This National Science Foundation (NSF) Division of Social and Economic Science Project Grant award of $234,489 supports collaborative research at New York University (NYU) from September 1, 2023 to August 31, 2026. The primary objectives are to:
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Conduct a series of experiments to understand how people form subjective causal models based on observed data patterns, and how these models may be influenced by preconceptions triggered by natural contexts. This will provide insights into how economic agents adopt (potentially incorrect) models and how this depends on available information.
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Use machine learning techniques to analyze news media content and identify heterogeneous causal models propagated by different outlets, to understand how media may shape divergent beliefs and policy preferences among the public.
The research aims to advance understanding of the processes by which people develop causal narratives to interpret the world, and how such models are influenced by data patterns, contextual framing, and media discourse. Findings could help inform the design of policies to correct misspecified beliefs and promote optimal decision-making.
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