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Recent advances in data science and AI technology may offer novel ways of dealing with complexity in ecology and may allow the development of knowledge synthesis tools that can manage context dependence. Especially promising seems the idea to bring together advanced AI-based technologies with conceptual causal models, because this may allow moving beyond pure pattern recognition towards causal inference. In an interdisciplinary setting including ecologists, data scientists, computational linguists and philosophers, the | Recent advances in data science and AI technology may offer novel ways of dealing with complexity in ecology and may allow the development of knowledge synthesis tools that can manage context dependence. Especially promising seems the idea to bring together advanced AI-based technologies with conceptual causal models, because this may allow moving beyond pure pattern recognition towards causal inference. In an interdisciplinary setting including ecologists, data scientists, computational linguists and philosophers, the Resident Group at the Center for Interdisciplinary Research (ZiF) in Bielefeld titled “Mapping Evidence to Theory in Ecology: Addressing the Challenges of Generalization and Causality” explores ways for combining ecological theory represented in the form of causal network graphs with evidence found in scientific papers. The vision is that complex, multifactorial hypotheses about ecological mechanisms would become the basis of a digital atlas of knowledge, and in this atlas the available empirical evidence would be mapped on these hypotheses to allow for case-specific explanations and predictions. | ||
Revision as of 09:05, 27 October 2024
Recent advances in data science and AI technology may offer novel ways of dealing with complexity in ecology and may allow the development of knowledge synthesis tools that can manage context dependence. Especially promising seems the idea to bring together advanced AI-based technologies with conceptual causal models, because this may allow moving beyond pure pattern recognition towards causal inference. In an interdisciplinary setting including ecologists, data scientists, computational linguists and philosophers, the Resident Group at the Center for Interdisciplinary Research (ZiF) in Bielefeld titled “Mapping Evidence to Theory in Ecology: Addressing the Challenges of Generalization and Causality” explores ways for combining ecological theory represented in the form of causal network graphs with evidence found in scientific papers. The vision is that complex, multifactorial hypotheses about ecological mechanisms would become the basis of a digital atlas of knowledge, and in this atlas the available empirical evidence would be mapped on these hypotheses to allow for case-specific explanations and predictions.