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原文連結
論文資訊
- 類型：已發表論文
- 日期：2023-01-31
摘要
The space of possible human cultures is vast, but some 文化 configurations are more consistent with 認知 and 社會 constraints than others. This leads to a "landscape" of possibilities that our 物種 has explored over millennia of 文化 演化. However, what does this 適應度景觀, which constrains and guides 文化 演化, look like? The machine-learning algorithms that can answer these questions are typically developed for large-scale datasets. Applications to the sparse, inconsistent, and incomplete data found in the historical record have received less attention, and standard recommendations can lead to bias against marginalized, under-studied, or minority cultures. We show how to adapt the minimum probability flow algorithm and the Inverse Ising模型, a physics-inspired workhorse of 機器學習, to the challenge. A series of
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