Date/Time

Date(s) - 05/12/2021
4:30 pm - 5:30 pm

Please use this zoom link for the event.

Please join us for a UW Data Science Seminar event on Wednesday, May 12th from 4:30 to 5:30 p.m. The seminar will feature Yan Leng, Assistant Professor at the McCombs School of Business at the University of Texas at Austin.

“Learning Quadratic Games on Networks”

Abstract: Individuals, or organizations, cooperate with or compete against one another in a wide range of practical situations. Such strategic interactions are often modeled as games played on networks, where an individual’s payoff depends not only on her action but also on that of her neighbors. The current literature has largely focused on analyzing the characteristics of network games in the scenario where the structure of the network, which is represented by a graph, is known beforehand. It is often the case, however, that the actions of the players are readily observable while the underly- ing interaction network remains hidden. In this paper, we propose two novel frameworks for learn- ing, from the observations on individual actions, network games with linear-quadratic payoffs, and in particular the structure of the interaction net- work. Our frameworks are based on the Nash equilibrium of such games and involve solving a joint optimization problem for the graph structure and the individual marginal benefits. Both synthetic and real-world experiments demonstrate the effectiveness of the proposed frameworks, which have theoretical as well as practical implications for understanding strategic interactions in a network environment.

Biography: I am a computational social scientist and network scientist using large-scale data sets, network theory, and machine learning techniques to understand human behavior over social networks. I am interested in the intricacy between individuals’ characteristics, actions, and their networks, my research tackles this area from four directions: how to infer the hidden network connections based on individuals’ decisions; how social influence spread over networks; how leverage influential nodes for selective network interventions; and how to incorporate network and other complex data structures for inference problems on networks.

The UW Data Science Seminar is an annual lecture series at the University of Washington that hosts scholars working across applied areas of data science, such as the sciences, engineering, humanities and arts along with methodological areas in data science, such as computer science, applied math and statistics. Our presenters come from all domain fields and include occasional external speakers from regional partners, governmental agencies and industry.

All seminars will be hosted virtually for the 2020-2021 academic year, and are free and open to the public.