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- Almås, Ingvild, et al.
(författare)
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Global evidence on the selfish rich inequality hypothesis
- 2022
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Ingår i: Proceedings of the National Academy of Sciences of the United States of America. - : Proceedings of the National Academy of Sciences. - 0027-8424 .- 1091-6490. ; 119:3
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Tidskriftsartikel (refereegranskat)abstract
- We report on a study of whether people believe that the rich are richer than the poor because they have been more selfish in life, using data from more than 26,000 individuals in 60 countries. The findings show a strong belief in the selfish rich inequality hypothesis at the global level; in the majority of countries, the mode is to strongly agree with it. However, we also identify important between- and within-country variation. We find that the belief in selfish rich inequality is much stronger in countries with extensive corruption and weak institutions and less strong among people who are higher in the income distribution in their society. Finally, we show that the belief in selfish rich inequality is predictive of people’s policy views on inequality and redistribution: It is significantly positively associated with agreeing that inequality in their country is unfair, and it is significantly positively associated with agreeing that the government should aim to reduce inequality. These relationships are highly significant both across and within countries and robust to including country-level or individual-level controls and using Lasso-selected regressors. Thus, the data provide compelling evidence of people believing that the rich are richer because they have been more selfish in life and perceiving selfish behavior as creating unfair inequality and justifying equalizing policies.
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| 2. |
- Anker, Andy S., et al.
(författare)
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Extracting structural motifs from pair distribution function data of nanostructures using explainable machine learning
- 2022
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Ingår i: npj Computational Materials. - : Springer Science and Business Media LLC. - 2057-3960. ; 8:1
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Tidskriftsartikel (refereegranskat)abstract
- Characterization of material structure with X-ray or neutron scattering using e.g. Pair Distribution Function (PDF) analysis most often rely on refining a structure model against an experimental dataset. However, identifying a suitable model is often a bottleneck. Recently, automated approaches have made it possible to test thousands of models for each dataset, but these methods are computationally expensive and analysing the output, i.e. extracting structural information from the resulting fits in a meaningful way, is challenging. Our Machine Learning based Motif Extractor (ML-MotEx) trains an ML algorithm on thousands of fits, and uses SHAP (SHapley Additive exPlanation) values to identify which model features are important for the fit quality. We use the method for 4 different chemical systems, including disordered nanomaterials and clusters. ML-MotEx opens for a type of modelling where each feature in a model is assigned an importance value for the fit quality based on explainable ML.
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