What question did this study set out to answer?

To examine how computational social science can reconcile the historical tension between interpretable causal explanation and empirical predictive accuracy.

February 2, 2017Science

Prediction and explanation in social systems

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Authors

JHJake M. HofmanMicrosoft (United States)ASAmit SharmaVivekananda Global University DWDuncan J. WattsUniversity of the Arts

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Implication

Methodological review demonstrates the necessity of integrating predictive accuracy with causal explanation in social systems, highlighting pathways toward more replicable social science.

Key Points

To examine how computational social science can reconcile the historical tension between interpretable causal explanation and empirical predictive accuracy.
Conceptual evaluation of historical paradigms in social science versus modern computational methodologies.
Identification and synthesis of three structural challenges facing predictive and explanatory modeling in complex social systems.
Evaluation practices for testing predictive accuracy in social science lack standardization and require systematic benchmarks.
Theoretical limits to predictability in complex social systems remain poorly characterized and must be established to calibrate modeling expectations.
Predictive accuracy and causal interpretability operate as complementary rather than competing criteria for validating scientific explanations.

Cite This Study

Hofman et al. (2017) studied this question.

synapsesocial.com/papers/69fff2a04716aad0cc8587b4 https://doi.org/10.1126/science.aal3856

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Implication

Methodological review demonstrates the necessity of integrating predictive accuracy with causal explanation in social systems, highlighting pathways toward more replicable social science.

Prediction and explanation in social systems

Discussion

Member takes

Implication

Key Points

Cite This Study

Prediction and explanation in social systems

Discussion

Member takes

Implication

Key Points

Cite This Study

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