Subsidy Allocations in the Presence of Income Shocks


  • Rediet Abebe Harvard University
  • Jon Kleinberg Cornell University
  • S. Matthew Weinberg Princeton University



Poverty and economic hardship are understood to be highly complex and dynamic phenomena. Due to the multi-faceted nature of welfare, assistance programs targeted at alleviating hardship can face challenges, as they often rely on simpler welfare measurements, such as income or wealth, that fail to capture to full complexity of each family's state. Here, we explore one important dimension – susceptibility to income shocks. We introduce a model of welfare that incorporates income, wealth, and income shocks and analyze this model to show that it can vary, at times substantially, from measures of welfare that only use income or wealth. We then study the algorithmic problem of optimally allocating subsidies in the presence of income shocks. We consider two well-studied objectives: the first aims to minimize the expected number of agents that fall below a given welfare threshold (a min-sum objective) and the second aims to minimize the likelihood that the most vulnerable agent falls below this threshold (a min-max objective). We present optimal and near-optimal algorithms for various general settings. We close with a discussion on future directions on allocating societal resources and ethical implications of related approaches.




How to Cite

Abebe, R., Kleinberg, J., & Weinberg, S. M. (2020). Subsidy Allocations in the Presence of Income Shocks. Proceedings of the AAAI Conference on Artificial Intelligence, 34(05), 7032-7039.



AAAI Technical Track: Multiagent Systems