Video URL

https://simons.berkeley.edu/talks/information-learning-and-incentive-design-societal-networks

Information, Learning, And Incentive Design For Societal Networks

(2022). Information, Learning, And Incentive Design For Societal Networks. The Simons Institute for the Theory of Computing. https://simons.berkeley.edu/talks/information-learning-and-incentive-design-societal-networks

Information, Learning, And Incentive Design For Societal Networks. The Simons Institute for the Theory of Computing, Feb. 11, 2022, https://simons.berkeley.edu/talks/information-learning-and-incentive-design-societal-networks 

          @misc{ scivideos_19613,
            doi = {},
            url = {https://simons.berkeley.edu/talks/information-learning-and-incentive-design-societal-networks},
            author = {},
            keywords = {},
            language = {en},
            title = {Information, Learning, And Incentive Design For Societal Networks},
            publisher = {The Simons Institute for the Theory of Computing},
            year = {2022},
            month = {feb},
            note = {19613 see, \url{https://scivideos.org/Simons-Institute/19613}}
          }
Manxi Wu (UC Berkeley)
Source Repository Simons Institute
Subject

Abstract

Today's data-rich platforms are reshaping the operations of societal networks by providing information, recommendations, and matching services to a large number of users. How can we model the behavior of human agents in response to services provided by these platforms, and develop tools to improve the aggregate outcomes in a socially desirable manner? In this talk, I will briefly summarize our works that tackle this question from three aspects: 1) Game-theoretic analysis of the impact of information platforms (navigation apps) on the strategic behavior and learning processes of travelers in uncertain networks; 2) Market mechanism design for efficient carpooling and toll pricing in the presence of autonomous driving technology; 3) Security analysis and resource allocation for robustness under random or adversarial disruptions.