Maximizing the Social Good: Markets without Money To create a truly sustainable world, we need to generate ample resources and allocate them appropriately. In traditional economics, these goals are achieved using money. However, in many settings of particular social significance, monetary transactions are infeasible, be it due to ethical considerations or technological constraints. In this talk, we will discuss alternatives to money, including risk, social status, and scarcity, and show how to use them to achieve socially-optimal outcomes. Risk helps determine a person’s value for a resource: the more someone is willing to risk for something, the more they value it. Using this insight, we propose an algorithm to find a good assignment of students in school choice programs. Social status helps motivate people to contribute to a public project. Using this insight, we design badges to maximize contributions to user-generated content websites. Scarcity forces people to evaluate trade-offs, allowing algorithms to infer the relative strength of their preference for different options. Using this insight, we design voting schemes that select the most highly-valued alternative.
Nicole’s research lies broadly within the field of algorithmic game theory. Using tools and modeling concepts from both theoretical computer science and economics, Nicole hopes to explain, predict, and shape behavioral patterns in various online and offline systems, markets, and games. Her areas of specialty include social networks and mechanism design. Nicole received her Ph.D. from MIT in Cambridge, MA in 2005 and then completed three years of postdocs at both Microsoft Research in Redmond, WA and CWI in Amsterdam, Netherlands before accepting a job as an assistant professor at Northwestern University in Chicago, IL in 2008. She joined the Microsoft Research New England Lab in 2012. She is the recipient of the NSF Career Award, the Sloan Fellowship, and the Microsoft New Faculty Fellowship.
Sensor-Driven Energy Management for Smart Buildings My recent work involves the instrumentation, control, and data collection of smart homes and buildings to help optimize energy consumption. Together with my collaborators at UMass Amherst, we have designed and deployed a “live” system that continuously gathers a wide variety of environmental and operational data in three real homes. In contrast to prior work, our focus has been on sensing depth, i.e., collecting as much data as possible from each home, rather than breadth, i.e., collecting data from as many homes as possible. Our data captures many important aspects of the home environment, including average household electricity usage every second, as well as usage at every circuit and nearly every plug load, electricity generation data from on-site solar panels and wind turbines, outdoor weather data, temperature data in indoor rooms, and, finally, data for a range of important binary events, e.g., at wall switches, the HVAC system, and from motion sensors. This data corpus has served as the foundation for much of our research. I will describe our sensing and control infrastructure, and discuss some of problems we have explored with our data sets related to occupancy detection and prediction, and how to prevent it.
Jeannie Albrecht is a Professor and Chair of the Computer Science Department at Williams College, and an Adjunct Professor in the Computer Science Department at UMASS Amherst. Her research focuses broadly on computer systems, including distributed systems and networks, with an emphasis on reliability and scalability. Her recent work involves instrumenting buildings with sensors and devices for monitoring and displaying energy consumption, to ultimately help occupants live more sustainably. She received a PhD in Computer Science from UC San Diego, an MS in Computer Science from Duke University, and a BS in Mathematics and Computer Science from Gettysburg College. Jeannie is a recipient of a National Science Foundation CAREER Award. She was born and raised in Baltimore, MD, and now lives in Williamstown, MA with her husband, eight year old son, five year old daughter, and dog.