally, we will interrogate the networked, screen-based interfaces (such as Zoom) upon which we have come to depend to connect and aggregate our disparate private spaces, with the aim of shaping, reconfiguring and inhabiting these spaces in ways yet to be imagined