Tutorial @ ACM EC

This tutorial will bring to bear tools from economics and computer science on a core problem of social good: provision of goods to vulnerable populations. By definition, forms of vulnerability such as poverty preclude access to goods through normal market channels. Without intervention, this can be ruinous individually and greatly suboptimal socially. Universal provision is one often-touted solution, but can be costly and inefficient. Consequently, more targeted approaches are common, but must contend with lack of priors about the population of interest. We survey the rich mechanism design and machine learning questions inherent in the most prevalent approaches to this unique resource allocation problem and suggest directions for future work from the EC community.


Exercises [1, 1 Solution, 2, 2 Solution]


Target Audience

The tutorial will be self-contained, and designed for students and researchers in computer science and economics without assuming previous knowledge of public / social service provision. It will emphasize applications of tools from machine learning and mechanism design in promoting social good. The audience will be exposed to potentially new models from economics and theoretical computer science which capture key issues in provision. The discussion of empirical work will illustrate the complexities of practical implementation, highlight the importance of experimentation to understanding relevant factors for vulnerable populations in particular, and suggest potential for further theoretical work.


Pre-recording will take place over the course of two days. EC tutorial watch parties will take place on July 13.


You have to register for the ACM EC Conference to participate in this tutorial. Please register here. Note, registration is free with a SIGecom membership ($5 for students and $10 for others).


Session 1A:

Session 1B:

Session 2A:

Session 2B:


Sera Linardi, University of Pittsburgh

Sera Linardi is an experimental economist and an associate professor of Economics at Graduate School of Public and International Affairs at the University of Pittsburgh. She holds a PhD in Social Science from California Institute of Technology and is the founding director of Center for Analytical Approaches to Social Innovation (CAASI), which builds interdisciplinary research teams to work on practical problems faced by organizations. She is a co-organizer of the 2020 NSF/CEME Decentralization conference on Mechanism Design for Vulnerable Populations. Her research focuses on the two ends of service provision: donor altruism and client utilization, with field experiments on homelessness and reintegration services. She is currently funded by the NSF, Heinz Foundation, Rapoport Foundation, and the Integrative Social Science Research Initiative.


Sam Taggart, Oberlin College

Sam Taggart is assistant professor of computer science at Oberlin College. He is co-organizer of the Mechanism Design for Social Good working group on inequality. His research interests lie at the intersection of theoretical computer science and microeconomic theory. Specific interests include applications of tools from mechanism design, statistical learning, and theoretical computer science to problems of social import, the interaction between economic incentives and statistical learning and in obtaining theoretical performance guarantees for practical resource allocation protocols such as the first-price auction. He holds a PhD in Computer Science from Northwestern University.


Support: Rediet Abebe, Irene Lo, and Ana-Andreea Stoica