• Corpus ID: 245124042

Outcome-Driven Dynamic Refugee Assignment with Allocation Balancing

  title={Outcome-Driven Dynamic Refugee Assignment with Allocation Balancing},
  author={Kirk Bansak and Elisabeth Paulson},
This study proposes two new dynamic assignment algorithms to match refugees and asylum seekers to geographic localities within a host country. The first, currently implemented in a multi-year pilot in Switzerland, seeks to maximize the average predicted employment level (or any measured outcome of interest) of refugees through a minimum-discord online assignment algorithm. Although the proposed algorithm achieves nearoptimal expected employment compared to the hindsight-optimal solution (and… 


Dynamic Placement in Refugee Resettlement
A dynamic allocation system based on two-stage stochastic programming to improve employment outcomes of resettled refugees and is able to achieve over 98 percent of the hindsight-optimal employment compared to under 90 percent of current greedy-like approaches.
Assigning refugees to landlords in Sweden: stable maximum matchings
The member states of the European Union received 1.2 million first time asylum applications in 2015 (a doubling compared to 2014). Even if asylum will be granted for many of the refugees that made
Improving refugee integration through data-driven algorithmic assignment
A flexible data-driven algorithm is developed that assigns refugees across resettlement locations to improve integration outcomes and can provide governments with a practical and cost-efficient policy tool that can be immediately implemented within existing institutional structures.
Dynamic Refugee Matching
This paper proposes an informed, intuitive, easy-to-implement and computationally efficient dynamic mechanism for matching asylum seekers to localities and shows that this mechanism outperforms uninformed mechanisms even in presence of severe misclassification error in the estimation of asylum seeker types.
Enabling Trade-offs in Machine Learning-based Matching for Refugee Resettlement
The Swiss State Secretariat for Migration recently announced a pilot project for a machine learning-based assignment process for refugee resettlement. This approach has the potential to substantially
Refugee Resettlement ∗
Over 100,000 refugees are permanently resettled from refugee camps to hosting countries every year. Nevertheless, refugee resettlement processes in most countries are ad hoc, accounting for neither
Tradable Immigration Quotas
Stability in Matching Markets with Complex Constraints
A model of many-to-one matching markets in which agents with multiunit demand aim to maximize a cardinal linear objective subject to multidimensional knapsack constraints is developed, which provides practical constraint violation bounds for applications in contexts, such as refugee resettlement, day care allocation, and college admissions with diversity requirements.
Migration as Submodular Optimization
This work casts the problem as the maximization of an approximately submodular function subject to matroid constraints, and proves that the worst-case guarantees given by the classic greedy algorithm extend to this setting.
Stability in Matching Markets with Complex Constraints
This work provides an algorithm that finds a group-stable matching that approximately satisfies all the multidimensional knapsack constraints and provides practical error bounds for applications in several contexts, such as refugee resettlement, matching of children to daycare centers, and meeting diversity requirements in colleges.