Presented at the 7th International Conference on Cross-Cultural Decision Making at the International Conference on Applied Human Factors and Ergonomics, Los Angeles, CA (July 2017).
There is a broad consensus that understanding causality is important for comprehending the world and making decisions. However, causality is notoriously difficult to understand and analyze, a challenge amplified in complex sociocultural systems. These challenges can be categorized as interactions among three critical areas of operationalizing causal analysis: Computational Formalisms, Mental Models, and Objective Truth. We provide a survey of causal research across these three key areas and identify existing gaps between objective truth, the way people understand and think about causality in mental models, and the modeling formalisms that have been developed to support representation and reasoning about causality. To bridge these gaps, we present a preliminary conceptual Meta-Causal Models framework to capture the semantic relationships between these three categories of causal analysis and identify the most effective combination of causal reasoning approaches to support decision-making requirements.
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