Please use this identifier to cite or link to this item: https://repository.isls.org//handle/1/2367
Title: Irreducible Complexity: How Do Causal Bayes Nets Theories of Human Causal Inference Inform the Design of a Virtual Ecosystem?
Authors: Tutwiler, M. Shane
Grotzer, Tina
Issue Date: Jul-2012
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.
URI: https://doi.dx.org/10.22318/icls2012.2.543
https://repository.isls.org//handle/1/2367
Appears in Collections:ICLS 2012

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