難民の統合をデータベースのアルゴリズムで改善する

Kirk Bansak^1,2, Jeremy Ferwerda^2,3, Jens Hainmueller^1,2,4

¹Department of Political Science, Stanford University, Stanford, CA 94305, USA.

Science (New York, N.Y.)

|January 20, 2018

まとめ

新しいアルゴリズムは難民の統合を機械学習を用いて再定住の場所と組み合わせることで改善しますこのデータベースのアプローチは,政府にとって実用的な政策ツールを提供することで,雇用の成果を大幅に高めます.

科学分野:

コンピュータ社会科学
機械学習アプリケーション
移住の社会学

背景:

発展した民主主義国は,ますます多くの難民を再定住している.
難民の受け入れ社会への統合は大きな課題です
現行の再定住の割り当ての慣行は統合の成果を最適化しないかもしれません

研究の目的:

難民の再定住場所の割り当てのためのデータベースのアルゴリズムを開発し評価する.
難民の統合の成果,特に雇用の改善
政府に実用的で費用対効果の高い政策ツールを提供すること.

主な方法:

機械学習と最適なマッチングを組み合わせた柔軟なデータ駆動アルゴリズムを開発しました
難民の特徴と再定住場所の連携を活用する
米国とスイスの過去の登録データでアルゴリズムをテストした.

主要な成果:

アルゴリズムは難民の雇用率を平均40%から70%まで大幅に改善しました
これらの利益は,研究対象国における既存の割り当て慣行と比較して観察された.
このアプローチは異なる割り当て制度と難民集団において効果的であることが示されました

結論:

開発されたアルゴリズムは,難民の統合を強化するための実用的で費用対効果の高い方法を提供します.
データを駆動するアプローチは,既存の政府構造内で容易に実施できます.
このツールは,再定住された難民の雇用の見通しを大幅に改善する可能性があります.

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