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1.
  • Grossmann, Igor, et al. (author)
  • Insights into the accuracy of social scientists' forecasts of societal change
  • 2023
  • In: Nature Human Behaviour. - : Springer Nature. - 2397-3374. ; 7, s. 484-501
  • Journal article (peer-reviewed)abstract
    • How well can social scientists predict societal change, and what processes underlie their predictions? To answer these questions, we ran two forecasting tournaments testing the accuracy of predictions of societal change in domains commonly studied in the social sciences: ideological preferences, political polarization, life satisfaction, sentiment on social media, and gender-career and racial bias. After we provided them with historical trend data on the relevant domain, social scientists submitted pre-registered monthly forecasts for a year (Tournament 1; N = 86 teams and 359 forecasts), with an opportunity to update forecasts on the basis of new data six months later (Tournament 2; N = 120 teams and 546 forecasts). Benchmarking forecasting accuracy revealed that social scientists' forecasts were on average no more accurate than those of simple statistical models (historical means, random walks or linear regressions) or the aggregate forecasts of a sample from the general public (N = 802). However, scientists were more accurate if they had scientific expertise in a prediction domain, were interdisciplinary, used simpler models and based predictions on prior data. How accurate are social scientists in predicting societal change, and what processes underlie their predictions? Grossmann et al. report the findings of two forecasting tournaments. Social scientists' forecasts were on average no more accurate than those of simple statistical models.
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2.
  • Conroy-Beam, Daniel, et al. (author)
  • Assortative mating and the evolution of desirability covariation
  • 2019
  • In: Evolution and human behavior. - : Elsevier. - 1090-5138 .- 1879-0607. ; 40:5, s. 479-491
  • Journal article (peer-reviewed)abstract
    • Mate choice lies dose to differential reproduction, the engine of evolution. Patterns of mate choice consequently have power to direct the course of evolution. Here we provide evidence suggesting one pattern of human mate choice-the tendency for mates to be similar in overall desirability-caused the evolution of a structure of correlations that we call the d factor. We use agent-based models to demonstrate that assortative mating causes the evolution of a positive manifold of desirability, d, such that an individual who is desirable as a mate along any one dimension tends to be desirable across all other dimensions. Further, we use a large cross-cultural sample with n = 14,478 from 45 countries around the world to show that this d-factor emerges in human samples, is a cross-cultural universal, and is patterned in a way consistent with an evolutionary history of assortative mating. Our results suggest that assortative mating can explain the evolution of a broad structure of human trait covariation.
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