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Complex Causality: Computational Formalisms, Mental Models, and Objective Truth

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Advances in Cross-Cultural Decision Making (AHFE 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 610))

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Abstract

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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Authors and Affiliations

  1. Charles River Analytics, Cambridge, MA, USA

    Mukesh Dalal, Amy Sliva & David Blumstein

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  1. Mukesh Dalal
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  2. Amy Sliva
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  3. David Blumstein
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Correspondence to David Blumstein .

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  1. Advanced Technology Labs, Lockheed Martin, Orlando, Florida, USA

    Mark Hoffman

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Dalal, M., Sliva, A., Blumstein, D. (2018). Complex Causality: Computational Formalisms, Mental Models, and Objective Truth. In: Hoffman, M. (eds) Advances in Cross-Cultural Decision Making. AHFE 2017. Advances in Intelligent Systems and Computing, vol 610. Springer, Cham. https://doi.org/10.1007/978-3-319-60747-4_11

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  • DOI: https://doi.org/10.1007/978-3-319-60747-4_11

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