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Templeton.org is in English. Only a few pages are translated into other languages.

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Yesterday’s largest threats and opportunities faced by our society often bear little resemblance to today’s. Scientific studies of social issues must therefore account for change and evolution. This perspective is often best captured using tools of the dynamical systems theory developed in physics and mathematics. A particularly useful and simple tool is coupled differential (or difference) equations. They are commonly used to model the dynamics of complex processes in the natural and applied sciences. Yet these techniques are still of little use in the social sciences. However, without the ability to model the dynamics of social systems, scientists will be unable to understand the important challenges and opportunities confronting our society and our species.

Dynamic modeling is also at the heart of recent advances in data analysis. Computational power has increased to the point that realistic, moderately complex causal models can be fit directly to data rather than using generic linear models. As larger and larger datasets describing short and long term changes in human societies are becoming available, dynamic models will come to be indispensable for testing various theories and for making predictions.

Cultural evolution brings a dynamical perspective from physics and the biological sciences to the study of social issues. The newly formed Cultural Evolution Society is an ideal institution to train social scientists to model dynamical systems. We propose to organize and develop web based educational materials on dynamic modeling for graduate students and post-docs from across the social sciences, as well as a textbook/review aimed at applying methods of the dynamical systems theory to the evolution of institutions, a topic bringing together many basic and applied issues in cultural evolution. These activities will lay the groundwork for a social scientific paradigm shift, and can provide policy tools by which we might humanely direct our own evolution.