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|Title:||Irreducible Complexity: How Do Causal Bayes Nets Theories of Human Causal Inference Inform the Design of a Virtual Ecosystem?|
|Authors:||Tutwiler, M. Shane|
|Publisher:||International Society of the Learning Sciences (ISLS)|
|Citation:||Tutwiler, M. S. & Grotzer, T. (2012). Irreducible Complexity: How Do Causal Bayes Nets Theories of Human Causal Inference Inform the Design of a Virtual Ecosystem?. In van Aalst, J., Thompson, K., Jacobson, M. J., & Reimann, P. (Eds.), The Future of Learning: Proceedings of the 10th International Conference of the Learning Sciences (ICLS 2012) – Volume 2, Short Papers, Symposia, and Abstracts (pp. 543-544). Sydney, NSW, AUSTRALIA: International Society of the Learning Sciences.|
|Abstract:||Recent computational theories on causal inference, developed by machine learning theorists and co-opted by psychologists and cognitive sciences, predict specific patterns of behavior when humans infer causal connections in simple systems. However, these theories may not be scalable to model complex causal systems, such as ecosystems. Said theories are reviewed herein, and future strands of research are suggested.|
|Appears in Collections:||ICLS 2012|
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