CCI Seminar Series, 2023-24

Anita Woolley, Carnegie Mellon University, Teaching Algorithms to Facilitate Collective Intelligence
April 17, 2024, 12:00-1:00, MIT Building E62, Room 687
Zoom recording here

Organizations are operating in increasingly complex environments, and the traditional ways of collaborating need to be adapted. Research over the past century on intelligence has identified the memory, attention and reasoning functions that enable a system to adapt and solve problems in a wide range of environments. My colleagues and I have built on this work by identifying how collective memory, attention and reasoning develop to enable collective intelligence in human systems, and I will present our recent work focused on developing AI to diagnose and strengthen these functions to enhance collective intelligence.

Anita Williams Woolley is the Associate Dean of Research and a Professor of Organizational Behavior and Theory at Carnegie Mellon University’s Tepper School of Business. Dr. Woolley received her doctorate in organizational behavior from Harvard University, and she is currently the PI on a number of projects funded by DARPA, NSF, ARO and AFRL on the development of collective intelligence in human and human-machine collaboration, and the ways that AI can enhance it. She is a Senior Editor at Organization Science and a founding Associate Editor of Collective Intelligence. For more, see https://sites.google.com/view/anitawoolley-com/home.

Michael Bernstein, Stanford University, Generative Agents: Interactive Simulacra of Human Behavior
March 5, 2024, 4:00-5:00, MIT Building 32, Room D463
Co-sponsored by CCI and the MIT Computer Science and Artificial Intelligence Lab (CSAIL)
Zoom recording of this seminar

Believable proxies of human attitudes and behavior can empower applications ranging from immersive environments to social policy interventions. However, the last quarter century has seen a slow recession of human behavioral simulation as a method, in part because traditional simulations have been unable to capture the complexity and contingency of human behavior. I argue that modern artificial intelligence models allow us to re-examine this limitation. I make my case through generative agents: computational software agents that simulate believable human behavior. Generative agents enable us to populate an interactive sandbox environment inspired by The Sims, where end users can interact with a small town of twenty-five agents using natural language. Our generative agent architecture empowers agents to remember, reflect, and plan — enabling them to act in ways reflective of their jobs and personalities, notice and remember each other, and even plan coordinated events. Extending this line of argument, I explore how proxying human behavior and attitudes can help us design more effective online social spaces, understand the societal disagreement underlying modern AI models, and better embed societal values into our algorithms.

Michael Bernstein is an Associate Professor of Computer Science at Stanford University, where he is a Bass University Fellow. His research focuses on human-computer interaction and social computing systems. This research has been reported in venues such as The New York Times, Wired, Science, and Nature, and Michael has been recognized with an Alfred P. Sloan Fellowship, UIST Lasting Impact Award, and the Computer History Museum’s Patrick J. McGovern Tech for Humanity Prize. He holds a bachelor’s degree in Symbolic Systems from Stanford University, as well as a master’s degree and a Ph.D. in Computer Science from MIT. For more, see https://hci.stanford.edu/msb/.

Scott Page, University of Michigan, Institutional Ensembles and Cultural Institutional Capacity
October 13, 2023, 1:00-2:30, MIT Building E62, Room 450
Zoom https://mit.zoom.us/j/98105285349 (Password: SDFA23)

We construct a series of models within a systems framework to analyze the interdependence between a society’s composition of institutions and its cultural-institutional capacity: the knowledge, behaviors, beliefs, norms, and networks that enables institutions to operate. In our models, a society selects a mixture of institutions of various types to allocate resources and take actions. These include markets, hierarchies, democracies, community-based institutions, or even algorithms. These institutional choices contribute to the production of cultural-institutional capacity, and, conversely, cultural-institutional capacity influences how well each institutional type performs. Cultural-institutional capacity building can be self-reinforcing. Markets can produce greater capacity for markets. It can also be generic and improve all institutional types. Neither of these forms of capacity building necessarily produces efficient ensembles of institutions. Paradoxically, systems with both forms can result in the collapse of an institutional type that builds generic capacity.

Scott Page is the John Seely Brown Distinguished University Professor of Complexity, Social Science, and Management at the University of Michigan, and the Williamson family Professor of Business Administration, professor of management and organizations, Stephen M. Ross School of Business. His research focuses on the myriad roles that diversity plays in complex systems. For example, how does diversity arise? Does diversity make a system more productive? How does diversity impact robustness? Does it make a system prone to large events? He has written five books and published papers in a variety of disciplines including economics, political science, computer science, management, physics, public health, geography, urban planning, engineering, and history. See more at https://sites.lsa.umich.edu/scottepage/bio/.

Deborah Gordon, Stanford University, The Ecology of Collective Behavior
October 30, 2023, 12:00-1:00, MIT Building E62, Room 446

Many natural systems, from brains to ant colonies, operate without central control, using networks of interactions that in the aggregate allow the system to adjust to the current situation. Examples from two ant species, harvester ants in the desert and turtle ants in the tropical forest, show how the dynamics of collective behavior fit the dynamics of the environment. Harvester ant colonies in the desert, in a stable but harsh environment, regulate foraging activity slowly, using centralized information flow with low modularity, and feedback in which the default is not to forage, and stimulation is needed activate foraging. Turtle ants form trail networks in the canopy of the tropical forest. In an unstable but humid environment, where activity is easy, the trail is regulated locally, at each node in the vegetation, with highly modular search that fits the modular distribution of resources. The feedback regime is set with the default to go unless inhibited. Their collective behavior responds rapidly to frequently changing conditions and resources. The talk will discuss broad analogies with the ways that rates, feedback regimes and modularity of interaction networks are used in other forms of collective behavior.

Deborah M. Gordon received her PhD from Duke University, then did postdoctoral research in the Harvard Society of Fellows, at Oxford University, and at the Centre for Population Biology at the University of London, and joined the faculty at Stanford in 1991. She is the author of three books, Ants at Work (Norton 2000); Ant Encounters: Interaction Networks and Colony Behavior (Primers in Complex Systems, Princeton University Press, 2010), and The Ecology of Collective Behavior (2023, Princeton University Press). Her awards include a Guggenheim Fellowship, fellowships at the Center for Advanced Study in Behavioral Sciences, and the Quest award of the Animal Behavior Society. Her lab studies collective behavior in ants. More broadly, she is interested in bridging insights from different disciplines that study dynamical systems and feedback control circuits, ranging across mathematical modeling, ecology, evolutionary biology, and neuroscience. http://www.stanford.edu/~dmgordon/