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Center for Analytical Approaches to Social Innovation (CAASI)

Bridging quantitative social science research and practical social innovation.

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About CAASI

Our mission is to bridge research and practice. 

Our center translates a real-world problem from the community into a set of quantitative research questions and engages experts across disciplines to solve them. Through coordination, we help each team produce outputs that are both publishable in their field and can be used by another team. We then integrate these disparate findings into a solution for the community. 

People

Meet the people of CAASI. 

  • Dr. Sera Linardi (Director)
  • Jinyong Jeong, PhD (Postdoctoral Scholar)
  • Mallory Avery (PhD student, Economics)
  • Lucy Gillespie (PhD student, GSPIA)
  • Jing Luo (PhD student, Katz School of Business)
  • Xiaohong Wang (PhD student, GSPIA)
  • Domonkos Vamossy (PhD student, Economics)

Partners and Affiliates

We're proud of our affiliate and funding partnerships, which help enable this research. 

  • Allegheny County Department of Human Services
  • Center for Field Experiments & Design (CFXd), Texas A&M University 
  • Foundation of HOPE: Care for Incarcerated and Released Individuals
  • Intelligent Systems Program — University of Pittsburgh 
  • Mechanism Design for Social Good (MD4SG) research group
  • Pittsburgh Experimental Economics Laboratory — University of Pittsburgh
  • SCI — Advanced Data Management Technologies Lab, Network Data Science Lab, Pitt Computational Social Dynamics Lab
  • United Way — 211 Helpline
  • Western Pennsylvania Regional Data Center

 

CAASI Reading Group

How can economics and institutional design solve real-world problems?

How can computational approaches help address the limitations of the economic approach?

Join us as we learn how mechanism design ("reverse game theory") is relevant to public policy and why bridging research and practice in this field leads us to the intersection between economics and computer science. Familiarity with mathematical notation is helpful, but no previous knowledge in either economics or CS is expected. 

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