Papers by Ventura R . alves
Work submited to the 10 years of EU-Nature Essay contest . Lisbon 2023. Alves, Ventura R. 2023. “CITY OF HOPE SCIENTIFIC DEVELOPMENT AND RESEARCH INSTITUTE (CHSDRI).” OSF Preprints. May 4. doi:10.31219/osf.io/5mepc, 2023
In this essay, the ideal scientific research utopia institute is represented by a futuristic scie... more In this essay, the ideal scientific research utopia institute is represented by a futuristic science city. The city of hope scientific development and research institute. (CHSDRI) As such. The main targets of scientific research, addressing modern society problems, like cancer.virus or antiaging. Are Positively resolved in a simplified way.
nature human Behaviour, 2023
How well can social scientists predict societal change, and what processes
underlie their predict... more 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 scientifc expertise in a prediction domain,
were interdisciplinary, used simpler models and based predictions on
prior data.
Insights into accuracy of social scientists' forecasts of societal change, 2022
How well can social scientists predict societal change, and what processes underlie their predict... more How well can social scientists predict societal change, and what processes underlie their predictions? To answer these questions, we ran two forecasting tournaments testing 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. Following provision of historical trend data on the domain, social scientists submitted pre-registered monthly forecasts for a year (Tournament 1; N=86 teams/359 forecasts), with an opportunity to update forecasts based on new data six months later (Tournament 2; N=120 teams/546 forecasts). Benchmarking forecasting accuracy revealed that social scientists’ forecasts were on average no more accurate than simple statistical models (historical means, random walk, or linear regressions) or the aggregate forecasts of a sample from the general public (N=802). However, scientists were more accurate ...
Insights into accuracy of social scientists´forecasts of societal change., 2022
Abstract: How well can social scientists predict societal change, and what processes underlie the... more Abstract: How well can social scientists predict societal change, and what processes underlie their predictions? To answer these questions, we ran two forecasting tournaments. Social scientists submitted pre-registered monthly forecasts for a year (Tournament 1; N=86 teams/359 forecasts), with an opportunity to update forecasts based on new data six months later (Tournament 2; N=120 teams/546 forecasts). Benchmarking forecasting accuracy revealed that social scientists’ forecasts were on average no more accurate than simple statistical models (historical means, random walk, or linear regressions) or the aggregate forecasts of a sample from the general public (N=802). However, teams were more accurate if they had scientific expertise in a prediction domain, were interdisciplinary, used simpler models, and based predictions on prior data.
One-Sentence Summary: When forecasting societal change, social scientists were no better than the general public or naïve statistical benchmarks.
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Papers by Ventura R . alves
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 scientifc expertise in a prediction domain,
were interdisciplinary, used simpler models and based predictions on
prior data.
One-Sentence Summary: When forecasting societal change, social scientists were no better than the general public or naïve statistical benchmarks.
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 scientifc expertise in a prediction domain,
were interdisciplinary, used simpler models and based predictions on
prior data.
One-Sentence Summary: When forecasting societal change, social scientists were no better than the general public or naïve statistical benchmarks.