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Duncan Watts: “Prediction and Explanation in Social Science”

Symposium on the Future of the Social Sciences IV
Oct 26, 2018
Historically, social scientists have sought out explanations of human and social phenomena that provide interpretable causal mechanisms, while often ignoring their predictive accuracy. We argue that the increasingly computational nature of social science is beginning to reverse this traditional bias against prediction; however, it has also highlighted three important issues that require resolution. First, current practices for evaluating predictions must be better standardized. Second, theoretical limits to predictive accuracy in complex social systems must be better characterized, thereby setting expectations for what can be predicted or explained. Third, predictive accuracy and interpretability must be recognized as complements, not substitutes, when evaluating explanations. Resolving these three issues will lead to better, more replicable, and more useful social science.
“Actors do not behave or decide as atoms outside a social context ... Their attempts at purposive action are instead embedded in concrete, ongoing systems of social relations.”— Mark Granovetter