Inferring Cultural Landscapes with the Inverse Ising Model
The space of possible human cultures is vast, but some cultural configurations are more consistent with cognitive and social constraints than others. This leads to a “landscape” of possibilities that our species has explored over millennia of cultural evolution. However, what does this fitness lands...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/25/2/264 |
| _version_ | 1827757583485632512 |
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| author | Victor Møller Poulsen Simon DeDeo |
| author_facet | Victor Møller Poulsen Simon DeDeo |
| author_sort | Victor Møller Poulsen |
| collection | DOAJ |
| description | The space of possible human cultures is vast, but some cultural configurations are more consistent with cognitive and social constraints than others. This leads to a “landscape” of possibilities that our species has explored over millennia of cultural evolution. However, what does this fitness landscape, which constrains and guides cultural evolution, 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 model, a physics-inspired workhorse of machine learning, to the challenge. A series of natural extensions—including dynamical estimation of missing data, and cross-validation with regularization—enables reliable reconstruction of the underlying constraints. We demonstrate our methods on a curated subset of the Database of Religious History: records from 407 religious groups throughout human history, ranging from the Bronze Age to the present day. This reveals a complex, rugged, landscape, with both sharp, well-defined peaks where state-endorsed religions tend to concentrate, and diffuse cultural floodplains where evangelical religions, non-state spiritual practices, and mystery religions can be found. |
| first_indexed | 2024-03-11T08:51:33Z |
| format | Article |
| id | doaj.art-23cd41feca6d437e93a0d617b506c228 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-11T08:51:33Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-23cd41feca6d437e93a0d617b506c2282023-11-16T20:23:05ZengMDPI AGEntropy1099-43002023-01-0125226410.3390/e25020264Inferring Cultural Landscapes with the Inverse Ising ModelVictor Møller Poulsen0Simon DeDeo1Department of Social and Decision Sciences, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USADepartment of Social and Decision Sciences, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USAThe space of possible human cultures is vast, but some cultural configurations are more consistent with cognitive and social constraints than others. This leads to a “landscape” of possibilities that our species has explored over millennia of cultural evolution. However, what does this fitness landscape, which constrains and guides cultural evolution, 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 model, a physics-inspired workhorse of machine learning, to the challenge. A series of natural extensions—including dynamical estimation of missing data, and cross-validation with regularization—enables reliable reconstruction of the underlying constraints. We demonstrate our methods on a curated subset of the Database of Religious History: records from 407 religious groups throughout human history, ranging from the Bronze Age to the present day. This reveals a complex, rugged, landscape, with both sharp, well-defined peaks where state-endorsed religions tend to concentrate, and diffuse cultural floodplains where evangelical religions, non-state spiritual practices, and mystery religions can be found.https://www.mdpi.com/1099-4300/25/2/264machine learninghistoryarchaeologyanthropologyreligioncultural evolution |
| spellingShingle | Victor Møller Poulsen Simon DeDeo Inferring Cultural Landscapes with the Inverse Ising Model Entropy machine learning history archaeology anthropology religion cultural evolution |
| title | Inferring Cultural Landscapes with the Inverse Ising Model |
| title_full | Inferring Cultural Landscapes with the Inverse Ising Model |
| title_fullStr | Inferring Cultural Landscapes with the Inverse Ising Model |
| title_full_unstemmed | Inferring Cultural Landscapes with the Inverse Ising Model |
| title_short | Inferring Cultural Landscapes with the Inverse Ising Model |
| title_sort | inferring cultural landscapes with the inverse ising model |
| topic | machine learning history archaeology anthropology religion cultural evolution |
| url | https://www.mdpi.com/1099-4300/25/2/264 |
| work_keys_str_mv | AT victormøllerpoulsen inferringculturallandscapeswiththeinverseisingmodel AT simondedeo inferringculturallandscapeswiththeinverseisingmodel |