社会水力学:社会行動のデータ駆動モデリング
Daniel S Seara1, Jonathan Colen1,2,3, Michel Fruchart1,2,4
1James Franck Institute, University of Chicago, Chicago, IL 60637.
まとめ
この研究は,住宅のダイナミクスを説明するために,データ主導の社会水力学モデルを導入します. 近所転落現象の 物理に基づく説明を明らかにしています
科学分野:
- 複雑なシステム
- 社会物理学
- コンピュータ社会科学
背景:
- 生物は物理的な力や意思決定によって 複雑な行動を示します
- 水力学理論は集団行動の簡素化された記述を提供しているが,しばしばデータ統合が欠けている.
- 既存の社会ダイナミクスモデルは 経験的データから切り離されていることが多いのです
研究 の 目的:
- 個人の動き (マイクロモチーフ) を集団行動 (マクロ行動) に結びつけるデータ主導のパイプラインを開発する.
- 米国における住宅の動態を理解するために社会水力学モデルを構築し,適用する.
- 現実世界のデータを用いて水力学的仮定を体系的に評価する.
主な方法:
- 運動を誘導する個人の好みによる水力学理論の増強
- 人口調査データ,社会学調査,ニューラルネットワーク分析を統合したデータ駆動パイプラインを使用します.
- 統計的推論を用いて 最小限の社会水力学モデルを 校正する.
主要な成果:
- カリブレーションされたモデルは,郡レベルでの米国の住宅ダイナミクスの重要な特徴を質的に捉えています.
- マグネティック・ヒステリシスに類似した 社会的記憶効果は 隔離-統合の移行時に現れる.
- このモデルは近隣のティッピングに 物理に基づいた類似性を提供し 急速な人口変化を説明しています
結論:
- 居住区隔離のような複雑な社会現象を効果的に説明できます.
- 集団行動のダイナミクスの新しい洞察を 提供しています
- このフレームワークは,微生物からヒト集団までの様々なシステムにおける意思決定主導の運動性の研究を容易にする.
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